Surgeon attributes in an online collaborative medical platform

Abstract

In a collaborative medical platform, a practitioner evaluation module generates attribute scores for practitioners based on various data collected by the platform including sensor data associated with real-world medical procedures performed by the practitioner and interactive data associated with the practitioner's interactions in the collaborative medical platform. Attribute scores may be employed for tasks such as recommending educational content to practitioners, optimizing assignments of practitioners to medical procedures, and configuring medical equipment (such as surgical robots) to meet the practitioner's skill level.

Description

SURGEON ATTRIBUTES IN AN ONLINE COLLABORATIVE MEDICAL PLATFORMCROSS-REFERENCE RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 605,879 filed on December 4, 2023, U.S. Provisional Patent Application No. 63 / 641,754 filed on May 2, 2024, U.S. Provisional Patent Application No. 63 / 661,015 filed on June 17, 2024, U.S. Provisional Patent Application No. 63 / 661,858 filed on June 19, 2024, U.S. Provisional Patent Application No. 63 / 717,950 filed on November 8, 2024, U.S. Provisional Patent Application No. 63 / 718,000 filed on November 8, 2024, and U.S. Provisional Patent Application No. 63 / 719,015 filed on November 11, 2024, the contents of which are each incorporated by reference herein.BACKGROUNDTECHNICAL FIELD

[0002] The described embodiments relate to a system and method for evaluating and characterizing surgeon attributes in collaborative medical platform.DESCRIPTION OF THE RELATED ART

[0003] In the field of medicine, particularly in robot-assisted surgical practice, the ability to accurately assess and differentiate between the performance levels of surgeons may be of significant importance. Surgical outcomes in robot-assisted procedures can vary based on the skill and expertise of the operating surgeon, making it beneficial to have reliable methods for evaluating surgical proficiency with robotic systems. Traditional assessment methods may not adequately capture the unique skills required for robot-assisted surgery, which can involve a combination of technical proficiency with robotic controls, spatial awareness in a remote operating environment, and the ability to adapt traditional surgical techniques to robotic platforms.

[0004] Meaningful surgeon evaluations can be valuable not only for the professional development of surgeons but also for healthcare institutions seeking to optimize patient care by assigning surgeons to procedures that best match their skill sets. Developing accurate assessment tools that can capture the nuances of surgical performance across diverse robotic platforms and procedures presents unique challenges in this evolving field.

[0005] Furthermore, the dynamic nature of robot-assisted surgical practice, with continual advancements in robotic technologies and techniques, necessitates ongoing evaluation and improvement of specialized surgical skills. Surgeons at all levels of experience may benefit from targeted feedback and opportunities for skill enhancement in areas specific to robot-assisted surgery, such as robotic system operation, hand-eye coordination in a digital interface, and effective utilization of robotic instruments.

[0006] The ability to distinguish between lower and higher performing surgeons in robot- assisted procedures may have implications for medical education and training programs focused on robotic surgery. Identifying key factors that contribute to superior outcomes in robot-assisted surgeries could inform curriculum development and help focus training efforts on critical skills and techniques specific to robotic platforms. Additionally, healthcare systems could potentially use this information to implement more effective quality improvement initiatives for robot- assisted surgical programs.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure (FIG.) 1 is an example embodiment of a computing environment for an electronically-assisted medical procedure.

[0008] FIG. 2 is a block diagram of an example architecture for a collaborative medical platform.

[0009] FIG. 3A shows a first view of an example practitioner dashboard associated with a collaborative medical platform.

[0010] FIG. 3B shows a second view of an example practitioner dashboard associated with a collaborative medical platform.

[0011] FIG. 4 shows an example practitioner dashboard displaying an educational content item to a medical practitioner associated with a collaborative medical platform.

[0012] FIG. 5 is an example embodiment of a case sharing interface associated with sharing a medical case in the collaborative medical platform.

[0013] FIG. 6 is an example embodiment of a case dashboard associated with a set of cases in a collaborative medical platform.

[0014] FIG. 7 is an example telepresence interface associated with a collaborative medical platform.

[0015] FIG. 8 is another example of a telepresence interface associated with a collaborative medical platform.

[0016] FIG. 9 is an example analytics dashboard associated with a collaborative medical platform.

[0017] FIG. 10 is an example video interface associated with a collaborative medical platform.

[0018] FIG. 11 is a first example embodiment of a practitioner profile page associated with a collaborative medical platform.

[0019] FIG. 12 is a second example embodiment of a practitioner profile page associated with a collaborative medical platform.

[0020] FIG. 13 is a first example embodiment of a notification page associated with a collaborative medical platform.

[0021] FIG. 14 is a second example embodiment of a notification page associated with a collaborative medical platform.

[0022] FIG. 15 is a first example embodiment of a program page associated with a collaborative medical platform.

[0023] FIG. 16 is an example embodiment of a token redemption page associated with a collaborative medical platform.

[0024] FIG. 17 is an example embodiment of an achievements page associated with a collaborative medical platform.

[0025] FIG. 18 is an example embodiment of a telepresence achievements page associated with a collaborative medical platform.

[0026] FIG. 19 is a flowchart of an example embodiment of a process for generating and presenting attribute scores for a medical practitioner in a collaborative medical platform.DETAILED DESCRIPTION

[0027] The Figures (FIGS.) and the following description describe certain embodiments by way of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. Reference will now be made to several embodiments, examples of which are illustrated in the accompanying figures. Wherever practicable, similar or like reference numbers may be used in the figures and may indicate similar or like functionality.

[0028] A collaborative medical platform facilitates exchange of data between remote medical practitioners in relation to medical cases during preprocedural, intraprocedural, and postprocedural stages. The collaborative medical platform stores or enables access to patientrecords, imaging data, video data, telemetry data from medical equipment, biometric sensor data from patients, and other medical information that may be obtained prior to medical procedures being performed, during medical procedures, or after medical procedures are performed. Based on the data collected in the collaborative medical platform, an attribute model generates attribute scores characterizing a medical practitioner’s skills across a set of pertinent attribute categories. A profile page for the practitioner may be dynamically updated based on the attribute scores. For example, the collaborative medical platform may present visual representations of the scores, present avatars associated with the practitioner’s skill levels, present badges, awards, or other achievements indicative of the scores, present leaderboards indicative of comparative rankings between practitioners, issue tokens for redeeming for real or virtual rewards based on score achievements, or initiate other actions related to the attribute scores.

[0029] Generating and presenting attribute scores in this manner encourages practitioners to improve their skills and enables medical facilities to better evaluate and deploy practitioners, ultimately improving patient outcomes. For example, practitioners may be motivated by competition among peers and to individually receive recognition for their skill levels in the form of badges, rewards, level recognition, or other aspects. Medical facilities may use the attribute scores to optimally assign practitioners to medical procedures, to facilitate hiring of balanced teams, and to conduct meaningful performance reviews. Scores can also be used to recommend educational content to practitioners such as courses, simulations, or other developmental processes. In some embodiments, medical equipment, such as surgical robots may be automatically configured for a particular medical practitioner dependent on their skill levels reflected in the attribute scores.

[0030] FIG. 1 illustrates an example embodiment of a computing environment 100 for a collaborative medical platform 140. The collaborative medical platform 140 may include one or more servers that are coupled by a network 130 to client devices 150 associated with users 155 of the collaborative medical platform 140, medical equipment 160, and various third-party servers 170. The collaborative medical platform 140 facilitates collaborative exchange of data between medical practitioners, patients, administrators, or other users 155 via the client devices 150 in support of preprocedural, intraprocedural, and postprocedural stages of medical cases. The collaborative medical platform 140 may furthermore facilitate access to telemetry data from medical equipment 160 (including, for example, real-time video, images, biometric sensing data, equipment control and / or status signals, etc.) that may be utilized in conjunction with performing medical procedures and managing patient cases. Furthermore, the collaborative medical platform 140 may facilitate access to various third-party servers 170 that provide external services such as, for example, electronic healthcare records (EHR) services, medical telepresenceservices, operating room scheduling, data analytics services, etc.

[0031] To further support the preprocedural stage of a medical case, the collaborative medical platform 140 may select one or more reference content items for presentation to a medical practitioner before performing a medical procedure. In various embodiments, the collaborative medical platform 140 maintains a store or a library of reference content items from which reference content items for the medical practitioner are selected. Alternatively or additionally, one or more third-party servers 170 maintain reference content items, and the collaborative medical platform 140 selects one or more reference content items from a third-party server 170. Reference content items for a medical practitioner may be retrieved from a combination of one or more third-party servers 170 and the collaborative medical platform 140. The collaborative medical platform 140 leverages data in a user profde of a medical practitioner to select one or more reference content items for the medical practitioner.

[0032] In support of an intraprocedural stage of a medical case, the collaborative medical platform 140 may facilitate presentation of various information to support the procedure such as preprocedural images, models, patient data, equipment information, or other data. The collaborative medical platform 140 may furthermore facilitate a telepresence session that enables one or more remote contributors to access video, images, 3D models, equipment telemetry data, or other data streams capturing during an ongoing medical procedure. The collaborative medical platform 140 may furthermore enable remote practitioners to provide annotations or other commentary related to real-time video, images, or three-dimensional models associated with a procedure. The collaborative medical platform 140 tracks and stores all data from the procedure (including video, medical equipment telemetry, and collaborative commentary) in association with the case identifier to enable subsequent access.

[0033] During the intraprocedural stage, the collaborative medical platform 140 may present educational content about a medical procedure being performed to one or more medical practitioners performing the medical procedure. Educational content describes performance of the medical procedure, such as information about techniques to use, movement of medical instruments or medical equipment, settings for medical equipment, or other information. The collaborative medical platform 140 compares telemetry data or video data of a medical procedure during the intraprocedural stage to baseline criteria associated with educational content and selects educational content associated with baseline criteria from which the telemetry data or video data deviates. Educational content may include instructions that, when executed by a piece of medical equipment 160, modify one or more settings of the piece of medical equipment based on the educational content, simplifying adjustment of operation of the piece of medical equipment 160.

[0034] In support of a postprocedural stage of a medical procedure, the collaborative medical platform 140 enables medical practitioners connected with a case to collaboratively monitor data associated with a patient’s recovery. For example, the collaborative medical platform 140 may provide interfaces for viewing health records associated with the patient’s recovery and facilitate collaborative exchange between medical practitioners through a case-specific content feed. The collaborative medical platform 140 may furthermore perform various analytics relating to performed medical procedures based on aggregations of data. The analytics may be useful to support patient recovery, to improve future procedures, and to track the performance of medical practitioners.

[0035] Educational content relevant to a medical procedure may be selected and presented to a medical practitioner who performed the medical procedure during the postprocedural stage by the collaborative medical platform 140. For example, metrics or analytics determined for the medical procedure by the collaborative medical platform 140 are compared to baseline criteria for various educational content. In various embodiments, the collaborative medical platform 140 selects educational content associated with baseline criteria from which a metric deviates and presents the selected educational content to the medical practitioner. For example, the collaborative medical platform 140 includes information identifying selected educational content in one or more interfaces generated for presentation to the medical practitioner, simplifying access to instructional information relative to the medical procedure.

[0036] The collaborative medical platform 140 may intelligently utilize data collected during preprocedural, intraprocedural, and / or postprocedural stages of a case during a different stage of the same case or other cases. For example, annotations of images or 3D models, practitioner comments from a content feed, or other information obtained during a preprocedural stage may be made available in the intraprocedural stage to aid the performing practitioner through the procedure. Analytical data relating to postprocedural data may be utilized to generate recommendations for future procedures, such as educational content, in order to improve efficiencies and / or outcomes.

[0037] The collaborative medical platform 140 may also facilitate functions such as managing clinical trials, facilitating education training and performance tracking, facilitating broadcasts of medical-related presentations, and facilitating procedure scheduling. Beneficially, the collaborative medical platform 140 stores complete records of medical cases (including video and telemetry from procedures) in a centralized and standardized platform that naturally allows for collaboration in an online environment, where practitioners may interact from disparate remote locations. The collaborative medical platform 140 may maintain data in a manner that adheres to data privacy and compliance obligations of medical practitioners and organizations.

[0038] The collaborative medical platform 140 may furthermore employ various machine learning techniques to infer recommendations, insights, or other artificially generated contributions based on the data collected into the collaborative medical platform 140. For example, the collaborative medical platform 140 may generate a recommendation for a medical practitioner to review educational content relevant to a medical practitioner based on data captured by the collaborative medical platform 140 during performance of a medical procedure. The collaborative medical platform 140 may select educational content for a medical practitioner based on telemetry data captured during a medical procedure performed by the medical practitioner. As another example, the collaborative medical platform selects educational content for a medical practitioner based on video data captured during a medical procedure performed by the medical practitioner. Educational content selected by the collaborative medical platform may be video, audio, text, or other data describing performance of a medical procedure. Additionally or alternatively, educational content configuration instructions or configuration data for one or more pieces of medical equipment 160.

[0039] The collaborative medical platform 140 may generate and present other recommendations to a medical practitioner based on stored information for the medical practitioner. For example, the collaborative medical platform 140 generates a recommendation for the medical practitioner based on a type of procedure scheduled to be performed by the medical practitioner; in various embodiments, the recommendation comprises case records associated with one or more historical cases captured in the collaborative medical platform 140 relating to prior performances of the type of procedure on a similarly situated patient. If granted appropriate permissions, the practitioner may then review an entire case record through the collaborative medical platform 140 including preprocedural information, videos or other data from the procedure itself, and postprocedural outcome data. In another example, the collaborative medical platform 140 may intelligently generate a recommendation to invite a particular medical practitioner to collaborate on a case based on that practitioner having relevant expertise, experience, and / or availability. An invitation may then be generated to the medical practitioner to enable access and collaboration on the case during at least one of the preprocedural, intraprocedural, and postprocedural stages. Furthermore, the collaborative medical platform 140 may intelligently identify and present patient risk factors relevant to procedure performance, planning, and postprocedural care. The collaborative medical platform 140 may also intelligently recommend educational content for training medical practitioners based on their individual tracked performance and various comparative analytics.

[0040] The collaborative medical platform 140 may be implemented using on-site computing or storage systems, cloud computing or storage systems, or a combination thereof and may beimplemented utilizing local or cloud-based servers, which may include physical or virtual machines, or a combination thereof. Cloud-based servers may include private cloud systems, public cloud systems, hybrid public / private cloud systems, or a combination thereof. Accordingly, the collaborative medical platform 140 may be local, remote, and / or distributed relative to the medical environments where procedures are performed and relative to the client devices 150 providing user access. Furthermore, different portions of the collaborative medical platform 140 may execute on different remote servers and various system elements of the collaborative medical platform 140 may be communicatively coupled over a network 130.

[0041] The client devices 150 may include, for example, a mobile phone, a tablet, a laptop or desktop computer, other computing device, or application executing thereon for accessing the collaborative medical platform 140 via the network 130. The client devices 150 may enable access to various user interfaces (which may comprise web-based interfaces accessed via a browser or application interfaces accessed via an application) for viewing and / or editing information associated with the collaborative medical platform 140. The client devices 150 may include conventional computer hardware such as a display, input device (e.g., touch screen), memory, a processor, and a non-transitory computer-readable storage medium that stores instructions for execution by the processor in order to carry out functions described herein. Examples of user interfaces are described in further detail below with respect to FIGs. 3-18.

[0042] The third-party servers 170 may facilitate diverse services utilized by the collaborative medical platform 140. For example, the third-party servers 170 may include various EHR systems for managing patient records, robotic control platforms for controlling surgical robots or other medical equipment, telepresence servers for facilitating telepresence services, patient scheduling systems, hospital information systems (HIS), or other servers. As another example, one or more third-party servers 170 include educational content about various medical procedures, such as articles about various medical procedures, audio data related to medical procedures, video data related to medical procedures, settings or configuration details for medical equipment 160 used in medical procedures, or other descriptive information about medical procedures. The third-party servers 170 may be implemented using various on-site computing or storage systems, cloud computing or storage systems such as private cloud systems, public cloud systems, hybrid public / private cloud systems, or a combination thereof.

[0043] The medical equipment 160 may include various sensors such as cameras or other imaging equipment, biometric monitors, or other sensing devices that collect data associated with a medical procedure being performed. Sensor data may include physiological or biological signals (such as pulse rate, blood pressure, body temperature, etc.), video, electrical signals representative of a state of a medical instruction, or other information. Cameras or imagesensors may include still image cameras, video cameras, 3-dimensional (3D) imaging devices, or a combination thereof. The cameras can include stationary cameras in a medical environment (e.g., operating room) or may include cameras integrated into medical instruments such as endoscopic cameras. Imaging systems may include computed tomography (CT) imaging systems, medical resonance imaging (MRI) systems, X-ray systems, or other imaging equipment. The medical equipment may furthermore include a robotic device that facilitates robotically- assisted medical procedures. The robotic device may include, for example, a robotic arm or other computer-controlled mechanical device that performs or assists with a medical procedure. The robotic device may be pre-programmed to perform a certain set of steps or tasks, and / or may be manually controlled by an operator. Telemetry data associated with a robotic device may include force data, positional data, or other sensor data, control signals, fault conditions, or other data relating to operation of the robotic device during a procedure. The medical equipment data may be streamed to the collaborative medical platform 140 in real-time or may be stored on a third-party server 170 and later uploaded to the collaborative medical platform 140.

[0044] The network 130 comprises communication pathways for communication between the collaborative medical platform 140, the medical equipment 160, the client devices 150, and the third-party servers 170. The network 130 may include one or more local area networks and / or one or more wide area networks (including the Internet). The network 130 may also include one or more direct wired or wireless connections (e.g., Ethernet, WiFi, cellular protocols, WiFi direct, Bluetooth, Universal Serial Bus (USB), or other communication link).

[0045] FIG. 2 is a block diagram showing an example architecture of an embodiment of the collaborative medical platform 140. In the embodiment of FIG. 2, the collaborative medical platform 140 includes a data ingestion module 205, an entity management module 210, an interface management module 215, a medical intelligence module 220, a telepresence module 225, an analytics module 230, a practitioner education module 235, a presentation module 240, an application integration module 245, a video library 250, a connection graph store 255, a user profile store 260, and a patient data store 265. In other embodiments, the collaborative medical platform 140 includes different or additional functional blocks than those shown in FIG. 2. Further, in some embodiments, a single functional block provides the functionality of multiple functional blocks shown in FIG. 2.

[0046] While in one embodiment, the illustrated functional blocks may execute entirely within the collaborative medical platform 140, alternative embodiments may include various modules or discrete functions of modules being executed by one or more third-party servers 170. Here, the collaborative medical platform 140 may interact with a third-party server 170 via an application programming interface (API) to enable the collaborative medical platform 140 torequest and utilize services provided by the third-party servers 170 to facilitate any of the functions described herein. For example, in an embodiment, electronic health records may be provided by a third-party server 170. Here, the collaborative medical platform 140 may query the third-party server 170 for relevant data but does not necessarily locally store complete patient records. Furthermore, third-party servers 170 may facilitate services such as telepresence sessions, presentation creation, access to video resources, three-dimensional model generation, or other aspects of the functions of the collaborative medical platform 140 described herein.

[0047] The data ingestion module 205 ingests various medical data used by the collaborative medical platform 140. The data ingestion module 205 may be electronically coupled to one or more external servers, databases, or other data sources that supply the medical data. The medical data may include, for example, profde data for patients (e.g., demographic information, health history, etc.), medical professionals (e.g., expertise, experience, etc.), or facilities, information about medical conditions, procedures, and medications, information about robotic systems, imaging systems, intervention tools, or other medical equipment, information about postprocedural outcomes, or other medical information discussed herein.

[0048] The data ingestion module 205 may aggregate data from various input data sources. For example, the data ingestion module 205 may obtain medical data from conventional electronic health records (EHR) systems. Here, the data ingestion module 205 may perform various preprocessing to normalize data to a standardized format used by the collaborative medical platform 140. For example, medical records may be organized in a database structure that includes values (strings, numerical values, binary values, or other data types) assigned to each of a set of predefined information fields.

[0049] The data ingestion module 205 may furthermore interface with one or more imaging systems to ingest preprocedural, intraprocedural, or postprocedural images, video, or three- dimensional models associated with patients. For example, the data ingestion module 205 may obtain and store X-ray images, magnetic resonance imaging (MRI) images, computed tomography (CT) scan images, visible light images, near infrared fluorescent (NIRF) images, or other medical images, video, or three-dimensional models derived from them. Image data may furthermore include image or video data from one or more cameras present in a medical environment where a medical procedure is being performed, such as one or more overhead cameras and / or one or more endoscopic cameras. Imaging data may include associated metadata such as telemetry data from one or more medical instruments used to perform the medical procedure, annotations or commentary associated with the video received from one or more medical practitioners associated with the medical procedure, segmentation data associated with dividing a video into segments relating to different steps of a procedure, or other informationrelating to image or video data.

[0050] To simplify subsequent retrieval and review of video of a medical procedure along with associated metadata, the data ingestion module 205 may perform various preprocessing and indexing of the content and associated metadata. For example, the data ingestion module 205 indexes video of a medical procedure with associated metadata to correlate different metadata with different portions of the video, synchronize videos associated with the same medical procedure, or perform various encoding or reformatting of video data. Videos may furthermore be automatically segmented and indexed into video segments corresponding to different steps of a procedure.

[0051] The data ingestion module 205 may furthermore integrate with various robotic platforms or other medical equipment to obtain telemetry data associated with procedures. For example, the data ingestion module 205 may obtain various sensor data from sensors utilized during medical procedures, identifying information associated with medical equipment, control data associated with control a robotic platform or other medical equipment, or other data generated from medical equipment in associated with performed medical procedures.

[0052] The data ingestion module 205 may furthermore provide interfaces accessible via the client device 150 for ingesting data input directly into the collaborative medical platform 140. For example, the data ingestion module 205 may present various forms or freeform entry elements to enable entry of medical information relevant to operation.

[0053] In an embodiment, the data ingestion module 205 may manage data in a manner consistent with various compliance and privacy policies. For example, the data ingestion module 205 may enable removal or redaction of portions of received data to preserve privacy of a patient when the data is used for purposes in which patient identification is not necessary.

[0054] The entity management module 210 manages presentation of entity pages associated with different entities affiliated with the collaborative medical platform 140 and manages connections between entities. Entities may include, for example, users 155 (which may medical practitioners, patients, administrators, etc.), medical cases associated with procedures, facilities, medical equipment 160, files or media content, events (e.g., conferences), presentations, training modules, or other data objects. Entity pages may comprise web pages accessible via a web browser of the client device 150 or may comprise pages of a desktop or mobile application installed on a client device 150.

[0055] Each entity page for an entity may enable viewing of information associated with the entity and / or interactions with the entity. For example, each user 155 of the collaborative medical platform may have a dedicated page that provides information about the user 155 suchas identifying information, role (e.g., surgeon, nurse, executive, administrator, patient, etc.) profile information (e.g., biography, credentials, etc.), assigned cases, procedure histories, connections to other users or cases, scheduling information, or other user-specific data. An entity page for a patient (whether or not the patient is a user 155 of the collaborative medical platform 140) may include patient profile information, health history, planned procedures, risk factors, or the medical information associated with the patient. An entity page for a medical case may include information about a patient associated with the case, descriptive information about a medical procedure (such as a type of medical procedure) associated with the case, a medical environment where the medical procedure is to be performed, other descriptive information about the medical procedure, a status of the procedure (e.g., preprocedural stage, intraprocedural stage, or postprocedural stage), or other information relevant to a medical case. Pages may furthermore include various interactive elements (e.g., content feeds) that enable users to share and interact with data associated with that entity as will be further described below.

[0056] The entity management module 210 also organizes pages and associated data received into the collaborative medical platform 140 into a connection graph (stored to the connection graph store 255) that captures relationships between different entities and associated data. Some connections may be configured as default connections, while other connections may be created based on specific actions from users 155. For example, users 155 may be connected by default to other users 155 (with at least viewing permissions) within the same organization.Alternatively, connections may be generated only when a user 155 expressly invites another user 155 to connect and the receiving user 155 accepts the connection request. Connections between medical practitioners and medical cases may similarly be created by default or in response to invitations to create a connection. For example, a default connection may be created between an entry for a planned medical procedure and a medical practitioner assigned responsibility for the procedure. Alternatively, all medical practitioners within an organization or within a relevant department may become connected to a planned procedure as a default. In other scenarios, a user may share a medical case with one or more other medical practitioners to generate a connection request that invites the other medical practitioners to collaborate with on the medical case. Accepting the connection request may then create a connection between the invited practitioner and the medical case. Supplemental connections may also automatically be generated (e.g., between the owner of the procedure and the invited contributor). Connections may furthermore be created between users 155 and individual videos, files, presentations, or other data objects. For example, a user 155 that creates or owns a video may share the video with one or more other users 155 to grant access rights to the video.

[0057] Connections between entities may be of diverse types and may be governed by differentpermissions. Generally, pages may be accessed only by users having appropriate access permissions. Different permission levels may dictate distinct levels of access for different pages. For example, depending on user-specific permissions for a particular page, the user may be permitted or blocked from accessing the data, editing the data, commenting or annotating the data, deleting the data, or performing other modifications. In an embodiment, a page may have a page owner with the highest level of access permissions. Generally, a medical practitioner may be the page owner for their own profile page and for procedures for which they have primary responsibility. Pages associated with facilities, medical equipment, or other entities may variably be owned by an assigned medical practitioner. Non-owners may have distinct levels of access to pages depending on the configured permissions. Permissions may be granted by the page owner or by another user that has appropriate permissions to assign or relinquish permissions to other users.

[0058] Based on different connections available to different users 155, the collaborative medical platform 140 enables a personalized experience for each user 155. For example, upon logging into the collaborative medical platform 140, a user 155 may be presented with personalized interfaces that relate to their connections to other users 155, medical cases, videos, presentations, or other content hosted by the collaborative medical platform 140.

[0059] An interface management module 215 manages content associated with various interfaces hosted by the collaborative medical platform 140 and accessible via the client devices 150. As described above, the interface management module 215 may manage pages associated with the various entities managed by the collaborative medical platform 140 including users 155 (which may include medical practitioners, patients, administrators, etc.), medical cases associated with procedures, facilities, medical equipment 160, files or media content, events (e.g., conferences), presentations, training modules, or other data objects. Access to different pages by a specific user 155 may be dependent on that user’s connections and permissions configured in the connection graph store 255. Furthermore, patient data may be pseudonymized for viewing by certain other users (dependent on the type of connection and / or permission) such that the patient data cannot be attributed to a specific individual.

[0060] A medical case page associated with a medical case may include information organized into preprocedural, intraprocedural, and postprocedural stages. At the preprocedural stage, a medical case page may include information about the patient, the procedure being performed, and the medical practitioner performing the procedure. The interface management module 215 may furthermore provide access to various analytical information (e.g., generated by the analytics module 230 described below) such as risk factors for the patient, experience / expertise of the medical practitioner, outcomes for the type of procedure being planned, or other data. Atthe intraprocedural stage, the medical case page may provide access to a telepresence session to enable remote collaborators to remotely collaborate with respect to an ongoing procedure. At a postprocedural stage, the medical case page may include information about the patient treatment plan, risk factors, follow up visits, or other postprocedural information.

[0061] Some entity pages in the collaborative medical platform 140 may include content feeds to facilitate collaboration between users 155. Content feeds may include various content (e.g., posts) such as text-based commentary, images, video, three-dimensional models, or other multimedia content relating to a medical case. Content may be directly posted to a page associated with a medical case or a post may comprise links to content stored by the collaborative medical platform 140 or on an external server. Posts may be grouped into conversations that hierarchically track the relationships between posts. For example, posts may be made as original posts (which start a new conversation) or as replies to existing posts (which become part of the conversation).

[0062] In an example use, a user 155 may invite one or more other users 155 to collaborate on a medical case and thereby gain access to a case page for the medical case. A content feed on the page enables the collaborating users 155 to post to the case page in association with the medical case. The content feed may therefore enable discussion about the procedure to be performed discussion of risk, best practices, or other information that may be useful to the practitioner performing the procedure. Furthermore, the contributing users 155 may post videos or three- dimensional models (or links to content) relating to historical procedures for similarly situated patients. Additionally, contributing users 155 could share links to entity pages associated with past procedures that may be of relevance, to enable a performing medical practitioner to view historical content feeds associated with those procedures. Patient data may optionally be pseudonymized when shared with other users (dependent on the type of connection and / or permission) such that the patient data cannot be attributed to a specific individual.

[0063] Content feeds may furthermore be utilized in relation to an ongoing procedure during a real-time telepresence session as discussed in further detail below. Here, a content feed may be presented as a real-time chat window that enables contributors to comment during a procedure, share video, images, or other media, provide links to relevant resources, or otherwise contribute content during the course of procedure.

[0064] In a postprocedural stage, a content feed may be utilized by contributors to discuss postprocedural treatments, patient recovery, risk management, or other information relevant to patient recovery. Examples of content feeds are provided in FIG. 7 which are described in further detail below.

[0065] The medical intelligence module 220 generates medical intelligence data that may be automatically added to content feeds or otherwise made available in the context of the collaborative medical platform 140. For example, the medical intelligence module 220 may automatically contribute posts to a content feed for a medical case that an artificial intelligence agent infers is relevant. Artificially generated posts may mimic posts provided by human contributors and may include text-based commentary, multimedia, links, etc. Medical intelligence data may be generated during a preprocedural stage, during a procedure, or during a postprocedural stage.

[0066] In an example implementation, the medical intelligence module 220 may include one or more machine-learned models trained to generate content that the models infer to be relevant to a particular medical case or more generally relevant to a user 155. In one implementation, the machine learned model generates an embedding for a medical case based on descriptive information about a medical procedure, characteristics of the patient on whom the medical procedure is to be performed, characteristics of medical practitioner performing the procedure, posts in the content feed, or other information available in the collaborative medical platform 140. The medical intelligence module 220 determines measures of similarity (e.g., cosine similarity, dot product) between the embedding for the medical case and embeddings for other content available in the collaborative medical platform 140 and that can be included in automated posts. The medical intelligence module 220 may then generate posts and / or select content for posts based on similarities of the embeddings. The medical intelligence module 220 may furthermore employ various Large Language Models (LLMs) to analyze text-based content associated with a medical case and artificially generate relevant natural language content for the content feed. Machine learning models may furthermore include one or more neural networks (such as convolutional neural network (CNN), artificial neural network (ANN), residual neural network (ResNet), or recurrent neural network (RNN)), regression-based models, generative models, or other type of machine -learned model capable of achieving the functions described herein.

[0067] In an example use case, the medical intelligence module 220 may identify one or more historical medical cases that are similar to a current medical case and automatically generate a link to a case page for the related case. A medical practitioner may then view videos, models, or other recorded data associated with the related medical case to help the practitioner prepare for a procedure. In other examples, the medical intelligence module 220 may automatically respond to a question posed by a user in the content feed. For example, the medical intelligence module 220 may operate like a chatbot that intelligently responds to text-based queries. In further embodiments, the medical intelligence module 220 may generate a recommendation to invite aspecific medical practitioner to collaborate on a medical case based on relevant expertise and experience. A user may then select to invite the recommended collaborator to collaborate on the medical case based on the artificially generated recommendation.

[0068] The telepresence module 225 facilitates a telepresence session during a procedure. The telepresence session may be joined by one or more collaborators that have been invited to collaborate on the medical case and enable the other users 155 to remotely access video, telemetry data from one or more medical instruments, or other real-time data captured during a medical procedure. As described above, a content feed may also be displayed in association with the telepresence session to enable contributors to comment or share multimedia or links relevant to the procedure.

[0069] The telepresence module 225 may furthermore enable contributors to provide real-time annotations on images, video, three-dimensional models, or other visual content of anatomy relevant to an ongoing procedure. For example, a contributor may mark locations in the visual content in association with provided comments. The telepresence module 225 may furthermore enable contributors to add overlaid drawings, highlighting, or other visual indicators during an ongoing telepresence session.

[0070] In an embodiment, the telepresence module 225 may enable remote contributors to take control of medical equipment 160. For example, a remote contributor may access a control interface that provides control elements for controlling a position or orientation of a camera, controlling a robotic arm, setting a configuration of a sensing device, or performing other control functions of medical equipment.

[0071] Upon completing a procedure, the telepresence module 225 may store the recorded video, telemetry data, content feed, annotations, and other captured data in association with the procedure. This information may be later accessed by users 155 of the collaborative medical platform 140 (with appropriate permissions) and / or may be utilized by the medical intelligence module 220 to further train machine learning models and / or generate inferences.

[0072] The analytics module 230 facilitates generation of various statistics, metrics, or other analytics associated with information stored in the collaborative medical platform 140.Analytics may generally be created based on a set of filtering parameters that yield some subset of data records for aggregating, and a combining function that specifies how the filtered data should be combined. The filtering parameters may filter medical procedure data based on data fields such as patient data, medical practitioner data, facility, procedure type, medical equipment used, etc. The combining function may comprise, for example, an averaging function, a median function, a histogram function, or other function. A specific analytics function may result in asingle output value or a series of values over one or more dimensions. Series outputs may be visually represented in a table, chart, graph, or other visual output.

[0073] For example, the analytics module 230 may generate metrics describing an average length of time for a specific medical practitioner or a group of medical practitioners to complete a medical procedure. Average times for various procedures performed by the same medical practitioner or group of practitioners may be presented together with similar metrics for other medical practitioners for comparison purposes. In another example, the analytics module 230 may generate metrics describing a number of times a medical practitioner has historically performed a specific type of medical procedure. Such counts could be further aggregated to indicate percentages that reflect how many times a medical practitioner has performed each different type of medical procedure out of a total number of procedures performed.

[0074] In further embodiments, the analytics module 230 may generate analytics based on interactions of the medical practitioner in the collaborative medical platform 140. For example, statistics can be derived based on counts of posts, comments, or other content contributed by a medical practitioner to the collaborative medical platform 140. Such analytics may be expressed in terms of counts of interactions, frequency of interactions, or other aggregations. These analytics could furthermore be separately aggregated based on whether interactions relate to preprocedural, intra-procedural, or post-procedural phases of procedures.

[0075] In an embodiment, the analytics module 230 may generate analytics based on specific filtering and / or combining functions specified by a user 155 of the collaborative medical platform 140. Additionally, the analytics module 230 may include various preset analytics that may be generated without necessarily receiving specific user inputs. Furthermore, in some embodiments, the medical intelligence module 220 may automatically generate analytics that it infers will be relevant to a specific user 155.

[0076] In some embodiments, the analytics module 230 may generate analytics based on any aspects of the collective case data including preprocedural data, telepresence sessions data (including recorded video, telemetry data, in-session content feed data, etc.), and postprocedural data. Analysis associated with telepresence session data may include performing various video processing, content recognition, or other advanced image processing techniques to extract useful information from videos. Furthermore, the analytics module 230 may leverage various medical intelligence data generated from the medical intelligence module 220 to generate analytics.

[0077] The practitioner education module 235 manages and stores training data for medical practitioners associated with medical procedures. In various embodiments, the training data comprises educational content including descriptive information about a medical procedure orabout a portion of a medical procedure. Example educational content includes training videos relating to performing medical procedures, articles about performing medical procedures, articles or videos about using one or more pieces of medical equipment 160 in a medical procedure, articles or videos about using one or more medical instruments in a medical procedure, best practices for a medical procedure, training manuals for medical procedures, instructional material for one or more medical instruments used in a medical procedure, digital training modules, webinars, audio data about a medical procedure (e.g., a podcast about a medical procedure) or other information for training medical practitioners in relation to medical procedures.

[0078] In various embodiments, educational content also includes configuration data or configuration instructions for one or more pieces of medical equipment 160 used in one or more medical procedures. For example, educational content includes a set of configuration instructions for configuring or for calibrating a robotic arm or other piece of medical equipment 160 for use in a medical procedure. Configuration instructions may include one or more settings for the piece of medical equipment 160. Example settings include: one or more limiting values for an amount of force applied by a piece of medical equipment 160, one or more limiting values for a range of motion of a piece of medical equipment 160, one or more limiting values for an amount of energy supplied by a piece of medical equipment 160, an identifier of a mode of operation for a piece of medical equipment 160, or values for one or more other settings of a piece of medical equipment 160. As another example, educational content comprises a set of instructions that, when executed by a piece of medical equipment 160, cause the piece of medical equipment 160 to perform a sequence of actions for calibration. Certain educational content may be executable by a piece of medical equipment 160 to modify values of one or more settings of the piece of medical equipment 160 or a mode of operation of the piece of medical equipment 160, allowing automatic modification of one or more settings of the piece of medical equipment 160 via the educational content item without a medical practitioner manually specifying values of settings of the piece of medical equipment 160.

[0079] The practitioner education module 235 stores educational content as different educational content items, with each educational content item comprising a discrete portion of content, such as a file. Each educational content item has one or more attributes providing descriptive information about the educational content item. For example, an attribute of an educational content item identifies one or more types of medical procedures associated with the educational content item, allowing identification of educational content items corresponding to different types of medical procedures. Other example attributes of an educational content item include: one or more medical practitioners associated with the educational content item (e.g., a medicalpractitioner who performed a medical procedure associated with the educational content item, a medical practitioner who created the educational content item), a location associated with the educational content item (e.g., a geographic location, a specific medical facility), a time associated with the educational content item (e.g., a time when the educational content item was created), identifiers of one or more pieces of medical equipment 160 associated with the educational content item, identifiers of one or more medical instruments used in a medical procedure associated with the educational content item, a format of the educational content item (e.g., audio, video, text), or other information describing the educational content item. Educational content items may be locally stored by the collaborative medical platform 140 (e.g., in the video library 250 or another storage device) or retrieved from one or more third-party servers 170 in various embodiments.

[0080] One or more educational content items may comprise reference cases, which are medical cases that a medical practitioner who performed a completed medical procedure in a medical case selected to be available to other medical practitioners. For a reference case, the practitioner education module 235 stores video data, telemetry data from medical equipment 160, or other data captured by the collaborative medical platform 140 during performance of the completed medical procedure. In various embodiments, a reference case includes a content feed including comments or other data obtained by the collaborative medical platform 140 from contributors during the completed medical procedure. For a reference case, the practitioner education module 235 pseudonymizes patient data to prevent the reference case from including patient data capable of being attributed to a specific patient. In some embodiments, the pseudonymized patent data in a reference case identifies ranges for one or more types of patent data to maintain relevant information about a patent on whom the completed medical procedure was performed for another medical practitioner while preventing identification of a specific patient on whom the completed medical procedure was performed.

[0081] Each educational content item is associated with one or more baseline criteria. Different baseline criteria specify values for metrics from performing a medical procedure, settings for a piece of medical equipment 160 used for a medical procedure, movement patterns of a piece of medical equipment 160 during a medical procedure, patterns of telemetry data obtained during a medical procedure, movement patterns of a medical practitioner during a medical procedure, or other descriptive information about performing a medical procedure. A baseline criterion specifies a standardized value for a metric, a standardized technique or approach used in a medical procedure, or other standardized value or technique related to a medical procedure. The practitioner education module 235 maintains one or more baseline criteria for different medical procedures, so different educational content items correspond to different medical procedures.An atribute of an educational content item comprises an identifier of a type of medical procedure to indicate the educational content item and its associated baseline criteria correspond to the type of medical procedure. This allows the practitioner education module 235 to identify different baseline criteria for different types of medical procedures.

[0082] In various embodiments, one or more medical practitioners input baseline criteria for a medical procedure to the practitioner education module 235. For example, a group of medical practitioners reach a consensus on values of metrics, paterns of telemetry data, paterns of movement, or values of other information describing performance of a medical procedure. A medical practitioner of the group inputs the agreed-upon baseline criteria to the practitioner education module 235 for storage in association with an educational content item. The group of medical practitioners may be associated with a particular medical facility (e.g., a hospital, a clinic), to provide facility-specific baseline criteria. The practitioner education module 235 stores an identifier of a medical facility as an atribute of an educational content item associated with the facility-specific baseline criteria to indicate baseline criteria associated with a specific medical facility. Additionally or alternatively, the group of medical practitioners who determined baseline criteria are not associated with a particular medical facility, but correspond to a larger organization or standards body, so the baseline criteria for the medical procedure are applicable across various medical facilities. The practitioner education module 235 may store facility-specific baseline criteria and more generally applicable baseline criteria as different atributes of an educational content item in various embodiments. This allows augmentation of more generally applicable baseline criteria associated with an educational content item with facility-specific baseline criteria.

[0083] In various embodiments, the practitioner education module 235 generates one or more baseline criteria associated with an educational content item by applying one or more trained machine learned models to metrics generated for multiple medical cases in which a type of medical procedure was performed by the analytics module 230. In various embodiments, the one or more trained machined learned models are also applied to telemetry data or video data captured by the telepresence module 225 during medical cases where the type of medical procedure was performed. For example, a machine learned model detects paterns in telemetry data captured during medical procedures of a specific type occurring in medical cases for which a specific value of a generated metric was generated or for which a value of a generated metric is within a range of values. The specific value of a generated metric or a range of values of the generated metric may correspond to one or more specific patient outcomes. For example, the specific value or range of values identifies successful patient outcomes for the type of medical procedure. One or more paterns of telemetry data detected with at least a threshold frequency inmedical procedures occurring in medical cases for which the generated metric has the specific value or has a value within a specified range are stored as baseline criteria for an educational content item associated with the specific type of medical procedure in various embodiments.

[0084] For example, application of a machine learned model to telemetry data identifies a specific sequence of movement of a piece of medical equipment 160 detected with at least a threshold frequency in completed medical procedures of the specific type performed in medical cases a metric corresponding to a positive outcome are stored as baseline criteria for an educational content item corresponding to movement of the piece of medical equipment 160 for the specific type of medical procedure. Telemetry data describing the specific sequence of movement of the piece of medical equipment 160 may be stored in the educational content item to specify limits of movement of the piece of medical equipment 160 during the specific type of medical procedure or to specify limits on force applied by the piece of medical equipment 160 during the specific type of medical procedure. As another example, captured telemetry data includes positional data for a piece of medical equipment 160 during occurrences of the type of medical procedure occurring in medical cases with one or more metrics correlated with positive outcomes for a patient. The practitioner education module 235 stores an educational content item associated with the type of medical procedure having the positional data in the captured telemetry data as a baseline criterion. This allows the practitioner education module 235 to dynamically generate an educational content item and associated baseline criteria for a type of medical procedure based on telemetry data captured during performance of the type of medical procedure over time, simplifying generation of educational content items for various medical procedures.

[0085] In other examples of generating educational content item from telemetry data, telemetry data from a piece of medical equipment includes bimanual dexterity of the medical practitioner during a medical procedure, with the bimanual dexterity information stored in an educational content item as baseline criteria in response to determining the medical procedure had a positive outcome. In various embodiments, the generated educational content item includes data for accessing a simulator for the piece of medical equipment 160 used during the medical procedure (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to further refine use of the piece of medical equipment 160 in response to telemetry data from a medical practitioner during a medical procedure including bimanual dexterity information deviating from the baseline criteria of the educational content item by at least a threshold amount. As another example, telemetry data from a piece of medical equipment 160 includes tissue tension for a patient during a medical procedure, with the tissue tension stored in an educational content item as baseline criteria in response to the practitioner educationmodule 235 determining the medical procedure had a positive outcome. The generated educational content item may include data for accessing a simulator for the piece of medical equipment 160 used during the medical procedure (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to further refine use of the piece of medical equipment 160 in response to telemetry data from a medical practitioner during a medical procedure including bimanual dexterity information deviating from the baseline criteria of the educational content item by at least a threshold amount.

[0086] Additionally or alternatively, the practitioner education module 235 applies one or more machine learned models to video data captured during performances of a specific type of medical procedure during prior medical cases to identify different pieces of medical equipment 160 used during the specific type of medical procedure, movement of different pieces of medical equipment 160 during the specific type of medical procedure, movement of the medical practitioner performing the specific type of medical procedure, or other information about performing the specific type of medical procedure. As further described above, applying a machine learning model to video data of prior performances of the specific type of medical procedure detects patterns of movement of the medical practitioner or of a piece of medical equipment 160 during performance of the specific type of medical procedure. A pattern or movement detected with at least a threshold frequency in video data of completed medical procedures of the specific type performed in medical cases having a metric corresponding to a positive outcome are stored as baseline criteria for one or more educational content items associated with the specific type of medical procedure. Such an educational content item associated with the type of medical procedure and baseline criteria describing a pattern of movement includes positional data or other data describing movement or positioning of the medical practitioner or for a piece of medical equipment 160 during the specific type of medical procedure for subsequent reference. Other information, such as depth perception data, proximity of a piece of medical equipment 160 to a structure of the patient, an angle of transection of a structure of a patient by a piece of medical equipment 160, path length of a piece of medical equipment 160, tissue tension, bimanual dexterity of a medical practitioner, or other data may be determined from video data by the practitioner education module 235 and stored as baseline criteria in response to being determined from video data of a medical procedure with a threshold frequency or in response to being determined from video data of a medical procedure having a metric corresponding to a positive outcome. This allows the practitioner education module 235 to determine baseline criteria for a type of medical procedure based on video data of one or more medical procedures.

[0087] To select an educational content item for a medical practitioner, the practitionereducation module 235 compares data describing performance of a medical procedure performed by the medical practitioner to baseline criteria associated with various educational content items. In various embodiments, after the medical practitioner completes the medical procedure, the practitioner education module 235 identifies educational content items associated with a type of the medical procedure and compares obtained information describing the medical procedure to one or more baseline criteria associated with the identified educational content items. For example, the practitioner education module 235 compares a metric generated for the medical procedure by the analytics module 230 from captured telemetry data, video data, or other data to a baseline criterion associated with educational content items associated with the type of the medical procedure. In response to the metric differing from the baseline criterion associated with an educational content item by at least a threshold amount, or otherwise failing to satisfy the baseline criterion, the practitioner education module 235 selects the educational content item associated with the baseline criterion for presentation to the medical practitioner. For example, in response to determining an amount of time for the medical practitioner to complete a medical procedure exceeds an average amount of time to complete the type of medical procedure or exceeds a baseline amount of time to complete the type of medical procedure, and selects one or more educational content items associated with the type of medical procedure and associated with baseline criteria specifying an amount of time to complete the type of medical procedure. In some embodiments, the practitioner education module 235 selects one or more educational content items associated with a type of medical procedure and associated with baseline criteria from which a metric determined for the medical procedure differs by at least a threshold amount, allowing the practitioner education module 235 to account for a specific amount of variance between a determined metric and a baseline criterion when selecting an educational content item.

[0088] Alternatively or additionally, the practitioner education module 235 compares one or more patterns or data detected within telemetry data captured during performance of the medical procedure to baseline criteria associated with educational content items. The practitioner education module 235 selects an educational content item associated with the type of the medical procedure and associated with a baseline criterion specifying a pattern of telemetry data differing from the captured telemetry data by at least a threshold amount. For example, telemetry data includes depth perception data during the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of the medical procedure and associated with depth perception data differing from the captured depth perception data by at least a threshold amount. The selected educational content item includes information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to be accessedby the medical practitioner in some embodiments. In another example, telemetry data includes tissue tension data captured during the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of the medical procedure and associated with tissue tension data differing from the captured tissue tension data by at least a threshold amount. The selected educational content item includes information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to be accessed by the medical practitioner in various embodiments.

[0089] Telemetry data from one or more sensors (e.g., sensors included in a piece of medical equipment 160) may also describe movement or positioning of medical equipment 160 or medical instruments during a medical procedure, and the practitioner education module 235 selects an educational content item including baseline criteria from which the movement of a piece of medical equipment or the positioning of a medical instrument in the telemetry data deviates by at least a threshold amount. For example, telemetry data includes a path length of a piece of medical equipment 160 during the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying a path length of the piece of medical equipment 160 from which the path length in the captured telemetry data deviated by at least a threshold amount. As another example, telemetry data includes positional data of a piece of medical equipment 160 during the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying positional data of the piece of medical equipment 160 from which the positional data of the piece of medical equipment 160 in the captured telemetry data deviated by at least a threshold amount. In an additional example, telemetry data includes bimanual dexterity data of the medical practitioner during the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying bimanual dexterity data of the piece of medical equipment 160 from which the bimanual dexterity data in the captured telemetry data deviated by at least a threshold amount. In the preceding examples, an educational content item selected based on the telemetry data includes information for accessing a simulator (e.g., through the collaborative medical platform 140) associated with the piece of medical equipment 160 corresponding to the telemetry data, providing the medical practitioner with increased interaction with the piece of medical equipment 160. Further, an educational content item selected based on deviation in positional data of a piece of medical equipment 160 from baseline criteria may include one or more of: a training video associated with the piece of medical equipment 160 anddescribing operation of the piece of medical equipment, audio data describing operation of the piece of medical equipment 160, and information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.). In some embodiments, the captured telemetry data describes a usage pattern of a piece of medical equipment 160 during the medical procedure. In response to determining the usage pattern of the piece of medical equipment 160 deviates from a baseline usage pattern of the piece of medical equipment in an educational content item, the practitioner education module 235 selects the educational content item, which may include benchmarking data describing a cost of the medical procedure based on the usage pattern and information about alternative usage patterns of the piece of medical equipment 160 to reduce the cost or information describing recommended usage patterns of the piece of medical equipment 160 or use of alternative pieces of medical equipment 160 in the type of medical procedure.

[0090] In various embodiments, the practitioner education module 235 compares one or more data identified from video data captured during performance of the medical procedure to baseline criteria associated with educational content items to select one or more educational content items for a medical practitioner. The practitioner education module 235 selects an educational content item associated with the type of the medical procedure and associated with a baseline criterion specifying specific data differing from the data identified from the captured video by at least a threshold amount. For example, the practitioner education module 235 obtains depth perception data during the medical procedure from video data of the medical procedure and selects an educational content item associated with the type of the medical procedure and associated with depth perception data differing from the depth perception data determined from the video data of the medical procedure by at least a threshold amount. The selected educational content item includes information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to be accessed by the medical practitioner in some embodiments. In another example, the practitioner education module 235 determines tissue tension data during the medical procedure and selects an educational content item associated with the type of the medical procedure and associated with tissue tension data differing from the tissue tension data determined from the video data by at least a threshold amount. The selected educational content item includes information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) to be accessed by the medical practitioner in various embodiments.

[0091] The practitioner education module 235 determines movement or positioning of medical equipment 160 or medical instruments during a medical procedure from video data of themedical procedure through one or more computer vision models or other models in various embodiments. Based on the movement or positioning information obtained from the video data, the practitioner education module 235 selects an educational content item including baseline criteria from which the movement of a piece of medical equipment or the positioning of a medical instrument in the telemetry data deviates by at least a threshold amount. For example, the practitioner education module 235 determines a path length of a piece of medical equipment 160 during the medical procedure from video data of the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying a path length of the piece of medical equipment 160 from which the path length from the video data deviated by at least a threshold amount. As another example, the practitioner education module 235 determines positional data of a piece of medical equipment 160 during the medical procedure from video of the medical procedure, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying positional data of the piece of medical equipment 160 from which the positional data of the piece of medical equipment 160 from the video data deviated by at least a threshold amount. In an additional example, the practitioner education module 235 determines bimanual dexterity data of the medical practitioner during the medical procedure from the video data, and the practitioner education module 235 selects an educational content item associated with the type of medical procedure and including baseline criteria specifying bimanual dexterity data of the piece of medical equipment 160 from which the bimanual dexterity data determined from the video data deviated by at least a threshold amount. In the preceding examples, an educational content item selected based on the telemetry data includes information for accessing a simulator (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.) associated with the piece of medical equipment 160 corresponding to the telemetry data, providing the medical practitioner with increased interaction with the piece of medical equipment. Further, an educational content item selected based on deviation in positional data of a piece of medical equipment 160 from baseline criteria may include one or more of: a training video associated with the piece of medical equipment 160 and describing operation of the piece of medical equipment, audio data describing operation of the piece of medical equipment 160, and information for accessing a simulator for the piece of medical equipment 160 (e.g., an identifier of a simulator, one or more exercises or techniques to perform on the identified simulator, etc.).

[0092] In some embodiments, the practitioner education module 235 determines an angle at which a structure of a patient (e.g., an organ of the patient) is transected by a piece of medicalequipment 160 (or by a medical instrument) during the medical practitioner form the video data of the medical procedure. The practitioner education module 235 selects an educational content item associated with the type of medical procedure and including an angle for transecting the structure of the patient from which the determined angle from the video data of the medical procedure deviated by at least a threshold amount. An educational content item selected based on deviation of a determined angle of transection of a structure of the patient from a baseline angle of transection may include content describing usage of the piece of medical equipment 160 (or medical instrument) transecting the structure of the patient during the medical procedure or content describing correlations between the angle of transection of the structure of the patient and one or more outcomes of the medical procedure (e.g., information depicting correlation between certain angles of transecting the structure of the patient and positive outcomes of the medical procedure or correlations between angles of transecting the structure of the patient and negative outcomes of the medical procedure). As another example, the practitioner education module 235 determines a proximity of a piece of medical equipment 160 (or a medical instrument) to one or more critical structures (e.g., an organ, a bone, an artery) of the patient during the medical procedure from video data of the medical procedure. The practitioner education module 235 selects an educational content item associated with the type of medical procedure and including a baseline proximity of the piece of medical equipment 160 (or medical instrument) from the critical structure of the patient from which the proximity of the piece of medical equipment 160 (or medical instrument) from the video data deviated by at least a threshold amount. In various embodiments, the educational content item with the baseline proximity to the critical structure of the patient includes content describing use of energy devices during medical procedures, which may include interactive content (e.g., content with questions to be answered by the medical practitioner), video or audio content describing use of energy devices during medical procedures, or other descriptive information about use of energy devices during medical procedures.

[0093] Further, the practitioner education module 235 may determine a usage pattern of a piece of medical equipment 160 during the medical procedure from video data of the medical procedure. In response to determining the usage pattern of the piece of medical equipment 160 deviates from a baseline usage pattern of the piece of medical equipment in an educational content item, the practitioner education module 235 selects the educational content item. The selected educational content item may include benchmarking data describing a cost of the medical procedure based on the usage pattern and information about alternative usage patterns of the piece of medical equipment 160 to reduce the cost. As another example, the selected educational content item may include information describing recommended usage patterns of thepiece of medical equipment 160 or use of alternative pieces of medical equipment 160 in the type of medical procedure.

[0094] In another example, the practitioner education module 235 compares one or more patterns of movement (e.g., movement of a piece of medical equipment 160, movement of a portion of the medical practitioner) detected within video data captured during the medical procedure to baseline criteria including a pattern of movement for the type of the medical procedure and selects an educational content item for the medical practitioner associated with a baseline criterion specifying a pattern of movement from which the detected pattern of movement differs by at least a threshold amount. Hence, the practitioner education module 235 may use data (e.g., video data or telemetry data) captured during performance of a medical procedure to determine when to select an educational content item for the medical practitioner. Different detected patterns within telemetry data or video data captured during performance of a medical procedure may be compared to different educational content items each associated with different baseline criteria. This allows the practitioner education module 235 to select an educational content item for a medical practitioner based on specific portions of the medical procedure that deviated from a corresponding baseline criterion based on video data or telemetry data captured during performance of a medical procedure, allowing tailoring of educational content item selection to specific portions of the medical procedure.

[0095] The practitioner education module 235 may apply one or more trained machine learning models to data describing performance of a medical procedure performed by the medical practitioner and to attributes of educational content items, such as educational content items associated with a type of the medical procedure, to select one or more educational content items for presentation to the medical practitioner. Example attributes of an educational content item include: a type of medical procedure associated with the reference content item, one or more medical practitioners associated with the reference content item, a location where the medical procedure was performed (e.g., a geographic location, an identifier of a medical facility), a format of the reference content item (e.g., text data, audio data, video data, etc.), feedback about the reference content item from or more medical practitioners (e.g., a rating, an amount of positive feedback received for the reference content item, etc.), or other descriptive information. The practitioner education module 235 trains one or more machine-learning models to select one or more educational content items for a medical practitioner based on attributes of educational content items and characteristics of the medical practitioner in various embodiments. Example machine learning models include regression models, support vector machines, naive Bayes, decision trees, k nearest neighbors, random forest, boosting algorithms, k-means, and hierarchical clustering. The machine learning models may also include neural networks, such asperceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, or transformers, while other types of machine learning models may additionally or alternatively be trained or applied by the practitioner education module 235 in various embodiments.

[0096] For example, to train a machine learning model to select one or more educational content items, the practitioner education module 235 generates a set of training examples, with each training example including data describing performance of a medical procedure performed by a medical practitioner and attributes of an educational content item and having a label indicating whether the medical practitioner in the training example accessed the educational content item included in the training example (or indicating whether the medical practitioner in the training example provided positive feedback for the educational content item included in the training example).

[0097] Applying the machine learning model to a training example generates a predicted likelihood of the medical practitioner in the training example accessing the educational content item in the training example (or a predicted likelihood of the medical practitioner in the training example providing positive feedback for the educational content item included in the training example). For each training example to which the practitioner education module 235 applies the machine learning model, the practitioner education module 235 generates a score for the machine learning model comprising an error term based on the label applied to the training example and the predicted likelihood of the medical practitioner in the training example accessing the educational content item in the training example (or a predicted likelihood of the medical practitioner in the training example providing positive feedback for the educational content item included in the training example). The error term, and accordingly the score, is larger when a difference between the label applied to the training example and the predicted likelihood of the medical practitioner in the training example accessing the educational content item in the training example (or the predicted likelihood of the medical practitioner in the training example providing positive feedback for the educational content item included in the training example) is larger and is smaller when the difference between label applied to the training example and the predicted likelihood of the medical practitioner in the training example accessing the educational content item in the training example (or the predicted likelihood of the medical practitioner in the training example providing positive feedback for the educational content item included in the training example) is smaller. In various embodiments, the practitioner education module 235 generates the score for the machine learning model applied to a training example using a loss function based on the difference between the label applied to the training example and the predicted likelihood of the medical practitioner in the training example accessing the educationalcontent item in the training example (or the predicted likelihood of the medical practitioner in the training example providing positive feedback for the educational content item included in the training example). Example loss functions include a mean square error function, a mean absolute error function, a hinge loss function, and a cross-entropy loss function.

[0098] The practitioner education module 235 backpropagates the error term to update a set of parameters comprising the machine learning model and stops backpropagation in response to the score, or to a loss function, satisfying one or more criteria. For example, the practitioner education module 235 backpropagates the score for the machine learning model through the layers of the machine learning model to update parameters of the machine learning model until the score has less than a threshold value. For example, the practitioner education module 235 uses gradient descent to update the set of parameters comprising the machine learning model. The practitioner education module 235 stores the trained machine learning model for application to data describing performance of a medical procedure performed by the medical practitioner and to attributes of one or more educational content items. In some embodiments, the practitioner education module 235 trains and maintains different machine learning models that each use different combinations of attributes of an educational content item and data describing performance of a medical procedure performed by the medical practitioner.

[0099] Alternatively or additionally, one or more machine learning models applied by the practitioner education module 235 to select an educational content item are nearest neighbor models applied to embeddings corresponding to educational content items and to characteristics of the medical practitioner, including data describing performance of a medical procedure performed by the medical practitioner. As further described above, attributes of an educational content item include: a type of medical procedure associated with the reference content item, one or more medical practitioners associated with the reference content item, a location where the medical procedure was performed (e.g., a geographic location, an identifier of a medical facility), a format of the reference content item (e.g., text data, audio data, video data, etc.), feedback about the reference content item from or more medical practitioners (e.g., a rating, an amount of positive feedback received for the reference content item, etc.), or other descriptive information. Example characteristics of a medical practitioner include: an area of specialization of the medical practitioner, types of prior medical procedures performed by the medical practitioner, medical procedures scheduled to be performed by the medical practitioner, a location where the medical practitioner performs medical procedures (e.g., a geographic location, an identifier of a medical facility, etc.), collaborators connected to the medical practitioner via the connection graph, or other descriptive information about the medical practitioner, as well as data further described above describing performance of a medical procedure by the medicalpractitioner.

[0100] In some embodiments, the practitioner education module 235 applies a nearest neighbor model to an embedding of the medical practitioner that determines a distance (or a measure of similarity) in a latent space between the embedding of the medical practitioner and embeddings for various educational content items. For example, the nearest neighbor model determines a Euclidean distance between the embedding of the medical practitioner and embeddings for educational content items. Based on the distances, the nearest neighbor model ranks educational content items by the distances (or measures of similarity) of their corresponding embeddings to the embedding of the medical practitioner and selects one or more educational content items having a threshold position in the ranking, so the selected one or more educational content items have embeddings nearest to the embedding of the medical practitioner. Alternatively, the nearest neighbor model selects one or more educational content items having less than a threshold distance from the embedding for the medical practitioner. Alternatively, the practitioner education module 235 generates an embedding for a medical procedure performed by the medical practitioner and selects one or more educational content items based on distances between the embedding for the medical procedure and embeddings for educational content items, as further described above.

[0101] Further, in some embodiments, the practitioner education module 235 generates embeddings for different medical practitioners based on characteristics of the medical practitioners, as further described above. The practitioner education module 235 determines distances between an embedding for a medical practitioner and embeddings for additional medical practitioners. For example, the practitioner education module 235 determines Euclidean distances between the embedding for the medical practitioner and embeddings for multiple additional medical practitioners. Based on the distances (or measure of similarity), the practitioner education module 235 selects a set of additional medical practitioners. For example, the practitioner education module 235 selects additional medical practitioners with embeddings within a threshold distance of the embedding of the medical practitioner. As another example, the practitioner education module 235 ranks additional medical practitioners based on distances between their embeddings and the embedding of the medical practitioner and selects additional medical practitioners having at least a threshold position in the ranking. The practitioner education module 235 selects one or more educational content items presented to one or more of the selected additional medical practitioners for presentation to the medical practitioner. Such embodiments allow the practitioner education module 235 to leverage similarity between various medical practitioners to select educational content items for presentation to the medical practitioner.

[0102] In various embodiments, the practitioner education module 235 determines one or more baseline criteria for educational content items by applying one or more clustering models to one or more attributes of medical cases in which a specific type of medical procedure was performed. Attributes of a medical case include one or more metrics generated for the medical case by the analytics module 230, telemetry data captured during performance of the medical procedure in the medical case, video data captured during performance of the medical procedure in the medical case, or other descriptive information about the medical case. Based on attributes of a medical case, the practitioner education module 235 generates an embedding for the medical case. The practitioner education module 235 applies a clustering model to embeddings for different medical cases in which the specific type of medical procedure was performed to generate different clusters of case where a specific type of medical procedure was performed. Different clusters are represented in a latent space including the embeddings for medical cases by different centroids, with a cluster including medical cases having embeddings within a threshold distance of the cluster’s centroid. In some embodiments, the practitioner education module 235 applies a k-means clustering model to embeddings for different medical cases in which the specific type of medical procedure was performed. Using k-means clustering causes a medical case in which the specific type of medical procedure was performed to be included in a cluster based on distances between the embedding for the medical case and centroids for different clusters. The medical case in which the specific type of medical procedure was performed is included in a cluster with a centroid having a minimum distance from the embedding for the medical case. Centroids of clusters are iteratively updated based on embeddings for medical cases in which the specific type of medical procedure was performed included in various clusters until one or more criteria are satisfied. This results in a specific number of clusters, each including medical cases in which the specific type of medical procedure was performed having similar embeddings.

[0103] The practitioner education module 235 may identify baseline criteria based on medical cases included in one or more clusters. For example, a cluster of cases in which the specific type of medical procedure was performed corresponds to positive outcomes for the specific type of medical procedure, while an alternative cluster corresponds to negative outcomes for the specific type of medical procedure. Based on captured telemetry data or video data during performance of the specific type of medical procedure in an additional case, the practitioner education module 235 generates an embedding for the additional case and determines a cluster including the additional case based on the centroids of the clusters and the embedding for the additional case. In response to determining the additional case is included in the alternative cluster corresponding to negative outcomes, the practitioner education module 235 selects one or more educationalcontent items for presentation to the medical practitioner performing the specific type of medical procedure during the additional case. The practitioner education module 235 compares video data or telemetry data captured during performance of the specific type of medical procedure in the additional medical case to video data or telemetry data associated with baseline criteria of educational content items associated with the specific type of medical procedure and selects one or more educational content associated with the specific type of the medical procedure and having baseline criteria specifying video data or telemetry data differing from the video data or telemetry data captured during performance of the specific type of medical procedure by at least a threshold amount.

[0104] Alternatively, the practitioner education module 235 selects an educational content item for a medical case in response to determining the embedding for the medical case is not included in a particular cluster. As an example, the practitioner education module 235 selects an educational content item for a medical case in response to determining an embedding for the medical case is greater than a threshold distance from a centroid of a particular cluster of medical cases. This may indicate that the medical case has characteristics that deviate at least a threshold amount from characteristics of other medical cases with positive patient outcomes in which the type of medical procedure was performed. As further described above, the practitioner education module 235 may select an educational content item associated with a specific type of medical procedure being performed in the medical case and having baseline criteria including video data or telemetry data differing from the video data or telemetry data captured during performance of the medical procedure in the medical case by at least a threshold amount.

[0105] When generating clusters of medical cases based on corresponding embeddings, the practitioner education module 235 may identify medical cases included in a particular cluster as reference cases for educational content items for a corresponding type of medical procedure. For example, in response to the practitioner education module 235 including a medical case in a specific cluster associated with positive outcomes, the practitioner education module 235 communicates a prompt to a medical practitioner associated with the medical case to generate a reference case based on the medical case. In response to receiving authorization from the medical practitioner to generate the reference case from the medical case, the practitioner education module 235 pseudonymizes patient data in the medical case and stores the pseudonymized patent data, video data captured during performance of the medical procedure, telemetry data captured during performance of the medical procedure, and one or more metrics generated for the medical procedure as an educational content item for the type of the medical procedure. One or more patterns determined from telemetry data or video data, or one or more generated metrics, are stored as baseline criteria associated with the educational content item.This simplifies creation of educational content items for a type of medical procedure by leveraging data captured by the collaborative medical platform 140 during performance of medical procedures to generate educational content items for subsequent reference about the medical procedure.

[0106] In various embodiments, an educational content item selected for a medical practitioner based on performance of a medical procedure by the medical practitioner is presented to the medical practitioner during a postprocedural stage. Presenting an educational content item to a medical practitioner during the postprocedural stage allows review of the educational content item after completion of a medical procedure. The practitioner education module 235 generates one or more interfaces that identify a selected educational content item to a medical practitioner. For example, the practitioner education module 235 includes information identifying a selected educational content item in a practitioner dashboard presented to the medical practitioner, such as a practitioner dashboard further described below in conjunction with FIG. 4. In various embodiments, information identifying a selected educational content item includes a link that, when selected by the medical practitioner, retrieves the selected educational content item for presentation. Alternatively, the practitioner education module 235 presents information identifying the educational content item in another interface or in another format. For example, the practitioner education module 235 transmits a notification message to a client device 150 of the medical practitioner that includes a link that, when selected by the medical practitioner, retrieves the selected educational content item for presentation.

[0107] The practitioner education module 235 may include a selected educational content item in one or more interfaces presented to the medical practitioner when accessing the collaborative medical platform 140 in various embodiments. For example, the practitioner education module 235 generates an interface including educational content and presents information describing a selected educational content item through the interface, allowing a medical practitioner to select the information describing the selected educational content item to access the selected educational content item. As another example, the practitioner education module 235 includes information identifying a selected educational content item in a medical case page generated by the interface management module 215 for a medical procedure for which the educational content item was selected. For example, a medical case page includes a section including notes or feedback for the medical practitioner about the medical case, with one or more educational content items selected by the practitioner education module 235 included in the section. Further, the interface management module 215 may generate one or more interfaces including recommendations for a medical practitioner based on metrics for the medical practitioner based on medical procedures, with the recommendation interface including one or more educationalcontent items selected by the practitioner education module for the medical practitioner based on data describing performance of one or more medical procedures.

[0108] In some embodiments, the practitioner education module 235 includes a selected educational content item in different interfaces depending on content of the selected educational content item. For example, educational content items describing the use of a piece of medical equipment or of a medical instrument are displayed in a recommendation interface. As another example, educational content items comprising interactive material or audio or video data for presentation to a medical practitioner are presented in a medical case page or in an education interface. However, in other embodiments, the practitioner education module 235 selects an interface for identifying a selected educational content item based on other characteristics of the educational content item.

[0109] Alternatively or additionally, the practitioner education module 235 presents a selected educational content item to a medical practitioner during an intraprocedural stage of a medical procedure. This presents the selected educational content item to the medical practitioner while the medical practitioner performs the medical procedure. In various embodiments, the practitioner education module 235 transmits a notification identifying the selected educational content item to a piece of medical equipment 160 or to a client device 150 that displays the notification or audibly presents the notification to the medical practitioner. The notification may include specific content from the selected educational content item to simplify access to relevant information from the selected educational content item by the medical practitioner. In various embodiments, the practitioner education module 235 transmits a notification identifying an educational content item to a piece of medical equipment 160 associated with the educational content item. For example, the educational content item includes recommended settings for the piece of medical equipment 160 (e.g., force thresholds, movement thresholds), so transmitting the notification to the piece of medical equipment 160 simplifies identification of the medical equipment 160 relevant to the educational content item. A notification transmitted to a piece of medical equipment 160 may include a link that, when selected by the medical practitioner, causes the piece of medical equipment 160 to execute one or more instructions that modify one or more settings based on the educational content item. Similarly, information identifying an educational content item associated with a piece of medical equipment 160 presented by a client device 150 may include instructions that, when selected, transmit instructions for modifying one or more settings of the piece of medical equipment 160. This simplifies modification of settings of a piece of medical equipment 160 based on a selected educational content item by reducing an amount of interaction by the medical practitioner with the piece of medical equipment 160. Alternatively, the practitioner education module 235 includes information identifying a selectededucational content item in an interface presented to the medical practitioner via a client device 150.

[0110] In some embodiments, a medical practitioner authorizes the practitioner education module 235 to automatically modify one or more settings of a piece of medical equipment 160 based on an educational content item selected for the medical practitioner. Such authorization may be specific to a particular medical procedure or limited to one or more specific pieces of medical equipment 160 used during a particular medical procedure. When the medical practitioner authorizes the practitioner education module 235 to automatically modify one or more settings of the piece of medical equipment 160, the practitioner education module 235 transmits a notification including one or more instructions corresponding to a selected educational content item to a piece of medical equipment 160 used in the medical procedure. The piece of medical equipment 160 executes the one or more instructions, modifying one or more settings of the piece of medical equipment 160 based on the selected educational content item. In various embodiments, the piece of medical equipment 160 displays a notification or otherwise notifies the medical practitioner that one or more settings have been modified or specified based on the selected educational content item. An indication that one or more settings are to be modified based on a selected educational content item may be presented to the medical practitioner by the piece of medical equipment 160 or by a client device 150 to alert the medical practitioner that one or more settings of the piece of medical equipment 160 are being automatically updated and provide the medical practitioner with an option to prevent modification of the one or more settings. Alternatively, the practitioner education module 235 automatically modifies one or more settings of a piece of medical equipment 160 based on an educational content item selected for a medical practitioner, as further described above, unless the medical practitioner indicates the practitioner education module 235 is not authorized to automatically modify one or more settings of a piece of medical equipment 160. This allows different embodiments to have a medical practitioner to opt-in to the practitioner education module 235 automatically modifying one or more settings of a piece of medical equipment 160 or to opt-out of the practitioner education module 235 automatically modifying one or more settings of a piece of medical equipment 160.

[0111] In other embodiments, presenting an educational content item during the intraprocedural stage of a medical case increases a number of interactions needed to modify one or more settings of a piece of medical equipment 160 used during a medical procedure. For example, presenting the educational content item via a piece of medical equipment 160 causes the piece of medical equipment 160 to request additional confirmation inputs from the medical practitioner subsequent to receiving input from the medical practitioner to change a specific setting of thepiece of medical equipment 160 to a value deviating from a corresponding value int eh educational content item or to specify a particular value for the specific setting of the piece of medical equipment 160 outside of a range corresponding to the educational content item. As an example, presenting the educational content item to the medical practitioner transmits an instruction to a piece of medical equipment 160 used during the medical procedure that, when executed, causes the piece of medical equipment 160 to display one or more warnings each requesting an input from the medical practitioner when the piece of medical equipment 160 receiving an input from the medical practitioner to a value of a setting of the piece of medical equipment 160 to a value outside of a range included in the educational content item. This increases difficulty of the medical practitioner configuring the piece of medical equipment 160 in a manner that is inconsistent with the selected educational content item to increase a likelihood that values of settings of the piece of medical equipment 160 are consistent with the selected educational item.

[0112] In various embodiments, the practitioner education module 235 also generates one or more interfaces for a medical practitioner to request an evaluating medical practitioner evaluate the medical practitioner’s performance of a medical procedure. The evaluating medical practitioner has a connection to the medical practitioner via the connection graph store 255 in various embodiments. For example, the evaluating medical practitioner collaborated with the medical practitioner in performing the medical procedure. As an example, the evaluating medical practitioner supervised the medical practitioner performing a medical procedure. Alternatively, the evaluating medical practitioner has a connection to the medical procedure in the connection graph store 255.

[0113] The presentation module 240 leverages stored information associated with a completed medical procedure to facilitate generation of presentations for education, research, training, or other purposes. Presentations may be in the form of slide decks, posters, videos, animations, or other multimedia content. Presentations may incorporate various multimedia (e.g., video, images, three-dimensional models, and associated metadata), patient record data, medical equipment telemetry data, information from content feeds, analytics, or other information generated and / or stored by the collaborative medical platform 140.

[0114] In an embodiment, the presentation module 240 may maintain one or more presentation templates for generating presentations. The template may include pre-formatted content with various information fields that may be automatically populated from a set of records. For example, a practitioner wanting to prepare a presentation relating to a set of recently performed procedures may specify the set of procedures to include in the presentation, and the presentation module 240 may automatically populate the presentation based on the data stored in associationwith those procedures, pages, with each page associated with one or more types of data about the completed medical procedure. In some embodiments, the presentation module 240 may apply one or more trained machine learned models to automatically generate and / or recommend presentation content that may be of interest to a medical practitioner. In further embodiments, the presentation module 240 may intelligently automatically de-identify patient data included in the presentations.

[0115] The presentation module 240 may furthermore include various editing tools for creating, viewing, and editing presentations. For example, the editing tools may enable editing of text, video, images, animations, three-dimensional models, or other content for including in a presentation.

[0116] In an embodiment, presentations may be presented through a presentation module 240 directly without data associated with the presentation being exported externally to the collaborative medical platform 140. For example, the presentation module 240 may enable live streamlining of a presentation during a telepresence session to a set of invited attendees. The invited attendees may be limited to users 155 of the collaborative medical platform 140 or may include outside attendees that may gain access via an external link. Sharing presentations in this manner enables practitioners to maintain data privacy and compliance and avoid issues that may arise when externally exporting medical data.

[0117] The application integration module 245 manages integration of applications with the collaborative medical platform 140. Applications may be utilized to add additional optional functionality to the collaborative medical platform 140. For example, applications may enable integration with a specific EHR system, scheduling system, or other existing medical system. Applications may furthermore enable users to selectively add specific functionality beyond the core features of the collaborative medical platform 140. The application integration module 245 may allow third parties to create applications that interface with the collaborative medical platform 140 and make these applications available to add.

[0118] The application integration module 245 may maintain a catalog of applications capable of interfacing with the collaborative medical platform 140 and may provide interfaces to enable users 155 to selectively add applications for integration. In various embodiments, applications identified by the application integration module 245 have been authorized or approved for installation by an administrator of the collaborative medical platform 140, allowing regulation of the applications capable of executing on the collaborative medical platform 140.

[0119] Additionally, the application integration module 245 may include one or more application programming interfaces (API) for an application installed through the applicationintegration module 245. An API for an application provides functionality for exchanging data between the application and one or more components of the collaborative medical platform 140, simplifying data exchange between the application and other portions of the collaborative medical platform 140.

[0120] The practitioner evaluation module 270 generates, tracks, and displays attribute scores that characterize practitioner's skill levels, expertise, and experience across a range of skill sets. These attributes may provide a dynamic and comprehensive view of a practitioner’s capabilities based on standardized metrics that can be directly compared between practitioners. Such attributes may be particularly useful for surgeons (including those involved with robot-assisted surgeries) and may facilitate tasks such as assigning surgeons to procedures, assigning supporting staff to the surgeon, recommending educational content to surgeons, automatically configuring a surgical robot or other medical instruments based on the surgeon’s skill set, or other tasks. Furthermore, characterizing and tracking these attributes can motivate a practitioner to continuously improve their skills. In some embodiments, the practitioner evaluation module 270 may facilitate goal tracking, issuance of rewards (real or virtual), maintaining leadership boards, or providing other incentives for a surgeon to achieve high performance and continue to improve.

[0121] The practitioner evaluation module 270 may generate attribute scores based on various information sources available in the collaborative medical platform 140 including telemetry data from robot-assisted surgical procedures, video data derived from video of procedures, data collected from simulations and / or training exercises, data relating to educational content viewed and / or interacted with, survey data received from other practitioners, health information relating to patients treated by the practitioner, or any other data sources described herein (e.g., including data obtained from the data ingestion module 205 described above). The practitioner evaluation module 270 may process this data through various algorithms, rule-based heuristics, and / or machine learning models to generate the set of attribute scores that reflect different aspects of surgical proficiency. For example, video and / or robot telemetry data may be processed through one or more machine learning models to characterize hand movements of the surgeon and to analyze how well a surgeon’s performance of a medical procedures conforms to one or more aspirational standards. Physical skills may be furthermore characterized by scores received in training and / or simulated procedures available through the collaborative medical platform 140. Surgeons may furthermore be evaluated based on experience (e.g., number of procedures performed as may be derived from hospital records), training courses completed, feedback from other practitioners, or other factors.

[0122] The attribute scores may correspond to predefined attribute fields that generallycharacterize the practitioner’s proficiency. Practitioners in different fields or different roles may be evaluated based on different attribute sets. For example, a surgeon may be evaluated according to attributes such as (1) surgeon workspace (characterizing an amount of time that the surgeon’s hands are in the surgeon workspace, e.g., as a percentage measurement); (2) spatial awareness (characterizing a measure of number of instruments in view, collisions, instrument crossing, etc.); (3) interceptions (e.g., characterizing ability to anticipate next steps or adverse events, which may be based on a pace of a procedure, pre-operative planning characteristics, patient selection based on comorbid conditions with known risk that can create intraoperative complications, or other factors); (4) accuracy (e.g., measured as a number of times the surgeon attempts to perform the same task); (5) patient selection (characterizing an ability of the surgeon to select a patient for a surgery at the appropriate time based on patient history, examination, and comorbid conditions); (6) completion of critical step (e.g., measured as a number of steps completed and / or time to complete steps); (7) tissue handling (e.g., based on sensor data characterizing level of force applied to tissue; (8) system stress; and (9) scoring (characterizing a time to complete a procedure). In further embodiments, attributes may characterize other aspects of a surgeon’s physical skills, precision in instrument control, cognitive skills, adaptability, problem solving, response to stress, or other characteristics. For surgeons that operate using robot-assisted surgical platforms, the characterized attributes may relate to the surgeon’s ability to interact with the robot in a safe and effective manner. In further embodiments, attributes may relate solely to interactions of the practitioner in the collaborative medical platform 140 (e.g., collaborative interactions, education content completed, training exercises, etc.) without necessarily relying on characterizing real -world performance.

[0123] In an embodiment, the practitioner evaluation module 270 may generate the attribute scores in a set of attribute categories on predefined scoring scales. For example, attributes scores may be generated in the range of 1-10 or 1-100 per attribute category. Alternatively, letter grades (e.g., A-F) may be used as attribute scores. In further embodiments, attribute scores may be generated according to any arbitrary scale which may vary between attributes. In yet further embodiments, attribute scores may be characterized as an achievement percentage towards a particular goal (e.g., in a range 0-100%). Attribute scores may furthermore be categorized into qualitative assessment categories that may each correspond to particular score ranges (e.g., excellent, very good, good, fair, poor, very poor).

[0124] The practitioner evaluation module 270 may furthermore aggregate attribute scores according to one or more combining functions to generate an overall score. Examples of combining functions may include an averaging function, a mean function, a summation function, or other combining function. The practitioner evaluation module 270 may assign a levelcategory based on an aggregate score such as, for example, beginner, intermediate, expert, master, etc. Alternatively, levels may be characterized using other categories such as bronze, silver, gold, platinum, titanium, diamond, etc. In such example implementations, a user’s profde page and / or avatar may be visually elevated in response to progressing through levels to create immediately visible hierarchies and promote competition.

[0125] The practitioner evaluation module 270 may furthermore facilitate various programs designed to assist practitioners in improving certain skills and achieving goals. For example, a surgical master program may be designed for practitioners that want to achieve master in robot- assisted surgical techniques. The practitioner evaluation module 270 may facilitate comparisons between practitioners in the program (e.g., via a leaderboard) to motivate practitioners through competition and opportunities to earn rewards.

[0126] In an embodiment, the practitioner evaluation module 270 may issue various real or virtual rewards based on achievements relating to a practitioner’s attribute scores. For example, a virtual reward may comprise a congratulatory notification of the practitioner’s achievement. Notifications of achievements may be presented when the user logs into the collaborative medical platform. Such notifications may be private to the practitioner, or may be displayed as a certification or badge on the practitioner’s profile page. Badges may be strategically designed to encourage certain behaviors and motivate practitioners to improve relevant skills. Rewards may also relate to visual appearance of an avatar presented on the practitioner’s profile page. The avatar may comprise a photograph of the practitioner or a cartoon drawing that represents the practitioner. The avatar may be automatically stylized in a manner that reflects the practitioner’s level as a form of reward for achieving a level. Furthermore, new milestones may be enabled that encourage the practitioner to strive for new achievements, badges, or other rewards.

[0127] Rewards can also include virtual tokens that may be used for purchases within the collaborative medical platform 140, such as gaining access to educational content. Tokens or other achievements may be used to track and certify the practitioner’s compliance with continuing education requirements or other specialized certifications. In some embodiments, the practitioner evaluation module 270 may facilitate issuance of real -world gifts from the practitioner’s employer (e.g., such as employee compensation, gift cards, etc.). Rewards may be based on the practitioner achieving certain predefined score thresholds in individual attributes or in aggregate, achieving a predefined threshold growth or growth rate, or a combination thereof. Achievements may also be recognized relating to data that is not necessarily tracked directly as an attribute, such as achieving a certain number of procedures performed, completing a procedure with at least a threshold accuracy, completing simulated challenges, etc.

[0128] The platform may also incorporate a comparative element, allowing surgeons to benchmark their performance against peers or established standards in robot-assisted surgery. This feature may provide motivation for continuous improvement and help identify industrywide trends in surgical skill development. Additionally, the system may offer personalized recommendations for skill enhancement based on the tracked attributes, suggesting specific training exercises or procedures that could help improve particular areas of performance.

[0129] The practitioner evaluation module 270 may facilitate tracking of practitioner leaderboards that enable comparison of practitioner attributes. Leaderboards may be tracked for aggregate scores, scores for individual attribute categories, and / or for other statistical data such as number of procedures performed, years in practice, number of tokens collected, number of hours of educational content completed, number or hours of training and / or simulation exercises performed, etc.

[0130] The practitioner evaluation module 270 may furthermore facilitate generation of a practitioner profile page that displays the practitioner’s attributes. In some embodiments, a public version of the profile page (accessible to other connected practitioners) may include a more limited set of attributes than a private profile page accessible only to the practitioner. Examples of profile pages are provided in FIGS. 11-18 described in further detail below.

[0131] The practitioner evaluation module 270 may furthermore automatically configure a surgical robot based at least in part on the practitioner’s attributes. For example, a robotic surgery platform may unlock features for a surgeon having attribute scores meeting curtained predefined criteria, designed to ensure that the surgeon is capable of using the features effectively and safely. In another example, certain warnings from the robotic platform may be enabled for surgeons at lower attribute levels and may be disabled for surgeons having higher attribute levels. In another example, a surgical robot platform may automatically adjust upper and / or lower boundaries around the speed, torque, or force applied by a robot arm or outputs of a medical instrument dependent on the surgeon’s skill level.

[0132] The practitioner evaluation module 270 may furthermore generate outputs to a hospital work assignment system that may affect how procedures or patients are assigned to practitioners, how procedures are staffed, and how procedures are scheduled. For example, based on case evaluations, the work assignment system may optimize assignments in a manner that best utilizes the respective skills of available practitioners. Score information may similarly be utilized for hiring decisions, by enabling facilities to evaluate and construct balanced teams with complementary skill sets.

[0133] The video library 250 stores videos of various medical procedures, training presentations,simulations, or other medical videos and metadata associated with the video. Examples of metadata associated with video of a medical procedure may include telemetry data of one or more medical instruments received in conjunction with the video, comments or annotations received from one or more medical practitioners through a surgical interface during the medical procedure included in the video, segmentation data that divides the video into temporal segments relating to different step of a procedure, profile information (e.g., age, body mass index, gender, etc.), associated with the patient in the video, or other information supplementing the video. Various reference content items including video data may be stored in the video library 250 for retrieval by the practitioner education module 235 in various embodiments.

[0134] The video library 250 may store videos in an indexed database that indexes videos based on various metadata. The video library 250 can then be browsed or searched via a video library interface to identify videos of relevance. The metadata associated with videos may include permissions stored in the connection graph store 255 that controls which users 155 have access to different videos. For example, a video in the video library 250 may be accessible only to users 155 that the video has been expressly shared with or that otherwise has viewing permissions for the video.

[0135] The connection graph store 255 comprises a database that stores information describing connections between entities or other objects (e.g., videos or other multimedia) managed by the collaborative medical platform 140. For example, as described above, the connection graph store 255 stores connections between users 155, connections between users 155 and procedures, connections between users 155 and multimedia content or other objects, or other connections between data entities of the collaborative medical platform 140.

[0136] The user profile store 260 stores profile data for users 155 of the collaborative medical platform 140. A user profile for a medical practitioner includes descriptive information such as a name of the medical practitioner, contact information for the medical practitioner, credentials or certifications of the medical practitioner, biographical information for the medical practitioner, types of medical procedures capable of being performed by the medical practitioner, medical facilities affiliated with the medical practitioner, operating room preferences (such as patient positioning, equipment setup, preferred instrumentation, typical procedure step order, etc.), equipment configuration preferences (e.g., ergonomic settings for a robot console), or other information describing the medical practitioner. Aspects of the user profile could be inferred using machine learning techniques. For example, a practitioner’s preferred instrumentation or step order may be inferred from application of a machine learning model trained to infer such preferences based on observed historical data. Additionally, a user profile for a medical practitioner includes medical procedures performed by or to be performed by the medicalpractitioner, as well as information describing the medical procedures. For example, the user profile identifies different types of medical procedures to be performed by, or performed by, the medical practitioner, and may include characteristics for each medical procedure (e.g., a length of time to complete the medical procedure, a number of times the medical practitioner performed a type of medical procedure matching the medical procedure, etc.). Further, one or more of the metrics determined by the analytics module 230 for the medical practitioner, as further described above, may be included in the user profile for the medical practitioner.

[0137] The patient data store 265 includes a patient profile for each patient associated with medical cases. A patient profile includes characteristics of a corresponding patient, which may be obtained from an electronic health record for the patient or may be provided via input from a medical practitioner. Characteristics of a patient include demographic information about the patient, medical conditions of the patient, medical procedures previously performed by the patient, allergies of the patient, contact information for the patient, current or prior prescriptions for the patient or other medically relevant information about the patient. A patient identifier is associated with a patient profile to uniquely identify the patient profile.

[0138] All of the data stored to the collaborative medical platform 140 (or otherwise made available through the collaborative medical platform 140) may be stored, presented, and in some cases restricted in a manner that ensures compliance with various data privacy and protection regulations.

[0139] FIGs. 3A-3B illustrate an example practitioner dashboard 300. FIG. 3A shows an upper portion of the dashboard 300 while FIG. 3B shows a lower portion of the dashboard 300 (which may be continuously scrollable). The practitioner dashboard 300 may operate as a home landing page for a medical practitioner upon logging into the collaborative medical platform 140. The practitioner dashboard 300 may include various content sections, at least some of which may be specifically targeted to the practitioner. A search bar 305 enables input of text-based search queries for searching content available in the collaborative medical platform 140 (e.g., case pages, other user pages, videos, presentations, etc.). In response to inputting a search query, a list of results may be displayed with links to content matching the search query. A video promotion section 310 shows a video recently added by the practitioner with user interface tools to enable the practitioner to promote the video by sharing it with other users, create a highlight reel, or view various statistical information about the video. An achievement section 315 presents an achievement relating to use of the collaborative medical platform 140. In this example, the achievement section 315 highlights that the user has recently reached 100 videos and provides links to view the user’s videos and access a video library. Other examples of achievements in the achievement section 315 could relate to number of cases managed, timeusing the platform 140, number of connections, count of frequency of interactions, or other usage achievements. The video library 320 includes video thumbnails, video tags, or other links to enable browsing of videos selected as potentially relevant to the medical practitioner. For example, relevant videos may be selected that relate to past or upcoming procedures associated with the medical practitioner, based on a history of videos viewed by the medical practitioner, based on a practice area or other profile information for the medical practitioner, or other factors. The webinar promotion section 325 includes a promotional banner for an upcoming webinar that will be viewable within the collaborative medical platform 140. The webinar may be identified as being of potential interest to the medical practitioner based on, for example, the subject matter of the webinar, the host of the webinar, or other factors. The shared cases section 330 provides summary information and links to case pages that have been shared with the medical practitioner. Examples of case pages are described in further detail below. The analytics summary 335 includes example analytics associated with the medical practitioner’s usage of the collaborative medical platform 140, procedures performed by the medical practitioner, or other analytics data derived from information stored in the collaborative medical platform 140. The analytical data may be presented in one or more visual representations such as a graph or chart. The feedback section 340 provides links to enable the medical practitioner to send feedback to an administrator of the collaborative medical platform 140.

[0140] FIGs. 3A-3B illustrate just one example of a practitioner dashboard 300. The types of content presented in the practitioner dashboard 300 may be different for different practitioners and / or may dynamically change over time for the same medical practitioner. Some of the sections may be fixed and always appear upon accessing the dashboard 300 (e.g., the search bar 305, video library 320, shared cases 330, analytics 335, and feedback sections 340), while other sections (e.g., video promotion 310, achievement 315, webinar promotion 325) may be dynamically inserted only in certain contexts. For example, webinar promotions 325 may be presented only when an upcoming webinar deemed to be of sufficient interest is upcoming. Achievements 315 may similarly be displayed only when a relevant achievement has recently been achieved. Furthermore, the dashboard 300 could be customized by the user to display desired sections in a configured order. The various sections 305, 310, 315, 320, 325, 330, 335, 340 when present, may furthermore be presented in different order in different contexts.

[0141] FIG. 4 shows an alternative embodiment of a practitioner dashboard 400. In the example shown by FIG. 4, the practitioner dashboard 400 includes an educational content item section 405 including information identifying an educational content item selected for a medical practitioner based on a previously performed medical procedure. The practitioner education module 235 selects the identified educational content item based on stored baseline criteria foreducational content items associated a type of the previously performed medical procedure and captured data describing performance of the previously performed medical procedure, as further described above in conjunction with FIG. 2. In various embodiments, the educational content item section 405 identifies one or more reasons why the identified educational content item is of potential interest to the medical practitioner. In the example of FIG. 4, the educational content item section 405 indicates that the identified educational content item includes suggested parameters or settings for a piece of medical equipment 160 (e.g., a robotic arm) used in the previously performed medical procedure. The educational content item section 405 in the example of FIG. 4 includes a link 410 that, when selected by the medical practitioner retrieves the educational content item for presentation to the medical practitioner via a client device 150 of the medical practitioner.

[0142] For purposes of illustration, FIG. 4 shows an example practitioner dashboard 400 where the educational content item section 405 is displayed proximate to a search bar 305. For example, the educational content item section 405 is displayed in a position of the practitioner dashboard 400 below the search bar 305, so the educational content item section 405 is prominently displayed in the practitioner dashboard 400 to increase a likelihood of the medical practitioner selecting the link 410 to the identified educational content item. However, in other embodiments, the practitioner dashboard 400 displays the educational content item section 405 in a different position relative to other sections. Similarly, while FIG. 4 shows an example where the achievement section 315 and the video library section 320 are displayed in conjunction with the educational content item section 405, in other embodiments, different or additional sections are displayed by the practitioner dashboard 400 in conjunction with the suggested reference content item section 405.

[0143] In various embodiments, the educational content item section 405 is dynamically inserted into the practitioner dashboard 400 in certain contexts and is not included in the practitioner dashboard 400 in other contexts. For example, the practitioner dashboard 400 displays the educational content item section 405 after the medical practitioner has completed a medical procedure. In an example, the practitioner dashboard 400 displays the educational content item section 405 starting a specific amount of time after the medical practitioner completed a medical procedure, but does not display the educational content section 405 before the specific amount of time lapses after completion of the medical procedure. The practitioner dashboard 400 displays the educational content item section 405 for a particular time interval after the medical practitioner completed the medical procedure in various embodiments.

[0144] While FIG. 4 shows an example where the educational content item section 405 identifies a single reference content item, in other embodiments, the educational content item section 405displays multiple reference content items selected for the medical practitioner. For example, the educational content item section 405 is a carousel content item having multiple slides, with each slide including information identifying a different selected educational content item and including a link to a different selected educational content item. In response to the medical practitioner performing a specific interaction with the educational content item section 405, the educational content item section 405 is updated to display a different slide including information identifying a different selected educational content item. For example, the educational item section 405 displays an alternative slide including information identifying a different selected educational content item in response to the medical practitioner performing a swiping gesture along an axis perpendicular to an axis including the search bar 305, the educational content item section 405, the achievement section 315, and the video library section 320. This allows a single section of the practitioner dashboard 400 to identify multiple educational content items to the medical practitioner.

[0145] In some embodiments, the collaborative medical platform 140 generates one or more education interfaces, such as an education dashboard. An education interface may additionally or alternatively display one or more suggested educational content items to a medical practitioner, providing an additional way for the medical practitioner to access the suggested educational content items. The practitioner dashboard 400 may include an interface element that, when selected by the medical practitioner, causes display of the education interface. The education interface may display information identifying multiple suggested educational content items in some embodiments, allowing the medical practitioner to more easily access a wider range of suggested educational content items.

[0146] FIG. 5 shows an example embodiment of a case sharing interface 500 for sharing a case with one or more contributors. Adding a contributor to a case may generate a connection between the contributor and the case and between the contributor and the case owner. The case sharing interface 500 includes a selection element 505 for receiving identifying information to identify a desired contributor. For example, the selection element 505 may receive an email address, name, a username, or another identifier of a medical practitioner or other requested contributor. In some embodiments, upon selecting identifying information for a desired contributor, the case sharing interface 500 may display all or a portion of profile data for the requested collaborator to enable the requestor to confirm if the matched profile data is the intended collaborator. The case sharing interface 500 then enables the requestor to confirm or decline selection of a collaborator and interact with a permission selection element 520 to set a desired permission level for the requested collaborator. Here, the permission level may place limits on an invited collaborator’s access to data about the case and / or may limit actions thecollaborator is permitted to perform in association with the case. In an example embodiment, the permission level may be selected between a “collaborator” level 525A and a “delegate” level 525B.

[0147] In response to receiving inputs to select a requested collaborator and set a desired permission level (via the permission selection element 520), the case sharing interface 500 may send an invitation to the requested contributor (e.g., via an email, text message, phone call, portal message, or other communication mechanism) to enable the requested collaborator to accept or decline the request. If the request is accepted, the case sharing interface 500 may add the identifier or other information for the new collaborator to a connected medical practitioner listing 510 that lists the contributors added to the case. For example, the illustrated example shows a connected medical practitioner listing 510 that includes the case owner 515 and three additional contributors that have been added to the case.

[0148] The case sharing interface 500 may furthermore enable the case owner to change permission levels of existing contributors in the connected medical practitioner listing 510. Furthermore, the case sharing interface 500 may include removal elements 530 associated with each contributor in the connected medical practitioner listing 510 that enables removal of a contributor from the case. Selection of a removal element 530 may remove the stored connection in between the practitioner and the case, such that the practitioner no longer has access to the case.

[0149] FIG. 6 is an example embodiment of a case dashboard 600 for a medical practitioner. The case dashboard 600 enables access to cases owned by the medical practitioner and cases shared with the medical practitioner by other users 155 as indicated in the case summary 610. In this example, the case dashboard 600 is organized as a set of case cards 605 that each graphically show a summary of a case. Selecting a case card 605 links to a case page for the case. In alternative embodiments, the dashboard 600 may be presented in a list view or other view without necessarily presenting case cards 605 in the visual form shown in FIG. 6.

[0150] FIG. 7 shows an example of a telepresence interface 700 associated with a telepresence session that may take place during an actual procedure or during a simulated procedure. Alternatively, the telepresence session may be utilized for live planning purposes without necessarily performing or simulating a procedure. In this example, the telepresence interface 700 displays a three-dimensional model of a target anatomy 705 associated with the procedure. The model may include annotated comments that may be obtained during the telepresence session or that were added in a preprocedural stage. Alternatively, the telepresence interface 700 may include a view of real-time video or images associated with an ongoing procedure. In anembodiment, each contributor may be able to switch between different relevant views such as real-time video or images, three-dimensional models, preprocedural images, or other relevant multimedia.

[0151] The telepresence interface 700 may furthermore include a telepresence content feed 715 for sending and receiving real-time messages between contributors. For example, a telepresence content feed 715 allows users to post messages and / or view messages from other participants. The messages may include text, media content (e.g., images, video, animations, etc.), or links to various media content or other resources (e.g., research articles). The telepresence interface 700 may furthermore enable participants to provide annotations on the target anatomy (presented in the form of an image, video, or model). For example, a participant may pin a comment to a specific location in the depicted anatomy, as may be indicated by an identifier 710.

[0152] Additionally, the telepresence interface 700 may display statistics 720 or other analytics that may be relevant to the procedure. The statistics 720 maybe include estimated or modeled values or metrics relating to the anatomy based on various sensed data from the medical equipment 160. The telepresence interface 700 may dynamically update the statistics 720 over time during the procedure.

[0153] FIG. 8 is another example of a telepresence interface 800 associated with a telepresence session. In this example, the telepresence interface 800 shows a live video of a procedure being performed together with a set of annotation tools 810 that enables a remote contributor to add annotation 805 overlaid on the video. The telepresence interface 800 also includes a set of alternative views 815 the contributor can switch between during the telepresence session. These alternative views 815 may include one or more different camera views (e.g., a view of the medical environment), one or more three-dimensional models (e.g., as shown in FIG. 7), views of preprocedural images, or other multimedia associated with the case. In various embodiments, telepresence interfaces, such as shown in FIGS. 7 or 8, are stored for a medical case and may be subsequently presented to other medical practitioners if a medical practitioner associated with the case authorizes generation of a reference case based on the medical case, as further described above in conjunction with FIG. 2.

[0154] In the example of FIG. 8, the telepresence interface 800 also displays an educational content item 820 to a medical practitioner, such as the medical practitioner performing the medical procedure. The educational content item 820 is dynamically selected by the telepresence interface 800 in various embodiments based on captured video data or telemetry data during the medical procedure. The telepresence interface 800 includes information describing the educational content item 820 or extracted from the educational content item 820,allowing the medical practitioner to discern content from the educational content item 820 via the telepresence interface 800. In various embodiments, the telepresence interface 800 limits presentation of the educational content item 820 to certain time intervals. For example, the telepresence interface 800 displays the educational content item 820 in response to the collaborative medical platform 140 determining that video data or telemetry data captured during performance of the medical procedure deviates by at least a threshold amount from baseline criteria associated with the educational content item 820. When captured video data or telemetry data does not deviate by at least the threshold amount from the corresponding baseline criteria for the educational content item 820, the telepresence interface 800 does not present the educational content item 820. For example, a client device 150 displaying the telepresence interface 800 receives a presentation instruction to present the educational content item 820 along with the educational content item 820 from the collaborative medical platform 140 and subsequently receives an alternative instruction to stop presenting the educational content item 820 from the collaborative medical platform 140. The alternative instruction may be received in response to the collaborative medical platform 140 determining video data or telemetry data received during performance of the medical procedure satisfies baseline criteria associated with the educational content item 820 or in response to determining video data or telemetry data no longer identifies a pattern corresponding to a baseline criterion associated with the educational content item 820.

[0155] To simplify incorporation of information from the educational content item 820 into the medical procedure, the telepresence interface 800 presents a modification instruction 825 in association with the educational content item 820. The modification instruction 825 includes an identifier of a piece of medical equipment 160 and values of one or more settings for the piece of medical equipment 160. In response to the medical practitioner selecting the modification instruction 825 via the telepresence interface 800, the collaborative medical platform 140 receives a request identifying the educational content item 820 and the piece of medical equipment 160. In response to receiving the request, the collaborative medical platform 140 transmits an instruction to the identified piece of medical equipment 160 to modify values of one or more settings to values retrieved from the educational content item 820 and included in the instruction transmitted to the piece of medical equipment 160. In various embodiments, the collaborative medical platform 140 determines the identifier of the piece of medical equipment 160 based on an identifier included in telemetry data received by the collaborative medical platform 140 or based on identifying information included in received video data of the medical procedure. This simplifies modification of one or more settings of the piece of medical equipment based on the educational content item 820 via interaction with the telepresenceinterface 800 rather than by manually entering values for settings identified by the educational content item to the piece of medical equipment 160.

[0156] FIG. 9 is an example embodiment of an analytics dashboard 900 for a medical practitioner. In this example, the analytics dashboard 900 displays a summary of cases managed by the medical practitioner includes, for example, a total number of cases, a number of cases in the current month, a number of cases in the current week, and a distribution of types of cases the practitioner has performed. In the example of FIG. 9, the analytics dashboard 900 also displays an educational content item section 905 to the medical practitioner. The educational content item section 905 includes information identifying an educational content item the collaborative medical platform 140 selected for the medical practitioner based on data describing performance of a medical procedure by the medical practitioner, as further described above in conjunction with FIG. 2. The educational content item section 905 includes a link that, when accessed, retrieves the educational content item from the collaborative medical platform 140 or from a third-party server 170 for presentation in various embodiments. The educational content item section 905 may identify an educational content item selected based on a medical procedure most recently completed by the medical practitioner in some embodiments. Alternatively, the analytics dashboard 900 includes multiple educational content item sections 905, with each educational content item section including an educational content item selected for a medical procedure previously performed by the medical practitioner, simplifying access to different educational content items relevant to various medical procedures performed by the medical practitioner.

[0157] FIG. 10 is an example embodiment of case video interface dashboard 1000 for viewing a case video. Case videos may be captured during a telepresence session or may be similarly captured during a procedure without a live streamed telepresence session. The case video interface 1000 includes a video interface that shows one or more views of a video associated with a medical procedure. The video interface 1000 may include multiple captured views, which may be from cameras in the medical environment, cameras inserted into the anatomy (e.g., endoscopy cameras), or other cameras. Captured views may furthermore include three- dimensional models, preprocedural images, procedure planning documents, or other visual information. The video may be segmented (manually or automatically using video processing and content recognition techniques) to divide the video into segments associated with different steps of the procedure. The video may include annotations provided by a medical practitioner during a telepresence session or in a postprocedural review. A content feed 1010 may be presented in association with a video to enable users 155 to post comments, links, media, or other content in association with the presentation. A reference content item, such as a referencecase, may display video and other information (e.g., a content feed 1010) of a medical procedure to a medical practitioner using the video interface 1000 described in conjunction with FIG. 10.

[0158] FIG. 11 is a first example of a profile page 1100 for a medical practitioner that enables viewing and interactions with practitioner attributes as further described above. In this example, the profile page 1100 presents an overall score 1102 for the practitioner and a progress graph 1104 that shows how the overall score 1102 has progressed over time. The profile page 1100 may also include other information such general information about the practitioner (e.g., name, hospital, practice area, etc.) and / or specific attribute scores in individual attribute categories.

[0159] FIG. 12 illustrates another example of a profile page 1200 for a medical practitioner. In this example, the profile page 1200 presents an overall score 1202, a level 1204, an attribute benchmark window 1206, an achievements window 1208, an enhancements window 1210, a leaderboard 1212, and a content feed 1214. In this example, the overall score 1202 may be presented as a percentage towards a predefined benchmark. The level 1204 may represent a qualitative description on a scale which may correspond to different ranges of the overall score 1202. The level 1204 may furthermore include a progress bar (or a progress graph) indicating progress through the different levels. The attribute benchmarks 1206 may present attributespecific scores in different attribute categories. Attribute benchmarks may be presented as a bar graph or other visual representation. The achievements window 1208 may present information about recent or upcoming achievements such as earning tokens, obtaining badges, unlocking levels, etc. The enhancements window 1210 may present links to various educational content, training sessions, simulations, or other content recommended for the practitioner. The leaderboard 1212 may present a ranking of the practitioner relative to other practitioners. The leaderboard 1212 could be filtered based on facility, geographic area, practice area, experience level, or other parameters. The content feed 1214 includes social interactions (e.g., posts, comments, replies, etc.) of the practitioner or other connected practitioners in the collaborative medical platform 140 as described above.

[0160] FIG. 13 illustrates an example of a notification page 1300 that includes a pop-up notification 1302 indicating that the practitioner has earned a new reward. For example, in this case, the pop-up notification 1302 indicates that the practitioner has “leveled up” based on performance improvements in certain attribute categories (e.g., precision, speed, decision making) associated with recent surgeries performed by the practitioner. This notification page 1300 may be automatically presented when the practitioner first logs into the collaborative medical platform 140 after achieving the reward criteria.

[0161] FIG. 14 illustrates another example of a notification page 1400. In this example, thenotification page 1400 indicates that the practitioner has earned tokens based on an increase in attribute scores. As described above, tokens may be used in the collaborative medical platform 140 to access educational content or obtain other awards. This notification page 1400 may be automatically presented when the practitioner first logs into the collaborative medical platform 140 after achieving the reward criteria.

[0162] FIG. 15 illustrates an example of a program page 1500 associated with a program accessible through the collaborative medical platform 140 intended to facilitate improvements in practitioner attributes. The program page 1500 may present some of the same data described above such as an overall score 1502 for a practitioner, a progress graph 1504, and leaderboard 1306, and a tokens window 1508. The program page 1500 may furthermore present a league information window 1510 that may identify other practitioners participating in the program and their respective overall scores. Such programs may be designed to enhance and improve surgeon skills while motivating these developments through collaboration, competition, and rewards.

[0163] FIG. 16 illustrates an example of a token redemption page 1600 for enabling practitioners to redeem tokens. Tokens may be redeemed for virtual or real world rewards such as, for example, access to educational courses or workshops, access to surgical tools, or other rewards.

[0164] FIG. 17 illustrates an example of an achievements page 1700 associated with a medical practitioner. The achievements page 1700 may present badges 1702 earned by the practitioner and / or progress towards those badges. Badges may relate to activities performed within the collaborative medical platform 140 or real-world medical procedures in which achievements can be derived from tracked video and / or telemetry data.

[0165] FIG. 18 illustrates an example of a telepresence achievement page 1800 that summarizes the reach of a practitioner through telepresence interactions. The telepresence achievement page 1800 may include session data 1802 indicative of various statistics associated with telepresence sessions (e.g., number of sessions, number of attendees, duration of sessions, etc.). A telepresence map 1804 may visually represent geographic locations of telepresence attendees to illustrate the reach and influence of the practitioner. The telepresence archive 1806 may provide links to historical telepresence sessions to enable the sessions to be reviewed and / or shared.

[0166] FIG. 19 is an example embodiment of a process for evaluating and presenting attribute scores for a medical practitioner in a collaborative medical platform 140. The collaborative medical platform 140 obtains 1902 sensor data relating to medical procedures performed by a practitioner. The sensor data may include image and / or video data depicting performance of the procedure (or aspects thereof), preprocedural imaging data, post-procedural imaging data, telemetry data from a surgical robot involved with the surgical procedure (e.g., control signals,sensor signals, instrumentation signals, etc.), sensor data from medical instruments, or other sensor data obtained during pre-operative, intraoperative, or post-operative phases of a medical procedure. The sensor data may be accumulated over the course of multiple procedures performed by the practitioner. Sensor data may furthermore include various metadata that is automatically captured or manually added. For example, metadata may include information about the date / time of the procedure, the practitioner(s) involved, the type of procedure being performed, steps of the procedure, medical equipment used during the procedure, patient data associated with the procedure, or other metadata. For video data, metadata may include spatial and / or temporal segmentation information indicative of object classes detected in the video frames, steps of a medical procedure being performed, or other information.

[0167] The collaborative medical platform 140 may also obtain 1904 interactive data relating to the practitioner’s interactions with the collaborative medical platform 140. The interactive data may include, for example, information from a connection graph indicating connections between the medical practitioner and other practitioners, facilities, cases, or other entities. The interactive data may also include usage data indicative of cases shared by the practitioner, participation in telepresence sessions by the practitioner, competition of educational courses, training sessions, or simulations by the practitioner, profile information of the practitioner, or other data collected and stored by the collaborative medical platform 140.

[0168] The collaborative medical platform 140 applies 1906 an attribute model to generate respective attribute scores characterizing skills of the medical practitioner. As described above, the attribute scores may reflect skills in a set of predefined categories relevant to the practitioner’s medical practice, and may reflect physical skills, cognitive skills, experience, or other factors. Different attribute models may be applied to generate different types of attribute scores depending on various factors such as the practitioner’s field of practice, role, experience, geographic location, or other factors.

[0169] The attribute model may generate scores relative to a predefined baseline criteria. In some embodiment, the attribute model may include one or more machine learning models trained to score performance of medical procedures (or simulations) based on the sensor data, interactive data, or a combination thereof. Such models may be trained in a supervised learning process by generating training feature vectors characterizing sensor data and / or interactive data of a medical procedure or simulation thereof and labels assigning scores to these training feature vectors. A learning algorithm then learns model parameters for mapping the training feature vectors to the scores. Alternatively, models may be applied to generate various intermediate metrics which may then be converted to scores according to various combining functions. For example, a video processing model may be applied to video to characterize a surgeon’s range ofhand movement when performing fine motor skill tasks. This metric may then be converted or combined with other metrics to generate an attribute score relating to motor skill performance. In another embodiment, an unsupervised learning approach may employed to train models that can identify anomalies relative to training datasets of sensor data and / or interactive data. In further embodiments, scores may be generated using various computational functions of heuristics applied to the sensor data and / or interactive data. For example, scores may be generated from combining various metrics such as counts of procedures performed, time to complete procedure or steps of procedures, etc.

[0170] The collaborative medial platform dynamically updates 1908 a profile page associated with the practitioner based on the attribute scores. In some embodiments, the profile page may directly list the attribute scores for the practitioner. In other cases, visual representations of the practitioner’s skills or progression of skill may be presented through charts, graphs, or other direct or indirect representations of the attribute scores. Updating the profile page may furthermore include identifying when criteria is met associated with certain predefined achievements and presenting notifications, badges, issuing tokens, or performing other actions associated with the achievements. For example, achievements may include leveling up to a new level (e.g., by meeting aggregate attribute score milestones), obtaining a particular score increase within a predefined time period, achieving a predefined goal set by the practitioner, or other achievement. Updating the profile page may furthermore include updating a visual representation of an avatar associated with the practitioner in a manner that reflects the scores. For example, a “master” level surgeon may have a more visually impactful avatar displayed on a profile page than an “apprentice” level surgeon. Updating the profile page may furthermore include tracking comparative scores between the practitioner and other peers (e.g., within the same facility, geographic area, practice area, associated with the same team, etc.). For example, comparative tracking may include updating a leaderboard ranking practitioners or determining a winner of a virtual competition.

[0171] The described embodiments incorporate multiple technical improvements that improve the functioning of computer systems, machine learning techniques, data management systems (particularly as related to healthcare data management), computer-based user interfaces, robotic and / or other medical instrumentation systems, and other technologies and technical fields. For example, the described embodiments provide technical improvements in data availability and data privacy by enabling third-parties to evaluate skill sets of medical practitioners (based on attribute scores alone) without performing manual performance evaluations of sensitive and / or restricted data such as operating room video, patient health records, or other sensitive health data.

[0172] The described embodiments furthermore include improvements in machine learning methods in that they combine information from disparate data sources including medical equipment telemetry data, video data, and mobile device data to improve predictive power relative to traditional machine learning techniques. Further still, by generating attribute scores for practitioners based on telemetry, video, and / or interactive data the described embodiments may generate various notifications, recommendations, or other content tailored to specific medical practitioners that enable them to improve their practice, accordingly resulting in better patient outcomes.

[0173] Furthermore, the described embodiments include technical improvements in the field of robotic-assisted surgery by enabling automated configuration of surgical robots based on the practitioner attribute scores. This results in improved performance of a surgical robot, improved human-robot interactions, and improved patient outcomes.

[0174] The foregoing description of the embodiments has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the embodiments to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.

[0175] Some portions of this description describe the embodiments in terms of algorithms and symbolic representations of operations on information. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.

[0176] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. Embodiments may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and / or it may include a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible non- transitory computer readable storage medium or any type of media suitable for storing electronic instructions and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may include architectures employing multiple processor designs for increased computing capability.

[0177] As used herein, unless expressly stated to the contrary, “or” refers to an inclusive “or”and not to an exclusive “or.” For example, a condition “A or B” is satisfied by any one of the following: A is true (or present) and B is false (or not present); A is false (or not present) and B is true (or present); and both A and B are true (or present). Similarly, a condition “A, B, or C” is satisfied by any combination of A, B, and C being true (or present). As a non-limiting example, the condition “A, B, or C” is satisfied when A and B are true (or present) and C is false (or not present). Similarly, as another non-limiting example, the condition “A, B, or C” is satisfied when A is true (or present) and B and C are false (or not present).

[0178] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope is not limited by this detailed description, but rather by any claims that issue on an application based hereon.Accordingly, the disclosure of the embodiments is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for generating attributes for a medical practitioner in an online collaborative medical platform, the method comprising: obtaining sensor data relating to medical procedures performed by the medical practitioner; obtaining interactive data characterizing interactions of the medical practitioner in the online collaborative medical platform; applying an attribute model to generate attribute scores for a set of attributes characterizing skills of the medical practitioner based at least in part on the sensor data and the interactive data; and dynamically updating a profde page associated with the medical practitioner in the online collaborative medical platform presenting the attribute scores.

2. The method of claim 1, wherein the sensor data comprises at least one of: video data of the medical procedures performed by the medical practitioner; and telemetry data associated with operation of a surgical robot controlled by the medical practitioner.

3. The method of claim 1, wherein the interactive data comprises at least one of: cases associated with the practitioner, educational programs completed by the practitioner, simulations scores achieved by the practitioner, training exercises completed by the practitioner, collaborative inputs provided by the practitioner, and telepresence interactions of the practitioner.

4. The method of claim 1, wherein dynamically updating the profde page comprises: detecting achievement of one or more predefined milestones associated with the attribute scores; and presenting in association with the profile page in response to the achievement, at least one of: a virtual badge, an achievement notification, and redeemable tokens.

5. The method of claim 1, wherein dynamically updating the profile page comprises: presenting a leadership board indicating a ranking of the medical practitioner in relation to peer medical practitioners having profiles in the collaborative medical platform.

6. The method of claim 1, further comprising: causing a surgical robot scheduled for operation by the medical practitioner to be configured based at least in part on the attribute scores.

7. The method of claim 1, wherein applying the attribute model comprises: applying on or more machine learning models trained to score performance of medical procedures from the sensor data based on deviations between the sensor data and baseline data.

8. The method of claim 1, wherein the set of attributes comprises at least one of: surgeon workspace data, spatial awareness, interceptions, accuracy, patient selection, completion of critical step, tissue handling, system stress, and scoring.

9. A non-transitory computer-readable storage medium storing instructions for generating attributes for a medical practitioner in an online collaborative medical platform, the instructions when executed by one or more processors causing the one or more processors to perform steps comprising: obtaining sensor data relating to medical procedures performed by the medical practitioner; obtaining interactive data characterizing interactions of the medical practitioner in the online collaborative medical platform; applying an attribute model to generate attribute scores for a set of attributes characterizing skills of the medical practitioner based at least in part on the sensor data and the interactive data; and dynamically updating a profde page associated with the medical practitioner in the online collaborative medical platform presenting the attribute scores.

10. The non-transitory computer-readable storage medium of claim 9, wherein the sensor data comprises at least one of: video data of the medical procedures performed by the medical practitioner; and telemetry data associated with operation of a surgical robot controlled by the medical practitioner.

11. The non-transitory computer-readable storage medium of claim 9, wherein the interactive data comprises at least one of: cases associated with the practitioner, educational programs completed by the practitioner, simulations scores achieved by the practitioner, training exercises completed by the practitioner, collaborative inputs provided by the practitioner, and telepresence interactions of the practitioner.

12. The non-transitory computer-readable storage medium of claim 9, wherein dynamically updating the profile page comprises: detecting achievement of one or more predefined milestones associated with the attribute scores; andpresenting in association with the profile page in response to the achievement, at least one of: a virtual badge, an achievement notification, and redeemable tokens.

13. The non-transitory computer-readable storage medium of claim 9, wherein dynamically updating the profile page comprises: presenting a leadership board indicating a ranking of the medical practitioner in relation to peer medical practitioners having profiles in the collaborative medical platform.

14. The non-transitory computer-readable storage medium of claim 9, the instructions when executed further causing the processor to perform a step of: causing a surgical robot scheduled for operation by the medical practitioner to be configured based at least in part on the attribute scores.

15. The non-transitory computer-readable storage medium of claim 9, wherein applying the attribute model comprises: applying on or more machine learning models trained to score performance of medical procedures from the sensor data based on deviations between the sensor data and baseline data.

16. The non-transitory computer-readable storage medium of claim 9, wherein the set of attributes comprises at least one of: surgeon workspace data, spatial awareness, interceptions, accuracy, patient selection, completion of critical step, tissue handling, system stress, and scoring.

17. A computer system comprising: one or more processors; and a non-transitory computer-readable storage medium storing instructions for generating attributes for a medical practitioner in an online collaborative medical platform, the instructions when executed by the one or more processors causing the one or more processors to perform steps comprising: obtaining sensor data relating to medical procedures performed by the medical practitioner; obtaining interactive data characterizing interactions of the medical practitioner in the online collaborative medical platform; applying an attribute model to generate attribute scores for a set of attributes characterizing skills of the medical practitioner based at least in part on the sensor data and the interactive data; anddynamically updating a profile page associated with the medical practitioner in the online collaborative medical platform presenting the attribute scores.

18. The computer system of claim 17, wherein the sensor data comprises at least one of: video data of the medical procedures performed by the medical practitioner; and telemetry data associated with operation of a surgical robot controlled by the medical practitioner.

19. The computer system of claim 17, wherein the interactive data comprises at least one of: cases associated with the practitioner, educational programs completed by the practitioner, simulations scores achieved by the practitioner, training exercises completed by the practitioner, collaborative inputs provided by the practitioner, and telepresence interactions of the practitioner.

20. The computer system of claim 17, wherein dynamically updating the profile page comprises: detecting achievement of one or more predefined milestones associated with the attribute scores; and presenting in association with the profile page in response to the achievement, at least one of: a virtual badge, an achievement notification, and redeemable tokens.

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