Systems and methods for providing guidance for robotic medical surgery

Abstract

The present disclosure relates to systems and methods for providing guidance for robotic medical surgery. A computer-implemented method for generating and presenting an electronic display of guidance for performing a robotic medical surgery may include: receiving a plurality of prior surgical data sets; receiving or identifying objective data defining one or more of a duration or a patient outcome of a robotic medical surgery; executing an algorithm to identify patterns across the plurality of prior surgical data sets; receiving information about instances of robotic medical surgery to be performed in the future on patients outside of a population; automatically generating guidance for performing the robotic medical surgery; and generating and presenting an electronic display of guidance for performing the robotic medical surgery.

Description

[0001] This application is a divisional application of an application with an application date of May 13, 2016, application number 201680035831.X, and invention name “System and method for providing guidance for robotic medical surgery”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to U.S. Provisional Application No. 62 / 162,430, filed May 15, 2015, the entire contents of which are incorporated herein by reference. Technical Field

[0004] Embodiments of the present disclosure generally relate to providing guidance for robotic surgical procedures. More specifically, the present disclosure relates to collecting and analyzing data from previous procedures to develop guidance for robotic surgical procedures. Background Art

[0005] Medical physicians can use robotic devices to help perform medical operations. Robotic devices can work with computer systems and other devices (e.g., components of a navigation system) to form a robotic system. The robotic system can receive and store various information related to the specific operation to be performed by the physician, such as information about the patient and the surgical plan. For example, for knee surgery, the surgical plan may include the type of operation to be performed (e.g., total knee replacement or unicompartmental surgery), tissue and bone modifications necessary to prepare the bone to receive the implant, and the type of implant to be implanted into the patient. The robotic system can also perform various functions during the execution of the surgical plan, such as tracking the patient and helping the physician modify the patient's anatomical structure (e.g., tissue, bone) according to the plan.

[0006] The robotic system may also acquire information during a medical procedure. Such information may relate to any number of characteristics of the medical procedure. Such information may describe specific characteristics of the performance of the procedure, such as how long a certain portion of the procedure took, which tools were used during the procedure, or how the physician completed certain bone modifications. Such information may also describe characteristics related to the procedure associated with the patient, the environment, or other inputs associated with the procedure. For example, characteristics may be the patient's height, weight, the type of procedure performed, or the operating room setup. Summary of the invention

[0007] Among other things, embodiments of the present disclosure relate particularly to retrieving and analyzing information acquired during a robotic medical procedure to provide guidance for the robotic medical procedure.Each embodiment disclosed herein may include one or more of the features described in conjunction with any other disclosed embodiment.

[0008] In one example, a computer-implemented method for generating and presenting an electronic display of instructions for performing a robotic medical procedure may include receiving, by one or more processors, a plurality of prior surgical datasets, wherein each prior surgical dataset: i) corresponds to an instance of a robotic medical procedure performed using a robotic tool on a patient within a population, and ii) defines one or more of a position or movement of a patient, a medical device, or the robotic tool involved in the robotic medical procedure; forces applied by the robotic tool; or the timing of steps of the robotic medical procedure; receiving or identifying, by the one or more processors, objective data defining one or more of a duration or a patient outcome of the robotic medical procedure; and executing an algorithm stored in a non-transitory computer-readable storage medium to identify patterns across the plurality of prior surgical datasets that describe an instance of a robotic medical procedure performed using a robotic tool on a patient within a population. one or more of the position or movement of a patient, a medical device, or a robotic tool involved, the forces applied by the robotic tool, or the timing of steps of a robotic medical procedure to achieve a duration or patient outcome defined by objective data; receiving, by one or more processors, information about instances of robotic medical procedures to be performed in the future on patients outside the group; automatically generating, by one or more processors, guidance for performing the robotic medical procedure based on evaluating patterns identified across multiple prior surgical data sets and the received information about the instances of the robotic medical procedure to be performed, the guidance including recommended positions or movements of a patient, a medical device, or a robotic tool involved in the robotic medical procedure, the forces applied by the robotic tool, or the timing of steps of the robotic medical procedure; and generating and presenting an electronic display of guidance for performing the robotic medical procedure.

[0009] The method may additionally or alternatively include one or more of the following features or steps: the duration defined by the objective data may be the duration of a portion of the robotic medical surgery; identifying patterns across multiple prior surgical data sets may include identifying levels of occurrence of at least one of the position or movement of a patient, a medical device, or a robotic tool across multiple prior surgical data sets; identifying patterns across multiple prior surgical data sets may include describing the movement of a robotic tool during a portion of a surgery that achieves the duration defined by the objective data, wherein the duration is the portion of the robotic medical surgery; information about the instance of the robotic medical surgery to be performed may include information about at least one of: the patient, the type of surgery, operating room characteristics, or prior experience of the user; guidance may include recommended timing of steps of the robotic medical surgery, and recommendations may include a recommended order of steps of the robotic medical surgery.

[0010] In another example, a system for generating and presenting an electronic display of instructions for performing a robotic medical procedure may include a computer-readable storage medium storing instructions for generating and presenting an electronic display of instructions for performing a robotic medical procedure; and one or more processors configured to execute the instructions to perform a method comprising: receiving a plurality of prior surgical data sets, wherein each prior surgical data set: i) corresponds to an instance of a robotic medical procedure performed using a robotic tool on a patient within a group, and ii) includes robotic data obtained from a robotic device associated with the robotic tool; receiving or identifying objective data defining one or more of a duration or a patient outcome of the robotic medical procedure; identifying a pattern across the plurality of prior surgical data sets that describes characteristics of the robotic medical procedure that achieves the duration or patient outcome defined by the objective data; receiving information about an instance of a robotic medical procedure to be performed in the future on a patient outside the group; automatically generating instructions for performing the robotic medical procedure based on the characteristics identified by the pattern and the received information about the instance of the robotic medical procedure to be performed; and generating and presenting an electronic display of instructions for performing the robotic medical procedure.

[0011] The system may additionally or alternatively include one or more of the following features or steps: the robotic data may be characterized by one or more of the position or movement of a patient, a medical device, or a robotic tool involved in the robotic medical procedure, forces applied by the robotic tool, or the timing of steps of the robotic medical procedure; the guidance may include recommended positions or movements of a patient, a medical device, or a robotic tool involved in the robotic medical procedure, forces applied by the robotic tool, or the timing of steps of the robotic medical procedure; the robotic data may include information collected by the robotic device during corresponding instances of the robotic medical procedure; recognizing patterns may include determining a level of occurrence of a characteristic across multiple input surgeries; the characteristic may be a sequence of surgical steps, and recognizing patterns may include determining the sequence of surgical steps to achieve a duration defined by the objective data; and information about the instance of the robotic medical procedure to be performed may include information about at least one of: the patient, the type of surgery, operating room characteristics, or prior experience of the user.

[0012] In yet another example, a non-transitory computer-readable storage medium may have instructions thereon that, when executed by a processor, cause the processor to perform a method for generating and presenting an electronic display of instructions for performing a robotic medical procedure, the method comprising: receiving a plurality of prior surgical data sets, wherein each prior surgical data set: (i) corresponds to an instance of a robotic medical procedure performed using a robotic tool on a patient within a population; and (ii) defines one or more of a position or movement of a patient, medical device, or robotic tool involved in the robotic medical procedure; a force applied by the robotic tool; or the timing of steps of the robotic medical procedure; receiving or identifying objective data defining one or more of a duration or a patient outcome of the robotic medical procedure; identifying a pattern across the plurality of prior surgical data sets that describes a robotic medical procedure; one or more of the position or movement of a patient, a medical device, or a robotic tool involved in a robotic medical procedure, the forces applied by the robotic tool, or the timing of steps of the robotic medical procedure to achieve a duration or patient outcome defined by objective data; receiving information about an instance of a robotic medical procedure to be performed in the future on a patient outside the group; automatically generating guidance for performing the robotic medical procedure based on evaluating patterns identified across multiple prior surgical data sets and the received information about the instance of the robotic medical procedure to be performed, the guidance including recommended positions or movements of a patient, a medical device, or a robotic tool involved in the robotic medical procedure, the forces applied by the robotic tool, or the timing of steps of the robotic medical procedure; and generating and presenting an electronic display of guidance for performing an instance of the robotic medical procedure.

[0013] The storage medium may additionally or alternatively include one or more of the following features: the duration defined by the objective data may be the duration of a portion of a robotic medical surgery; each previous surgery data set may include information collected by a robotic device associated with a robotic tool during the corresponding robotic medical surgery; identifying patterns may include identifying a level of occurrence of at least one of a position or movement of a patient, a medical device, or a robotic tool across multiple previous surgery data sets; identifying patterns across multiple previous surgery data sets may include describing the movement of the robotic tool during a portion of a surgery that achieves the duration defined by the objective data, wherein the duration is the portion of the robotic medical surgery; information about the instance of the robotic medical surgery to be performed may include information about at least one of: the patient, the type of surgery, operating room characteristics, or previous experience of the user; and the medical device may be a table supporting the patient.

[0014] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0016] Figure 1 A system for providing guidance for robotic medical surgery is illustrated according to an exemplary embodiment.

[0017] Figure 2 A method for providing guidance for robotic medical surgery according to an exemplary embodiment is illustrated.

[0018] Figure 3 Input information related to assisting robotic medical surgery is illustrated according to an exemplary embodiment.

[0019] Figure 4 An output of a surgery optimizer according to an exemplary embodiment is illustrated.

[0020] Figure 5 The flow from entering a procedure to various consumer input information is illustrated according to an exemplary embodiment. DETAILED DESCRIPTION

[0021] The present disclosure relates to systems and methods for providing guidance for robotic medical surgery. In one embodiment, a surgery optimizer may receive information corresponding to an input surgery. The information may then be analyzed by the optimizer to determine patterns. The optimizer may also receive input related to the robotic medical surgery for which the optimizer will formulate guidance. The optimizer may formulate guidance based on the patterns and information related to the robotic medical surgery to assist in implementing the robotic medical surgery.

[0022] Exemplary Embodiments

[0023] Figure 1 A system 2 for developing guidance for robotic medical surgery is illustrated, and Figure 2 The method for developing guidance is illustrated. The system 2 includes a surgical optimizer 10. The surgical optimizer 10 can receive input surgical data 4 ( Figure 2, step 210). In one embodiment, each set of input surgical data 4 corresponds to a completed or ongoing robotic medical procedure performed by a user using the robotic system 5. In the present disclosure, "user" is synonymous with "physician" and can be anyone who performs the described actions (e.g., surgeon, technician, nurse, etc.). Among other components, each robotic system 5 may include, among other things, a robotic device, a guidance module, and a camera support for tracking patients and other objects. The guidance module and camera support (referred to herein as the guidance component 24) may include a screen for providing output to the user. One or more of the robotic device, guidance module, or camera support may store input surgical data 4 that can be retrieved by the surgical optimizer 10. The input surgical data 4 may alternatively be stored in any other component of the robotic system 5, or may be stored external to the robotic system 5. The stored input surgical data 4 may be analyzed by the surgical optimizer 10 to determine a pattern of characteristic features of the corresponding medical procedure ( Figure 2 , step 220).

[0024] The surgery optimizer 10 may also receive information about assisting robotic medical surgery (referred to as assisting surgery information 6) ( Figure 1 ; Figure 2 , step 230). The surgery optimizer 10 can then formulate guidance for the assisted surgery based on the analysis of the input data 4 and the assisted surgery information 6 ( Figure 1 ; Figure 2 , step 240). In the present disclosure, "assisted surgery" refers to a robotic medical surgery to which guidance from the surgery optimizer 10 will be associated. The term "assisted surgery" is used herein to distinguish between a surgery for which guidance is directed or associated and a surgery and its corresponding data 4 used as input to the surgery optimizer 10. When guidance is developed or provided to the user, the assisted surgery may or may not have been initiated or completed. In addition, the assisted surgery may be a planned surgery that never actually occurred, a partially completed surgery, or an already completed surgery. The output provided by the surgery optimizer 10, which may be guidance related to the assisted surgery, may or may not actually be acted upon by the user, and if action is taken, the guidance may or may not actually optimize the surgery with respect to a given metric.

[0025] Surgery Optimizer

[0026] The surgical optimizer 10 can be used to implement various functions (e.g., calculations, processing, analysis) described herein. The surgical optimizer 10 may include a processing circuit 12 having a processor 14 and a memory 16. The processor 14 may be implemented as a general-purpose processor, an application-specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. The memory 16 (e.g., memory, memory unit, storage device, etc.) may be one or more devices (e.g., RAM, ROM, flash memory, hard disk storage, etc.) for storing data and / or computer code to complete or facilitate the various processes described in this application. The memory 16 may be or include a volatile memory or a non-volatile memory. The memory 16 may include a database component, an object code component, a script component, or any other type of information structure for supporting the various activities described in this application. According to an exemplary embodiment, the memory 16 may be communicatively connected to the processor 14 and may include computer code for performing one or more processes described herein. The memory 16 may include various modules, each of which is capable of storing data and / or computer code associated with a specific type of function. In one embodiment, the memory 16 contains several modules related to a medical procedure, such as an input module 18 , an analysis module 20 , and an output module 22 .

[0027] It should be understood that the surgical optimizer 10 need not be contained in a single housing. Instead, the components of the surgical optimizer 10 may be located in Figure 1 Components of the surgical optimizer 10 (including components of the processor 14 and memory 16) may be located, for example, in components of a different robotic system 5, or in components of a robotic system that assists in the surgery (e.g., in the guidance component 24).

[0028] The present disclosure contemplates methods, systems, and program products for performing various operations on any machine-readable medium. The machine-readable medium may be part of the surgical optimizer 10 or may interface with the surgical optimizer 10. The embodiments of the present disclosure may be implemented using an existing computer processor or by a dedicated computer processor for an appropriate system combined for this or other purposes or by a hardwired system. Embodiments within the scope of the present disclosure include program products that include a machine-readable medium for carrying or having machine-executable instructions or data structures stored thereon. Such a machine-readable medium may be any available medium that can be accessed by a general or special-purpose computer or other machine with a processor. For example, such a machine-readable medium may include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage device, magnetic disk storage device, other magnetic storage device, solid-state storage device, or any other medium that can be used to carry or store the desired program code in the form of a machine-executable instruction or data structure and can be accessed by a general or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hardwired, wireless, or hardwired or wireless connection), the machine appropriately regards the connection as a machine-readable medium. Therefore, any such connection is properly referred to as a machine-readable medium. The above combinations are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing machine to perform each function or group of functions.

[0029] Reference again Figure 1 , the surgical optimizer 10 also includes one or more communication interfaces 28. The communication interface 28 can be or include a wired or wireless interface (e.g., a jack, antenna, transmitter, receiver, transceiver, wire terminal, etc.) for communicating data with an external source via a direct connection or a network connection (e.g., an Internet connection, a LAN, WAN or WLAN connection, etc.). For example, the communication interface 28 may include an Ethernet card and port for sending and receiving data via an Ethernet-based communication link or network. In another example, the communication interface 28 may include a Wi-Fi transceiver for communicating via a wireless communication network. Thus, if the surgical optimizer 10 is physically connected to the surgical optimizer 10, the surgical optimizer 10 may be connected to the surgical optimizer 10. Figure 1 If the other components of the system 2 shown in FIG. 1 (such as the robotic system 5 , the original location of the auxiliary surgery information 6 , or the components that assist the surgery) are separated, the communication interface 28 can enable wireless communication between the surgery optimizer 10 and these separate components.

[0030] Enter surgical data

[0031] The input surgical data 4 is data corresponding to a robotic medical procedure performed using the robotic system 5. The data 4 may relate to any characteristics of the corresponding input. Figure 5 , the input surgical data 4 may include, for example, patient information (e.g., biometrics, patient images, comorbidities, allergies, etc.), operating room characteristics (e.g., size, setup), preoperative information (e.g., surgical plan, type of surgery), intraoperative information (e.g., the execution of the surgery, how long certain steps of the surgery took), postoperative information (e.g., the final positioning of any implants), and / or information related to any equipment used in the surgery (such as a robot). The data 4 may have been input into the robotic system 5 before the surgery (e.g., manually by the user or through any form of data transfer), or may have been collected, measured, recorded, or otherwise obtained by the robotic system 5 during the surgery. Robotic data refers to any data 4 associated with a medical surgery performed using a robotic device that is input to, measured, recorded, or otherwise collected / obtained by a robotic device. The data obtained during the surgery may be in the form of a log file, although the input surgical data 4 corresponding to the surgery may include other data corresponding to the surgery in addition to the log file. In one embodiment, a set of input surgical data 4 corresponds to a single medical surgery performed using a robotic system 5.

[0032] refer to Figure 1 as well as Figure 2 At step 210, the surgery optimizer 10 may receive any number of sets of input surgery data 4, each set corresponding to a medical procedure. In one embodiment, the surgery optimizer 10 may receive sets of input surgery data 4 from several robotic systems 5, where multiple sets are collected from each robotic system 5. Each set of input surgery data 4 collected from a single robotic system 5 may correspond to a different procedure performed using that robotic system 5. In other embodiments, the medical procedures corresponding to the sets of input surgery data 4 received by the surgery optimizer 10 may all be performed using the same robotic system 5, or each medical procedure may be performed using a different robotic system 5.

[0033] The input surgical data 4 received by the surgical optimizer 10 may include information indicative of characteristics of the corresponding input surgical procedure. For example, the input surgical data 4 from a certain surgical procedure may indicate, among many other characteristics, the techniques used by the physician to modify bone or perform other portions of the surgery, how long a certain portion of the surgery took, the forces applied to the bone during a portion of the surgery, or the positions of certain components of a robotic system or a person during the surgery.

[0034] The input procedure data 4 may indicate the technique used by the physician to modify the bone, for example, by including measurement lines associated with the position and orientation of the surgical tools over time. The robotic system 5 may include a tracking system that monitors the position and orientation of the patient and the robotic device (including the surgical tools) during surgery. The data 4 associated with the position and orientation of various components may be stored in a log file located in any component of the robotic system 5, such as in the robotic device, in the guidance module, or in the camera support of the tracking system. The log file with the data 4 conveying the position and orientation of the tools during surgery may be received by the surgical optimizer 10 (e.g., by executing instructions contained in the input module 18 of the memory 16) and may be analyzed by the surgical optimizer 10 (e.g., by executing instructions contained in the analysis module 20 of the memory 16) to determine how the physician implemented a certain portion of the surgery.

[0035] In one example, the position and orientation data may indicate that the professional swept the tool from side to side to complete a portion of the surgery. However, a log file from a different surgery may include data 4 indicating that the physician inserted the surgical tool into the bone multiple times to complete the same portion of the surgery instead of sweeping the tool from side to side. Thus, each set of input surgery data 4 may indicate characteristics of the corresponding surgery, such as the physician's technique during a portion of the surgery.

[0036] In a second example, the input surgical data 4 may include information indicating how the physician completed a preoperative range of motion test. For example, in a knee surgery, different physicians may use different techniques to map the patient's range of motion. The input surgical data 4 may include measurement lines describing the position and orientation of the tibia and femur during the range of motion test for the corresponding surgery. The position and orientation data may be analyzed to determine how the physician manipulated the tibia and femur during the range of motion test. Data 4 from various surgeries may be compared to determine how different physicians complete similar ranges of motion tests, or to determine whether a single physician uses the same technique during different surgeries.

[0037] In another example, the input surgery data 4 may include information indicating the length of time for a certain portion of the surgery. Many tasks may be performed in a single surgery. For example, in a knee replacement surgery, tasks may include the preoperative range of motion test described above; femoral preparation, which may include using different tools to cut, carve, or otherwise modify the femur; tibial preparation, which may similarly include using different tools to cut, carve, or otherwise modify the bone; and implant placement. Analysis of the input surgery data 4 may indicate how long each portion and sub-portion of the surgery took. For example, the data 4 may indicate the total length of time for the femoral preparation or the length of time to complete a single bone cut.

[0038] In yet another example, the input surgical data 4 may indicate forces applied to a bone during a portion of a corresponding surgery. In this embodiment, the robotic device may include a force sensor, and information related to the force sensor readings may be stored in a log file. The data 4 of the log file may then be analyzed to track the forces applied to the bone during the surgery (e.g., by a surgical tool).

[0039] The tracking system used during surgery can generate data 4 stored in corresponding log files related to the placement of various components in the operating room. For example, the data 4 can indicate the position of the robotic device (including the surgical tools and base of the robotic device), the guidance module, the patient's anatomy (e.g., femur, tibia), and the tracking system camera over time, along with any other items tracked by the tracking system. The data 4 can be analyzed to determine the position of the components during surgery.

[0040] Data analysis

[0041] refer to Figure 2 At step 220, the input surgical data 4 received from the robotic medical system 5 (or other location) may be analyzed by the surgical optimizer 10 (e.g., by executing instructions stored in the analysis module 20). In one embodiment, the data 4 may be analyzed to determine patterns of characteristics across multiple input surgeries. The characteristics analyzed may relate to physician technique, length of a portion of the surgery, forces applied to bone, position of an object or person during the surgery, or any other characteristics that may be identified by analyzing data corresponding to a robotic medical procedure.

[0042] The analysis module 20 of the surgery optimizer 10 may include instructions for analyzing the input surgery data 4 initially contained in a log file or other form to determine patterns of one or more characteristics across multiple surgeries. In one embodiment, the analysis module 20 may process the log file using a standard extract, transform, and load procedure to transform a plain text file into a database record. The analysis module 20 may also include statistical analysis processing to determine patterns in the input surgery data 4.

[0043] The pattern of a feature can be a description of the general occurrence (or lack thereof) of the feature, a specific feature of the feature, a factor associated with the occurrence of the feature, or any other description of the feature. For example, in the above example involving the movement of a surgical tool from side to side, the pattern can be that the side-to-side technique is used in 75% of the surgeries (e.g., the side-to-side movement is seen in 75% of the analyzed log files). In another embodiment, the pattern can be a curve mapping the distance of a single sweep of the tool during the side-to-side movement portion of multiple surgeries. Such a curve can provide information about the average distance swept by the tool. In yet another embodiment, the pattern can be that 80% of the physicians using the side-to-side technique are left-handed.

[0044] In another embodiment, the characteristic of interest may be the technique used to complete a series of motion tests. The pattern of characteristics identified by the surgery optimizer 10 may be, for example, that 30% of physicians use a first technique, 30% of physicians use a second technique, and 40% of physicians use a third technique. The pattern may additionally or alternatively identify details of the implementation of each technique (e.g., where the leg is positioned during a certain phase of testing across multiple surgeries), or differences between the implementations of a certain technique among physicians. The pattern may additionally or alternatively identify other information related to the use of each technique (e.g., whether certain physicians are more likely to use a certain technique).

[0045] If the characteristic involves time, then the pattern may be the average length of time for a certain portion of a procedure. In another embodiment, the pattern may be that using a certain technique for a task corresponds to a shorter completion time for that task. In yet another embodiment, the pattern may be that performing a collection of tasks in a certain order results in a faster overall completion time. A pattern of characteristics may be any description of a characteristic developed by analyzing multiple procedures.

[0046] A pattern associated with the force applied to the bone may be the average force applied during a certain portion of the procedure (e.g., the average force applied during a specific type of bone cut, where measurements were taken during multiple procedures). In other examples, a pattern may be that physicians with more experience use different force levels than physicians with less experience; that a certain technique corresponds to using higher or lower force; or that a certain physician uses different force levels depending on the setting of the operating room.

[0047] In yet another example, a pattern may relate to the positioning of an object or person during surgery. A pattern may be, for example, that a particular operating room layout or size typically results in a robotic device, patient table, camera holder, patient, or user being placed in a certain position. A pattern may additionally or alternatively be that certain types of surgery are typically performed with a robotic device in a certain position relative to a patient or other reference point, or that a certain physician typically positions objects in an operating room in a certain manner.

[0048] In summary, surgery optimizer 10 may analyze data 4 to identify patterns of any characteristics of interest. Patterns may relate to occurrences, specific features, implementations, related factors, or any other information describing characteristics that may be useful during development of guidance for assisting surgery.

[0049] Auxiliary surgery information

[0050] refer to Figure 1 and Figure 2 In step 230, the surgery optimizer 10 may receive auxiliary surgery information 6 about the surgery for which the formulated guidance will be directed. The auxiliary surgery information 6 may be any information about the auxiliary surgery, including information about the patient of the auxiliary surgery. Figure 3 Several examples of auxiliary surgical information 6 are illustrated, including patient information 30, surgical information type 32, operating room characteristics 34, and user prior experience information 36. The auxiliary surgical information 6 may be provided in any form, such as images (e.g., of the patient or operating room), data files, manuals input by a user or received from a patient monitoring device, and may be sent to the surgical optimizer 10 via the communication interface 28.

[0051] The patient information 30 may include various characteristics of the patient that assist in the surgery, such as height, weight, body mass index, bone density, bone structure, cartilage thickness, etc. The patient information 30 may be manually entered by a user, uploaded from a pre-existing image or text file, received from a patient monitoring device, or transmitted in any other manner to the surgery optimizer 10 via the communication interface 28. The patient monitoring device may be, for example, a device that monitors the patient's neurological or cardiac activity.

[0052] The surgery type information 32 may include the general type of ancillary surgery (e.g., total or partial knee replacement; hip replacement; ankle, shoulder, or spine surgery; surgery on bones outside of a joint; or non-orthopedic surgery (such as on soft tissue). The information 32 may additionally or alternatively include a surgical plan with details of the ancillary surgery, such as the shape and order of planned bone modifications or which tools (e.g., saws, bone drills) will be used during the surgery.

[0053] The operating room characteristics 34 may include the shape and size of the operating room and the planned placement of various people and objects during surgery. For example, the surgical plan may indicate the initial placement of one or more of the following: a patient table; a patient; a physician; other operating room personnel; a robotic device; a guidance module; tracking system components; tools (e.g., retractors and other objects used during surgery); or wires, tubing, and other equipment. During assisted surgery, the system 5 may track any of these components (including parts of a person (e.g., an arm) or parts of an object) using one or more tracking systems.

[0054] Finally, user prior experience 36 may include information related to the user's prior experience performing robotic medical procedures. Information 36 may include general information about the number of procedures performed by the user (e.g., the user has not performed robotic medical procedures, the user has performed more than one, or the user has performed many procedures), or may include detailed information about specific procedures performed by the user. Surgery optimizer 10 may take the user's prior experience into account when developing guidance for assisted surgery.

[0055] In one embodiment, the surgical optimizer 10 may receive further input in the form of rules. Rules may define the results and guidance that should be generated when the surgical optimizer 10 receives certain inputs. For example, a rule may define that for patients weighing more than 300 pounds, the surgical optimizer 10 should never recommend an implant size that is less than a certain threshold. Similarly, in another example, a rule may define that the surgical optimizer should not recommend an implant size greater than a certain threshold for patients with brittle femurs. Rules may govern any aspect of a surgical or decision-making step.

[0056] Although the rules may be general rules applicable to many surgeries, the rules may be considered part of the auxiliary surgical information 6 because they may be applied to assist the surgery. The surgical optimizer 10 may consider and apply the rules when developing the guidance. Similar to the other auxiliary surgical information 6, the rules may be manually entered by a user, uploaded from a pre-existing image or text file, or transmitted to the surgical optimizer 10 via the communication interface 28 in any other manner and stored in the memory 16. The rules may be developed through manual review of the literature, through analysis of the input surgical information 4 (either manually or by the surgical optimizer 10), or through any other mechanism.

[0057] guide

[0058] refer to Figure 1 and Figure 2At step 240, the surgery optimizer 10 may formulate an output 26 by executing an algorithm stored in the output module 22. In one embodiment, the output may be guidance for the physician to implement during the assisted surgery. Formulating the guidance may include generating revised robotic instructions that, when executed, cause the robot to provide guidance to the user. The guidance may be displayed on a screen of one or more guidance components 24. The guidance may be based on patterns of characteristics of the input surgery identified by the surgery optimizer 10 and based on the assisted surgery information 6 received by the surgery optimizer 10.

[0059] Figure 4 Several exemplary types of guidance that may be developed by surgery optimizer 10 are illustrated. Figure 4 The categories shown in and the examples provided below are illustrative only. Certain types of instruction may fit into more than one category, or may fall into Figure 4 In one example, guidance relates to clinical decision support 40. Clinical decision support 40 may include any type of guidance intended to assist a physician in making decisions before, during, or after a robotic surgical procedure.

[0060] In one embodiment, guidance can assist the physician in making decisions related to tool movement 42. Guidance can be a recommendation to move the tool in a side-to-side motion to complete a certain portion of the surgery. This recommendation can be based on the recognition of the pattern of 75% of physicians using side-to-side motion as described above. Guidance can also be based on any other pattern identified by the surgery optimizer. Guidance can be tailored to the specific auxiliary surgery by also being based on the auxiliary surgery information 6. Thus, if certain information contained in the auxiliary surgery information 6 strongly opposes the recommendation for side-to-side motion (for example, if side-to-side motion is more difficult to achieve and the user has no prior experience), then the surgery optimizer 10 can take this into account and recommend a different technique. In this way, the surgery optimizer 10 can synthesize data from the input surgery and information about the auxiliary surgery in order to develop targeted and appropriate guidance for the auxiliary surgery.

[0061] In another example, the surgery optimizer 10 can formulate guidance related to implant type 44 (e.g., implants or parts of implants to be used in knee or hip replacement surgery) or implant positioning. The implant type 44 can be the size of the implant, the brand of the implant, the material of the implant, or any other feature related to the implant. The recommendation related to the implant type 44 can be based on the pattern of the implant type of the patient identified for input surgery. For example, guidance can be based on the type of implant selected for patients with bone size and structure similar to the patient of the auxiliary surgery. In another example, the implant type 44 can be based on the type of implant selected for other patients with similar weight to the patient of the auxiliary surgery. Therefore, the formulation of guidance related to the implant type 44 can be based on the pattern identified in the data of the input surgery and the information about the patient of the auxiliary surgery. Similarly, the guidance related to implant positioning can be based on the positioning pattern of the implant in the previous surgery of the patient with similar characteristics (e.g., bone size, structure, weight) to the patient of the auxiliary surgery. In one embodiment, the surgery optimizer 10 can receive information about the long-term results of the patient of the input surgery as part of the input surgery data 4. The surgical optimizer 10 may base its guidance for an assisted procedure at least in part on the type or positioning of the implant that results in the best long-term outcome for the patient undergoing the procedure.

[0062] Although in some cases, the pattern of characteristics and auxiliary surgery information can relate to the information of same type (for example, the bone structure of the patient of input surgery and the bone structure of the patient of auxiliary surgery), in other embodiments, pattern and auxiliary surgery information can relate to different types of information.For example, the guidance relevant to implant type 44 can be based on the pattern of certain type of input surgery (for example, unicompartmental knee replacement surgery) usually using a certain type of implant and the auxiliary surgery information about the bone structure of the patient.

[0063] In another example, the guidance may relate to the order of the bone modifications 46. The surgery optimizer 10 may have recognized that physicians of input surgeries of similar types to the auxiliary surgery typically perform the bone modifications in a certain order. Therefore, the surgery optimizer 10 may formulate guidance that recommends that the physician of the auxiliary surgery perform the bone modifications in a similar order. However, if other factors argue against performing the modifications in the same order as the input surgery (such as the bone structure of the patient of the auxiliary surgery or even operating room characteristics (e.g., initial placement of robotic equipment)), then the surgery optimizer 10 may recommend a different order of the bone modifications.

[0064] Another type of clinical decision support guidance may be user-specific guidance 48. User-specific guidance 48 may be any type of guidance that takes into account the experience, qualifications, or other characteristics of the physician assisting the procedure. User-specific guidance 48 may take into account factors such as how many robotic surgical procedures the physician has previously performed, the techniques the physician used to perform those surgical procedures, the physician's handedness, or the physician's preferences. Such information about the physician may be included in the auxiliary surgical information 6 or may be included in the input surgical data 4 corresponding to the procedure performed by the physician.

[0065] In one embodiment of user-specific guidance, the surgery optimizer 10 may output different orders or numbers of guidance steps to physicians with different experience levels. For example, during a surgery performed by a less experienced physician, the surgery optimizer 10 may provide a greater number of surgical steps, thereby including more details, than provided to a physician with more experience. Therefore, more experienced physicians may receive more simplified guidance commensurate with their experience during surgery. Over time, as a novice physician becomes more experienced, the surgery optimizer 10 may reduce the number of details and / or steps displayed to the physician during that physician's surgery. Similarly, in another embodiment, the surgery optimizer 10 may display certain messages to novice physicians that are not necessarily displayed to more experienced physicians. For example, relevant warning messages (e.g., about common but undesirable actions) may be displayed during the novice physician's surgery. When the physician becomes more experienced and does not perform undesirable actions, the surgery optimizer 10 may stop displaying the message. On the other hand, if a particular experienced user can benefit from a message (e.g., because the user is performing some undesirable action during surgery or performed undesirable actions during past surgeries), the surgery optimizer may display the message for that user.

[0066] Another category of guidance may include operating room guidance 50. This type of guidance may relate to the placement of objects or people within the operating room. Equipment placement guidance 52 may be recommended locations for placing robotic devices, guidance modules, tracking systems, patient tables, or any other objects within the operating room. The recommendations may be based on the positioning of these items during input surgeries with similar operating room features (e.g., shape, size), and may take into account fixed objects or other constraints of the operating room used to assist in the surgery. For example, some operating rooms may have a patient table that is fixed to the floor. Thus, in addition to the pattern of placement of objects during the input surgery, the recommendations for positioning the remaining objects may be based on the positioning of the fixed patient table.

[0067] The guidance related to patient placement 54 may recommend that the user place the patient in a certain orientation, or may be more specific and relate to a certain specific positioning of the patient's legs or other parts of the anatomy. For example, if the patient was positioned facing a certain orientation relative to the robotic device in a previous surgery having similar operating room characteristics to the assisted surgery operating room characteristics 34, then the guidance may be a recommendation that the patient be similarly positioned relative to the robotic device to be used in the assisted surgery. Similarly, if the patient's legs were positioned at a certain angle with the knee at the beginning of most input surgeries having similar characteristics to the assisted surgery, then the guidance may be a recommendation to position the patient's knee at that angle at the beginning of the assisted surgery.

[0068] In another embodiment, the output guidance 26 may be a recommendation related to user placement 56. The user may be a person operating the robotic device, or may be any other person in the operating room (e.g., a technician, an assistant, a doctor, a nurse, etc.). The guidance related to user placement 56 may be based on the user placement pattern identified from the input surgical data 4 and based on the auxiliary surgical information 6 (e.g., the type of surgery 32, operating room characteristics 34, etc.). Analysis of the input surgical data 4 may have revealed that certain types of surgeries are completed faster when the user stands in a certain position relative to the patient and the robotic device. Therefore, for auxiliary surgeries with similar characteristics, the guidance 26 may recommend that the user stand in a similar position relative to the patient and the robotic device to perform the surgery.

[0069] Example workflow

[0070] Figure 5 An exemplary workflow for providing guidance based on patterns in input surgical data 4 is illustrated. Figure 5 Step 510 includes input surgical data 4 that may be received by the surgical optimizer 10. As described above, the input surgical data 4 may include any information about the robotic medical procedure, such as patient information received in any manner, information about the operating room (OR) (e.g., OR size, OR setup), pre-operative information (e.g., procedures performed prior to surgery, such as the range of motion test described above), intra-operative information (e.g., tracking information, locations of bones to be contacted for bone registration), post-operative information (e.g., desired final implant location, information from rehabilitation), and / or information related to the surgical robot (e.g., joint angles, tolerances, whether the robot performed correctly during surgery, movement of the robot) and information related to other devices / equipment used during the surgery.

[0071] The processed data illustrated in step 520 may include information resulting from the analysis of the raw case data of step 510 and may be derived from the raw case data shown in step 510. The processed data may include values ​​that are comparable between more than one surgery and may help determine trend data in step 530. For example, the processed data may include information related to operating room efficiency (e.g., how quickly the OR staff completes the surgery). Information about efficiency may be derived from various types of raw case data, such as intraoperative data related to the timing of each part of the surgery and robotic data related to the movement of the robot. The processed data may also include information about surgeon performance (e.g., how the surgeon holds certain tools). Surgeon performance information may be derived from preoperative data, intraoperative data, and / or robotic data, for example, by comparing the preoperative plan with intraoperative tracking data and information about robot movement obtained during the surgery. Step accuracy (e.g., how accurately the surgeon / OR staff completes the steps of the surgery) may similarly be derived by comparing preoperative information with intraoperative information. Patient outcome information may include a comparison between preoperative and postoperative data. Finally, information related to the health of the robot (or the health of other instruments or equipment used during surgery) (such as whether a part of the robot needs to be replaced, determining how much a motor has been used, or determining whether other instruments need maintenance) can be derived from the raw case data associated with the robot or other equipment.

[0072] By reviewing the input surgical data 4 and processed data from multiple surgeries, surgeons, operating rooms, hospitals, and / or regions, the surgical optimizer 10 may determine trend data in step 530. Trend data may include patterns that have been identified by the surgical optimizer 10. Examples of trend data include surgeon technique (e.g., different techniques are used in different regions / hospitals, different surgeons use different techniques), case reports (e.g., a synopsis of multiple different surgeries that includes information about the surgeries, such as bone registration accuracy, comparison of preoperative plans to postoperative outcomes, etc.), population health (e.g., patients in different regions / hospitals have better or different outcomes, patients undergoing a certain surgery have better or different outcomes), OR practices (e.g., certain OR settings, staffing, or surgeries lead to better / different outcomes), feature efficacy (e.g., how well a user implements steps of a surgery, how well a feature (e.g., a button) works), and / or maintenance scheduling (e.g., does the trend data indicate that certain robotic devices or other equipment require maintenance, either because the outcomes of surgeries are changing, or because the equipment has been through a certain number of surgeries).

[0073] In step 540, the trend data obtained in step 530 can be applied in various settings and provided to multiple customers to provide guidance to the customers. For example, in order to provide guidance to the surgeon, information about the patient and surgical data (e.g., preoperative, intraoperative, and postoperative) can be processed to determine surgeon performance (e.g., how well the implant was placed), procedural accuracy (e.g., how well the surgeon performed in the bone registration procedure), and patient outcomes. The surgeon can then receive a case report summarizing information about the various surgeries. Based on the information in the case report, the surgical optimizer 10 can provide guidance to the surgeon to improve future surgeries.

[0074] In another example, the surgical optimizer 10 may provide guidance to an organization, such as a hospital, an insurance company, or a government. For example, a hospital may be interested in various trend data, including data related to surgeon technique or maintenance scheduling. In one example, a pattern related to maintenance scheduling may indicate that maintenance is being performed too frequently, which may indicate that the hospital should investigate the equipment later.

[0075] In addition, the systems and methods described herein can be used by hospitals to optimize OR staffing and / or scheduling. In one example, the input surgical data 4 may include information related to the staff working during the corresponding medical surgery (e.g., name, case load, personality profile, experience, etc.). In some cases, the location of each staff member can be tracked using a navigation system during surgery. Trend data may include patterns related to the composition of the surgical staff and the clinical outcomes of the patient. These trends may allow the surgical optimizer 10 to provide guidance on assigning specific people to certain surgeries. The surgical optimizer 10 may also receive information related to case scheduling for one or more surgeons and / or operating rooms. Scheduling may help the surgical optimizer 10 recommend certain personnel for certain surgeries. For example, for complex cases, the surgical optimizer 10 may recommend personnel who have experience in the surgery and produce good clinical outcomes for patient operations, personnel who have not been too busy (e.g., overworked) in the previous few days, and / or personnel available in the scheduled or desired time period (e.g., morning). In one example, the surgical optimizer 10 may recommend a first OR staff group for robot setup and a second OR staff group for overtime surgery (e.g., a review of patients, surgeries, and other details before a surgical operation).

[0076] In another example, the surgery optimizer 10 can be linked to the hospital's alarm system to optimize OR utilization and scheduling. For example, if an OR staff member does not show up for a scheduled surgery, the surgery optimizer 10 can send an alarm to the hospital's scheduling system. In another example, if a scheduled surgeon typically takes a long time to perform a surgery, the surgery optimizer 10 can send an alarm to the hospital's scheduling system to allow for appropriate planning and scheduling of subsequent surgeries. In yet another example, if a surgeon takes longer than the scheduled time in surgery, an alarm can be sent.

[0077] The robotic system 5 may include tracking methods that can assist in inventory tracking and billing. In one example, the surgical optimizer 10 may receive information related to implants and other inventories to be used during a medical procedure (e.g., the item number or type of implant, price, available quantity, or quantity to be used during surgery). In some prior art systems, personnel can track inventory, and bills based on manual tracking can be submitted to the hospital by the device manufacturer. In one example, the robotic system 5 may include an implant or other device / tool ​​identifier (such as a barcode scanner or other visual or radio frequency identification system) to verify component use (e.g., by scanning the package). This information can then be used to automatically generate and export electronic surgical orders to speed up the billing process. In addition, surgical orders can provide a dynamic record of hospital inventory consumption. Tracking of inventory can also help account for disposable OR items (such as surgical sponges).

[0078] Reference again Figure 5 , insurance companies may be interested in group health status to help set insurance rates. For example, governments may be interested in group health status for research or policy development. Group health status can be determined by analyzing patient data, preoperative information, and postoperative information to determine patient outcomes for multiple patients.

[0079] The systems and methods for developing guidance described in this application can be used to train physicians. Trend data can be provided to equipment manufacturers, surgeons, or any other personnel for training purposes. Trends related to surgeon technique and OR practice may be particularly helpful for training. For example, training related to surgeon technique may recommend that the surgeon not hold the probe in a certain position. In another example, training may include a recommendation to place the OR camera in a certain position.

[0080] Analysis of data from many existing surgeries can illuminate general best practices (e.g., techniques, equipment, and user placement) that can be provided to the physician prior to or during robotic surgery or during other training sessions. Best practice guidance may have been extracted by the surgery optimizer 10 during analysis of the input surgical information 4, or may have been received by the surgery optimizer 10 in the form of rules or other inputs. Providing guidance to the physician regarding best practices can accelerate learning surgery for the physician using the robotic system 5.

[0081] The systems and methods described herein may also be used to fine-tune a physician's technique by comparing input surgical data 4 from other physicians with input surgical data 4 from the physician's own past surgeries. For example, the surgical optimizer 10 may recognize that certain changes in the physician's technique will shorten the overall length of the surgery and may provide this guidance to the physician. In this example, the guidance provided to the trainee may be user-specific guidance 48, which is in the form of clinical decision support, the "assisted surgery" may be a future surgery to be performed by the physician, and the assisted surgery information 6 may be information about the physician. In one embodiment, the guidance provided to the physician may be a recommendation to watch a training video. For example, the surgical optimizer 10 may recognize that a physician takes longer than other users to complete a certain stage of the surgery and may recommend a training video on the technique to reduce the time to completion.

[0082] Trend data may also be provided to a team at a device manufacturer (e.g., a team that uses a device in a medical procedure associated with the input surgical data 4). For example, a manufacturer's sales / marketing team may be interested in trends related to population health and / or OR practices. Understanding these trends may help the sales / marketing team sell the medical device and / or advise hospital personnel on how to use the medical device. A device manufacturer's engineering team may be interested in surgeon technique and / or feature efficacy to help make design improvements to the device's software and / or hardware. Finally, a service team may be interested in trends in maintenance scheduling to help reduce maintenance.

[0083] Although the principles of the present disclosure are described herein with reference to illustrative embodiments of specific applications, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art and those who have access to the teachings provided herein will recognize that additional modifications, applications, embodiments, and replacements of equivalents all fall within the scope of the embodiments described herein. Thus, the present invention is not considered to be limited by the foregoing description.

Claims

1. A computer-implemented method for generating and displaying instructions for performing a robotic medical procedure, comprising: receiving, by one or more processors, a plurality of prior surgery data sets from a plurality of robotic systems, wherein the robotic systems include robotic tools and electronic displays, wherein each prior surgery data set: i) corresponds to a prior instance of a robotic medical procedure performed using the robotic tool on a patient within the population, ii) defines one or more of a position or movement of the robotic tool involved in the robotic medical procedure, and iii) includes whether the robotic tool performed correctly during the robotic medical procedure; receiving or identifying, by the one or more processors, objective data defining one or more of a duration or a patient outcome of a robotic medical procedure; executing an algorithm stored in a non-transitory computer-readable storage medium to identify a pattern across the plurality of prior surgical data sets describing one or more of position or movement of at least one patient, medical device, or robotic tool involved in the robotic medical procedure, forces applied by the robotic tool, or timing of steps in the robotic medical procedure to achieve a duration or patient outcome defined by the objective data; receiving, by the one or more processors, information regarding instances of robotic medical procedures to be performed in the future on patients outside the group; automatically generating, by the one or more processors, guidance for performing a robotic medical procedure based on evaluating the identified patterns across the plurality of prior surgical data sets and the received information regarding the instance of the robotic medical procedure to be performed, the guidance comprising recommended movements of a robotic tool during the robotic medical procedure; and Instructions for performing a robotic medical procedure are generated and presented on an electronic display.

2. The method of claim 1, wherein the duration defined by the objective data is the duration of a portion of a robotic medical procedure. 3 . The method of claim 1 , wherein identifying a pattern across the plurality of prior surgical data sets comprises identifying a level of occurrence of a position or movement of a robotic tool across the plurality of prior surgical data sets.

4. The method of claim 1, wherein identifying patterns across the plurality of prior surgical data sets comprises describing movement of a robotic tool during a portion of a surgery performed for a duration defined by objective data, wherein the duration is the portion of the robotic medical surgery.

5. The method of claim 1, wherein the information about the instance of the robotic medical procedure to be performed includes information about at least one of: the patient, the type of procedure, operating room characteristics, or prior experience of the user.

6. The method of claim 1, wherein the guidance includes a recommended timing of steps of a robotic medical procedure involving the robotic system, and the recommendation further includes a recommended order of the steps of the robotic medical procedure.

7. A system for generating and displaying instructions for performing a robotic medical procedure, comprising: a computer-readable storage medium storing instructions for generating and displaying instructions for performing a robotic medical procedure; as well as One or more processors are configured to execute the instructions to perform a method comprising: receiving a plurality of prior surgery data sets from a plurality of robotic systems, wherein the robotic systems include robotic tools and electronic displays, wherein each prior surgery data set: i) corresponds to an instance of a robotic medical procedure performed using the robotic tool on a patient within the population, ii) includes robotic data obtained from a robotic device associated with the robotic tool; and iii) includes whether the robotic tool was correctly performed during the robotic medical procedure; receiving or identifying objective data defining one or more of a duration or a patient outcome of a robotic medical procedure; Identify patterns across multiple prior surgical datasets that describe characteristics of robotic medical procedures that achieve durations or patient outcomes defined by objective data; receiving information regarding instances of robotic medical procedures to be performed in the future on patients outside the group; automatically generating instructions for performing a robotic medical procedure based on the features identified by the pattern and the information received regarding the instance of the robotic medical procedure to be performed, wherein the instructions include forces to be applied intraoperatively by a robotic tool of the robotic system; A display for guidance in performing a robotic medical procedure is generated and presented, wherein the display is shown on an electronic display of a robotic system.

8. The system of claim 7, wherein the characteristics of the robotic medical procedure include movement of a robotic tool.

9. The system of claim 7, wherein the guidance further comprises recommended positions or movements of a patient, medical device, or robotic tool involved in the robotic medical procedure, or timing of steps of the robotic medical procedure.

10. The system of claim 7, wherein the robotic data comprises information gathered by the robotic device during corresponding instances of robotic medical procedures.

11. The system of claim 7, wherein identifying a pattern comprises determining a level of occurrence of a characteristic across a plurality of input procedures.

12. The system of claim 7, wherein the characteristic is a sequence of surgical steps, and identifying the pattern comprises determining the sequence of surgical steps to achieve a duration defined by the objective data.

13. The system of claim 7, wherein the information about the instance of the robotic medical procedure to be performed includes information about at least one of: the patient, the type of procedure, operating room characteristics, or prior experience of the user.

14. A non-transitory computer-readable storage medium having instructions thereon, which, when executed by a processor, cause the processor to perform a method for generating and displaying instructions for performing a robotic medical procedure, the method comprising: receiving a plurality of prior surgical data sets from a plurality of robotic systems, wherein each prior surgical data set: (i) corresponds to an instance of a robotic medical procedure performed using a robotic tool of the robotic system on a patient within the population, wherein the robotic system includes the robotic tool and an electronic display; (ii) defines one or more of a position or movement of at least one patient, medical device, or robotic tool involved in the robotic medical procedure; and a force applied by the robotic tool; or the timing of steps of a robotic medical procedure; and iii) including whether the robotic tool is being performed correctly during the robotic medical procedure; receiving or identifying objective data defining one or more of a duration or a patient outcome of a robotic medical procedure; identifying a pattern across the plurality of prior surgical data sets that describes one or more of position or movement of at least one patient, medical device, or robotic tool involved in the robotic medical procedure, forces applied by the robotic tool, or timing of steps of the robotic medical procedure to achieve a duration or patient outcome defined by the objective data; receiving information regarding instances of robotic medical procedures to be performed in the future on patients outside the group; automatically generating guidance for performing an instance of a robotic medical procedure, the guidance comprising i) recommended movements of a robotic tool of a robotic system during the robotic medical procedure, wherein the guidance is based on iii) evaluating patterns identified across the plurality of prior surgical datasets and information received about the instance of the robotic medical procedure, and iv) intraoperative data from the robotic system during the instance of the robotic medical procedure; and A display of instructions for performing an instance of a robotic medical procedure is generated and presented, wherein the display is displayed on an electronic display of a robotic system.

15. The storage medium of claim 14, wherein the duration defined by the objective data is a duration of a portion of a robotic medical procedure.

16. The storage medium of claim 14, wherein each prior surgical data set comprises information gathered by a robotic device associated with a robotic tool during a corresponding robotic medical procedure.

17. The storage medium of claim 14, wherein identifying a pattern comprises identifying a level of occurrence of at least one of a position or a movement of a robotic tool across the plurality of prior surgical data sets.

18. The storage medium of claim 14, wherein identifying patterns across the plurality of prior surgical data sets comprises describing movement of a robotic tool during a portion of a surgery performed for a duration defined by objective data, wherein the duration is the portion of a robotic medical surgery.

19. The storage medium of claim 14, wherein the information about the instance of the robotic medical procedure to be performed comprises information about at least one of: the patient, the type of procedure, operating room characteristics, or prior experience of the user.

20. The storage medium of claim 14, wherein each prior surgery data set includes information related to a position or movement of a robotic tool.

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