Radiation Oncology Operations Command Center (ROCC) Local Technician - Super Technician Matching

Abstract

A method 100 for connecting a local medical imaging device operator LO with remote medical imaging experts RE during an imaging examination performed using a medical imaging device 2 includes the steps of determining characteristics for available remote medical imaging experts who are available to assist the local medical imaging device operator, matching one or more of the available remote medical imaging experts with characteristics of the imaging examination based on the determined characteristics for the remote medical imaging experts, and providing a user interface (UI) 28, 28' on at least one display device 24, 24' operable by the local medical imaging device operator and the remote medical imaging experts, the UI displaying a list 44 of matched available remote medical imaging experts and allowing the local medical imaging device operator to select one of the matched available medical imaging experts from the displayed list via the UI.

Description

Technical Field

[0001]

[0001] The following generally relates to imaging technology, remote imaging assistance technology, remote imaging inspection monitoring technology, and related technologies.

Background Art

[0002]

[0002] Currently, there is a high demand for image diagnosis. As the world's population ages, the demand for rapid, safe, and high-quality imaging continues to increase, thereby placing additional pressure on imaging centers and their staff. To rapidly and safely perform imaging examinations on patients while maintaining high throughput and quality standards, imaging providers must establish and protect an efficient workflow from disruption.

Summary of the Invention

Problems to be Solved by the Invention

[0003]

[0003] Some common workflow disruptions include the lack of appropriate guidance for new technicians, the lack of technician experience with imaging machines from various vendors with different control interfaces and features, the lack of knowledge of imaging protocols, etc. These are examples of some common events that, if encountered during scanning, can have an adverse effect on current imaging examinations, delay subsequent examinations, and thereby disrupt the entire workday. With proper preparation, many of these disruptions could be mitigated or completely avoided by providing over-the-shoulder guidance to the technician.

[0004]

[0004] Some improvements for overcoming these problems and others are disclosed below.

Means for Solving the Problems

[0005]

[0005] In one aspect, a non-transitory computer-readable medium stores instructions executable by at least one electronic processor to perform a method of connecting a local medical imaging device operator with a remote medical imaging expert during an imaging examination performed using a medical imaging device. The method includes determining characteristics of available remote medical imaging experts who are available to assist the local medical imaging device operator; matching one or more of the available remote medical imaging experts to the characteristics of the imaging examination based on the determined characteristics of the remote medical imaging experts; and providing a user interface (UI) to at least one display device operable by the local medical imaging device operator and the remote medical imaging expert, the UI displaying a list of the matched available remote medical imaging experts and the local medical imaging device operator being able to select, via the UI, one matched available medical imaging expert from the displayed list.

[0006] In another aspect, an apparatus for connecting a local medical imaging device operator during an imaging examination performed using a medical imaging device comprises a screen sharing device for sharing the screen of a controller of the medical imaging device. A telephone or video communication link is operably connected to an electronic network to provide telephone or video communication to one of a set of remote medical imaging experts. A database stores determining characteristics for the remote medical imaging experts of the set of remote medical imaging experts, the characteristics including at least one of an experience level of the modality of the imaging examination, an experience level of the anatomy of the patient to be imaged during the imaging examination, an experience level of working with the local medical imaging device operator, and an experience level of the types of problems that occur in the imaging examination. At least one electronic processor retrieves from the database characteristics for one or more remote medical imaging experts of the set of remote medical imaging experts capable of assisting the local medical imaging device operator during the imaging examination, ranks the remote medical imaging experts capable of assisting based on matching the characteristics of the remote medical imaging experts capable of assisting with the characteristics of the imaging examination, displays a list of the ranked remote medical imaging experts capable of assisting to enable the local medical imaging device operator to select one of the listed remote medical imaging experts capable of assisting, and provides a user interface to establish an assistance session between the local medical imaging device operator and the selected remote medical imaging expert via the screen sharing device and the telephone or video communication link.

[0007]

[0007] In another aspect, a method of connecting a remote medical imaging expert to a local operator to provide assistance during an imaging examination includes retrieving from a database characteristics of one or more remote medical imaging experts capable of assisting the local operator in the imaging examination; ranking the remote medical imaging experts capable of responding using a score indicating the characteristics of the remote medical imaging experts capable of responding along with the characteristics of the imaging examination; providing a user interface (UI) that displays a list of the ranked remote medical imaging experts capable of responding; receiving, via at least one user input device, user input indicating a selection of one of the listed remote medical imaging experts; collecting data regarding the effectiveness of the assistance provided by the selected remote medical imaging expert and the local operator; determining one or more quality metrics regarding the collected data; and updating information stored in the database regarding the selected remote medical imaging expert using the calculated one or more quality metrics.

Advantages of the Invention

[0008]

[0008] One advantage is to provide a remote expert or radiographer who assists technicians when performing a medical imaging examination, by using the awareness of the local imaging examination situation, which facilitates effectively assisting one or more local operators in different facilities.

[0009]

[0009] Another advantage is to provide a remote expert or radiographer to assist technicians when performing a medical imaging examination, and to ensure that the remote expert or radiographer is well-matched to the imaging modality and the imaging examination.

[0010]

[0010] Another advantage is that a remote expert or radiographer is pre-selected to assist a technician during an ongoing medical imaging examination, the remote expert or radiographer is well-matched to the imaging modality and the imaging examination, and the pre-selected remote expert or radiographer is prepared to quickly respond to an error condition automatically detected during the ongoing imaging examination.

[0011]

[0011] Another advantage is to provide information about steps already performed during the workflow to a remote expert to provide assistance for subsequent steps in the workflow.

[0012]

[0012] Another advantage is to match the optimal remote expert to assist one or more local operators based on the experience level of the remote expert and the characteristics of the imaging examination performed by one or more local operators.

[0013]

[0013] Another advantage is to provide status information regarding a medical imaging examination to a remote operator or radiographer using a standard display format that is independent of the controller display of the medical imaging device performing the medical imaging examination.

[0014]

[0014] A given embodiment may provide none of the above advantages, may provide one, two, three or more, or all of the above advantages, and / or may provide other advantages that will be apparent to those skilled in the art upon reading and understanding the present disclosure.

[0015]

[0015] The present disclosure may be embodied in various components and configurations of the components, as well as in various steps and configurations of the steps. The drawings are merely illustrative of the preferred embodiments and should not be construed as limiting the present disclosure.

Brief Description of the Drawings

[0016]

Figure 1

[0016] FIG. is a diagram schematically showing an exemplary apparatus for providing remote assistance according to the present disclosure.

Figure 2

[0017] FIG. is a diagram schematically showing modules implemented by the apparatus of FIG. 1.

Figure 3

[0018] FIG. is a diagram showing an example of an output generated by the apparatus of FIG. 1.

Figure 4

[0019] FIG. is a diagram showing an exemplary flowchart of operations suitably executed by the apparatus of FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

[0017]

[0020] The following relates to a radiation medical operations command center (ROCC) system and method for providing remote expert or “supertech” assistance to a local technician performing an imaging examination. It is valuable to quickly identify a supertech with sufficient qualifications to assist in a given imaging examination. Delays in providing a supertech can adversely affect the imaging laboratory workflow. Moreover, in some disclosed embodiments, it is contemplated to provide supertech assistance in response to some automatically detected errors or problems in an imaging examination, and again, in such cases, it is required that the supertech be able to respond immediately when such an error is detected.

[0018]

[0021] In some embodiments disclosed herein, a super-technology matching system matches the best available super-technologies with local technicians and / or current imaging examinations. For this purpose, the system tracks the available super-technologies. The database stores information about each super-technology regarding the expertise of each super-technology in various imaging modalities, the anatomy being imaged, etc. For matching to a specific local technician, the database stores similar information about that local technician. For matching to a specific imaging procedure, information regarding that imaging procedure is stored. The database may have an auxiliary information mining system for obtaining information about the current imaging examination directly from the imaging scanner controller or from a radiology information system (RIS) or other examination scheduling system.

[0019]

[0022] When the database stores a wide range of features for characterizing super-technologies, local technicians, and / or imaging examinations, a feature selection or reduction process is optionally performed for a given matching situation. For example, this is implemented as principal component analysis (PCA) to generate highly discriminatory features.

[0020]

[0023] Based on the (optionally, reduced) set of features, the available super-technologies are matched to local technicians and / or imaging examinations. In one matching approach, a clustering algorithm or other machine learning (ML) component groups the available super-technologies by experience in various modalities (e.g., to tech levels 1 - 5 for a given modality and anatomy), and ranks the available super-technologies based on these groupings. In another approach, a (non-machine learning) scoring system is employed to score how well each available super-technology matches a local technician and / or examination, and the super-technologies are ranked by score.

[0021]

[0024] A user interface (UI) is provided through which the most well - matched super - tech is presented to the local tech. In one approach, in a selection dialog, a list of the top N most well - matched super - techs is provided. The local tech selects a super - tech from the top N selection dialog of the top N most well - matched super - techs, and more detailed information about the selected super - tech is displayed in a presentation window of the UI. If the local technician is satisfied with the selected super - tech, a button such as "Connect" is selected to start a ROCC session with the selected super - tech. In a variant of this approach, the UI is presented to a third party such as a ROCC administrator who selects the super - tech and starts the ROCC session.

[0022]

[0025] In another alternative embodiment, instead, the UI is presented to the super - tech. This approach is suitable for ROCC sessions triggered by an automatically detected error state in an ongoing imaging examination. In this case, the UI displays a pop - up to the top - ranked super - tech, and information about the imaging examination in which the error occurred (and in some cases, information about the local technician performing the examination) is displayed. This serves as an invitation that the super - tech can accept by activating a button such as "Connect" to start the ROCC session. Alternatively, the super - tech can reject the invitation using a suitable UI dialog selector, in which case the system presents the invitation to the next - highest - qualified super - tech.

[0023]

[0026] Optionally, the system can collect quality metrics, such as collecting data on whether a given matched supertech was able to effectively assist in an imaging examination. This collected data can be used by human maintenance operators to fine-tune database features, machine learning components, etc. to optimize the performance of the system. In one variant approach, the system can employ adaptive learning, such as pattern matching, to adaptively adjust machine learning components to maximize metrics, such as the effectiveness of the assistance provided by the matched supertech.

[0024]

[0027] Referring to FIG. 1, an apparatus for providing assistance from a remote medical imaging expert RE (or supertech) to a local technician operator LO is shown. As shown in FIG. 1, a local operator LO who operates a medical imaging device 2 (also referred to as an image collection device, an imaging device, etc.) is located at a medical imaging device bay 3, and a remote operator RE is located at a remote service location or center 4. It should be noted that the "remote operator" RE does not necessarily directly operate the medical imaging device 2, but rather provides assistance to the local operator LO in the form of advice, guidance, instructions, etc. The remote location 4 can be a remote service center, a radiographer's office, a radiology department, etc. The remote location 4 may be in the same building as the medical imaging device bay 3 (this is the case, for example, for the "remote operator" RE who is a radiographer responsible for tasks around the examination image review), but more generally, the remote service center 4 and the medical imaging device bay 3 are in different buildings and can actually be located in different cities, different countries, and / or different continents. Generally, the remote location 4 is remote from the medical imaging device bay 3 in the sense that the remote operator RE cannot directly visually observe the imaging device 2 in the imaging device bay 3 (thus, optionally providing a video feed as further described herein).

[0025]

[0028] The image acquisition device 2 may be a magnetic resonance (MR) image acquisition device, a computed tomography (CT) image acquisition device, a positron emission tomography (PET) image acquisition device, a single photon emission computed tomography (SPECT) image acquisition device, an X-ray image acquisition device, an ultrasonic (US) image acquisition device, or a medical imaging device of another modality. The imaging device 2 can also be a hybrid imaging device, such as a PET / CT or SPECT / CT imaging system. Although a single image acquisition device 2 is shown as an example in FIG. 1, more generally, a medical imaging laboratory has a plurality of image acquisition devices that are image acquisition devices of the same and / or different imaging modalities. For example, if a hospital performs many CT imaging examinations, a relatively small number of MRI examinations, and an even smaller number of PET examinations, the hospital's imaging laboratory (which may be called a "radiology laboratory" or some other similar name) may have three CT scanners, two MRI scanners, and only one PET scanner. This is just an example. Moreover, the remote service center 4 can provide services to multiple hospitals. The local operator controls the medical imaging device 2 via the imaging device controller 10. The remote operator is located at the remote workstation 12 (or, more generally, the electronic controller 12).

[0026]

[0029] As used herein, the term "medical imaging device bay" (and variations thereof) refers to the room that houses the medical imaging device 2 and also to the adjacent control room that houses the medical imaging device controller 10 for controlling the medical imaging device. For example, with respect to an MRI device, the medical imaging device bay 3 may include a radio frequency (RF) shielded room that houses the MRI device 2, as well as an adjacent control room that stores the medical imaging device controller 10, as understood in the art of MRI devices and procedures. On the other hand, in the case of other imaging modalities such as CT, the imaging device controller 10 is located in the same room as the imaging device 2, and thus there is no adjacent control room, and the medical bay 3 may be the only room that houses the medical imaging device 2. Further, although FIG. 1 shows a single medical imaging device bay 3, the remote service center 4 (more particularly, the remote workstation 12) communicates with a plurality of medical bays via a communication link 14, which generally includes the Internet extended by a local area network at the remote operator RE end and the local operator LO end for electronic data communication, as will be understood.

[0027]

[0030] As schematically shown in FIG. 1, in some embodiments, a camera 16 (e.g., a video camera) is disposed that at least includes the area of the imaging device 2 where the local operator LO interacts with the patient there, and optionally further includes the imaging device controller 10, in order to collect a video stream 17 of a portion of the medical imaging device bay 3. The video stream 17 is sent via the communication link 14 to the remote workstation 12 as a streaming video feed received, for example, via a secure Internet link.

[0028]

[0031] In other embodiments, the live video feed 17 is provided, in an exemplary embodiment, by a video cable splitter 15 (e.g., a DVI splitter, an HDMI splitter, etc.). In other embodiments, the live video feed 17 is provided by a video cable that connects an auxiliary video output (e.g., aux vid out) port of the imaging device controller 10 to a remote workstation 12 that is operated by a remote expert RE.

[0029]

[0032] Additionally or alternatively, a screen mirroring data stream 18 is generated by the screen sharing device 13 and sent from the imaging device controller 10 to the remote workstation 12. The communication link 14 also provides a natural language communication path 19 for oral and / or text communication between the local operator and the remote operator. For example, the natural language communication link 19 is a Voice over Internet Protocol (VOIP) phone connection, an online video chat link, a computerized instant messaging service, etc. Alternatively, the natural language communication path 19 is provided by a dedicated communication link that is separate from the communication link 14 that provides the data communications 17, 18. For example, the natural language communication path 19 is provided via a landline phone.

[0030]

[0033] FIG. 1 also shows a remote service center 4 including a remote workstation 12, such as an electronic processing device, a workstation computer, or more generally a computer, operably connected to receive, present, and mirror the video 17 from the medical imaging device bay 3 from the camera 16 and present the screen mirroring data stream 18 as a mirrored screen. Additionally or alternatively, the remote workstation 12 can be implemented as a single server computer or as a plurality of server computers interconnected to form, for example, a server cluster, cloud computing resources, etc. The workstation 12 includes common components such as an electronic processor 20 (e.g., a microprocessor), at least one user input device (e.g., a mouse, keyboard, trackball, etc.) 22, and at least one display device 24 (e.g., an LCD display, a plasma display, a cathode ray tube display, etc.). In some embodiments, the display device 24 can be a separate component from the workstation 12. The display device 24 may also include two or more display devices. For example, one display presents the video 17 and the other display presents the shared screen of the imaging device controller 10 generated from the screen mirroring data stream 18. Alternatively, the video and the shared screen are presented on a single display in their respective windows. The electronic processor 20 is operably connected to one or more non-transitory storage media 26. The non-transitory storage media 26 includes, by way of non-limiting exemplary examples, one or more of a magnetic disk, RAID, or other magnetic storage media, a solid state drive, a flash drive, an electronically erasable read-only memory (EEROM) or other electronic memory, an optical disk or other optical storage, various combinations thereof, etc., and can be, for example, network storage, an internal hard drive of the workstation 12, various combinations thereof, etc.It should be understood that references to one or more non-transitory media 26 in this specification are to be construed broadly as encompassing a single medium or multiple media of the same or different types. Similarly, the electronic processor 20 is implemented as a single electronic processor or as two or more electronic processors. The non-transitory storage medium 26 stores instructions executable by at least one electronic processor 20. Those instructions include instructions for generating a graphical user interface (GUI) 28 for display on the remote operator display device 24.

[0031]

[0034] The medical imaging device controller 10 in the medical imaging device bay 3 also includes components similar to those of the remote workstation 12 disposed in the remote service center 4. Unless otherwise indicated herein, features of the medical imaging device controller 10 including the local workstation 12' disposed in the medical imaging device bay 3, similar to the features of the remote workstation 12 disposed in the remote service center 4, have a common reference number followed by a "prime" symbol, and descriptions of the components of the medical imaging device controller 10 will not be repeated. In particular, the medical imaging device controller 10 is configured to display a GUI 28' on a display device or controller display 24' that presents information related to the control of the medical imaging device 2, such as a configuration display for adjusting configuration settings, an alert 30 that can be sensed at a remote location when status information regarding a medical imaging examination meets the alert criteria of the imaging device 2, imaging collection monitoring information, and presentation of the collected medical images. It will be understood that the screen mirroring data stream 18 conveys the content presented on the display device 24' of the medical imaging device controller 10. The communication link 14 enables screen sharing between the display device 24 in the remote service center 4 and the display device 24' in the medical imaging device bay 3. The GUI 28' includes one or more dialog screens, including, for example, among others, an examination / scanning selection dialog screen, a scanning settings dialog screen, and a collection monitoring dialog screen. The GUI 28' can be included in the video feed 17 or mirroring data stream 17' and displayed on the remote workstation display 24 at the remote location 4.

[0032]

[0035] FIG. 1 also shows a remote workstation 12 communicating with a database 31 that stores patient information (such as an electronic health record (EHR) database, an electronic medical record (EMR) database, a radiology information system (RIS) database, etc.).

[0033]

[0036] FIG. 1 shows an exemplary local operator LO and an exemplary remote expert RE (i.e., an expert, such as a super tech). However, in the radiation medical operations command center (ROCC) contemplated herein, the ROCC provides a staff of super techs that can respond to assist local operators LO in different hospitals, radiation medical labs, etc. The ROCC can be stored in a single physical location or geographically dispersed. For example, in one contemplated implementation, remote operators RO are recruited from across the United States and / or internationally to provide a staff of super techs with a wide range of expertise in various imaging modalities and imaging procedures targeting various imaged anatomies. In view of this diversity of local operators LO and remote operators RO, the disclosed communication link 14 includes a server computer 14 (or a cluster of servers, cloud computing resources comprising multiple servers, etc.) programmed to establish a connection between a selected local operator LO / remote expert RE pair. For example, if the server computer 14 is Internet-based, connecting a particular selected local operator LO / remote expert RE pair can be done using Internet Protocol (IP) addresses of various components 16, 10, 12, telephone or video terminals of the natural language communication path 19, etc. The server computer 14 is operably connected to one or more non-transitory storage media 26. The non-transitory storage media 26 includes, by way of non-limiting exemplary examples, one or more of a magnetic disk, RAID, or other magnetic storage media, solid state drives, flash drives, electronically erasable read only memory (EEROM) or other electronic memory, optical disks or other optical storage, various combinations thereof, etc., and can be, for example, network storage, an internal hard drive of the server computer 14, various combinations thereof, etc. It should be understood that references herein to one or more non-transitory media 26 should be construed broadly as encompassing a single medium or multiple media of the same or different types.Similarly, the server computer 14 can be implemented as a single electronic processor or as two or more electronic processors. The non-transitory storage medium 26 stores instructions executable by the server computer 14. Further, the non-transitory computer-readable medium 26 (or another database) stores data regarding a set of remote experts RE and / or a set of local operators LO. The remote expert data may include, for example, skill set data, business experience data, data regarding the ability to handle multi-vendor modalities, data regarding experience with local operator LO, etc.

[0034]

[0037] Further, as disclosed herein, the server 14 executes an expert matching method or process 100 that matches a remote expert RE having sufficient qualifications to handle a given local operator LO.

[0035]

[0038] Next, referring to FIG. 2 while continuing to refer to FIG. 1, in one embodiment of the expert matching method or process 100, the server 14 is programmed using several components to assist a remote expert RE. The principal component analysis model 32 analyzes remote expert data stored in a database 41 (which can be the non-transitory computer-readable medium 26), and is configured to identify features for performing a medical imaging examination, such as the modality of the medical imaging device 2, the vendor of the medical imaging device, the type of protocol to be used in the medical imaging examination, the anatomy to be imaged, the condition of the patient to be imaged, the previous business experience of the local operator LO in handling critical cases, the previous business experience of the remote expert RE in handling critical cases, the responsiveness of the remote expert, etc. In particular, the responsiveness of each remote expert RE is searched. In some examples, the principal component analysis model 32 can be a machine learning (ML) model.

[0036]

[0039] The principal component analysis model 32 is configured to classify the remote expert RE into skill levels on a scale of level 1 to 5. A "level 1 expert" has approximately 0 to 2 years of experience, may have sufficient skills in patient care and safety, and requires support and guidance in making the basics of obtaining images complete (e.g., from a super tech with more experience). A "level 2 expert" has approximately 2 to 3 years of experience, has sufficient skills in patient care and safety, may be able to generate high-quality images, needs to solidify knowledge to gain confidence in making independent decisions, and needs to touch on new and more difficult cases. A "level 3 expert" has at least 3 to 5 years of experience and has strong capabilities in obtaining standard images. A "level 4 expert" has more than 5 years of experience, has strong knowledge of advanced imaging, can advise on inspection cards, can anticipate most imaging protocols, should be able to maintain and update relationships with other technicians according to preferences. A "level 5 expert" has at least 10 years of experience, has strong knowledge of advanced medical imaging, can set inspection cards, can anticipate all imaging protocols, should be able to support in retaining all knowledge to assist the local operator LO, and should be able to maintain up-to-date relationships with technicians according to preferences.

[0037]

[0040] As an input to the principal component analysis model 32, data characterizing the remote expert RE can be provided. To do so, the remote expert RE can, for example, fill out a questionnaire when participating in the ROCC to provide information regarding their experience in different modalities, the anatomy being imaged, etc. In one embodiment, the principal component analysis model 32 includes five features for use in the model, where the features are calculated therein based on, for example, the business experience associated with the remote expert and the corresponding medical imaging examination complexity conditions. For example, one set of features can be obtained from the business experience of the super tech (labeled ST-WE) combined with the complexity of the medical imaging examination to generate a binarized feature. If the first quartile, median, and third quartile of ST-WE and the correlation (REC) with the examination complexity or the support required to perform the scan are 10, 20, and 30, respectively, the following four features can be defined: "Is the ST-WE_REC value less than 10?", "Is the ST-WE_REC value 10 or greater?", "Is the ST-WE_REC value 20 or greater?", and "Is the ST-WE_REC value 30 or greater?". Other binarized features such as ST-WE_ScannerType, ST-WE_Vendor, etc. can be obtained similarly. Other constraints on the principal component analysis model 32 may include constraints on accuracy, such as the area under the curve (AUC) value.

[0038]

[0041] The ML module 34 is configured to group all of the remote experts RE of a set of remote experts into various categories, for example, based on their experience in operations using different imaging modalities, the patient's condition, the complexity of the medical imaging examination, the modality, the modality vendor, the experience of the local operator LO, etc. One or more ML models 35 can be generated and used to predict a set of variables important for the medical imaging examination. To find the "best fit" remote expert to assist the local operator LO in the medical imaging examination, the model 35, together with the principal component analysis model 32 and the remote expert RE data stored in the non-transitory computer-readable medium 26, is used. For example, the model 35 can be used to prioritize the remote experts RE into the following categories: exact match, important experience, has complete examination experience but no modality experience, has modality experience but no examination experience, has neither modality experience nor examination experience.

[0039]

[0042] The quality metric inspection module 36 is configured to simulate the remote expert RE - local operator LO pairing to perform future pairing predictions and also monitor quality metric values 37, such as the AUC value from the principal component analysis model 32 and the model 35.

[0040]

[0043] The decision-making module 38 is configured to determine whether the results from the ML module 34 and the quality metric inspection module 36 meet a predetermined sufficient threshold. If the threshold is not met, the remote expert RE can change the input constraints for the principal component analysis model 32 and re-acquire a new set of remote experts based on the new input constraints, together with a new set of quality metric values 37.

[0041]

[0044] When the threshold is met, the assignment module 40 identifies the best remote expert RE (or a ranked list of the top N most highly ranked experts RE) for the medical imaging examination (e.g., selecting the correct sequence for imaging and successfully obtaining high-quality images), and is configured to match the best remote expert with the local operator LO. If a match is made, the generated model 35 and quality metric value 37 can be stored in the database 41 (or alternatively, on the non-transitory computer-readable medium 26) for use in future matching.

[0042]

[0045] The GUI output module 42 is configured to output, on the display device 24 of the remote workstation 12 (via the GUI 28) and / or on the display device 24' of the medical imaging device controller 10 (via the GUI 28'), a list 44 of the best addressable remote experts RE, along with one or more corresponding quality metric values 37 (e.g., reliability values, AUC values, etc.). The list 44 may include only the best addressable remote experts RE for a set number of people, such as the three best addressable experts, and the corresponding quality metric values 37.

[0043]

[0046] Figure 3 shows an example of List 44. As shown in Figure 3, List 44 includes the three best available remote experts RE along with corresponding quality metric values 37 that include reliability values and AUC values. When one of the remote experts RE listed via at least one user input device 22, 22' is selected, a drop-down menu 46 listing a set of experience metrics 48 for the remote expert RE may be presented. As shown in Figure 3, the set of experience metrics 48 may include an experience metric for brain imaging, an experience metric for spinal imaging, an experience metric for liver imaging, an experience metric for heart imaging, an experience metric for knee imaging, and an experience metric for whole body imaging. These metrics 48 are compared with corresponding experience metrics 50 shown on a drop-down menu 52 for the local operator LO. From this, the best available remote expert RE can be selected to assist the local operator LO. Further, List 44 may also include a communication button 54 that can be selected by the remote expert RE or the local operator LO to establish a natural language communication path 19 via a communication link 14 between the two parties.

[0044]

[0047] When a remote expert RE is selected to assist a given local operator LO, the communication link 14 connects the local operator LO / the selected remote expert RE. The remote workstation 12 of the selected remote expert RE and / or the medical imaging device controller 10 operated by the local operator LO are configured to execute a method or process 200 for providing assistance from the remote expert RE to the local operator LO. For the sake of brevity, method 200 will be described as being executed by the remote workstation 12. The non-transitory storage medium 26 stores instructions that are readable and executable by at least one electronic processor 20 (of one or more electronic processors of the workstation 12 as shown and / or of one or more servers on the local area network or the Internet) for performing the disclosed operations, including executing the method or process 200.

[0045]

[0048] A preferred implementation of the support method or process 200 is as follows. The method 200 is executed over the course of a medical imaging examination (at least in part) performed using the medical imaging device 2, and the local expert RE is one selected via the matching method 100. As used herein, the term "duration of a medical imaging examination" (or a variation thereof) refers to the time period of a medical imaging examination that includes (i) the actual image acquisition time, (ii) the post-imaging processing time, and (iii) up to the time of patient release. To execute the method 200, the workstation 12 at the remote location 4 is programmed to receive at least one of (i) the video 17 from the video camera 16 of the medical imaging device 2 located in the medical imaging device bay 3, (ii) the screen share 18 from the screen sharing device 19, and / or (iii) the video 17 tapped by the video cable splitter 15. The video feed 17 and / or the screen share 18 can generally be displayed in a separate window of the GUI 28 on the remote workstation display 24. The video feed 17 and / or the screen share 18 can be screen scraped to determine information regarding the medical imaging examination (e.g., modality, vendor, anatomy to be imaged, cause of the problem to be solved, etc.). In particular, the GUI 28 presented on the display 24 of the remote workstation 12 preferably includes a window presenting the video 17, a window presenting the mirrored screen of the medical imaging device controller 10 constructed from the screen mirroring data stream 18, and status information regarding the medical imaging examination maintained using at least in part the screen scraped information. Thereby, the remote operator RE can be aware of the content of the display of the medical imaging device controller 10 (via the shared screen), the physical situation (e.g., the position of the patient in the medical imaging device 2) (via the video 17), and further, the status of the imaging examination summarized by the status information.During the imaging procedure, the natural language communication path 19 is preferably used to enable the local operator LO and the remote operator RE to discuss the procedure, and in particular to enable the remote operator to give advice to the local operator.

[0046]

[0049] Continuing to refer to FIGS. 1-3 and referring to FIG. 4, an exemplary embodiment of the expert matching method 100 is schematically shown as a flowchart. In operation 102, information regarding a medical imaging examination to be performed by the local operator LO is collected. In one approach, that information is communicated to the server 14 by the imaging laboratory scheduling system that scheduled the imaging examination. In another contemplated approach, the local operator LO fills out an electronic expert assistance request form pushed to the local operator LO by the server 14 (e.g., as a web page), and the form requests important information about the imaging examination (e.g., modality, vendor, anatomy to be imaged, cause of the problem to be solved, etc.). In yet another contemplated approach, the video feed 17 and / or screen sharing 18 is captured at the server 14 and screen scraped to determine information regarding the medical imaging examination (e.g., modality, vendor, anatomy to be imaged, cause of the problem to be solved, etc.). In some examples, the information collected during operation 102 may include the availability of different remote experts RE.

[0047]

[0050] In operation 104, characteristics of a responsive remote medical imaging expert RE, who can assist local operator LO, are determined. To do so, remote expert data stored in non-transitory computer-readable medium 26 can be retrieved and input into principal component analysis model 32. A feature selection process, such as principal component analysis (PCA), can be performed on the data retrieved from video 17 and screen sharing 18 and the information screen scraped to generate principal component analysis model 32 and determine the characteristics of responsive remote expert RE. The characteristics of responsive remote expert RE can include at least one of, for example, the level of experience in the imaging modality of the imaging examination, the level of experience in the anatomy of the patient to be imaged during the imaging examination, the level of experience in working with local operator LO, and the level of experience in the types of problems that occur during the imaging examination. In some examples, not only which remote expert RE is responsive but also to determine the duration of the responsive state, the responsiveness data collected in operation 102 can be combined with the scheduling information of the imaging examination. This enables a comparison between the expected duration of the imaging examination and the duration of the responsive state of the remote expert, avoiding situations where the remote expert becomes unresponsive before the imaging examination (including subsequent processing time, until patient release) is completed. This reduces the likelihood that the patient will need to revisit. A list of remote experts RE who will "soon be responsive" may also be included.

[0048]

[0051] In operation 106, the manageable remote experts REs are ranked. To do so, an ML operation is applied to the principal component analysis model 32 to rank the manageable remote experts REs. This is executed using the ML module 34 and the generated model 35. Further, a quality metric value 37 is generated using the quality metric inspection module 38, and the quality metric value 37 is also used to rank the manageable remote experts REs. The quality metric value 37 is used as a score for ranking the remote experts REs. The decision-making module 38 then ranks the manageable remote experts REs based on the quality metric 37 and the results of the model 35 generated by the ML module 34.

[0049]

[0052] In operation 108, one or more of the manageable remote experts REs are matched with a local operator LO who performs a medical imaging examination. This is executed using the assignment module 40. The best remote expert RE for a medical imaging examination (e.g., selecting the correct sequence for imaging and successfully obtaining high-quality images) is matched with the local operator LO.

[0050]

[0053] In operation 110, a list 44 of the ranked manageable remote experts REs is displayed via the GUI 28 on the display device 24 of the remote workstation 12 and / or via the GUI 28' on the display device 24' of the medical imaging device controller 10. Referring again to FIG. 2, the list 44 may include the quality metric 37 as a score used to rank the manageable remote experts REs. The quality metric 37 can be displayed with the corresponding remote expert RE, and the remote experts can be ranked according to the highest quality metric. The list 44 may also include "soon-to-be-available" remote experts REs based on schedule availability data.

[0051]

[0054] In operation 110, one of the remote experts RE listed on list 44 is selected by the local operator LO. To do so, the local operator LO uses at least one user input device 22' to select one of the listed remote experts RE. In some examples, the local operator LO can select a remote expert RE to open a drop-down menu 46 to show a set of experience metrics 48 of the selected remote expert. The local operator LO can also select a communication button 54 selectable by the remote expert RE or the local operator LO to establish a natural language communication path 19 via a communication link 14 between the two parties so that the screens of the medical imaging device controller 10 and the remote workstation 12 are shared. In some examples, the selected remote expert RE can reject the natural language communication path 19 (e.g., if the remote expert RE is busy or feels that the problem to be addressed in the medical imaging examination is not suitable for his or her skills). In this case, a communication link can be established with another one of the listed remote experts RE (e.g., the remotely expert with the next highest rank). This process can continue until one of the remote experts RE accepts the natural language communication path 19.

[0052]

[0055] In random operation 112, the database 41 can be updated based on the interaction between the selected remote expert RE and the local operator LO. In one example, if the selected remote expert RE refuses to assist the local operator LO, the database 41 can be updated, for example, so as not to recommend that remote expert for that type of problem for that particular local operator LO. Further, data regarding the effectiveness of the assistance provided by the selected remote expert RE and the local operator LO can be collected. This can be done via a feedback form filled in by the local operator LO and / or the remote expert RE. In one example, one or more of the quality metrics 37 for the selected remote expert RE can be recalculated and stored in the database 41 (along with updates regarding the additional experience of the remote expert by dealing with problems with the local operator LO). In another example, an adaptive learning process for the collected data can be carried out to maximize the quality metric 37 regarding the effectiveness of the assistance, and those quality metrics 37 can also be stored in the database 41.

[0053]

[0056] The present disclosure has been described with reference to the preferred embodiments. Upon reading and understanding the foregoing modes for carrying out the invention, others may conceive of modifications and changes. The exemplary embodiments are to be construed as including all such modifications and changes as long as they fall within the scope of the appended claims or the scope of their equivalents.

Claims

1. A non - transitory computer - readable medium storing instructions executable by at least one electronic processor to perform a method of connecting a local medical imaging device operator with a remote medical imaging expert during an imaging examination performed using a medical imaging device, the method comprising: Determining characteristics of available remote medical imaging experts who are available to assist the local medical imaging device operator; Based on the determined characteristics of the remote medical imaging experts, matching one or more of the available remote medical imaging experts with the characteristics of the imaging examination; Providing a user interface to at least one display device operable by the local medical imaging device operator and the remote medical imaging expert, the user interface displaying a list of the matched available remote medical imaging experts and allowing the local medical imaging device operator to select one matched available medical imaging expert from the displayed list via the user interface; And The step of determining characteristics of a remote medical imaging expert among a set of remote medical imaging experts available to assist the local medical imaging device operator comprises: Performing a feature selection process on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination A non - transitory computer - readable medium having.

2. The method further comprises: Ranking the available remote medical imaging experts based on a score of the characteristics of the available remote medical imaging experts and the characteristics of the imaging examination The non - transitory computer - readable medium according to claim 1.

3. The step of providing the user interface that displays the list of the matched available remote medical imaging experts comprises: Providing the list as a ranked list of the available remote medical imaging experts according to the score The non-transitory computer-readable medium according to claim 2, having **Claim 4** The step of providing the user interface for displaying the list of the matched available remote medical imaging experts comprises The step of providing the list including the highest-ranked available remote medical imaging expert according to the score The non-transitory computer-readable medium according to claim 2, having **Claim 5** The step of providing the user interface for displaying the list of the matched available remote medical imaging experts comprises The step of listing the scores of the available remote medical imaging experts listed on the user interface The non-transitory computer-readable medium according to claim 3 or 4, having **Claim 6** The step of determining the characteristics for the available remote medical imaging experts comprises the step of determining the scheduling availability of the remote medical imaging experts, and the step of providing the user interface for displaying the list comprises the step of providing the list together with the scheduling availability of the remote medical imaging experts. The non-transitory computer-readable medium according to any one of claims 3 to 5. **Claim 7** The step of ranking the available remote medical imaging experts comprises The step of applying a machine learning operation to the characteristics of the available remote medical imaging experts to rank the available remote medical imaging experts The non-transitory computer-readable medium according to any one of claims 2 to 6, having **Claim 8** The non-transitory computer-readable medium according to any one of claims 1 to 7, wherein the feature selection process is a principal component analysis (PCA) process. **Claim 9** The characteristics of the available remote medical imaging experts are The level of experience in the imaging modality of the imaging examination, and The level of experience in the anatomy of the patient to be imaged during the imaging examination, and The level of experience in working with the local medical imaging device operator, and The level of experience in the type of problems occurring in the imaging examination The non-transitory computer-readable medium according to any one of claims 1 to 8, including at least one of **Claim 10** The step of providing the user interface for displaying the list of the matched responsive remote medical imaging experts comprises receiving, via at least one user input device, a user input indicating a selection of one of the listed remote medical imaging experts, and providing a drop-down menu for displaying information about the selected remote medical imaging expert The non-transitory computer-readable medium according to any one of claims 1 to 9, comprising **Claim 11** The step of providing the user interface for displaying the list of the matched responsive remote medical imaging experts comprises receiving, via at least one user input device, a user input indicating a selection of one of the listed remote medical imaging experts, establishing a two-way telephone or video communication link between the selected remote medical imaging expert and the local medical imaging device operator, and sharing the display of the medical imaging device at the workstation of the selected remote medical imaging expert The non-transitory computer-readable medium according to any one of claims 1 to 10, comprising **Claim 12** The step of providing the user interface for displaying the list of the matched responsive remote medical imaging experts comprises upon receiving an instruction to reject the two-way telephone or video communication link from the selected remote medical imaging expert, establishing a communication link between another one of the listed remote medical imaging experts and the local medical imaging device operator The non-transitory computer-readable medium according to claim 11, comprising **Claim 13** The characteristics of the remote medical imaging expert are stored in a database, and the method comprises collecting data regarding the effectiveness of the assistance provided by the selected remote medical imaging expert and the local medical imaging device operator calculating one or more quality metrics regarding the collected data updating the information stored in the database regarding the selected remote medical imaging expert using the calculated one or more quality metrics The non-transitory computer-readable medium according to any one of claims 1 to 12, further comprising

14. wherein the method further comprises collecting data regarding the effectiveness of the assistance provided by the selected remote medical imaging expert and the local medical imaging device operator; performing an adaptive learning process on the collected data to maximize a quality metric regarding the effectiveness of the assistance; updating the information stored in the database regarding the selected remote medical imaging expert using the maximized quality metric The non-transitory computer-readable medium according to claim 13, further comprising

15. An apparatus for connecting a local medical imaging device operator during an imaging examination performed using a medical imaging device, the apparatus comprising: a screen sharing device for sharing a screen of a controller of the medical imaging device; a telephone or video communication link operably connected to an electronic network for providing telephone or video communication to one remote medical imaging expert of a set of remote medical imaging experts; a database storing determining characteristics for the remote medical imaging expert of the set of remote medical imaging experts, the characteristics including at least one of an experience level of a modality of the imaging examination, an experience level of an anatomy of a patient to be imaged during the imaging examination, an experience level of working with the local medical imaging device operator, and an experience level of types of problems occurring in the imaging examination; performing a feature selection process on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination to retrieve from the database characteristics for one or more remote medical imaging experts of the set of remote medical imaging experts who are capable of assisting the local medical imaging device operator during the imaging examination; ranking the capable remote medical imaging experts based on matching the characteristics of the capable remote medical imaging experts with the characteristics of the imaging examination Display a list of the ranked available remote medical imaging experts, enable the local medical imaging device operator to select one of the listed available remote medical imaging experts, and provide a user interface for establishing an assistance session between the local medical imaging device operator and the selected remote medical imaging expert via the screen sharing device and the telephone or video communication link At least one electronic processor programmed to perform the above An apparatus comprising the above

16. The at least one electronic processor Upon receiving a user input indicating a selection of one of the listed remote medical imaging experts via at least one user input device, establish a communication link between the selected remote medical imaging expert and the local medical imaging device operator The apparatus according to claim 15, further programmed as above

17. The at least one electronic processor Apply a machine learning operation to the characteristics of the available remote medical imaging experts to rank the available remote medical imaging experts The apparatus according to claim 15 or 16, further programmed as above

18. The at least one electronic processor Upon receiving a user input indicating a selection of one of the listed remote medical imaging experts via at least one user input device, provide a drop-down menu for displaying information about the selected remote medical imaging expert The apparatus according to any one of claims 15 to 17, further programmed as above

19. A method of connecting a remote medical imaging expert to a local operator to provide assistance during an imaging examination, the method comprising Performing a feature selection process on the characteristics of the remote medical imaging expert and the characteristics of the imaging examination to retrieve from a database the characteristics of one or more remote medical imaging experts capable of assisting the local operator in the imaging examination Ranking the responsive remote medical imaging experts using a score indicating the characteristics of the responsive remote medical imaging experts along with the characteristics of the imaging examination; Providing a user interface for displaying a list of the ranked responsive remote medical imaging experts; Receiving, via at least one user input device, a user input indicating a selection of one of the listed remote medical imaging experts; Collecting data regarding the effectiveness of the support provided by the selected remote medical imaging expert and the local operator; Determining one or more quality metrics regarding the collected data; Updating the information stored in the database for the selected remote medical imaging expert using the calculated one or more quality metrics; A method comprising the steps above.

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