MEDICAL PROCEDURE CASE SCHEDULING

Example medical systems and techniques are disclosed. An example medical system includes processing circuitry coupled to memory. The processing circuitry is configured to determine at least one of a predicted duration or a predicted end time of a medical procedure. The processing circuitry is configured to monitor data associated with the current medical procedure during the current medical procedure and determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure. The processing circuitry is configured to determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time and output a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

TECHNICAL FIELD

This disclosure relates to scheduling of medical procedures.

BACKGROUND

Medical procedures may include diagnostic and interventional medical procedures. Sometimes such medical procedures may take more time or less time than expected. In some examples, a medical procedure that begins as diagnostic medical procedure may become an interventional medical procedure, such as when a clinician discovers a medical issue during a diagnostic procedure that requires immediate medical attention. Such an event may cause the amount of time for which the medical procedure was scheduled or estimated to exceed the allotted time, which may impact other scheduled medical procedures.

SUMMARY

Medical procedures performed in an operating room or a Catheterization (Cath) lab are typically scheduled according to a certain predetermined cadence, sometimes including buffers in between procedures, to minimize wasted time by clinicians, such as doctors and key staff. For example, each medical procedure may be allotted a duration of time during which hospital staff, equipment, rooms and/or the like, may be dedicated to the medical procedure. These predetermined cadences and buffers may be carefully considered, but ultimately are based on limited data. Patients often wait significantly longer for their medical procedure to start than they were led to expect. In some instances, a patient's medical procedure may need to be rescheduled to another day or another facility due to unforeseen or unaccounted for delays in medical procedures prior to the patient's medical procedure. It is fairly common for an upcoming medical procedure to be rescheduled due to prior medical procedures taking significantly longer than expected.

Patients waiting for a medical procedure are likely already uncomfortable and/or in a state of reduced health. Requiring a patient to remain in a waiting room for a longer period of time, to have to return on a second day, and/or to travel to another facility, may exasperate patient discomfort, increase exposure of the patient to other patients (which may increase a risk of communicable disease, fungus, infection, etc.), increase patient stress, patient expense, patient driver lost time, and/or patient inconvenience.

As such, it may be desirable to improve medical procedure scheduling so as to reschedule medical procedures which are likely to be impacted based on events occurring during a current medical procedure.

It also may be an inefficient use of medical facility resources to schedule buffer times between procedures, if such buffer times are not necessary. For example, if a buffer time is scheduled after a medical procedure's expected end time and the medical procedure ends on time or early, medical facility resources, such as the room, medical equipment, and/or clinicians, may be waiting unused until after the buffer period expires.

In general, this disclosure is directed to various techniques and medical systems for revising schedules for future medical procedures based on events occurring during a current medical procedure. For example, a medical system may determine that an event occurs during the current medical procedure. The event may be indicative of the end of a phase of the medical procedure, the beginning of a phase of the medical procedure, a patient reaction or the like, which may be indicative of a change in an expected or predicted duration or end time of the current medical event. If the predicted duration or end time of the current medical procedure is revised, the medical system may output a scheduling indication and/or reschedule a future medical procedure.

In one example, the disclosure describes a medical system comprising: memory configured to store at least one of a predicted duration or predicted end time of a current medical procedure; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine at least one of the predicted duration or the predicted end time; monitor data associated with the current medical procedure during the current medical procedure; determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and output a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

In another example, the disclosure describes a method comprising: determining, by processing circuitry, at least one of a predicted duration or a predicted end time of a current medical procedure; monitoring, by the processing circuitry, data associated with the current medical procedure during the current medical procedure; determining, by the processing circuitry, that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determining, by the processing circuitry, at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and outputting, by the processing circuitry, a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

In yet another example, the disclosure describes a non-transitory computer readable medium comprising instructions, which when executed, cause processing circuitry to: determine at least one of a predicted duration or a predicted end time of a current medical procedure; monitor data associated with the current medical procedure during the current medical procedure; determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and output a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

These and other aspects of the present disclosure will be apparent from the detailed description below. In no event, however, should the above summaries be construed as limitations on the claimed subject matter, which subject matter is defined solely by the attached claims.

This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the apparatus and methods described in detail within the accompanying drawings and description below. Further details of one or more examples are set forth in the accompanying drawings and the description below.

DETAILED DESCRIPTION

As mentioned above, medical procedures performed in an operating room or a Cath lab are typically scheduled following a certain predetermined cadence, sometimes including buffers in between procedures, to minimize wasted time by clinicians, such as doctors and key staff. However, some medical procedures may take significantly longer than scheduled, even with a buffer period, due to unforeseen complications or other issues which may arise or be discovered during the medical procedure. Such medical procedures may impact scheduling of subsequent medical procedures.

Rescheduling a patient of a subsequent medical procedure may cause additional stress, discomfort, and/or inconvenience for the patient and may increase a risk that the patient be exposed to communicable disease, fungus, infection, as the patient may unnecessarily spend more time in a medical facility waiting room due to the rescheduling. In some cases, a medical procedure may be completed early or before expiration of the buffer period, thereby leaving medical facility resources unused until the start of the next scheduled medical procedure.

According to the techniques of this disclosure, a medical system may predict or estimate a predicted duration or end time of a medical procedure. The medical system may monitor and log medical procedure time and significant events (e.g., milestones) within the medical procedure. The medical system may use such information to assist with subsequent and/or current medical procedure scheduling, to provide live (e.g., real-time) medical procedure duration projections, and assess the impact on upcoming medical procedures, for example, subsequent medical procedures scheduled for that same day that may be affected by the extension or contraction of a duration of a current medical procedure. It should be noted that the predicted duration or end time may be represented in any number of formats, such as a median or mean, a confidence interval, a historical range, or the like.

Additionally, facility or individual medical procedure techniques (e.g., surgical techniques) are currently changed in an ad hoc manner. For example, one clinician may notice that different techniques may reduce medical procedure time, or periodic audits may flag a particular clinician as being associated with relatively long (or speedy) medical procedure times, but additional information would need to be gathered to determine why the particular clinician is involved in longer or shorter (in time) medical procedures. Improvements in medical procedure techniques inspired by other facilities are even more unusual with many of such improvements coming from yearly conventions, journal articles, or new staff. The techniques of this disclosure may identify medical techniques which are more efficient than other medical techniques and, as such, facilitate proliferation of such techniques.

The techniques of this disclosure may not only provide scheduling staff with a better initial estimate of how long medical procedures typically take, but may provide scheduling staff with real-time notice of extending medical procedure times. This real-time notice may be used to better manage rescheduling of upcoming medical procedures, patient expectations, and patient requirements or burdens, thus improving hospital resource allocation and patient experience. Additionally, the techniques of this disclosure may provide a medical facility with information that could help them better match clinicians, such as staff, physicians, and technicians, with a particular upcoming medical procedure and/or with each other, to improve medical procedure time. The techniques of this disclosure may also provide evidence of successful best practices or technology other facilities are implementing.

FIG.1is a schematic perspective view of one example of a system for determining a recommended treatment strategy according to one or more aspects of this disclosure. System100includes a display device110, a table120, an imager140, and a computing device150. In some examples, system100includes additional equipment170, such as one or more cameras which may capture video image data of occurrences in the environment surrounding system100, one or more microphones which may capture audio data of occurrences in the environment surrounding system100, a procedural device (e.g., a renal artery denervation (RDN) device, a laparoscopic tool, a surgical tool, etc.) which may be used to treat a patient, an anesthesia delivery device, an FFR device, a barcode scanner which may be configured to scan a barcode of a medical instrument or device indicative of the identity of the medical instrument or device, and/or other medical instruments or medical devices. System100may be an example of a system for use in a medical facility, such as in an operating room or a Cath lab. In some examples, system100may include other devices, not shown for simplicity purposes. In some examples, system100may also include server160, which may be co-located with the other devices of system100or may be located elsewhere. System100may be used during a medical procedure, such as a diagnostic and/or an interventional medical procedure to treat a medical condition of a patient. System100may determine a predicted duration and/or end time of a medical procedure. During such a medical procedure, system100may monitor data associated with the medical procedure and revise a predicted duration and/or end time of the medical procedure based on data collected by system100, according to the techniques of this disclosure.

For example, computing device150may be coupled to a plurality of common pieces of equipment that may provide data to computing device150. Such equipment may include imager140, which may include a C-Arm imager, or other imager, and additional equipment170, which may include equipment monitoring a patient's vitals, one or more cameras, one or more microphones, or the like. In some examples, additional equipment170may include specialty equipment, such as an RDN generator, an IVUS imaging device (which may be an example of imager140), specialty FFR estimating equipment or reports, as well as relevant anesthesia equipment, or the like. In some examples, the connections between imager140, additional equipment170and computing device150may include physical audio/visual (A/V) and/or data ports.

System100may include one or more machine learning models. A machine learning model may be trained to determine a predicted duration and/or end time of a medical procedure. Additionally, or alternatively, the machine learning model may be trained to, based on data associated with the medical procedure, such as image data, audio data, video data, and/or other data, determine a revised duration and/or end time for the medical procedure. For example, the machine learning model may be trained to determine an event, such as the end of a phase of the medical procedure, the beginning of a phase of the medical procedure, the use of an unexpected medical instrument, or the like, based on data associated with the medical procedure.

System100may use the revised duration and/or end time to automatically reschedule other subsequent medical procedures that may be impacted by the revised duration or end time of the current medical procedure or to alert medical facility staff and/or an impacted patient of the likelihood that the subsequent medical procedure may need to be rescheduled.

In some examples, system100may communicate with a computing device, patient device180, associated with a patient of a subsequent medical procedure, such as to notify the patient of the subsequent medical procedure of a change in scheduling of their medical procedure. Patient device180may include a mobile device, such as a smartphone or other cellular phone, a tablet computer, a laptop computer, a desktop computer, or the like. For example, computing device150and/or server160may communicate with patient device180via automated phone call, short message service (SMS) text message, email, web portal notification, or the like.

Computing device150may include, for example, an off-the-shelf device such as a laptop computer, desktop computer, tablet computer, smart phone, or other similar device or may include a specific purpose device. Computing device150may perform various control functions with respect to imager140. In some examples, computing device150may include a guidance workstation. Computing device150may control the operation of imager140and receive the output of imager140.

Display device110may be configured to output instructions, images, and messages relating to the medical procedure. Table120may be, for example, an operating table or other table suitable for use during a medical procedure.

In the example ofFIG.1, imager140, such as a fluoroscopy imager, an angiography imager, or other imaging device, may be used to image relevant portions of the patient's anatomy during the medical procedure to visualize the anatomy, characteristics and locations of lesions or other issues inside the patient's body through the generation of imaging data. While described primarily as a fluoroscopy imager, imager140may be any type of imaging device, such as an angiography device, an intravascular ultrasound (IVUS) device, an optical coherence tomography (OCT)-fractional flow reserve (FFR) device, a computed tomography (CT) device, a magnetic resonance imaging (MRI) device, a positron emission tomography (PET) device, an ultrasound device, or the like. In some examples, imager140may represent more than one imaging device, such as a plurality of any of the aforementioned devices.

Imager140may image a region of interest in the patient's body. The particular region of interest may be dependent on anatomy, the medical procedure, patient symptoms, and/or the like. For example, when performing a cardiac medical procedure, a portion of the vasculature and/or the heart may be within the region of interest.

Computing device150may be communicatively coupled to imager140, display device110and/or server160, for example, by wired, optical, or wireless communications. Server160may be a hospital server which may or may not be located in an emergency room or Cath lab of a hospital, a cloud-based server, or the like. Server160may be configured to store patient imaging data, electronic healthcare or medical records, or the like.

Any of, or any combination of, computing device150, imager140, and/or server160may include one or more machine learning model(s). For example, computing device150, imager140, and/or server160may obtain image data of the cardiac anatomy of the patient. Computing device150, imager140, and/or server160may, based on data associated with a medical procedure determine that an event occurs during the medical procedure that may impact a predicted duration or end time of the medical procedure. In some examples, computing device150, imager140, and/or server160may execute a machine learning model to determine a predicted duration and/or end time for a medical procedure. For example, computing device150, imager140, and/or server160in executing the machine learning model may determine confidence intervals which may be used to determine the predicted duration and/or end time for the medical procedure. In some examples, computing device150, imager140, and/or server160may execute a machine learning model to determine the event occurs and/or to determine a revised duration or end time for the medical procedure. In some examples, computing device150, imager140, and/or server160may output a scheduling revision for a subsequent medical procedure based on the revised duration and/or end time of the current medical procedure.

By outputting a scheduling revision for the subsequent medical procedure, system100may improve the efficiency of medical facility resource usage and/or reduce patient stress, discomfort, and/or inconvenience due to unexpected, “last minute” rescheduling of a subsequent medical procedure.

FIG.2is a schematic view of one example of a computing device150of system10ofFIG.1. Computing device150may include a workstation, a desktop computer, a laptop computer, a smart phone, a tablet, a dedicated computing device, or any other computing device capable of performing the techniques of this disclosure.

Computing device150may be configured to perform processing, control and other functions associated with imager140. As shown inFIG.2, computing device150may represent multiple instances of computing devices, each of which may be associated with imager140. Computing device150may include, for example, a memory202, processing circuitry204, a display206, a network interface208, input device(s)210, and/or output device(s)212, each of which may represent any of multiple instances of such a device within the computing system, for case of description.

While processing circuitry204appears in computing device150inFIG.2, in some examples, features attributed to processing circuitry204may be performed by processing circuitry of any of computing device150, imager140, or server160, or combinations thereof. In some examples, one or more processors associated with processing circuitry204in computing system may be distributed and shared across any combination of computing device150, imager140, and server160. Computing device150may be used to perform any of the techniques described in this disclosure, and may form all or part of devices or systems configured to perform such techniques, alone or in conjunction with other components, such as components of computing device150, imager140, server160, or a system including any or all of such systems.

Memory202of computing device150includes any non-transitory computer-readable storage media for storing data or software that is executable by processing circuitry204and that controls the operation of computing device150and/or imager140, as applicable. In one or more examples, memory202may include one or more solid-state storage devices such as flash memory chips. In one or more examples, memory202may include one or more mass storage devices connected to the processing circuitry204through a mass storage controller (not shown) and a communications bus (not shown).

Although the description of computer-readable media herein refers to a solid-state storage, it should be appreciated by those skilled in the art that computer-readable storage media may be any available media that may be accessed by the processing circuitry204. That is, computer readable storage media includes non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. For example, computer-readable storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, Blu-Ray or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store the desired information and that may be accessed by computing device150. In one or more examples, computer-readable storage media may be stored in the cloud or remote storage and accessed using any suitable technique or techniques through at least one of a wired or wireless connection.

Memory202may store image data214, audio data216, other data224, video data230, estimated time for procedure226, revised estimated time for procedure228, threshold(s)232, and/or scheduling information234. Image data214may be captured by imager140(FIG.1) during a medical procedure of a patient. Processing circuitry204may obtain imaging data214from imager140and store image data214in memory202. In some examples, image data214may include image data of the patient captured during a previous or prior medical procedure as well as imaged data of the patient captured during a current medical procedure. Processing circuitry204may execute user interface218so as to cause display206(and/or display device110ofFIG.1) to present user interface218to one or more clinicians performing the medical procedure. For example, image data214may be generated by imager140of anatomy of the patient and obtained by computing device150via network interface208which may be communicatively coupled to imager140. In some examples, image data214may include image data indicative of medical devices and/or medical instruments being implanted or used during a medical procedure, such as a stent, sternal ties, balloons, guidewires, or the like.

Memory202may store audio data216. For example, additional equipment170(FIG.1) may include one or more microphones which may capture audio data. Processing circuitry204may obtain the audio data from additional equipment170via network interface208and store the obtained audio data in audio data216. Audio data216may be indicative of events occurring during a medical procedure. For example, a clinician may verbally ask a nurse for a particular medical instrument which may be indicative of that medical instrument being deployed during the medical procedure.

Memory202may store other data224. For example, a clinician may input via input device(s)210other information relevant to the medical procedure, such as information identifying the clinicians performing the medical procedure, roles for each such clinician, a type of procedure, equipment to be used during the procedure, or the like. Other data224may also include data from additional equipment170.

Memory202may store video data230. For example, additional equipment170(FIG.1) may include one or more cameras which may capture video data. Processing circuitry204may obtain the video data from additional equipment170via network interface208and store the obtained video data in video data230. Video data230may be indicative of events occurring during a medical procedure. For example, a clinician may pick up or move a particular medical instrument which may be indicative of that medical instrument being deployed during the medical procedure.

Memory202may store estimated time for procedure226. Estimated time for procedure226may include an estimated duration of the medical procedure and/or an estimated end time for the medical procedure. In some examples, estimated time for procedure226may be generated by processing circuitry204executing machine learning model(s)222. In other examples, estimated time for procedure226may be entered by a clinician or scheduling staff via input device(s)210or on another computing device via network interface208.

Memory202may store revised estimated time for procedure228. Revised estimated time for procedure228may include a revised estimated duration of the medical procedure and/or a revised estimated end time for the medical procedure. For example, processing circuitry204executing machine learning model(s)222may determine an event occurs during a current medical procedure. Processing circuitry204executing machine learning model(s)222may determine revised estimated time for procedure228based on the event occurring.

Memory202may store threshold(s)232. Threshold(s)232may include a threshold amount which may be used to determine whether to reschedule a subsequent medical procedure. The threshold amount may be an amount of time. Threshold(s)232may include a threshold time which may be used to determine whether to prompt a clinician to seek assistance. These thresholds are discussed further later in this disclosure.

Memory202may include scheduling information234. For example, scheduling information234may include information relating to scheduled medical procedures, such as those medical procedures scheduled for a current day, week or other time period. Scheduling information234may include predicted durations, start times, and/or end times for subsequent medical procedures. In some examples, when processing circuitry204outputs a scheduling indication, the scheduling indication may be stored in scheduling information234.

Memory202may also store one or more machine learning model(s)222and user interface218. Machine learning model(s)222may be configured to, when executed by processing circuitry204, to determine estimated time for procedure226, determine that an event occurs in a current medical procedure, and/or revised estimated time for procedure228.

For example, processing circuitry204executing machine learning model(s)222may determine estimated time for procedure226based on a type of procedure, clinicians scheduled to perform the procedure, image data214(which may include image data from a current medical procedure, as well as image data from one or more previous medical procedures), audio data216, other data224, video data230, and/or the like.

In some examples, processing circuitry204may execute machine learning model(s)222to determine revised estimated time for procedure228. For example, processing circuitry204executing machine learning model(s)222may monitor progress of the current medical procedure through image data214, audio data216, other data224, and/or video data230. Processing circuitry204executing machine learning model(s)222may determine an event has occurred during the current medical procedure based on one or more of image data214, audio data216, other data224, and/or video data230. For example, a medical instrument may appear in image data214that may indicate that a current diagnostic medical procedure has now become an interventional medical procedure. This may be an example event. Other example events are discussed further hereinafter. Based on determining the event has occurred, processing circuitry204executing machine learning model(s)222may determine revised estimated time for procedure228. For example, machine learning model(s)222may have been trained on training data which may include the occurrence of that event or a similar event and may be able to determine revised estimated time for procedure228based on times associated with the occurrence of that event or a similar event in the training data.

In such examples, machine learning model(s)222may be trained using image data, audio data, video data, and/or other data of a plurality of medical procedures, durations associated with the plurality of medical procedures, durations between events and/or an end of the plurality of medical procedures, durations associated with clinicians and/or clinician roles in the plurality of medical procedures, annotations, and/or the like.

In some examples, machine learning model(s)222may be trained to recognize various medical instruments and/or devices in image data214, in video data230, and/or based on other data224(e.g., based on a scanned barcode). As such, processing circuitry204executing machine learning model(s)222may determine a state of the medical procedure, such as a phase of the medical procedure based on which medical instrument(s) and/or device(s) may be being used in the medical procedure.

In some examples, machine learning model(s)222may be trained to recognize spoken words in audio data216. As such, processing circuitry204executing machine learning model(s)222may determine a state of the medical procedure, such as a phase of the medical procedure based on which words spoken and captured by one or more microphone(s) of additional equipment170during the medical procedure.

In some examples, processing circuitry204executing machine learning model(s)222may continuously monitor image data214, audio data216, other data224, and/or video data230for indications of any events which may impact a duration or end time of the current medical procedure so that processing circuitry204may determine revised estimated time for procedure228upon occurrence of such an event. In this manner, processing circuitry204may, for example, via network interface208, output a scheduling indication. For example, the scheduling indication may include a notification to a patient scheduled for a upcoming or subsequent medical procedure and/or scheduling staff of a likelihood of a need to reschedule the upcoming or subsequent medical procedure and/or may automatically reschedule the upcoming or subsequent medical procedure when the current medical procedure is estimated to impact the schedule (e.g., scheduled time, location, etc.) of the upcoming or subsequent medical procedure. For example, in some cases, a 1 minute difference between estimated time for procedure226and revised estimated time for procedure228may not impact scheduling and may not trigger processing circuitry to output the scheduling indication. In some examples, processing circuitry204may determine whether revised estimated time for procedure228varies (e.g., differs) more than a threshold amount (of time) of threshold(s)232from estimated time for procedure226before outputting a scheduling indication.

Additionally, because more than one phase of a medical procedure may take longer than predicted or less time than predicted, or phases may be added to or subtracted from a medical procedure more than once, it may be desirable to selectively output scheduling indications. As such, in some examples, prior to outputting a scheduling indication, processing circuitry204may determine whether the revised predicted duration or the revised predicted end time varies by more than a threshold amount from the predicted duration or the predicted end time. For example, if the revised predicted duration or the revised predicted end time does vary by more than a threshold amount from the predicted duration or the predicted end time, this may be considered a significant scheduling impact and processing circuitry204may output the scheduling indication. If the revised predicted duration or the revised predicted end time does not vary by more than a threshold amount from the predicted duration or the predicted end time, in some examples, processing circuitry204may not output the scheduling indication. In this manner, a scheduling indication may not be output for every time a new revised estimated time for procedure is determined, which may reduce the number of notifications sent to the patient and/or scheduling staff when scheduling impact is minimal. In some examples, this threshold amount is programmable by a user. The threshold amount may be a fixed amount of time or an amount of time that may be based on other factors. It may be desirable that scheduling indications only be output for scheduling inputs that would cause a subsequent medical procedure to be pushed from the current date to another date. As such, the threshold amount may take into account scheduling information234relating to subsequent medical procedures, the current time of day, or the like.

User input data of other data224, such as the type of medical procedure, identification of clinicians involved in the current medical procedure, and/or roles of each such clinician, may be useful to processing circuitry204in determining estimated time for procedure226, as different types of medical procedures may take different amounts of time to complete, and different clinicians may take different amounts of time to perform the same type of procedure. Prior to a first medical procedure, users (e.g., clinicians) may fill out simple profiles detailing their name and/or badge number and the roles they may typically fill in the Cath lab or operating room. In some examples, a user may also enter a room in which computing device150resides. Such information may be entered into computing device150and/or server160via an input device, user interface, or the like.

With regards to a particular medical procedure, a user may enter or select the type of medical procedure, the profiles (or other identification) of the clinicians involved in the medical procedure and, if appropriate, the role of each such clinician, e.g., via input device(s)210, user interface218, display206, network interface208, or the like. Processing circuitry204may store such information in other data224and use such information to determine estimated time for procedure226. In some examples, rather than determine estimated time for procedure226based on the information referred to above, processing circuitry204may determine estimated time for procedure226based on an estimate of the duration or end time of the procedure entered by a user.

Over time, processing circuitry204may determine trends for specific clinician teams, such as physicians or combinations of physicians and staff, and their impact on medical procedure time. For example, if a particular clinician typically needs more time to complete one type of medical procedure, processing circuitry may take this into account when determining estimated time for procedure226for subsequent medical procedures involving the particular clinician, for example, in the same role, performing the same type of procedure.

In some examples, processing circuitry204may provide information to scheduling staff in advance including an estimated time for procedure226for a plurality of medical procedures to assist the scheduling staff in scheduling the plurality of medical procedures. In some examples, processing circuitry204may automatically schedule the plurality of medical procedures.

During a medical procedure, if processing circuitry204detects something that may shorten or lengthen the medical procedure (e.g., for example, based on image data214, audio data216, video data230, and/or other data224), processing circuitry204may output a scheduling indication to push that information to scheduling staff and/or may automatically reschedule patients and/or resources. For scheduling staff, this advanced knowledge may allow for the better management of the pool of patients and hospital resources. For example, the advanced knowledge may allow a scheduling staff to notify a patient that their medical procedure will be bumped to the following day, shift, or week, reducing or avoiding time spent by the patient in a waiting room the day on which the procedure was originally scheduled.

With a coronary medical procedure, processing circuitry204executing machine learning model(s)222may detect a switch from a diagnostic medical procedure to an interventional medical procedure, for example, by detecting the introduction of a medical instrument into the vasculature of the patient in image data214. For example, processing circuitry204executing machine learning model(s)222may identify a change from a diagnostic catheter to a guide catheter and the introduction of a crossing balloon in fluoroscopy image data of image data214. Processing circuitry204executing machine learning model(s)222may then determine that such action is likely to extend the medical procedure time by a particular amount or range of time (e.g., X-Y minutes).

With an RDN procedure, processing circuitry204executing machine learning model(s)222may determine, based on an initial patient scan (e.g., based on image data214) that a generator will need to provide14energy deliveries per protocol. The example patient may have a tortuous anatomy and sleep apnea. Processing circuitry204executing machine learning model(s)222may determine that previous medical procedures similar to the RDN procedure have taken between X and Y minutes to complete.

With a chronic total occlusion (CTO) medical procedure, the clinician may start with an antegrade approach, but convert to retrograde approach, which would require more time.

Processing circuitry204executing machine learning model(s)222may recognize in image data214the change to the retrograde approach and may determine that previous medical procedures similar to the current CTO procedure have taken between X and Y minutes to complete once the change to the retrograde approach has been made.

In some examples, where there are multiple options available for treatment, such as with a CTO, (e.g., antegrade and retrograde approaches), processing circuitry204may include enough time for both approaches when determining estimated time for procedure226. In this manner, any determined revised estimated time for procedure228is very likely to be of a shorter duration than estimated time for procedure226, which may make it less likely that a subsequent medical procedure may be pushed back in time.

In some examples, system100may provide a “check-in” feature. In the example where computing device150may notify scheduling staff that there is a delay in the current medical procedure, processing circuitry204may, for example, upon request from a user, such as scheduling staff, determine an estimated time until a next phase of the current procedure is completed. For example, a delay in the current medical procedure may be caused by a diagnostic procedure becoming an interventional procedure. Such a delay may be a relatively large delay or a relatively small delay. The extent of such a delay may be more apparent or determinable once intravascular imaging (e.g., IVUS) imaging is taken and analyzed. As such, processing circuitry204may control display206, display device110, our output device(s)212, to output a message such as “Intravascular imaging is typically taken in the next 10 minutes, please check back.” Such a feature may reduce the amount of interactions the scheduling staff may have with the clinicians performing the current medical procedure, thereby increasing the time the clinicians performing the current medical procedure may spend focused on the current medical procedure itself. In some examples, the scheduling staff may request that processing circuitry provide updates on the progress of the current medical procedure. In some examples, such updates may be provided based on progress through each of a number of phases of the current medical procedure.

Because processing circuitry204may have knowledge of clinicians and their roles associated with a medical procedure, as well as the time such clinicians may take to complete each medical procedure, over time, processing circuitry204may improve staffing by suggesting to scheduling staff clinicians that perform relatively well with each other. Processing circuitry204may also identify clinicians that are most efficient at specific procedures and recommend such clinicians for those specific procedures. Processing circuitry204may also draw associations between specific equipment and/or technology used and any associated reduced medical procedure time. Processing circuitry204may therefore, highlight or propose treatment strategies associated with reduced medical procedure time to, e.g., clinicians.

Processing circuitry204may be implemented by one or more processors, which may include any number of fixed-function circuits, programmable circuits, or a combination thereof. In various examples, control of any function by processing circuitry204may be implemented directly or in conjunction with any suitable electronic circuitry appropriate for the specified function. Fixed-function circuits refer to circuits that provide particular functionality and are preset on the operations that may be performed. Programmable circuits refer to circuits that may programmed to perform various tasks and provide flexible functionality in the operations that may be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, the one or more of the units may be distinct circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs) or other equivalent integrated or discrete logic circuitry. Accordingly, the term processing circuitry204as used herein may refer to one or more processors having any of the foregoing processor or processing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

Display206may be touch sensitive or voice activated (e.g., via one or more microphones of additional equipment170), enabling display206to serve as both an input and output device. Alternatively, a keyboard (not shown), mouse (not shown), or other data input devices (e.g., input device(s)210) may be employed.

Network interface208may be adapted to connect to a network such as a local area network (LAN) that includes a wired network or a wireless network, a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the Internet. For example, computing device150may obtain image data214from imager140during a medical procedure. Computing device150may receive updates to its software, for example, application(s)217, via network interface208. Computing device150may also display notifications on display206that a software update is available.

Input device(s)210may include any device that enables a user to interact with computing device150, such as, for example, a mouse, keyboard, foot pedal, touch screen, augmented-reality input device receiving inputs such as hand gestures or body movements, or voice interface.

Output device(s)212may include any connectivity port or bus, such as, for example, parallel ports, serial ports, universal serial busses (USB), or any other similar connectivity port known to those skilled in the art.

Application(s)217may be one or more software programs stored in memory202and executed by processing circuitry204of computing device150. Processing circuitry204may execute user interface218, which may display image data214, other data224, video data230, estimated time for procedure226, and/or revised estimated time for procedure228on display206and/or display device110.

FIG.3is a tabular diagram illustrating example inputs that may be used to determine a duration or an end time and/or a revised duration or a revised end time of a medical procedure according to one or more aspects of this disclosure. In some examples, one or more of such inputs may be used by processing circuitry204to determine estimated time for procedure226and/or revised estimated time for procedure228. For example, processing circuitry204may determine estimated time for procedure226based on the type of medical procedure to be performed (or being performed). Processing circuitry204may determine the type of medical procedure based on data associated with the medical procedure, such as image data214, audio data216, video data230, and/or other data224(e.g., user input data and/or data from additional equipment170). For example, the type of medical procedure may be included in other data224and have been input into computing device150by a user. In some examples, processing circuitry204may determine estimated time for procedure226based on information, in addition to, or in lieu of, the type of medical procedure.

In some examples, image data214may include patient demographic information, evidence of previous interventions (e.g., a bypass), the existence and/or type of existing implanted device(s), vessel information (e.g., vessel morphology), presence of medical instruments and/or medical equipment (e.g., guide catheters), treatment quantities and/or locations (e.g., for RDN treatments), vital signs, case outcome, vessel spasm, and/or the like. In some examples, processing circuitry204executing machine learning model(s)222may utilize such information when determining estimated time for procedure226, and/or revised estimated time for procedure228.

In some examples, other data224may include data from a procedural device, such as an RDN device, a laparoscopic tool, a surgical tool, an anesthesia delivery device, FFR device, a barcode scanner, or the like. For example, other data224may include ablation quantity and locations, temperature and impedance data, measures or indications of procedural complexity, lesion quantity and/or morphology, FFR score(s), estimates of change impact on a vessel over time, or the like. In some examples, other data224may include user input data, such as clinician identification of clinicians involved in the medical procedure, clinician roles in the medical procedure, or the like. In some examples, processing circuitry204executing machine learning model(s)222may utilize such information when determining estimated time for procedure226, and/or revised estimated time for procedure228. While an RDN device is specifically mentioned inFIG.3, the RDN device is set forth as an example. Other data224may include data from any of or any combination of devices of additional equipment170discussed herein.

For example, audio data216, video data230, and/or other data224(e.g., user input data and/or data from additional equipment170) may be used by processing circuitry204executing machine learning model(s)222to determine various time points or phases associated with estimated time for procedure226and/or revised estimated time for procedure228. For example, processing circuitry204executing machine learning model(s)222may determine that a room (e.g., operating room, Cath lab, or the like) is clear, that the room is clean, that staff has begun preparing for the medical procedure, that the patient is present in the room, that the patient is sedated, that the clinician is present in the room, that an initial scan by imager140has begun, that medical instruments and/or medical devices are introduced to and or withdrawn from the patient, that patient closure begins, the medical procedure ends, and/or that the room has been cleared. One or more of these time points may be considered to mark the beginning or end of a phase of the medical procedure.

In some examples, processing circuitry204may support the use of custom tags for events. For example, a user may designate an event as being a milestone to be monitored. The system may then monitor for the events, such as through image data214, audio data216, video data230, and/or other data224. For example, an event may include placement of a medical instrument or equipment, such as guide catheter placement or an RDN device, energy delivery, or the like. In some examples, processing circuitry204may flag anatomy and/or patient reactions (e.g., vessel spasm) as events that may be indicative of a predicted change in duration of the current medical procedure. For example, processing circuitry204may, for an RDN procedure, flag vessels that meet ablation criteria, tortuosity scoring, etc., as being an event.

Machine learning model(s)222may be trained to determine what phases are included in a particular type of medical procedure and to predict a time duration associated with each phase of the medical procedure. In this manner, processing circuitry204executing machine learning model(s)222may predict estimated time for procedure226. While monitoring data associated with the medical procedure, such as image data214, audio data216, video data230, and/or other data224, processing circuitry204executing machine learning model(s)222may determine an event occurs in the medical procedure. This event may be indicative of the end of a phase or the beginning of a phase of the medical procedure or the beginning of a phase not previously associated with the medical procedure (e.g., the introduction of an invasive medical instrument to the patient during a diagnostic medical procedure). Machine learning model(s)222may be trained to determine revised estimated time for procedure228based on such events. For example, if a new phase is added to the medical procedure, processing circuitry204executing machine learning model(s)222may reclassify the medical procedure adding that new phase and any other associated phases expected by machine learning model(s)222to now be part of the medical procedure and revise the original estimated time for procedure226based on expected time durations for the new phase(s) of the medical procedure. Processing circuitry204may store this revision as revised estimated time for procedure228.

To determine estimated time for procedure226, and/or revised estimated time for procedure228, machine learning model(s)222may have been trained to associate various phases with a given type of medical procedure and a time duration associated with each phase. In this manner, processing circuitry204executing machine learning mode(s)222may determine estimated time for procedure226. When processing circuitry204executing machine learning model(s)222determines an event that may impact estimated time for procedure226(e.g., a new phase, an early or late ending to a phase, or the like), processing circuitry204executing machine learning model(s)222may determine revised estimated time for procedure228based on the occurrence of the event.

For example, once trained, processing circuitry204executing machine learning model(s)222may identify medical procedures that are likely to extend beyond estimated time for procedure226, for example, based on previous similar patients (procedure type, age, gender, BMI, etc.). Processing circuitry204may also include other factors, such as in which room the medical procedure is being performed, the identity and/or roll of the clinicians performing the procedure, or the like. Processing circuitry204may determine an expected duration of the medical procedure, which in some examples, may include a range of durations. Scheduling staff may use such an estimate to assist with front end scheduling. During the medical procedure, processing circuitry204may provide live updates of progress of the medical procedure (e.g., of phases occurring) back to the scheduling staff to assist with rescheduling clinician, equipment, and/or patient schedules. For example, if a complex renal anatomy is captured on fluoroscopy in an RDN medical procedure, or a clinician introduces therapeutic devices (ex. crossing balloons) in a scheduled diagnostic medical procedure (e.g., a medical procedure not expected to include therapy), processing circuitry204may determine an expected impact of such unanticipated events and notify hospital staff as to schedule impacts. These live updates may be used to give as much advanced notice as possible to the staff and/or patients involved with subsequent medical procedures or with scheduling thereby allowing for greater resource flexibility.

Different phases of a medical procedure and predicted times associated therewith, may be used to allow for scheduling resources in a staggered manner for the medical procedure. For example, it may be desirable to administer anesthesia about X minutes after the medical procedure starts so as to avoid negatively impacting the medical procedure duration. This information may also help with staggering the timing of resources after the medical procedure, such as patient room availability and nursing staff.

FIG.4is a flow diagram illustrating example medical procedure rescheduling techniques according to one or more aspects of this disclosure. The techniques ofFIG.4are described below with respect to processing circuitry204, but such techniques may be performed by any of, or any combination of, processing circuitry of devices depicted inFIG.1or capable of performing such techniques.

Processing circuitry204may determine at least one of a predicted duration or a predicted end time of a current medical procedure (400). For example, processing circuitry204may execute machine learning model(s)222on input data from image data214, audio data216, other data224, and/or video data230to determine estimated time for procedure226. In some examples, processing circuitry204may determine estimated time for procedure226without executing machine learning model(s)222. For example, a clinician may enter other data224which may include a predicted duration and/or a predicted end time of the current medical procedure. In such a case, processing circuitry204may simply use the predicted duration and/or a predicted end time as estimated time for procedure226.

Processing circuitry204may monitor data associated with the current medical procedure during the current medical procedure (402). For example, processing circuitry204executing machine learning model(s)222may monitor image data214, audio data216, video data230, and/or other data224during the current medical procedure.

Processing circuitry204may determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure (404). For example, processing circuitry204executing machine learning model(s)222may determine the event occurs based on image data214, audio data216, video data230, and/or other data224collected during the medical procedure.

Processing circuitry204may determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time (406). For example, processing circuitry204executing machine learning model(s)222may determine revised estimated time for procedure228based on the determination of the event. For example, the event may be indicative of an additional phase or phases being added to the current medical procedure which may lengthen the actual duration of the current medical procedure, thereby impacting a schedule of a subsequent medical procedure.

Processing circuitry204may output a scheduling indication for a subsequent medical procedure based on the at least one of the revised duration or the revised end time (408). For example, processing circuitry204may output a scheduling indication to a patient, scheduling staff, a scheduling algorithm, or the like. The scheduling indication may include an indication of revised estimated time for procedure228, a scheduling impact to the subsequent medical procedure, or the like. In some examples, the scheduling indication may include an automatic rescheduling of the subsequent medical procedure. For example, the scheduling indication may cause the rescheduling of elements associated with the medical procedure, such as time, date, location, equipment, clinician(s) involved in the subsequent medical procedure, and may include or cause a notification to the patient of any of the rescheduled elements.

When automatically determining whether to automatically reschedule the subsequent medical procedure, in some examples, processing circuitry204may determine a risk to the health of the patient associated with rescheduling the subsequent procedure. If such a risk is relatively high (e.g., meets a threshold), processing circuitry204may not automatically reschedule the subsequent procedure (e.g., allowing the subsequent procedure to proceed on a delayed basis, but in a same facility on a same date). If such a risk is not relatively high (e.g., does not meet the threshold), processing circuitry204may reschedule the subsequent procedure.

When automatically rescheduling the subsequent medical procedure, in some examples, processing circuitry204may determine a risk associated with each of one or more medical procedures scheduled or to be scheduled on a given day, the risk being a risk that a respective medical procedure of the one or more medical procedures takes a longer time to complete than is scheduled. For example, if such risks are relatively high (e.g., meet a threshold), such that the subsequent medical procedure may have to be rescheduled again, processing circuitry204may forgo rescheduling the subsequent medical procedure for that given day.

In some examples, the event lengthens a duration of the current medical procedure. In some examples, the event includes at least one of an end of a phase of the medical event, a beginning of a phase of the medical event, a patient reaction, or an identification of new medical instrument being used.

In some examples, the data associated with the current medical procedure comprises at least one of image data214, audio data216, video data230, user input data (of other data224), fractional flow reserve data (of other data224), or renal artery denervation data (of other data224).

In some examples, the current medical procedure includes a plurality of phases. In some examples, as part of predicting the at least one of a revised duration or end time of the medical procedure, processing circuitry204is configured to at least one of add a new phase from the current medical procedure or remove an existing phase from the current medical procedure.

In some examples, the predicted duration or the predicted end time of the current medical procedure is based at least in part on at least one of a type of the current medical procedure, clinician identification, or clinician roles. For example, estimated time for procedure226may be based the type of the current medical procedure, identities of clinicians involved in the current medical procedure, and/or clinician roles assigned to clinicians involved in the current medical procedure.

In some examples, processing circuitry204is configured to determine that the event has not occurred by a threshold time, for example, of threshold(s)232. Based on the event not occurring by the threshold time, processing circuitry204may prompt a clinician to seek assistance. For example, processing circuitry204may output to display206and/or display device110a message asking if the clinician would like to contact a manufacturer's representative, another clinician, another specialty group (e.g., a heart team), or the like.

In some examples, processing circuitry204is configured to determine that the event has not occurred after use of a threshold number of therapy devices being utilized during the current medical procedure. The threshold number of devices may be stored in threshold(s)232. Based on the event not occurring after the use of the threshold number of therapy devices, processing circuitry204may prompt a clinician to seek assistance. For example, processing circuitry204may output to display206and/or display device110a message asking if the clinician would like to contact a manufacturer's representative, another clinician, another specialty group (e.g., a heart team), or the like.

In some examples, as part of at least one of determining the at least one of the predicted duration or the end time, or determining the at least one of a revised duration or revised end time of the medical procedure, processing circuitry204may executed machine learning model(s)222. Machine learning model(s)222may be trained on at least one of image data, audio data, video data, user input data, renal artery denervation device data, or fractional flow reserve data.

In some examples, as part of outputting the scheduling indication, processing circuitry204is configured to at least one of automatically reschedule the subsequent medical appointment or send a notification to administration staff responsible for scheduling. In some examples, as part of automatically rescheduling the subsequent medical appointment, processing circuitry204may reschedule at least one of staff, hospital equipment, rooms, or a patient. In some examples, as part of outputting the scheduling revision, processing circuitry204is configured to send a notification to an electronic device (e.g., patient device180ofFIG.1) associated with a patient, wherein the notification comprises at least one of a new time, a new date, or a new medical facility for the subsequent medical procedure.

In some examples, processing circuitry204is configured to send the notification to administration staff responsible for scheduling, and processing circuitry204is further configured to receive a request from a user to determine an estimated time until a next phase of the current procedure is completed. In such examples, processing circuitry204is configured to determine the estimated time until the next phase of the current procedure is completed and output an indication of the estimated time until the next phase of the current procedure is completed.

In some examples, processing circuitry204is further configured to determine that at least one phase of the medical procedure is completed prior to an expected end time of the at least one phase and identify one or more techniques used during the at least one phase based on imaging data. In some examples, processing circuitry204is further configured to determine that the medical procedure is completed at least one of in a shorter duration then the predicted duration or prior to the predicted end time of the medical procedure and identify a team of clinicians associated with the medical procedure as an efficient team.

In some examples, processing circuitry204is further configured to determine that the revised predicted duration or the revised predicted end time varies by more than a threshold amount from the predicted duration or the predicted end time. In some examples, outputting the scheduling indication is further based on the revised predicted duration or the revised predicted end time varying by more than the threshold amount from the predicted duration or the predicted end time. In some examples, the threshold amount is programmable.

In some examples, the event is a first event, the revised predicted duration is a first revised predicted duration, the revised predicted end time is a first revised predicted end time, and the scheduling indication is a first scheduling indication. In some examples, processing circuitry204is further configured to determine that a second event occurs in the current medical procedure based on the data associated with the current medical procedure. In such examples, processing circuitry204is further configured to determine at least one of a second revised predicted duration or a second revised predicted end time of the current medical procedure based on the second event occurring and output a second scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

In some examples, processing circuitry204is configured to determine at least one of a respective predicted duration or a respective predicted end time for each of a plurality of current medical procedures. In such examples, processing circuitry204is configured to monitor respective data associated with each of the plurality of current medical procedures during each of the plurality of current medical procedures. In such examples, processing circuitry204is configured to determine that a respective event occurs in at least two of the plurality of current medical procedures based on the respective data associated with each of the plurality of current medical procedures. In such examples, processing circuitry204is configured to determine at least one of a respective revised predicted duration or a respective revised predicted end time of each of the at least two of the plurality of current medical procedures based on the respective event occurring. In such examples, processing circuitry204is configured to output a respective scheduling indication for each of a plurality of subsequent medical procedures based on at least one of the at least one respective revised predicted duration or revised predicted end time. For example, the techniques of this disclosure may be used to monitor each medical procedure being performed in a given medical facility and perform rescheduling of medical procedures based on events occurring within current medical procedures so as to reduce or minimize waiting room times for patients of subsequent medical procedures.

FIG.5is a conceptual diagram illustrating an example machine learning model according to one or more aspects of this disclosure. Machine learning model500may be an example of machine learning model(s)222. Machine learning model500may be an example of a deep learning model, or deep learning algorithm, trained to determine an identification of an implanted medical device and/or to determine a recommended treatment strategy. One or more of computing device150and/or server160may train, store, and/or utilize machine learning model500, but other devices of system100may apply inputs to machine learning model500in some examples. In some examples, other types of machine learning and deep learning models or algorithms may be utilized in other examples. For examples, a convolutional neural network model of ResNet-18 may be used. Some non-limiting examples of models that may be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, or DenseNet, etc. Some non-limiting examples of machine learning techniques include Support Vector Machines, K-Nearest Neighbor algorithm, and Multi-layer Perceptron. In some examples, machine learning model500may be a continuous learning model which may use input data to improve training over time.

As shown in the example ofFIG.5, machine learning model500may include three types of layers. These three types of layers include input layer502, hidden layers504, and output layer506. Output layer506comprises the output from the transfer function505of output layer506. Input layer502represents each of the input values X1 through X4 provided to machine learning model500. In some examples, the input values may include any of the values input into the machine learning model, as described above. For example, the input values may include or be based on image data214, audio data216, video data230, and/or other data224, as described above. In addition, in some examples, input values of machine learning model500may include additional data, such as other data that may be collected by or stored in system100.

Each of the input values for each node in the input layer502is provided to each node of a first layer of hidden layers504. In the example ofFIG.5, hidden layers504include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples. Each input from input layer502is multiplied by a weight and then summed at each node of hidden layers504. During training of machine learning model500, the weights for each input are adjusted to establish the relationship between image data214, audio data216, video data230, and/or other data224, and estimated time for procedure226and/or revised estimated time for procedure228. In some examples, one hidden layer may be incorporated into machine learning model500, or three or more hidden layers may be incorporated into machine learning model500, where each layer includes the same or different number of nodes.

The result of each node within hidden layers504is applied to the transfer function of output layer506. The transfer function may be linear or non-linear, depending on the number of layers within machine learning model500. Example non-linear transfer functions may be a sigmoid function or a rectifier function. The output507of the transfer function may be a classification that image data214, audio data216, video data230, and/or other data224is indicative of a particular phase of a medical procedure and/or a duration to complete the medical procedure.

As shown in the example above, by applying machine learning model500to input data such as image data214, audio data216, video data230, and/or other data224, processing circuitry204is able to determine estimated time for procedure226and/or revised estimated time for procedure228. This may improve the efficiency of the use of medical resources, the adoption of techniques to improve the time a medical procedure may take, the ability to reschedule patients of subsequent medical procedures earlier, and/or the like.

FIG.6is a conceptual diagram illustrating an example training process for a machine learning model according to one or more aspects of this disclosure. Process670may be used to train machine learning model(s)222or machine learning model500. A machine learning model674(which may be an example of machine learning model500and/or machine learning model(s)222) may be implemented using any number of models for supervised and/or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, naïve Bayes network, support vector machine, or k-nearest neighbor model, CNN, RNN, LSTM, ensemble network, to name only a few examples. In some examples, one or more of computing device150and/or server160initially trains machine learning model674based on a corpus of training data672. Training data672may include, image data, audio data, video data, and/or other data (e.g., user input data, data from other equipment, etc.), of a plurality of past medical procedures performed on a plurality of patients having different patient conditions, durations associated with the plurality of medical procedures, durations between events and/or an end of the plurality of medical procedures, durations associated with clinicians and/or clinician roles in the plurality of medical procedures, annotations, other training data mentioned herein, and/or the like.

While training machine learning model674, processing circuitry of system100may compare676a prediction or classification with a target output678. Processing circuitry204may utilize an error signal from the comparison to train (learning/training680) machine learning model674. Processing circuitry204may generate machine learning model weights or other modifications which processing circuitry204may use to modify machine learning model674. For examples, processing circuitry204may modify the weights of machine learning model674based on the learning/training680. For example, one or more of computing device150and/or server160, may, for each training instance in training data672, modify, based on training data672, the manner in which estimated time for procedure226and/or revised estimated time for procedure228is determined.

The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors or processing circuitry, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The terms “controller”, “processor”, or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit comprising hardware may also perform one or more of the techniques of this disclosure. Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various operations and functions described in this disclosure. In addition, any of the described units, circuits or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as circuits or units is intended to highlight different functional aspects and does not necessarily imply that such circuits or units must be realized by separate hardware or software components. Rather, functionality associated with one or more circuits or units may be performed by separate hardware or software components or integrated within common or separate hardware or software components.

The techniques described in this disclosure may also be embodied or encoded in a computer-readable medium, such as a computer-readable storage medium, containing instructions. Instructions embedded or encoded in a computer-readable storage medium may cause a programmable processor, or other processor, to perform the method, e.g., when the instructions are executed. Computer readable storage media may include random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), or electronically erasable programmable read only memory (EEPROM), or other computer readable media.

This disclosure includes the following non-limiting examples.

Example 1. A medical system comprising: memory configured to store at least one of a predicted duration or predicted end time of a current medical procedure; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine at least one of the predicted duration or the predicted end time; monitor data associated with the current medical procedure during the current medical procedure; determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and output a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

Example 2. The medical system of example 1, wherein the event lengthens a duration of the current medical procedure.

Example 3. The medical system of example 1 or example 2, wherein the event comprises at least one of an end of a phase of the medical event, a beginning of a phase of the medical event, a patient reaction, or an identification of new medical instrument being used.

Example 4. The medical system of any of examples 1-3, wherein the data associated with the current medical procedure comprises at least one of image data, audio data, video data, user input data, fractional flow reserve data, or procedural device data.

Example 5. The medical system of any of examples 1-4, wherein the current medical procedure comprises a plurality of phases.

Example 6. The medical system of example 5, wherein as part of predicting the at least one of a revised duration or end time of the medical procedure, the processing circuitry is configured to at least one of add a new phase from the medical procedure or remove an existing phase from the medical procedure.

Example 7. The medical system of any examples 1-6, wherein the predicted duration or the predicted end time of the current medical procedure is based at least in part on at least one of a type of the current medical procedure, clinician identification, or clinician roles.

Example 8. The medical system of examples 1-7, wherein the processing circuitry is further configured to: determine that the event has not occurred by a threshold time; and based on the event not occurring by the threshold time, prompting a clinician to seek assistance.

Example 9. The medical system of any of examples 1-8, wherein as part of at least one of determining the at least one of the predicted duration or the end time, or determining the at least one of a revised duration or revised end time of the medical procedure, the processing circuitry is configured to execute a machine learning model, and wherein the machine learning model is trained on at least one of image data, audio data, video data, user input data, procedural device data, or fractional flow reserve data.

Example 10. The medical system of any of examples 1-9, wherein as part of outputting the scheduling indication, the processing circuitry is configured to at least one of automatically reschedule the subsequent medical appointment or send a notification to administration staff responsible for scheduling.

Example 11. The medical system of example 10, wherein as part of automatically rescheduling the subsequent medical appointment, the processing circuitry is configured to reschedule at least one of staff, hospital equipment, rooms, or a patient.

Example 12. The medical system of example 10 or example 11, wherein as part of outputting the scheduling revision, the processing circuitry is configured to send a notification to an electronic device associated with a patient, wherein the notification comprises at least one of a new time, a new date, or a new medical facility for the subsequent medical procedure.

Example 13. The medical system of any of examples 10-12, wherein the processing circuitry is configured to send the notification to administration staff responsible for scheduling, and wherein the processing circuitry is further configured to: receive a request from a user to determine an estimated time until a next phase of the current procedure is completed; determine the estimated time until the next phase of the current procedure is completed; and output an indication of the estimated time until the next phase of the current procedure is completed.

Example 14. The medical system of any of examples 1-13, wherein the processing circuitry is further configured to: determine that at least one phase of the medical procedure is completed prior to an expected end time of the at least one phase; and identify one or more techniques used during the at least one phase based on imaging data.

Example 15. The medical system of any of examples 1-14, wherein the processing circuitry is further configured to: determine that the medical procedure is completed at least one of in a shorter duration then the predicted duration or prior to the predicted end time of the medical procedure; and identify a team of clinicians associated with the medical procedure as an efficient team.

Example 16. The medical system of any of examples 1-15, wherein the processing circuitry is further configured to determine that the revised predicted duration or the revised predicted end time varies by more than a threshold amount from the predicted duration or the predicted end time, wherein the outputting the scheduling indication is further based on the revised predicted duration or the revised predicted end time varying by more than the threshold amount from the predicted duration or the predicted end time.

Example 17. The medical system of any of examples 1-16, wherein the event is a first event, the revised predicted duration is a first revised predicted duration, the revised predicted end time is a first revised predicted end time, and the scheduling indication is a first scheduling indication, and wherein the processing circuitry is further configured to: determine that a second event occurs in the current medical procedure based on the data associated with the current medical procedure; determine at least one of a second revised predicted duration or a second revised predicted end time of the current medical procedure based on the second event occurring; and output a second scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

Example 18. The medical system of any of examples 1-17, wherein the processing circuitry is further configured to: determine at least one of a respective predicted duration or a respective predicted end time for each of a plurality of current medical procedures; monitor respective data associated with each of the plurality of current medical procedures during each of the plurality of current medical procedures; determine that a respective event occurs in at least two of the plurality of current medical procedures based on the respective data associated with each of the plurality of current medical procedures; determine at least one of a respective revised predicted duration or a respective revised predicted end time of each of the at least two of the plurality of current medical procedures based on the respective event occurring; and output a respective scheduling indication for each of a plurality of subsequent medical procedures based on at least one of the at least one respective revised predicted duration or revised predicted end time.

Example 19. A method comprising: determining, by processing circuitry, at least one of a predicted duration or a predicted end time of a current medical procedure; monitoring, by the processing circuitry, data associated with the current medical procedure during the current medical procedure; determining, by the processing circuitry, that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determining, by the processing circuitry, at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and outputting, by the processing circuitry, a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.

Example 20. A non-transitory computer-readable storage medium storing instructions, which when executed cause processing circuitry to: determine at least one of a predicted duration or a predicted end time of a current medical procedure; monitor data associated with the current medical procedure during the current medical procedure; determine that an event occurs in the current medical procedure at a first time based on the data associated with the current medical procedure; determine at least one of a revised predicted duration or a revised predicted end time of the current medical procedure based on the event occurring at the first time; and output a scheduling indication for a subsequent medical procedure based on the at least one of the revised predicted duration or the revised predicted end time.