ROBOTIC MEDICAL SYSTEM HAVING HUMAN COLLABORATIVE MODES

Abstract

A system for use in health care, configured to diagnose, instruct, and plan treatment for a patient. The system comprises a controller having three modes of operation: an autonomous decision-making mode; a patient interactive mode; and a doctor interactive mode. The controller is configured to use artificial intelligence in operation of the modes to analyze data obtained from at least some of the patient, health care provider, a diagnostic test, or a medical database. If indicated by an output from the analyzed data, the controller recommends diagnostic tests or clinical procedures, or provides instruction to robotically perform the test. Iterative operation of the system refines the output of analyzed data, enabling the system to provide a diagnosis and treatment plan for a patient. The system may be configured to process an outpatient visit to the emergency room from presentation to discharge.

Claims

1. A system for use in a medical care environment, the system comprising: a controller, comprising: (a) a patient collaborative decision-making mode in which the system exchanges data with a patient; (b) an autonomous decision-making mode in which the system is fully autonomous; and (c) a doctor collaborative decision-making mode in which the system exchanges data with at least one health care provider; the controller being configured to alternate among the modes to: (i) analyze, using artificial intelligence, data obtained from at least one of: the patient, the at least one health care provider, at least one clinical procedure, at least one diagnostic test, or at least one medical database; (ii) use the analyzed data to select at least one diagnostic test, clinical procedure, or clinical recommendation to be performed by either a health care provider or a medical robot; and (iii) if indicated by an output from the analyzed data, autonomously instruct the at least one medical robot to perform at least one of a clinical procedure or a diagnostic test; wherein iterative operation of the system, comprising at least one alternation between at least two of the modes, achieves a diagnosis of the patient, and at least one of a treatment plan or instructions to the patient.

2. The system according to claim 1, wherein the controller is further configured to do at least one of: (i) in the autonomous decision-making mode, improve decision-making capabilities of the robotic control system based on at least one of: a) the data, b) statistical analysis performed by the robotic control system, and c) the diagnostic tests performed by the at least one medical robot; (ii) in the doctor collaborative mode, incorporate the expertise of the at least one health care provider with the data and with the statistical analysis of the robotic control system to improve collaborative decision-making capabilities; and (iii) in the patient collaborative mode, output questions to the patient likely to lead an accurate diagnosis, wherein the learning paths for autonomous mode, doctor collaborative mode, and patient collaborative mode are different.

3. The system according to either of claim 1 or 2, wherein at least one of collected patient data, analyzed information, doctor judgments/decisions, and system judgments/decisions, is available to doctor collaborative mode, patient collaborative mode, and autonomous mode for use in at least one of i) making further judgments/decisions and ii) as training set data for artificial intelligence algorithms.

4. The system according to either of claim 2 or 3, wherein the system is configured to arrive at the same diagnosis for the patient via a first method when the system is in a first mode or first sequential combination of modes, and via a second method when the system is in a second mode or second sequential combination of modes.

5. The system according to any of the previous claims, wherein the robotic control system is configured to provide information to the patient in response to a patient query in at least one of the patient interactive mode and the autonomous mode.

6. The system according to any of the previous claims, wherein the robotic control system in autonomous mode is adapted to exchange analyzed data with, and to learn from, any number of other control systems.

7. The system according to any of the previous claims, wherein the controller is configured to be used in at least one of the modes for long-term patient monitoring in a health care setting.

8. The system according to any of the previous claims, wherein the controller is configured in the autonomous mode to instruct the at least one medical robot to perform any number of diagnostic procedures, comprising at least one of blood drawing, imaging studies, and vital sign acquisition.

9. The system according to any of the previous claims, wherein the controller in the autonomous mode is configured to instruct at least one of the medical robots to perform any number of therapeutic procedures, comprising at least one of prescribing or dispensing medication; providing written or oral instructions; and administering IV fluids.

10. The system according to any of the previous claims, wherein, if iterative sharing of the exchanged, analyzed data between the patient collaborative mode and the autonomous mode results in different output from iterative sharing of the exchanged, analyzed data between the doctor collaborative mode and the autonomous mode, comparison of output from the modes enables achievement of improved diagnostic accuracy.

11. The system according to any of the previous claims, wherein iterative operation of the controller uses artificial intelligence to reach at least one of the diagnosis and the treatment plan, and the patient instructions.

12. The system according to any of the previous claims, wherein the artificial intelligence comprises machine learning.

13. The system according to any of the previous claims, wherein the controller, based on the iterative operation, is configured to switch among the modes, and wherein the decision of at least one of whether to switch to a different mode, when to switch to a different mode, and which mode to switch to, is made by the system using at least collected patient data and artificial intelligence, such that diagnostic accuracy is improved.

14. The system according to any of the previous claims, wherein the instructions to the patient comprise any of: to fill a prescription, to take a prescription, to return for a scheduled follow-up visit, to carry out routine home care tasks, to contact a health care provider, to make an appointment, to allow a medical robot to carry out a test such as blood drawing, to transfer to a different department, or other instructions that are routinely provided to a patient by a human provider in a medical setting.

15. The system according to any of the previous claims, further comprising at least one user interface adapted to exchange the data between the controller and at least one of the patient and the health care provider.

16. The system according to any of the previous claims, wherein said data comprises any of the patient's current age, gender, height, weight, BMI, blood pressure, heart rate, blood laboratory test results, imaging study results, biopsy test results, a previous medical diagnosis, or the patient's past medical data.

17. The system according to any of the previous claims, wherein the analyzing of the data comprises at least one of computational statistics, data mining using exploratory data analysis, or data mining through unsupervised or supervised learning, the data being obtained from at least one of the patient, the health care provider, the patient's previous medical records, the at least one medical database, or previous learning by the robotic control system.

18. The system according to any of the previous claims, wherein the alternation among the modes is determined by the system obtaining maximum data/information from a given mode, whereupon it switches to a different mode, the controller beginning its operation in the patient collaborative mode, switching to the autonomous mode, and, if indicated, switching to the doctor collaborative mode.

19. A method for automated assessment of a patient, comprising the steps of: i) providing a controller capable of iteratively alternating among three decision-making modes, the modes comprising: a patient information-exchange mode, an autonomous mode, and a health care provider information-exchange mode, ii) using the controller to obtain data from the patient and from one or more medical databases, iii) using artificial intelligence and statistical analysis capabilities of the controller, analyzing the data, at least one of the data or the analyzed data being exchanged among at least two of the modes; iv) using iterative operation of the controller to: a) in the autonomous decision-making mode, make decisions based on i) the analyzed information and ii) input from at least one of the collaborative modes; b) in the health care provider information-exchange mode, make decisions based on the analyzed information and incorporating the expertise of at least one health care provider; and c) in the patient information-exchange mode, output questions, analyze answers provided by the patient, and provide instructions to the patient; v) performing at least one iterative operation in at least two of the modes; and vi) arriving at a diagnosis of the patient, and at least one of a treatment plan or patient instructions.

20. The method according to claim 19, wherein the controller is further configured to do at least one of: (i) in the autonomous decision-making mode, improve decision-making capabilities of the robotic control system based on at least one of: a) the data, b) statistical analysis performed by the robotic control system, and c) the diagnostic tests performed by the at least one medical robot; (ii) in the doctor collaborative mode, incorporate the expertise of the at least one health care provider with the data and with the statistical analysis of the robotic control system to improve collaborative decision-making capabilities; and (iii) in the patient collaborative mode, output questions to the patient likely to lead to an accurate diagnosis, wherein the learning paths for autonomous mode, doctor collaborative mode, and patient collaborative mode are different.

21. The method according to either of claim 19 or 20, further comprising the step of providing at least one of the collected patient data, analyzed information, and controller judgments/decisions, to doctor collaborative mode, patient collaborative mode, and autonomous mode for use in at least one of i) making further judgments/decisions and ii) as training set data for artificial intelligence algorithms.

22. A system according to any of claims 19-21, wherein due to different learning paths, the method of arriving at a diagnosis in a first mode or first sequential combination of modes, are different for a second patient with the same symptoms in the same situation.

23. The method according to any of claims 19-22, further comprising the step of providing information to the patient in response to a patient query in at least one of the patient interactive mode and the autonomous mode.

24. The method according to any of claims 19-23, further comprising the step of the controller in autonomous mode exchanging analyzed data with, and learning from, any number of other controllers.

25. The method according to any of claims 19-24, further comprising the step of the controller in autonomous mode instructing at least one medical robot to perform any number of diagnostic procedures, comprising at least one of blood drawing, imaging studies, and vital sign acquisition.

26. The method according to any of claims 19-25, wherein, if iterative exchange of the exchanged, analyzed data between the patient collaborative mode and the autonomous mode results in different output from iterative exchange of the exchanged, analyzed data between the doctor collaborative mode and the autonomous mode, comparison of output from the modes achieves improved diagnostic accuracy.

27. The method according to any of claims 19-26, wherein iterative operation of the controller uses artificial intelligence to reach the diagnosis and the treatment plan, or the patient instructions.

28. The method according to any of claims 19-27, wherein the artificial intelligence comprises machine learning.

29. The method according to any of claims 19-28, wherein the controller, based on the iterative operation, switches among the modes, and wherein the decision of at least one of i) whether to switch to a different mode, ii) when to switch to a different mode, and iii) which mode to switch to, is made by the controller using at least collected patient data and artificial intelligence, such that diagnostic accuracy is improved.

30. The method according to any of claims 19-29, wherein the data comprises any of the patient's current age, gender, height, weight, BMI, blood pressure, heart rate, blood laboratory test results, imaging study results, biopsy test results, a previous medical diagnosis, or the patient's past medical data.

31. The method according to any of claims 19-30, wherein the analyzing of the data comprises at least one of computational statistics, data mining using exploratory data analysis, or data mining through unsupervised learning, the data being obtained from at least one of the patient, the health care provider, the patient's previous medical records, the at least one medical database, or previous learning by the controller.

32. The method according to any of claims 19-31, wherein the alternating among the modes is determined by the controller obtaining maximum data/information from a given mode, whereupon it switches to a different mode, the controller beginning its operation in the patient collaborative mode, switching to the autonomous mode, and, if indicated, switching to the doctor collaborative mode.

33. The system according to claim 19, wherein the diagnosis comprises any one of a provisional diagnosis, a differential diagnosis, or a final diagnosis.

34. The system according to claim 1, wherein for a given patient with given symptoms, a first mode or first sequential combination of modes, and a second mode or a second sequential combination of modes, are configured to perform different methods that are driven by different optimization goals or optimization strategies yet arrive at the same ultimate treatment plan or diagnosis.

35. The system according to claim 33, wherein the first mode or sequential combination of modes improves diagnosis over time using artificial intelligence according to a first optimization goal or strategy, and wherein the second mode or sequential combination of modes improves methods over time using artificial intelligence according to a second optimization goal or strategy, such that diagnostic accuracy is improved.

36. The system according to any of claims 1-18, wherein the diagnosis comprises any one of a provisional diagnosis, a differential diagnosis, or a final diagnosis.

Description

BRIEF DESCRIPTION OF FIGURES

[0067] The present invention will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which:

[0068] FIG. 1 is a diagram showing several possible paths and steps in human-controller interactions;

[0069] FIG. 2 is a schematic representation of the flow of information in an exemplary implementation of the system, showing multiple integrated loops of information exchange;

[0070] FIG. 3 is a schematic representation of the three modes of operation, showing possible ways in which the three modes of operation perform iterative loops of bidirectional information exchange;

[0071] FIG. 4 shows an exemplary flow chart of an exemplary method of the system; and

[0072] FIG. 5 illustrates a more detailed exemplary implementation of step 114 of FIG. 4, which is an exemplary flow chart of a method of the system, detailing the steps involved in carrying out specific tests.

DETAILED DESCRIPTION

[0073] Reference is first made to FIG. 1, which shows a description of human-robot interactions, indicating several possible paths and levels of such a relationship. The two collaborative modes of the present disclosure use features of the collaboration column in FIG. 1. In a collaboration, the robotic system and human beings, in this case either the patient or the health care provider (doctor, nurse, or other medical professional), interact in a collaboration. Information is exchanged, such that each component of the interaction provides specific information. The robotic system and human beings accomplish tasks together toward a shared goal. The process entails mutual learning, adaptation, and eventually trust, as further outlined herewithin below. Thus, in this context, ‘collaboration’ has a distinct definition as will be clarified.

[0074] Reference is now made to FIG. 2, a schematic representation of an exemplary system for use as a comprehensive medical robotic system 200, showing the flow of information. The system comprises a controller 210, which includes a memory 230 and a processor 220, and three distinct modes of operation, 240, 250, and 260. The ‘modes of operation’ are drawn in the same form as the hardware components of the system, though it is to be understood that they are routines or algorithms which are implemented by the hardware components. Multiple integrated loops of information are exchanged, for example, between autonomous mode 240 and doctor decision-making mode 250; between autonomous mode 240 and patient decision-making mode 260; between doctor 251 decision-making mode 250 and patient decision-making mode 260. The controller 210 draws information from at least one medical database 270. Exchange of information also takes place between the system 200 operating in autonomous mode 240 and a medical robot 280, which may be instructed by the system to perform a diagnostic test or procedure 262 on the patient 261. The results 263 of the test or procedure are then reported back to the system 210 and the data from the test are added to the medical database. Doctors or other medical care providers 251 and patients 261 interact with the system via at least one user interface 290. Information obtained in any mode is made available to the other modes, and iterative operation of the entire system 200, or two of the three modes, improves the diagnostic accuracy of the operation. The camera 264 provides inputs to the system, and the camera may alternatively be comprised within a medical robot. Arrows contacting a specific box or element indicate electronic or physical input or flow of information between elements in the direction of the arrow, either unidirectional or bidirectional, according to the direction of the arrow. Arrows, shown as dashed lines, that connect two elements, albeit without direct contact of the arrow with the box, indicate alternate modes of operation.

[0075] FIG. 3 shows an exemplary manner in which the system may use the three modes of operation to access and process information. More details of each individual step are shown in a slightly different format in the flowchart of FIG. 4 below. In step 301, the patient approaches the intake module. In step 302, the system operates in patient interactive mode, then switches in step 303 to autonomous mode to acquire data from the patient and, optionally in step 304, from a medical database. Such data may comprise, for example, past medical records of the patient under evaluation. In step 305, the system formulates specific questions based on the data acquired and analyzed in the previous steps. Based optionally and additionally on the patient responses in step 306, the system in step 307 decides whether to involve a health care provider. If so, bidirectional information exchange occurs with the physician in step 308. The data produced from this interaction is then analyzed, possibly in conjunction with the previously acquired raw and processed information. At each subsequent step of operation, the clinical picture becomes clearer as more information is exchanged, analyzed, and made available to the controller. In step 309, if enough information is available to make a diagnosis in autonomous mode, the system reports in step 310 to the health care provider. It is to be understood that providing a definite or tentative diagnosis could occur at many points along the illustrated path. In step 311, the system decides whether to order diagnostic tests, which leads to a loop with step 320 and returning to step 307; although the system could return to step 308 or to a different step using either autonomous or doctor-interactive modes. The system at any point, for example step 312, may decide to ask the patient additional questions, may decide in step 313 that more information is needed, and either query the database in step 316, or access doctor-interactive mode in step 314. In step 317, or in a subsequent step depending on how many iterations of mode operation are required and how much information needs to be obtained and analyzed, the system may provide a diagnosis and treatment plan, or provide recommendations to a physician in step 319. In some implementations, the system may handle the patient encounter without accessing doctor interactive mode. For example, in step 318, the system provides the patient with answers to questions, instructions, and may discharge the patient to home.

[0076] FIG. 4 is a flowchart showing an exemplary method used in the systems of the present disclosure, as shown in outline in FIG. 3 above, but now incorporating more details and decision processes than the outline of FIG. 3. The chart provides an overview of the manner in which the robotic control system would carry a patient through the emergency room experience, from initial entry to the point of autonomously finalizing a diagnosis, developing a treatment plan, providing patient instructions, or turning the patient over to a medical provider. It is to be understood that such a flowchart highlights only the main components or steps of the procedure, and only one possible implementation of the system. Such a complex and complicated decision-making process with input from artificial intelligence and other data sources may necessitate many more steps and decision points than can be illustrated in a figure. Also, depending on the patient's condition, in alternative embodiments of the present disclosure, some steps of the evaluation may be performed in a different order. It is also to be understood that in autonomous mode, any of the tests or measurements required may be performed, if circumstances prescribe, by medical staff.

[0077] In step 401 of the method, the patient enters the emergency department and approaches the patient intake system, a user interface for gathering initial patient information, which uses the control system operating in patient-interactive mode. In alternative implementations of the present invention, the medical setting may be a medical clinic. This interactive system is appropriate for those apparently reported 60% of emergency visits, in which the person requiring treatment is 1) ambulatory with or without assistance and capable of sitting or reclining in a chair or entry station, 2) conscious and able to respond to queries, or accompanied by a friend or relative knowledgeable of the patient's condition and able to interact with the control system and input accurate and reliable answers into the system, 3) in a stable physiological state, i.e., not at risk of rapid deterioration if not given immediate treatment. In alternative implementations of the present invention, the system, after evaluation of the patient, skips steps 401 and 402 and starts immediately with the acquisition of data such as vital signs in step 403.

[0078] In step 402, the system acquires basic data about the patient in any language in a patient interactive mode. Basic information regarding date of birth, gender, address, known drug allergies, chronic conditions, past history would all be acquired by entering the specifics or selecting from a list of choice. The data may, for example, be acquired in written format by touch-mediated selection or keyboard entry, or by voice recording. Alternatively, the data may be acquired by downloading an existing documentation of patient history from a cloud, server, mobile phone, computer, or the like, for example, upon identifying the patient via facial recognition. After the basic demographic and past history information is input or downloaded, the system may access records of any past admissions or emergency visits. This data may help in subsequent steps for decision-making in either autonomous or doctor interactive mode. Information regarding the patient's main complaint and symptoms may be entered according to a physiological system. The patient data may be stored in the memory of the controller for future use.

[0079] In step 403, when sufficient patient data has been collected, the system switches to the autonomous mode to acquire patient vital signs including such parameters as height and weight of the patient using a scale and measurement device that may be conveniently inbuilt into a robotic patient intake station, blood pressure and heart rate using an automatic cuff, respiratory rate and oxygen concentration using a pulse oximeter, and temperature using a non-contact thermometer. All of these devices may be used in cooperation with the system. Once sufficient vital signs data has been collected, the system operating in autonomous mode in step 404 will analyze the data acquired in steps 402 and 403, to formulate specific questions likely to lead to a diagnosis, treatment plan, or useful knowledge extraction based on the patient's main complaint, vital signs, age, gender, past history and on any other relevant information gleaned from a medical database and based on artificial intelligence.

[0080] In step 105, after the analysis is complete to the extent possible with the current collected data, the system switches to patient interactive mode to acquire specific information about the subject's condition based on the answers provided by the subject in step 402. It is understood that some patients are more articulate, more aware, and more capable of providing specific and accurate information about their condition and complaints than are others. Such a limitation may be overcome by making the data acquired in this step only one component of the total assessment and not highly dependent on the patient's mental capabilities or mental status and/or by using the nature of the inability or difficulty of the patient to respond as a factor for diagnosis. For this reason, the user interface should be as simple as possible, and the system should be aware of the quality and level of input provided by the patient or his/her representative. If the system determines that the input provided by the patient is inconsistent or unhelpful, it would be able to tailor the questions to a simpler level, ask the same question in different formats, or request input in pictorial format, e.g., show a pain scale from 1 to 10 with pictures to represent different levels of pain. Alternately or additionally, if the quality of the patient input is poor, system may ask the patient if there is someone assisting him/her that may be able to help answer the questions, or switch to doctor collaborative mode and request assistance from a health care provider.

[0081] Based on the patient's responses to initial symptom-based questions, the system continues asking more and more detailed and specific questions as needed. For example, if an otherwise healthy and active patient presented with a high spiking fever and other specific symptoms, the system might suspect exposure to an exotic infectious agent and could ask questions regarding recent activities including travel, identify endemic infectious conditions in the distant location, and present a likely differential diagnosis.

[0082] According to alternative methods of the present invention, if the system in step 104 assesses from the information in steps 402, 403, and/or 404 that the patient is in an unstable, rapidly deteriorating, or medically dangerous situation, the method bypasses step 105 and goes directly to step 106, as shown in the dashed path. Some examples of situations requiring such a response might include systolic blood pressure above a certain reading, fever greater than a predetermined cutoff temperature, heart rate above an age-specific and age-appropriate number, unresponsiveness, sudden change in mental status or appearance of deterioration. The deterioration may be determined either by a sudden change in monitored parameters, or by real-time images acquired via an associated camera, as shown in FIG. 2. The camera may be an optical device for acquiring images of the whole patient, or may be adapted to identify and image only the face; alternatively, the camera may use infrared, movement detection, blood flow velocity, or any other measureable change to identify a shift in the patient's status requiring immediate attention by a health care provider. The camera 264 feeds images to the system 210 for evaluation by the controller.

[0083] In step 406, the system operates in autonomous mode to decide if the patient requires a direct referral to a medical provider, based on physical signs as described above and alternatively or additionally, based on patient responses in step 405. Step 406 may additionally or alternatively comprise comparing the data and test results obtained from the patient to gathered data from multiple patients, and based on that information, performing a triage or prioritization for medical provider treatment of the patient relative to one or more other patients based on the severity of the patient's condition. This determination is an important part of the system design, because of the critical importance of triage and identifying patients in need of the most urgent care. The system may, for example, be able to distinguish between subjective input from a patient trying to bypass the wait by recording extreme responses, e.g., extreme pain levels, and patient input indicating a true medical emergency. The system may consider the limited number of human medical staff present in a particular medical setting.

[0084] If the system makes a decision in step 406 to alert the medical provider, in step 415 the system alerts the physician of a situation requiring at least some immediate human attention and turn care of the patient over to the medical provider and system in doctor interactive mode. “At least some immediate human attention” may be as minimal as comforting a patient who is upset, or may be as complex as preparing for emergency surgery. Before the medical provider and system take over care, the medical provider would have the option of asking questions and interacting with the system. Subsequently the medical provider may assess the patient together with the system and administer appropriate treatment. This is more advantageous than merely alerting a medical provider, since the doctor collaborative mode is more powerful and more likely to arrive at an accurate diagnosis than a doctor acting alone. If a determination were made by the doctor that the robotic control system would be helpful to continue the assessment, the physician may instruct the control system to continue the assessment at a specific point in the process determined by the doctor.

[0085] In step 406 of the method, if the system determines that no emergency alert is warranted and/or that autonomous mode is capable of continuing to assess the patient, the method continues to step 407, in which the system in autonomous mode makes an assessment of whether enough information had been collected and evaluated to make a tentative diagnosis. If so, the method proceeds to step 416 in doctor interactive mode. Alternatively, the system may store the tentative diagnosis for later use and remain in autonomous mode, since the medical provider may access outputs made when the system is in autonomous mode.

[0086] In step 416, the system reports a tentative diagnosis and/or make recommendations to the doctor for next steps in the assessment of the patient in doctor interactive mode based on at least some of 1) the input collected from steps 402-405, 2) information in an extracted form from a generic medical database, 3) past medical outcomes and patient records from the specific patient and others, for example, treated in the same department, by the same doctor, or in other area hospitals, and 4) artificial intelligence or machine learning and learning from past experience, such as using a database with data from previous patients with the same symptoms, or earlier ER visits by the same patient. Upon receiving the tentative diagnosis, the medical provider may ask further questions to the system regarding the patient's condition, request that the system obtain more information from a database, or direct the system to search for further diagnostic possibilities. In this step, not shown in FIG. 4 but understood to comprise further operation of the system continuing from step 416 or any other step using doctor-interactive mode, the robotic control system and the medical provider are having a “conversation” regarding the best course of action for the patient under evaluation, which may include verbal dialogue, outputs on a display, keyboard or touchscreen input, and the like. The system may recommend a CT scan, invasive radiological test, e.g., angiography, or other evaluation. The system may also recommend hospitalization, release with or without a course of treatment, or other action. At a point mutually determined by the doctor and the control system or by the doctor alone, the doctor takes over sole care of the patient, in step 419. This may occur when the doctor and system have mutually arrived at a diagnosis that the doctor is satisfied with.

[0087] In step 417, following drawing of blood with or without IV line insertion in step 412 or 413, the system communicates with the medical provider and optionally turns care of the patient over to one or more humans. This could occur in any of steps 415, 416, 419-421, or others. The system is not constrained by what is illustrated in FIG. 4, which exemplifies a limited representation of one possible implementation. It is difficult to diagram all of the steps in system operation in a single diagram. In some alternative embodiments of the present invention, the system may return to autonomous mode, or patient collaborative mode, to inform the patient of its assessment and next anticipated steps in the treatment protocol. The system further provides relevant medical information regarding the patient's condition to the degree known and appropriate for a given situation. In step 418, the system enters patient interactive mode and answers questions the patient might have regarding his/her condition, the probable course of treatment, or other aspects of the visit. The questions may be provided by the system or may be entered by the patient, using word-recognition software and commonly employed search algorithms, tailored and possibly limited by the information acquired and collected by the system regarding this specific case.

[0088] In step 407, operating in autonomous mode, if the system determines that it lacks sufficient data to make a tentative diagnosis and that further tests are warranted which it is capable of performing in autonomous mode, the system will proceed to step 408 in autonomous mode. In step 408, continuing in autonomous mode, the system will decide whether to instruct a medical robot to perform one or more diagnostic medical tests such as ECG; x-ray of a specific part of the body, e.g. to assess a potential fracture; or infrared imaging to evaluate temperature, blood flow to a specific region, or other diagnostic purpose. If the decision is made to instruct a robot to perform a test, the system proceeds to step 414 in autonomous mode.

[0089] In step 414, at least one diagnostic test is performed. While the system operates in autonomous mode, the system simultaneously provides instructions as needed to the patient regarding positioning of him/herself or body parts to undergo the test, e.g., placing a limb to be x-rayed in a specific pose. The tests listed in step 408 are all exemplary tests that could be built into a robotic control system intake module, such that the patient under evaluation may not need to move from his/her position during the entire intake evaluation, which may be from initial entry into the ER until the system outputs a diagnosis. Such a robotic system intake module may comprise any of a camera, a display, and a keyboard, a touchscreen, a haptic interface, a voice recognition system with a microphone, or other means for the patient to input information or questions. Further details of the specific tests and their order of performance are detailed in FIG. 5.

[0090] The results of the test or tests performed in step 414 are evaluated by the system in autonomous mode to identify pathological features requiring medical attention. If the same patient had previously been treated in the same department or hospital, or if electronic medical records were available from another source, the system may compare the current images or results with the previous set to identify changes potentially indicative of a progressive or new pathological process. The system may use machine learning or artificial intelligence to identify normal and abnormal features of an imaging study or dynamic evaluation, and use this information to formulate the next step of the evaluation. At this point, the system returns to step 106 in autonomous mode, adds the information obtained in step 414 or from the steps described in FIG. 5, and assesses whether referral to a human medical provider may be warranted.

[0091] Returning to step 406 and operating in autonomous mode, the system proceeds iteratively as described above until in step 408 no further diagnostic tests are indicated that the system could perform in autonomous mode. In this eventuality, the system continues in autonomous mode and the method proceeds to step 409, where the system makes a decision as to whether more patient input is required. If positive, the system returns to step 405 in patient interactive mode.

[0092] Although not shown in FIG. 4, the method according to alternative embodiments of the present invention returns to one or more prior steps at any point in the evaluation. For example, after step 414, the method may, based on a system decision, go back to step 405 to operate in patient interactive mode and acquire more specific and relevant information from the patient based on the results of the imaging studies or ECG. If, e.g., the patient presented with shortness of breath and rapid respiration, the system may decide to acquire a set of chest x-rays to evaluate for pneumonia. If no infiltrate or opacity were observed but a broken rib was identified, the system may decide to direct the investigation in a different direction to identify the cause of the fracture and possible associated injuries.

[0093] If in step 409, the system decides that no further patient input is warranted, the system proceeds in autonomous mode to step 410 of the evaluation and assess whether blood tests, or collection of other body fluids, are required. It is understood that, based on the results of earlier steps, the system may decide to order blood tests, step before performing imaging tests in step 414. Blood tests that could be ordered are any of the commonly ordered sets to determine levels, distribution and types and other parameters of red and white blood cells; sodium and other ion levels (chemistry panel); levels of alcohol, other drugs or suspected toxins, blood cultures, or other diagnostic blood test. The decision on the best order of performing tests would be made based on the most critical information needed, and on other practical considerations, e.g., mobility of the patient's limbs before and after possible insertion of an intravenous line, and the length of time required to return results of the blood test or imaging study.

[0094] In step 410, if blood tests are not indicated, the system proceeds to step 420 and enters doctor interactive mode. In step 420, the system may make suggestions, present evaluation results until this point, ask questions, or request further input from the physician as to additional diagnostic tests to be performed. The physician may review the data and decide to take over care of the patient, or ask the system to search for other information such as medical case reports or articles on PubMed that either physician or robotic control system could then use to contribute to decisions, or which both could use to collaborate on a desired treatment plan. The physician may also provide input that would allow the system to perform additional evaluations in autonomous mode. Although not shown in the figure, the system could proceed from step 120 to step 119 at this point and turn case management over to the physician.

[0095] If blood tests are indicated based on the autonomous mode evaluation in step 410, the system proceeds to step 411 and determines whether to insert an intravenous (IV) line at the time of blood drawing. The decision to insert an IV line may depend on factors such as the likelihood that the patient would require IV fluids, IV medicines, or other indication for venous access. The decision to insert an IV line may be based on the probability of the anticipated need for venous access, and on the ease of inserting an IV line. The ease of insertion may depend on factors such as the age of the patient, patient cooperation, hydration status. If the system, for example, decides the likelihood of requiring venous access warrants insertion of an IV line and simultaneously determines that the likelihood of success in line insertion is greater than a defined probability, the system will proceed to step 412.

[0096] In step 412, the system continues to operate in autonomous mode to instruct a medical robot to draw blood and insert an IV line of the appropriate gauge. This task may be accomplished, e.g., by a robotic arm, in some embodiments attached to the robotic control system, the robotic arm having infrared capability to detect the vein and ultrasound imaging to detect blood flow. Any such system could be conveniently adapted for this purpose, such as that manufactured by Veebot. If in step 411, the system determines not to insert an IV line, the method will proceed to step 413 and the system instructs the medical robot to draw blood as described in step 412, omitting IV insertion. In the course of these steps, 412 and 413, the control system operates autonomously, but preferably provides feedback to the patient as to the next steps of the process and give instructions as needed, for example, to position the patient's arm in a suitable location for the phlebotomy. These instructions may be either auditory, visual, tactile, or a combination of all three.

[0097] At this point of the evaluation, the system determines whether to return to any previous step, and whether to provide the patient with information in step 417, answer any questions in step 418, wait for the results of the blood tests, or proceed to step 419 in doctor interactive mode and report the current findings and results to the medical provider, for example, highlighting any abnormal test results, providing a differential diagnosis and suggesting a course of treatment or further evaluation. Here the control system and the doctor interact as in step 420 described above, until a mutual decision is made to turn care of the patient over to the human provider, which may occur, for example, when the doctor and system mutually arrive at the same diagnosis.

[0098] FIG. 5 illustrates a more detailed implementation of step 414 of FIG. 4, in which the system autonomously performs one or more tests such as an ECG, x-ray, infrared imaging, or other study that may be performed without transferring the patient and without medical staff intervention.

[0099] In step 501, the system decides if further diagnostic information is indicated. If yes, the system may then decide for instance which imaging test would be most informative for arriving at a tentative or conclusive diagnosis, for example, by going through the list of limited available options. This decision is based on the information acquired in previous steps, and whether and at what point a clear diagnostic picture begins to emerge. For example, if the patient presents with a fever, high blood pressure, and shortness of breath, if the fever is very high, above a given temperature, and the blood pressure is only mildly elevated, the system may decide first to perform an x-ray to rule out pneumonia. However, if the fever is mild and the blood pressure highly elevated, the system may decide first to perform an ECG. This example is merely for the purposes of explaining how the system would operate, and is not meant to limit the scope of the system's capabilities.

[0100] In step 502, the system decides if an x-ray is needed, and if so, proceeds to step 505 where the system instructs a medical robot to perform the x-ray. Based on an autonomous evaluation of the imaging results in autonomous mode, the method returns to step 501 where the system determines if further diagnostic studies are indicated. If not, the method returns to step 409 of FIG. 4. If yes, the method proceeds to step 203 to evaluate whether to perform an ECG. If yes, the system instructs a robot to perform the EKG in step 506; if not, the method proceeds to step 504 to evaluate the need for infrared imaging. If yes, the imaging would be performed in step 507; if not, the system would proceed to further iterations of decisions regarding other medical tests in step 50x, and in performance of them in step 50y. This process could continue by the system in autonomous mode until a clear result was obtained or the system ran out of potential diagnostic tests that it could instruct a robot to perform. Again, based on the individual patient's clinical picture, the system would prioritize tests according to those most likely to provide diagnostic information and the importance of a given test result for a successful clinical outcome.

[0101] As stated above, the flow of information in FIG. 4 is meant to be an exemplary implementation of a method according to the invention, and variations on this specific scheme could be adapted, based on a given patient's needs, on system learning over time, and on advances in artificial intelligence. The system may be programmed to operate in a curtailed mode, for example, if an emergency situation such as a natural disaster or terror attack placed intense demands on the resources of a given emergency department, such that many patients needed rapid assessment and triage at once. The system may be programmed to learn, based on number of patients waiting, how much time to allocate to an individual. The system may make tentative diagnoses based on limited information in times of great demand. In other circumstances, the system may perform an extensive analysis and evaluation and provide a detailed assessment and list of differential diagnoses, with weights given to each possible diagnosis. When a given patient has a complex condition and has been evaluated thoroughly by physicians, the system may be asked to provide a detailed assessment that could take considerable time to perform, with extensive mining of electronic data sources and literature searches. For example, the system may utilize a higher representation of the autonomous mode versus the doctor in making decisions, and/or make autonomous mode a higher priority than doctor collaborative mode, with suggested time limitations for doctor collaborative mode based on severity of one or more patient conditions.

[0102] The system may evaluate multiple patients simultaneously in a specific mode of operation. In a most efficient operation, the system may take a patient from his/her initial presentation to the emergency room in step 101 to a tentative or conclusive diagnosis in a matter of under an hour. It is even possible that, for a clear-cut case, the system may be able to suggest a course of outpatient treatment, write a prescription, and send the patient homeward with minimal if any input from a medical provider. In such a scenario, at the time of discharge, the system may make notes in the patient's electronic medical record by storing information in the memory to the effect that if this patient returned to the emergency department within a specified time period, a specific course of action should be immediately taken. Patients sent home may be provided by the system operating in autonomous mode with a list of instructions for self-care, routine follow up, and signs indicating a need for further immediate return to the emergency department for medical care.

[0103] It is appreciated by persons skilled in the art that the present invention is not limited by what has been particularly shown and described hereinabove. Rather the scope of the present invention includes both combinations and subcombinations of various features described hereinabove as well as variations and modifications thereto which would occur to a person of skill in the art upon reading the above description and which are not in the prior art.