Clinical resource allocation and optimisation system

Abstract

A method for improving the health of a population is provided comprising the use of a trained artificial intelligence to: • receive requests for provision of clinical services in relation to a patient, the request comprising a specification of the clinical services required and an indication of presence or absence of relevant patient acuity factors in the patient concerned; • allocate or assist in allocation of patients to clinical service providers wherein allocation is based on optimising use and clinical effectiveness of clinical personnel and / or resources by the healthcare provider taking into account the specified clinical services and patient acuity factors; • receive feedback on performance of one or more of the specified clinical services the artificial intelligence being trained on performance data relating to clinical personnel, clinical service providers, clinical resources, referrers, patient acuity factors, and patient outcome, the performance data being modified continuously or periodically to reflect the feedback.

Description

[0001] Clinical resource allocation and optimisation system

[0002] This invention relates to systems and methods for allocating clinical personnel and / or resources and optimising the use and clinical effectiveness of such personnel and / or resources.

[0003] Modern medicine is a complex affair involving multiple actors (e.g. doctors, nurses, medical technicians, support staff and administrators at a variety of different competency levels) working in multiple care or support settings (such as social, community, primary, secondary, tertiary, quaternary care settings and including ancillary or specialist provision such as medical laboratories, pharmacies, dental practices, rehabilitation practices, palliative care facilities etc.,) to provide health care services to or for a patient.

[0004] Health care may be managed or delivered at a personal, local, community, regional or national basis by healthcare providers who are organised or structured as single providers, small or large group providers, and whether generalist or specialist, local community regional national or state levels. In the following "healthcare provider" should be taken to mean a body having responsibility at least in part for delivering health related services to a population or part of a population howsoever segmented. The population served by an individual practising alone might range up to 2000-3000 patients, whilst those in larger provider arrangements might range anywhere between that level and 3,000,000 patients. In the UK National Health Service (NHS), there are over 600 million patient interactions each year across all levels of heathcare provision or on average in excess of 1.7 million per day (estimated 2022). No individual healthcare provider is capable of providing all of the health services a patient might require, many interactions involve single or multiple referrals to other providers.

[0005] In the following "Clinical" shall be taken at its broadest as encompassing anything done to, for, or in relation to a patient and shall not be interpreted narrowly as implying the need for presence of a patient for a particular service.

[0006] Health care services include not only services provided in person, e.g. at a point of care, but also related services provided remotely from the patient (e.g. laboratory analysis, radiological interpretation). These services may be provided at different venues (e.g. an initial consultation at a doctor's surgery, a sample being taken at a clinic, analysis of a sample at a laboratory, , a diagnostic or treatment procedure at a general or a more specialised hospital, etc.,), with different types of clinical plant, equipment, instruments, or consumables (e.g., an x-ray, MRI scan, operating theatre, consultation room, etc.,) or by different individuals or teams of clinical personnel (such as specialist consultants, specialty and associate specialty doctors, doctors in training, general practitioners, nurses, associate nurses and trainee nurses all at carrying levels of experience and capability).

[0007] In the following "clinical personnel" shall be taken to mean all individuals providing clinical services, and "clinical resources" shall be taken to mean anything else required to provide clinical services (e.g. all clinical facilities, equipment and consumables).

[0008] Unlike a factory assembly line, where a uniform procedure can be applied to manufacture of uniform objects; each patient and their condition may be different and may require either a standard or a more personalised or bespoke care pathway , or episodes of care on or related to that clinical care pathway.

[0009] By care pathway is meant a defined episode or linked series of episodes of care related to the assessment, investigation, diagnosis, treatment of a health condition, or combination of them, and final discharge of a patient. A pathway may include for example, episodes such as from initial assessment in primary care or elsewhere to referral for more specialised assessment, and / or subsequent investigation (as an outpatient or an inpatient at a hospital or other care facility), diagnosis, planning and fixing of treatment or treatments, execution of treatment(s), or a repeat of such episodes or some of them, until the patient is discharged or referred into another care setting (such as further rehabilitation, community care or social care) or care pathway for continued or altered treatment, until discharge from each care pathway.

[0010] It will be apparent that a patient may start on one pathway and as knowledge / treatment progresses be moved to a different or further pathway, or concurrent pathway if there are multi-morbidities (multiple unrelated health conditions) or comorbidities (multiple health conditions related to an index or primary condition). Upon discharge, a patient may return to that same or a different or other care pathway(s) with complications or other comorbidities.

[0011] Many systems exist for allocating clinical personnel and resources to a specific, or quite limited set of, episodes of care. For example:

[0012] • US2017 / 0177806 is directed to a system and method of automatically creating a surgical team schedule and managing the supplies and medicines used in such resources. The system operates by receiving patient data indicating medical service and treatment requirements, evaluating the availability of medical staff and supplies (optionally by a bidding process), and presenting the information to an administrator to schedule and review availability. In essence it provides a database of staff with performance criteria monitored at a hospital or surgical centre level.

[0013] • US2019 / 0304595 is directed to methods and apparatus for healthcare team performance optimization and management in a hospital. Machine learning techniques are used to provide predictions of outcome of various compositions of personnel and resources based on previous outcomes of other compositions of personnel and resources in the hospital; and to generate, for example, ranked pools of nurses or ranked pools of doctors. The system permits changes in personnel and resources based on availability [e.g. nurses trading shifts] within the ranking criteria of the system. Data such as outcome and patient satisfaction can be stored and used in predicting outcomes.

[0014] Although such systems can provide a tool for scheduling appropriate resources in specific care settings to adequately deal with specific, or quite limited set of, episodes of care that are required by a patient, they do not provide a tool for allocating and optimising the provision of appropriate clinical resources for some or all (end-to-end) episodes of care along a standard or more personalised care pathway in a specific care setting or from a variety of different providers. Nor do they provide a tool for optimising the clinical effectiveness of such episodes or care pathway (i.e., the relative clinical outcomes for patients, the relative clinical quality of clinical facilities and personnel used along the care pathway, and clinical productivity (e.g., measured by time taken, marginal cost, and efficiency). Nor do they provide a tool for matching a patient's assessed acuity (the urgency, complexity such as arises from multi-morbidities or co-morbidities, or acuity to immediate or long term health arising from complications) with the assessed competency of an individual or team of individual clinicians, or clinical facilities, allocated or attending to that patient's healthcare needs. The many shortcomings of such systems result from their attention to serving a particular patient rather than optimising clinical effectiveness across a relevant population served by the healthcare provider.

[0015] Clinical facilities deliver clinical services to large numbers of patients and these patients are competing for clinical resources in the form of clinical personnel (or variable capacity, capability or quality) and the physical resources (theatres, consulting rooms, equipment, again of variable capacity, capability or quality) required to examine and treat the patients.

[0016] Poor allocation or matching of resources to patients with variable presentation of risk or acuity can result in poor healthcare across a population, with some patients occupying disproportionate resources to deliver only marginal improvements in health at the expense of delay in treatment to others, with the increased risk of poor outcomes that may entail. To this extent the "worried well" can cause harm to the silent ill.

[0017] Similarly, inappropriate or poorly planned procedures occupy clinical resources that might best be used for those in more pressing or appropriate need of those resources; or result in excessive resources being devoted to clinical services to the detriment of resources that can be applied to other services.

[0018] The present invention improves clinical effectiveness across a population by utilizing an Al-driven system to optimize patient referral, matching of patients and clinical resource(s) by reference to risk or acuity, consultation, diagnosis, treatment, and care strategies, resulting in enhanced clinical decision-making, reduced treatment errors, and improved quality of care assessments.

[0019] The method enhances treatment accuracy and quality while increasing available capacity for earlier patient appointments and procedures, leading to better patient outcomes. The system can incorporate best practice, standard or optimised patient pathways to minimize unnecessary referrals, scans, and procedures, thereby streamlining the care process and ensuring a greater capacity to deliver healthcare without increasing the required resources. Further, the method can modify a patient's treatment dynamically in response to information gathered in provision of services.

[0020] Accordingly, the present invention provides a method for improving healthcare provision to a population for which a healthcare provider is at least in part responsible, by optimising allocation, use, and clinical effectiveness of clinical personnel and / or clinical resources, the method comprising the use of a system comprising a trained artificial intelligence to: a) receive from a referrer a request for provision of one or more specified clinical services in relation to a patient, the request comprising a specification of the clinical services required and an indication of presence or absence of relevant patient acuity factors in the patient concerned; b) allocate or assist in allocation of patients to clinical service providers comprising one or more clinical personnel, the clinical service providers being suitable for and having access to clinical resources necessary to provide the specified clinical services, wherein allocation is based on optimising use and clinical effectiveness of clinical personnel and / or resources by the healthcare provider taking into account the specified clinical services and patient acuity factors c) receive feedback on performance of one or more of the specified clinical services from one or more of

[0021] • the clinical service providers;

[0022] • one or more of the clinical personnel;

[0023] • the patient; • the referrer wherein the artificial intelligence is trained on performance data relating to clinical personnel, clinical service providers, clinical resources, referrers and patient acuity factors, and the performance data is modified continuously or periodically to reflect the feedback.

[0024] By "artificial intelligence" is meant a machine or system showing aspects of intelligence, and the term encompasses but is not limited to the use of tools such as machine learning to find or be trained to find associations between disparate data sources.

[0025] By "referrer" is meant anyone that makes the initial request, whether a general practitioner, hospital doctor, nurse, or otherwise, and whether within the healthcare provider or external of the healthcare provider.

[0026] "Performance data" may include not only data, information, and feedback sourced from operation of the method or system of the present invention, but also information and data however sourced relating to patients having comparable healthcare needs.

[0027] For the avoidance of doubt, "clinical service providers" is to be interpreted in its broadest sense as encompassing both clinical personnel having access to appropriate clinical resources, and also clinical establishments and facilities employing clinical personnel.

[0028] Requesting the one or more specified clinical services required may comprise provision of a clinical care pathway identifying a single or bundle of episodes required for completion of the specified clinical services; and this may be in the form of standardised care codes identifying the clinical care pathway or episode of care. The system may match a provided clinical care pathway to a standardised care code.

[0029] By "patient acuity factor" is meant one or more of:

[0030] • an assessed level of urgency,

[0031] • an assessed level of complexity (as a result of actual or probable multi-morbidities or comorbidities or the potential need for involvement of a for multi-disciplinary team),

[0032] • an assessed level of risk (of possible or probable complications), and

[0033] • an assessed clinical priority (including for example strategic priorities related to population healthcare, inequalities in healthcare, or clinically relevant protected characteristics)

[0034] • one or more objective factors relevant to patient health.

[0035] Indicating presence or absence of patient acuity factors may comprise indicating one or more of referrer perceived: acuity, urgency, complication risk, multi-morbidities or co-morbidities, pharmaceutical use, drug use, patient history, psychological factors, or social factors.

[0036] Other patient acuity factors may be inferred by the artificial intelligence from objective patient data without a specific assessment by the referrer. For example, birth date, place of birth, and post code can provide important information independent of referrer assessment. The history of outcomes of referrals from a referrer can itself provide useful information as a referrer may have a tendency to over-assess or under-assess patient acuity for particular conditions; or may have subjective biases that affect accuracy or quality of referral.

[0037] Information gathered in provision of one of the specified services may be used to modify the specification of the clinical services required. For example, where the referrer is a general practitioner, the first service required may be for a specialist to assess the general practitioner's diagnosis, perception of acuity, and suggested services. This may result in the specialist determining that the initial diagnosis and specification of the clinical services required is inappropriate. If so the specialist may recommend a different specification of clinical services, which in some cases may result in discharge of the patient without treatment, or further referral to another clinical service provider.

[0038] An important feature of the invention is the provision of feedback from participants in the clinical process to provide additional data about each step in the process and the process overall. Such feedback forms part of the data on which the artificial intelligence operates. Accordingly, "performance data" includes not only objective criteria, but also the subjective feedback of participants in the provision of the clinical services, including patient satisfaction.

[0039] The feedback may comprise structured elements, for example:

[0040] • tick boxes [e.g. selecting from options such as was the clinical establishment 1 clean, 2 dirty, 3 don't know],

[0041] • scales [e.g. on a scale of 1 to 10, how do you rate your health now?],

[0042] • QALY (quality adjusted life year) assessments,

[0043] • or otherwise organised structured response.

[0044] The feedback may also include unstructured matter (e.g. free text, images from medical imaging devices, ECG traces and similar). In this case the artificial intelligence may apply appropriate processing (e.g. natural language processing for free text, or image processing for images) to identify parameters of significance that are additional to or instead of any structured matter.

[0045] In similar manner, processing of unstructured matter in the referral may identify parameters of significance that are additional to or instead of any structured matter.

[0046] Patient feedback can include intangible aspect such as patient satisfaction.

[0047] Allocation or assisting allocation may require the artificial intelligence to: d) identify the nature of the clinical resources and clinical personnel required for one or more of the specified clinical services; e) define appropriateness criteria for delivery of the specified clinical services; f) identify one or more clinical service providers potentially able to meet the appropriateness criteria; and optionally g) rank those clinical service providers for ability to meet appropriateness criteria in relation to the specified clinical services; h) offer to the referrer and / or one or more entities having payment responsibility for all or part of the clinical services, a high-ranking subset of the one or more clinical service providers; receive acceptance of one or more of the offered clinical service providers from the referrer and / or the one or more entities having payment responsibility for all or part of the clinical services.

[0048] Identifying one or more clinical service providers suitable for and having access to clinical resources necessary to provide the specified clinical services may include;

[0049] • communicating the nature of the specified clinical services to potential clinical service providers appropriate to the clinical service concerned;

[0050] • receiving from the potential clinical service providers a bid or bids for all or part of the clinical services, either alone or in combination with other clinical service providers. and may include defining at least one clinical service provider by;

[0051] • identifying appropriate and available clinical resources;

[0052] • identifying appropriate and available clinical personnel;

[0053] • defining a clinical service provider comprising a notional team of appropriate and available clinical resources and appropriate and available clinical personnel.

[0054] The appropriateness criteria may include one or more of:-

[0055] • required skill level with respect to the specified clinical services and patient acuity criteria;

[0056] • availability of clinical service providers with respect to urgency of specified clinical services;

[0057] • scheduling of the availability of clinical resources;

[0058] • efficiency of utilisation of clinical resources;

[0059] • efficiency of utilisation of clinical personnel;

[0060] • geographical proximity of clinical resources and clinical personnel;

[0061] • cost of provision of the specified clinical services.

[0062] The present invention further encompasses computer implemented systems for executing the methods of the invention, and software for executing the methods of the invention.

[0063] Further features of the invention are evident from the claims appended hereto and are illustrated in the following non-limitative description of embodiments of the invention.

[0064] At its broadest, the present invention provides a system in which an artificial intelligence trained on performance data relating to clinical personnel, clinical service providers, clinical resources, referrers, patient acuity factors, and clinical outcomes and care or patient quality assessments, receives feedback on the performance of one or more of

[0065] • clinical service providers comprising one or more clinical personnel, the clinical service providers being suitable for and having access to clinical resources necessary to provide the specified clinical services;

[0066] • one or more of the clinical personnel;

[0067] • the patient;

[0068] • the referrer

[0069] This permits the artificial intelligence to rate the clinical service providers, clinical personnel, and the referrer for their performance in relation to requested clinical services taking into account patient acuity factors.

[0070] Further, by rating the various parties, improvements in service and use of resources can be achieved.

[0071] By rating the performance of referrers, it is possible to detect and mitigate practices such as ordering unnecessary services (whether through inadequate referrer training or the practice of "defensive medicine").

[0072] By rating the performance of clinical personnel, it is possible to match high performing personnel with acute or complex cases, while using low performing personnel to handle cases within their skill set, but of lower acuity or complexity. By rating the performance of clinical service providers a measure of the overall capability of the provider can be obtained.

[0073] By rating the health impact (and resultant economic evaluation) of clinical interventions at a population or individual patient level, individual and system wide effectiveness of such interventions can be assessed.

[0074] Further, by rating the performance of clinical service providers and personnel over time in relation to specific pathways or episodes of care, it is possible to detect changes in relative performance that may indicate:-

[0075] • increasing capability that may reflect increased skill or changes in practice that might be adopted by other providers;

[0076] • decreasing capability that may indicate systemic problems with the clinical service provider and / or personnel;

[0077] • changes in the nature of patients referred (e.g. the onset of a new factor affecting outcome)

[0078] Further, such performance data can provide a check on potentially damaging processes that otherwise would require a "whistleblower" to raise an alarm.

[0079] In short, improved healthcare provision is provided that increases the efficiency of healthcare, improves patient outcomes both individually and across a population, and minimises waste of valuable clinical resources.

[0080] The invention is illustrated in the appended drawings in which:

[0081] Fig. 1 is a schematic process diagram for a very simple interaction using the system of the present invention.

[0082] Fig. 2 is a schematic process diagram for a more complex interaction using the system of the present invention.

[0083] Fig. 3 is a schematic process diagram illustrating a client care pathway.

[0084] In Fig. 1 a system 1 comprises one or more computing devices and houses a trained artificial intelligence 2. System 1 may comprise a self-contained digital platform managed and controlled by a body that is accountable for authorisation, and payment, of all or any part of the cost of care potentially, or actually, provided to a referred patient; or it may comprise separately managed devices to provide different aspects of the system's functionality.

[0085] The artificial intelligence is trained on data relating to the performance of various clinical personnel and clinical resources 3, in relation to various clinical episodes of care, or episodes of care along a clinical care pathway.

[0086] In Fig, 1 a referrer 4 provides a request 5 to the system 1 for the provision of clinical services relating to a patient 13. That request 5 may be as simple as a request for the services of clinical personnel alone [e.g. a second opinion], or may require both clinical personnel and clinical resources. Based on appropriateness criteria and data (including feedback) held on the clinical personnel and resources 3, artificial intelligence 1, and system 1 selects and refers 6 the request 5 to an appropriate clinical practitioner, e.g. consultant 3*.

[0087] Consultant 3* performs the one or more clinical services and consultant 3* reports 7 the result of the clinical services provided to the system 1 and optionally to the patient 13 and / or the referrer 4. Consultant 3* also responds to system 1 with feedback 9 concerning as relevant the appropriateness of the referrer's request and / or the performance of any other clinical personnel or resources used in provision of the services . Patent 13 (and optionally referrer 5) also responds to system 1 with feedback 10 concerning the performance of the clinical services provided. The feedback (from whichever source) becomes part of the data on which the artificial intelligence 2 operates and in this manner the system improves its ability to make clinically effective referrals to appropriate clinical personnel.

[0088] The request 5 may comprise a series of services 11 (e.g. a series of episodes along a client care pathway), and the consultant 3* may determine that some or all are inappropriate for the patient (e.g. due to the request being inaccurate or having a poor assessment of acuity) and this too forms part of the feedback. The consultant 3* may determine that a different series of services 12 is more appropriate, and if necessary the system 1 may then select other clinical personnel and resources relevant to those services.

[0089] The system may also provide feedback 14 to the referrer 4 and / or clinical personnel and / or clinical resources involved in the provision of clinical services for training and / or quality control purposes.

[0090] Fig. 2 shows a more complicated interaction in which the system 1 identifies multiple parties to provide the required clinical services, e.g. a consultant 3*, a clinical facility 3**, and a medical technician 3***. in each case reporting back with feedback to the system 1 improves the data on which the artificial intelligence operates.

[0091] Fig. 3 shows indicatively a process of treatment in which a referrer 301 provides a client care pathway comprising an investigatory and diagnosis phase and a treatment phase. The investigatory and diagnosis phase comprises examination 302 and optional investigations 303 (e.g. ECG) and 304 (e.g. X-ray). The pathway defines under what circumstances investigations 303 and 304 are called for and calls for a treatment phase comprising either a first care episode 305, or second and third episodes 306 / 307 depending on the outcome of the investigatory and diagnostic phase. The pathway may also define under what circumstances a patient may be discharged 308, or should be transferred to a different pathway or referred 309 to different clinical personnel and / or resources.

[0092] By providing systematised clinical pathways defining one or more routes the system enables comparisons to be made between the providers of each episode on the pathway, and between presented patient acuity factors and which route of the pathway provides optimum outcome.

[0093] In addition to the data on which the artificial intelligence 2 operates comprising data on individual clinical personnel and establishments and their performance of particular steps of a series of services, data on the overall performance of the services enables identification of particularly effective combinations of clinical personnel and establishments.

[0094] The data on which the artificial intelligence operates may be different from the data on which the artificial intelligence is initially trained.

[0095] The data on which the artificial intelligence is initially trained may include data on clinical outcomes for particular procedures independent of the practitioners involved [e.g. anonymised data sets from one or more providers concerning outcomes of particular clinical procedures]. Such data may be used in operation to provide benchmarking for particular clinical services, but in the absence of participant feedback will not necessarily provide any teaching concerning the clinical personnel or resources involved in provision of the clinical services. As a body of data and feedback is generated, retraining may be required, and such retraining may comprise comparison of predicted outcomes from anonymised data and predicted outcomes from data and feedback concerning the particular personnel and resources used.

[0096] In this manner, with increasing data, including feedback, on clinical personnel and clinical resources the predictive ability concerning them and the way in which they interact increases.

[0097] The artificial intelligence should therefore periodically or continuously be retrained, and the data and feedback provided can be used to compare predicted outcome with actual outcome as part of this process. It may also be necessary from time to time to remove data concerning no longer practising clinical personnel and / or no longer practiced clinical procedures.

[0098] In addition, the provision of patient feedback, whether structured or unstructured, may assist in capturing aspects of patient care that are not necessarily captured in assessments of clinical outcome. For example, bedside manner and hospital cleanliness are important aspects of patient satisfaction that are not necessarily indicated in data concerning outcomes.

[0099] The following illustrative embodiment of the invention is based on clinical procedure and arrangements in the UK, but applies, with appropriate modification to meet local requirements, in any jurisdiction and provides a ranking, bidding, and tendering system for clinical services. Not all features of this embodiment are required for a system or method to fall within the appended claims.

[0100] General overview A digital platform (hereafter called "Digital Platform") employing machine learning and artificial intelligence capability (hereafter "ML / AI") is used to expand the clinical capacity (hereafter "Secondary Healthcare Capacity") and improve the clinical effectiveness (hereafter "Clinical Effectiveness") of secondary healthcare provision.

[0101] The Digital Platform enables a strategic healthcare commissioning body ("Commissioning Body") to design, create, tender and then manage delivery of secondary care clinical work (as distinguished from primary care, care in the community or social care). This clinical work is organised in single care episodes, or multiple care episodes, on a specific care pathway for patients (hereafter called "Clinical Care Pathway").

[0102] Typically these patients have been referred (hereafter called "Referred Patient") by primary care general personnel (hereafter "GPs") to, or by doctors working within or across, public and / or private hospitals (or other analogous diagnostic or treatment facilities). These facilities are hereafter, together with their clinical or non-clinical facilities, plant, equipment or consumables, called "Secondary Care Facilities".

[0103] The clinical personnel (consultant or associate level specialists, nurses or nurse assistants or other healthcare workers who work within Secondary Care Facilities) providing the healthcare in Secondary Care Facilities are hereafter called "Clinical Personnel".

[0104] The Referred Patients need clinical assessment, investigation, diagnosis or treatment, whether as inpatients requiring a bed and stay (hereafter called "Inpatients") or as outpatients that do not require a bed or stay (hereafter called "Outpatients"). Their referrals are to Clinical Care Pathways or specific episodes along those pathways. The referrals are identified by standardised care codes (hereafter called "Care Codes"). The Care Codes might identify a single, or a bundle, of care episodes along a Care Pathway. The Care Codes incorporate a standardised bundled cost for the relevant Secondary Care Facility and required Clinical Personnel. The Care Code costs can be un-bundled. Care Codes are generally reviewed and revised annually. As an example of care codes, showing structured indications of activities relating to patient acuity, are the NHS Healthcare Resource Group codes which are standard groupings of clinically similar treatments which use common levels of healthcare resource.

[0105] The Digital Platform automates the entire tender process that results in deployment of Secondary Care Facilities and Clinical Personnel to meet the secondary healthcare needs of Referred Patients on the same or very similar Clinical Care Pathways. It employs ML / AI functionality to analyse and learn from data and feedback related to the Clinical Effectiveness, which may include the:

[0106] • Appropriateness (measured by reported accuracy of both the referral and the referring clinician's assessment of clinical acuity (level of urgency, complexity and acuity)) (hereafter called "Acuity")

[0107] • Outcome (measured by actual reported clinical outcome, productivity and patient experience) of the clinical assessment, investigation, diagnosis or treatment of Referred Patients

[0108] • Efficiency (measured by a value for money assessment of the costs and benefits of public money expenditure) of the allocation and application of public monies.

[0109] The Digital Platform can design and create tenders (hereafter "Tenders") to be bid (hereafter "Bid") and executed within, or outside of, normal business as usual working hours.

[0110] Typically, normal or "business as usual" working hours in Secondary Care Facilities (hereafter "BAU") hours are between 0800-1700 on normal workdays, but excluding all weekends or public holidays; whilst other times are called non-BAU (namely after 1700 on normal workdays, or on weekends or public holidays).

[0111] Non-BAU capacity, if fully utilised, is typically equivalent to about 70% of BAU capacity. The Digital Platform can be adapted to create tenders to be executed in non-BAU or BAU business hours.

[0112] The Digital Platform enables a Commissioning Body to Tender part or all of the available non-BAU as well as any available un-used BAU capacity. This results in expanded Secondary Healthcare Capacity.

[0113] The Secondary Care Facilities have spare capacity whose availability they can bid at a price. Clinical Personnel can bid spare capacity (if not working full-time) and / or additional capacity (over and above their full-time capacity) and, that expanded combined capacity enables more Referred Patients to be provided with the healthcare that they need or require.

[0114] A tender (hereafter called a "Tender") can be issued to, or bid by, Secondary Care Facilities acting alone or collaboratively; and, separately thereafter, by Clinical Personnel acting alone or collaboratively.

[0115] The tendered Clinical Care Pathway can encompass an end-to-end patient journey starting with a referral to a Clinical Care Pathway at a Secondary Care Facility (from a primary care GP or from secondary care clinician within a secondary care setting) and ending with a clinical discharge (i.e., clinical assessment, investigation, diagnosis and / or treatment within the secondary care setting completed).

[0116] The tender empowers Secondary Care Facilities to choose whether, and if so when and for what price it will bid to make available its expanded available capacity; and it empowers Clinical Personnel to choose whether, and if so when, where, with which colleagues and for what price it will bid to make available its expanded available capacity. This is hereafter called the "Bid" of its clinical capacity for the Tendered Clinical Care Pathway work.

[0117] The Tender requires all participants (Secondary Care Facilities, Clinical Personnel and Referred Patients) to provide structured feedback on the key determinants of Clinical Effectiveness. These include individually reported Referred Patient clinical outcomes, quality of care as reported by Secondary Care Facility, Clinical Personnel and Referred Patients, tender compliance and performance as reported by Secondary Care Facilities or Clinical Personnel or observed by a Commissioning Body, and clinical productivity as measured by the Commissioning Body.

[0118] The Referred Patients can be automatically triaged according to clinical urgency, complexity or risk, then stratified and prioritised according to assessed clinical Acuity ("Referred Patient Acuity").

[0119] The participant Secondary Care Facilities can be ranked by contribution to overall Clinical Effectiveness ("Ranked Secondary Care Facilities").

[0120] The participant Clinical Personnel can be ranked by contribution to overall Clinical Effectiveness and / or the constituent elements of Clinical Effectiveness ("Ranked Clinical Personnel").

[0121] The Commissioning Body can provide evidence of improved Clinical Effectiveness, or elements of it, that demonstrate improvement in the value for money assessment of increments to state or other funding ("value for money assessment").

[0122] Using machine learning and artificial intelligence (ML / Al), the digital platform can automatically:

[0123] • optimise or moderate the design of Tenders created by the Commissioning Body

[0124] • optimise the bids of Secondary Care Facilities and / or Clinical Personnel to achieve preferred Tender outcomes such as lowest marginal cost, fastest execution, or other Clinical Effectiveness elements

[0125] • match Referred Patient Acuity (or specific elements of it) with Ranked Secondary Care Facilities and / or Ranked Clinical Personnel (or specific elements of the ranking) to achieve optimal or selected healthcare outcomes

[0126] • provide or recommended further education or improvement options to participating Secondary Care Facilities and / or Clinical Personnel

[0127] • evidence, for the state or other funder that finances the Commissioning Body, the 'value for money' outcomes for such funding over time

[0128] • empower a single Commissioning Body, or multiple Commissioning Bodies acting collaboratively, to tender clinical work (related to Referred Patients on a common Clinical Care Pathway) within a single, or across multiple, geographic commissioning areas.

[0129] It will be evident that key aspects of this digital platform include a precise specification of the clinical services required, a tendering process for those clinical services or parts of those clinical services, a feedback mechanism for assessing performance of all involved in those clinical services, and the use of an artificial intelligence to identify appropriate resources to be applied to the services.

[0130] The disclosed system and method provides a Commissioning Body with a centrally managed system employing a trained artificial intelligence that is capable of designing and managing the execution of an end-to-end process that enables available Secondary Care Facilities and / or Clinical Personnel within a single, or across multiple, geographic commissioning areas to be automatically matched with Referred Patients (whether triaged according to Referred Patient Acuity or otherwise) awaiting secondary healthcare so as to achieve and promote optimal Clinical Effectiveness (e.g. improved patient clinical outcomes, improved quality of delivered care, and improved clinical productivity at lowest marginal cost) or some specific determinant or determinants of Clinical Effectiveness (such as elements of acuity, speed, cost, or such like) during either non-BAU or BAU secondary healthcare work periods.

[0131] The system and method can automatically optimise or moderate the design of Tenders to Secondary Care Facilities and / or Clinical Personnel for individual or multiple secondary healthcare Clinical Care Pathways for Referred Patients, as defined by individual Care Codes, so that the secondary healthcare is delivered at optimal Clinical Effectiveness or delivered to promote or achieve one or more selected elements of Clinical Effectiveness.

[0132] The system and method can optimise or moderate Bids (by Secondary Care Facilities and / or Clinical Personnel) so that they are automatically matched with Referred Patients to promote or deliver optimal Clinical Effectiveness or to promote or deliver one or more selected elements of Clinical Effectiveness.

[0133] The system and method can optimise or moderate Bids (by Secondary Care Facilities and / or Clinical Personnel) so that Referred Patients with similar levels of Referred Patient Acuity, or specific elements of such acuity, are automatically matched with Ranked Secondary Care Facilities and / or Ranked Clinical Personnel to promote or deliver optimal Clinical Effectiveness or to promote or deliver one or more selected elements of Clinical Effectiveness.

[0134] The system and method can systematically provide or recommend further education, training, mentoring, development or clinical capability improvement options to participating Secondary Care Facilities and / or Clinical Personnel to improve their ranking over time and / or to promote or deliver improved Clinical Effectiveness or selected elements of Clinical Effectiveness.

[0135] The system and method can enable the Commissioning Body to automatically evidence changes in Value for Money Assessment outcomes over time to the state, or other funder, that finances increments in the secondary healthcare provision.

[0136] The system and method can empower a single Commissioning Body, or multiple Commissioning Bodies acting collaboratively, to Tender clinical work (Referred Patients on Clinical Care Pathways) within a single, or across multiple, geographic, commissioning areas to further promote and deliver optimised Clinical Effectiveness or selected elements of Clinical Effectiveness.

[0137] To effect the system and method this embodiment will normally require:

[0138] Appropriate system integration to assure:

[0139] • Access to required operation data such as bundled or un-bundled Care Codes, identifier codes for Secondary Care Facilities, and Clinical Personnel data (from employment or contractor databases held by their Secondary Care Facilities)

[0140] •Appropriate IT / Digital Infrastructure to effect computation and communication

[0141] • System Security

[0142] • Means to ensure Referred Patients' data privacy and security

[0143] Enrolment of participants on the digital platform

[0144] Each participating:

[0145] • Commissioning Body (administrator)

[0146] • Clinical Care Facility (administrator) • Clinical person (clinical consultant, associate specialist, trainee doctor, nurse, nurse assistant) enrols, provides relevant authentication as well as the healthcare system and personal profiles and data required to participate.

[0147] Enrolment extends also to preferred clinical collaborators (amongst Secondary Care Facilities; or amongst Clinical Personnel)

[0148] Management of Referred Patients

[0149] Referred Patients awaiting secondary healthcare who may be classified and / or triaged and then stratified by:

[0150] • Clinical Care Pathway, using single or multiple Care Codes

[0151] • Acuity, using aggregated or specific standardised assessment levels of clinical urgency, complexity and acuity

[0152] • Geographic proximity or accessibility of the Referred Patients to participating Secondary Care Facilities

[0153] • Targeted determinants or elements of Clinical Effectiveness (such as time to initial or final episode of a Clinical Care Pathway, improved patient clinical outcomes, improved quality of delivered care, or improved clinical productivity at lowest marginal cost)

[0154] • Location within or referral from the geographic areas of a single or multiple number of participating Commissioning Bodies

[0155] Tender design The Commissioning Body administrator, normally being funded by the state or other funding body, may make a strategic decision on which Referred Patients and / or Clinical Care Pathways are to be the subject of a Tender, carving out a list of Referred Patients whose care is to be tendered by reference to the targeted:

[0156] • Number of Referred Patients

[0157] • Geographic location or accessibility of Referred Patients

[0158] • Care Code(s)

[0159] • Schedule within which the tendered care is to be provided (tender executed)

[0160] • Secondary Care Facility or multiple Secondary Care Facilities at which the tendered care is to be provided

[0161] • Minimum or threshold level of bid required whether in terms of the percentage of: o Tendered time slots for the clinical work to be done o Tendered limits (+ or -) by reference to the standardised unbundled cost attributed to the tendered Clinical Code

[0162] • Cost per Referred Patients and / or Clinical Code

[0163] • Referred Patient Acuity

[0164] • Ranked Clinical Care Facilities and / or Ranked Clinical Personnel to be matched with the Referred Patient Acuity

[0165] Bidding - stage 1

[0166] The selected Secondary Care Facilities are requested to bid their:

[0167] • capacity availability, referenced to non-BAU or BAU hours (generally, clinical activity is organised around 4-hour (or other - e.g., surgical procedures are organized on longer time slots) programmed activity (PA) time slots within the BAU or non-BAU periods

[0168] • price for each time-slot that it bids, with the price being a percentage (%) (whether + or -) of the standardised cost attributed to the Clinical Code for their un-bundled services.

[0169] The Clinical Care Facilities may bid:

[0170] • alone, if tendered

[0171] • collaboratively with other Clinical Care Facilities, if the Tender permits Tender questions or queries to the Commissioning Body's administrator are dealt with on the Digital Platform and available to all bidders.

[0172] Matching - stage 1

[0173] The Digital Platform automatically matches the bid capacity and prices to obtain:

[0174] • Earliest (fastest) completion of the clinical work

[0175] • Lowest overall cost to complete the work

[0176] But such matching may be moderated on the Digital Platform by the administrator to take other Clinical Effectiveness factors into consideration (e.g., historical service quality or performance). Once matching (whether moderated or not) is settled, the winning bidder(s) are awarded the clinical work subject to completion of Bidding - Stage 2.

[0177] Bidding - stage 2

[0178] The selected Clinical Personnel are requested to bid their:

[0179] • capacity availability, referenced to time slots within non-BAU or BAU hours as determined by the Commissioning Body based on the options automatically derived by the Digital Platform to achieve the Clinical Effectiveness objectives set by the administrator

[0180] • price for each time-slot that they bids, with the price being a percentage (%) (whether + or -) of the standardised cost attributed to the Clinical Code for their services.

[0181] The Clinical Personnel may bid:

[0182] • alone, if tendered

[0183] • collaboratively with other Clinical Personnel, if the Tender permits

[0184] Tender questions or queries to the Commissioning Body's administrator are dealt with on the Digital Platform and available to all bidders. t

[0185] Matching - stage 2 The Digital Platform automatically matches the bid capacity and prices to obtain:

[0186] • Earliest (fastest) completion of the clinical work

[0187] • Lowest overall cost to complete the work

[0188] But such matching may be moderated on the Digital Platform by the administrator to take other Clinical Effectiveness factors into consideration (e.g., historical levels of clinical outcomes or productivity).

[0189] Once matching (whether moderated or not) is settled, the winning bidder(s) are awarded the clinical work and the awarded Tender moves to execution phase.

[0190] Clinical Work Execution and Feedback

[0191] During and at the end of each episode of work, the provision of feedback into the Digital Platform by each of the Referred Patients, each Secondary Care Facility and each of the participating Clinical Personnel, is required. Subsequently other universally mandated data (e.g., on the clinical outcomes with respect to each Referred Patient) is also provided into the Digital Platform. The feedback may include structured (such as QALY or similar standard or generally recognised or accepted selfassessments of the health impact of care provided) unstructured or less structured feedback from patients.

[0192] This data is automatically analysed, using ML / AI, to inform the assessment of the Clinical Effectiveness and each of the determinants or elements of the clinical work undertaken by each Clinical Care Facility and Clinical Personnel (and their respective collaborators, if any, in each Tender).

[0193] The Clinical Effectiveness assessments are automatically applied to adjust the rating of each Clinical Care Facility and Clinical Personnel (and their respective collaborators, if any, in each Tender) so that in future: • Tender designs

[0194] • Matching of Referred Patients and / or Clinical Codes with Rated Clinical Care Facility and / or Rated Clinical Personnel the awarding of clinical work will optimise the level of delivered Clinical Effectiveness.

[0195] The Clinical Effectiveness assessments are also automatically applied to inform the provision or recommended further education or improvement options for:

[0196] • participating Patient Referrers

[0197] • participating Secondary Care Facilities and / or Clinical Personnel (or their respective collaborators, if any)

[0198] Tender completion and payment

[0199] Subject to the provision of required feedback, the Digital Platform may automatically execute the payment of relevant Clinical Care Facilities and Clinical Personnel

[0200] While the above embodiment is directed to the system being operated by a Commissioning Body, it will be evident that the same approach can apply to use by a patient directly interacting with a system configured to act as a medical market place (e.g. to arrange testing or diagnostic services).

[0201] Advantages and industrial application

[0202] By providing feedback to participants in provision of clinical services, the system may improve medical training by providing feedback to clinical personnel and clinical resources.

[0203] For example the system can analyse the performance data for the purpose of providing to one or more of the referrers, clinical personnel, or clinical resources one or more of:-

[0204] • one or more comparative assessments of performance;

[0205] • one or more indication of areas for improvement;

[0206] • one or more ratings of performance;

[0207] • one or more indications of changes of performance over time.

[0208] By ratings is meant a simple indicator (number(s), letter(s), combination of number(s) and letter(s) or otherwise) of overall performance. By comparative assessment is meant a more nuanced indication of strengths and weaknesses across a number of attributes. For example, a rating might be on a scale of 1 to 5 indicating an overall performance ranging from excellent to appalling; whereas an assessment might indicate a strong clinical performance judged by comparing patient outcomes, but a low clinical effectiveness judged by comparing use of clinical resources.

[0209] By receiving feedback from participants in a clinical care pathway the system can monitor patient progress along that pathway which enables the system to:-

[0210] • Provide alerts if progress does not match defined criteria;

[0211] • Release clinical personnel and resources for other use, if no longer required for that pathway;

[0212] • Call for other clinical personnel and resources if clinical services not part of the clinical care pathway are required.

[0213] By providing a system that allocates resources based on meeting defined appropriateness criteria the method and system of the present invention:-

[0214] • Improves clinical outcomes - by appropriate matching of clinical personnel and resources with patient needs. • Improves allocation of resources - by minimising the use of high cost clinical personnel and resources for low acuity cases.

[0215] • Provides responsiveness to changes in clinical personnel, clinical resources, and clinical practice.

[0216] By providing a system that uses an artificial intelligence trained on performance data and feedback relating to clinical care pathways as experienced by many patients, the system can identify wasted or superfluous elements of the pathways. For example, if the system identifies that for patients having a particular attribute one route through the pathway provides better results than the normal route through the pathway, it can flag or steer the patient to that route without the unnecessary use of resources.

[0217] In effect the system provides a network. This network nodes are the various resources (humans, physical items, physical places, etc). The nodes can be active, passive or in between. The links that form (or are encouraged to form) are dynamic and may be bid based. The network is "alive" and multiscale. By studying the signals (their frequency and strength), the performance of the network can be quantified on: local scale (patient), sub system scale (OR, Hospital / Trust), and all the way to system scale (NHS England / beyond).

[0218] By providing a system that uses artificial intelligence to identify factors not normally considered relevant to patient outcome the system can prioritise or de-prioritise patients independent of referrer perceived patient acuity or user defined appropriateness criteria.

[0219] By using both structured data and unstructured data the system can identify variables relevant to outcome not normally considered as such by referrers. For example if the system identifies a particular condition being associated with a particular geographical area, it may flag this variable as worthy of investigation.

[0220] Further, by operating a bidding / tendering system the present invention improves the use of otherwise dormant clinical facilities and optimises resource allocation. Known bidding / tendering processes typically look to marginal cost factors for specific patients or groups of patients rather than optimising the system as a whole for ability to deliver.

[0221] In more detail the performance of the following are improved by the method and system of the present invention:-

[0222] A. The actors:

[0223] 1. Referrers:

[0224] Inaccurate or unnecessary referrals to treatment can be identified and assessed. These could be incorrectly triaged clinical condition (e.g., cancer versus muscle or nerve damage) or incorrectly risk assessed (e.g., correctly triaged but inadequately risked: such as a stage 4 versus a stage 1 cancer) or simply inappropriate referrals (where a Referrer refers to accommodate patient demands or concerns or to meet performance constraints, such as short appointment time, affecting his or her own clinical practice).

[0225] The assessment of a referrer may be based on that referrer's data (including their referrals to treatment) and all other referrers across either or both

[0226] (i) the system's accumulating dataset of referrals to treatment; and / or (ii) external data concerning referrals to treatment (e.g. the NHS's data).

[0227] The outcome is that:

[0228] (i) the referrer may be informed of their comparative referrals to treatment accuracy;

[0229] (ii) the referrer may be directed to mandatory, or recommended, continuing education to improve the accuracy of referrals

[0230] (iii) the system can take into account a referrer's accuracy in assessing requests for clinical services from that referrer.

[0231] 2. Clinicians to whom referrals are made:

[0232] The system permits identification and assessment of o inaccurate or inappropriate investigations (e.g., seeking an MRI scan or blood test or biopsy where not strictly necessary or where excessively sought relative to peer clinicians); o missed or inaccurate diagnoses (e.g., on the basis of subsequent assessments by either other peer level clinicians, multi-disciplinary teams of clinicians or treatments; unidentified or missed co-morbidities; poor risk rating of the diagnosed condition - e.g. rating a procedure low risk that turned out to be high risk (thereby endangering the patient) or rating a procedure high risk that turned out to be low risk) thereby devoting excessive resources; o treatments that are less successful than prognosed or expected measured by reference to treatments of similar cohorts of patients.

[0233] The assessment of a clinician is based on that clinician's data (all his investigations, diagnoses or treatments) and all other treatment data across either or both

[0234] (i) the Platform's accumulating investigations, diagnoses, or treatments data-sets; and

[0235] (ii) externally held similar data (e.g. the NHS's systematic collection of similar data)

[0236] The outcome is that:

[0237] (i) the clinician may be informed of their comparative data;

[0238] (ii) the clinician may be directed to mandatory, or recommended, continuing education to improve the comparative quality of their clinical performance

[0239] (iii) the system can take into account a clinician's performance in assessing requests for clinical services from that referrer.

[0240] B. The patient or clinical providers' experienced quality of care provided by hospitals and / or clinical personnel:

[0241] Each patient provides feedback (desirably structured feedback) on the experienced quality of each of the:

[0242] Hospital and its plant / equipment / consumables All clinical personnel providing care Each of the relevant clinical personnel hospital provides (inputs) structured feedback on the experienced quality of the Hospital (and its plant, equipment or consumables), and optionally of other clinical personnel.

[0243] This feedback may be used to rate and rank the relevant hospital and / or clinical personnel (whether individuals or teams) so that such data can be used, on a comparative basis, to:

[0244] • Inform the relevant hospital and clinical personnel (whether individuals or teams) of the comparative rating (e.g., this is already done to some extent in an NHS program called Getting it Right First Time or GIRFT: see https: / / gettingitrightfirsttime.co.uk / )

[0245] • Recommend or mandate improvements on the basis of such feedback

[0246] • Amend any tendering process used (e.g., in extremis, excluding hospitals or clinical personnel from a tender)

[0247] • Inform a comparative ranking of such hospitals and clinical personnel (whether individuals or teams) in subsequent tenders.

[0248] C. Optimising clinical effectiveness (maximising availability of hospitals and / or clinical personnel; improving clinical outcomes; improving experienced quality of care; and improving clinical productivity):

[0249] The overall outcome of the system, in practice, is to improve across an entire health system the aggregate clinical effectiveness of all hospital and clinical resources when applied to cohorts of patients and care pathways on waiting lists. This includes:

[0250] • maximising the availability of hospitals and / or clinical personnel - because it offers scheduling and flexible team construction

[0251] • improving clinical outcomes - because improved allocation of resources allows lower quality resources to be used safely on low acuity cases, and high quality resources to be used when required by patient acuity

[0252] • improving experienced quality of care - because feedback is used to educate and to moderate referrer, clinician, and hospital behaviour

[0253] • improving clinical productivity - because feedback is used to educate and to moderate the referrers, clinicians, and hospitals behaviour, resulting in improved patient allocation and reduced wastage on superfluous procedures.

[0254] The outcome can be measured, in aggregate or for cohorts of patients on the same care pathway, in terms of health impact (by QALYs - quality adjusted life years, a standardised measure of impact on a patient of a care pathway) or economic impact (net increase in a person's standardised production after deducting their standardised consumption). In aggregate, a healthy population is a productive population.

[0255] In short, the present invention has the technical effects of improving patient outcomes; reducing waste; and improving individual and institutional performance; by improving management, allocation, education, and assessment of clinical resources and personnel.

Claims

CLAIMS1. A method for improving healthcare provision to a population for which a healthcare provider is at least in part responsible, by optimising allocation, use, and clinical effectiveness of clinical personnel and / or resources, the method comprising the use of a system comprising a trained artificial intelligence to: a) receive from a referrer a request for provision of one or more specified clinical services in relation to a patient, the request comprising a specification of the clinical services required and an indication of presence or absence of relevant patient acuity factors in the patient concerned; b) allocate or assist in allocation of patients to clinical service providers comprising one or more clinical personnel, the clinical service providers being suitable for and having access to clinical resources necessary to provide the specified clinical services, wherein allocation is based on optimising use and clinical effectiveness of clinical personnel and / or resources by the healthcare provider taking into account the specified clinical services and patient acuity factors; c) receive feedback on performance of one or more of the specified clinical services from one or more of• the clinical service providers;• one or more of the clinical personnel;• the patient;• the referrer wherein the artificial intelligence is trained on performance data relating to clinical personnel, clinical service providers, clinical resources, referrers, patient acuity factors, and patient outcome, and the performance data is modified continuously or periodically to reflect the feedback.

2. A method as claimed in Claim 1, in which indicating presence or absence of relevant patient acuity factors in the patient concerned includes indicating one or more of referrer perceived: acuity, urgency, complexity, multi-morbidities or co-morbidities, pharmaceutical use, drug use, patient history, psychological factors, or social factors.

3. A method as claimed in Claim 1 or Claim 2, wherein requesting the one or more specified clinical services required comprises provision of a clinical care pathway identifying a single or bundle of episodes required for provision of the specified clinical services.

4. A method as claimed in Claim 3, wherein the clinical care pathway is matched to or provided as one or more standardised care codes identifying the clinical care pathway.

5. A method as claimed in any of Claims 1 to 4, in which information gathered in provision of one or more of the specified services is used to modify the specification of the clinical services required to provide a modified specification of clinical services.

6. A method as claimed in Claim 5 as dependent on Claim 3 or Claim 4, in which the clinical care pathway comprises diagnostic and / or investigatory episodes and conditional episodes conditionally delivered dependent on the outcome of the diagnostic and / or investigatory episodes.

7. A method as claimed in Claim 5 or Claim 6, in which the modified specification of clinical services comprises one or more of: discharge of the patient without completion of the specified clinical services; referral to another clinical service provider;• entry to another clinical care pathway.

8. A method as claimed in any of Claims 1 to 7 , wherein providing an indication of presence or absence of relevant patient acuity factors in the patient concerned includes provision of structured data, unstructured data, or both, relating to the patient.

9. A method as claimed in any of Claims 1 to 8, wherein all participants in the provision of the clinical services provide feedback concerning performance of their own and other participant's performance.

10. A method as claimed in any of Claims 1 to 9, wherein the patient provides feedback concerning one or more participants in the provision of the clinical services.

11. A method as claimed in any of Claims 1 to 10 wherein feedback on performance includes provision of structured data, unstructured data, or both, relating to one or more of the clinical personnel, clinical service providers, clinical resources, referrers, patient acuity factors, and patient outcome.

12. A method as claimed in any of Claims 1 to 11, wherein one or more participants in the provision of the clinical services receive data and / or feedback concerning their performance.

13. A method as claimed in any of Claims 3 to 12, in which data and / or feedback is provided after completion of an episode of care, and the system monitors progress along the clinical care pathway.

14. A method as claimed in Claim 13, in which the system:-• provides an alert if progress does not match defined criteria for completion of the episode; and / or• releases and / or allocates appropriate clinical personnel and / or clinical resources dependent upon progress along the clinical care pathway.

15. A method as claimed in any of Claims 1 to 9, wherein the system analyses the performance data for the purpose of providing to one or more of the referrers, clinical personnel, or clinical resources one or more of:-• one or more comparative assessments of performance;• one or more indication of areas for improvement;• one or more ratings of performance;• one or more indications of changes of performance over time.

16. A method as claimed in any of Claims 1 to 15, wherein in allocation or assisting allocation the trained artificial intelligence is used to:- d) identify the nature of the clinical resources and clinical personnel required for one or more of the specified clinical services; e) define appropriateness criteria for delivery of the specified clinical services; f) identify one or more clinical service providers potentially able to meet the appropriateness criteria.

17. A method as claimed in Claim 16, wherein in allocation or assisting allocation the trained artificial intelligence is further used to g) rank those clinical service providers for ability to meet appropriateness criteria in relation to the specified clinical services;18. A method as claimed in any of Claims 1 to 17, wherein all or part of the clinical services are allocated to clinical service providers and / or clinical personnel based on a bidding and / or tendering process.

19. A method as claimed in Claim 18 as dependent on Claim 17 wherein in allocation or assisting allocation the trained artificial intelligence is further used to: h) offer to the referrer and / or one or more entities having payment responsibility for all or part of the clinical services, a high-ranking subset of the one or more clinical service providers; i) receive acceptance of one or more of the offered clinical service providers from the referrer and / or the one or more entities having payment responsibility for all or part of the clinical services.

20. A method as claimed in any of Claims 16, 17, 19, or Claim 18 as dependent on Claim 17, wherein identifying one or more clinical service providers includes;• communicating the nature of the specified clinical services to potential clinical service providers by putting out to tender all or part of the specified clinical services;• receiving from the potential clinical service providers bids for all or part of the specified clinical services.

21. A method as claimed in any of Claims 16, 17, 19, 20, or Claim 18 as dependent on Claim 17, wherein identifying one or more clinical service providers suitable for and having access to clinical resources necessary to provide the specified clinical services includes defining at least one clinical service provider by;• identifying appropriate and available clinical resources;• identifying appropriate and available clinical personnel;• defining a clinical service provider comprising a notional team of appropriate and available clinical resources and appropriate and available clinical personnel.

22. A method as claimed in any of Claims 16, 17, 19, 20, 21, or Claim 18 as dependent on Claim 17, wherein the appropriateness criteria include one or more of:-• required competency or skill level with respect to the specified clinical services and patient acuity criteria;• availability of clinical personnel and resources with respect to urgency of specified clinical services;• availability of clinical personnel and resources with respect to complexity of specified clinical services;• availability of clinical personnel and resources with respect to any multi-morbidities or co-morbidities of a referred patient specified clinical services;• availability of clinical personnel and resources with respect to the risk of complications for a referred patient and the specified clinical services;• historical reliability of clinical personnel and resources in providing relevant clinical services;• efficiency of utilisation of clinical resources;• efficiency of utilisation of clinical personnel;• geographical proximity of clinical resources, clinical personnel, and patient;• cost of provision of the specified clinical personnel and clinical resources.

23. A method as claimed in any of Claims 1 to 22, wherein the referrer and / or one or more entities having payment responsibility for all or part of the clinical services is permitted to modify one or more of the appropriateness criteria in response to the offered subset.

24. A method as claimed in any of Claims 16, 17, 19, 20, 21, 22 23, or Claim 18 as dependent on Claim 17, wherein the appropriateness criteria include the referrer's history and accuracy of previous referrals.

25. A method as claimed in any of Claims 16, or 20 to 24 as dependent on Claim 16, wherein after acceptance of the one or more of the offered clinical service providers an appointment is made for provision of the specified clinical services.

26. A method as claimed in Claim 25 wherein an inability of patient or clinical service providers to meet the appointment is automatically signalled to all potential participants and a fresh high-ranking subset of the one or more clinical service providers is offered for selection by the referrer.

27. A method as claimed in any of Claims 19, or 20 to 26 as dependent on Claim 16, wherein patients are triaged according to clinical acuity or any other relevant clinical factor(s) selected by one or more entities having payment responsibility for all or part of the clinical services.

28. A method as claimed in any of Claims 16, or 20 to 27 as dependent on Claim 16, in which the high-ranking subset comprises a single clinical service provider.

29. A method as claimed in any of Claims 1 to 28, in which the artificial intelligence is periodically retrained on the performance data and feedback.

30. A system comprising one or more computing devices, the system comprising an artificial intelligence, and configured to perform the method of one or more of the preceding claims.

31. A computer-readable storage medium comprising software that when operated performs the method of one or more of Claims I to 29.

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