OpRESTORE AI-Patient Navigator Study

The OpRESTORE AI-Patient Navigator Study: Developing and Testing an AI-enhanced Patient Navigator for UK Veterans With Service-related Physical Health Problems

OpRESTORE is a national NHS service that supports UK veterans with complex physical health problems linked to their military service. Veterans referred to OpRESTORE often need care from many different specialists, including surgeons, pain teams, rehabilitation, and mental health services. Currently, decisions about which service is most appropriate are made by a multidisciplinary team (MDT) of clinicians. While effective, this process can be slow, resource-intensive, and sometimes difficult for patients to navigate.

This study will develop and test a new digital "navigator" tool that uses artificial intelligence (AI) to support these referral decisions. The aim is to see whether the tool can safely and accurately match veterans to the right care pathway, while reducing delays and improving patient experience.

The project will be carried out in several stages:

• Reviewing past OpRESTORE records to design the AI model.
• Testing the tool alongside the MDT ("shadow testing") to check whether its recommendations match the clinical decisions.
• Running a case-control study to compare outcomes between patients referred using AI support and those referred by the MDT alone.
• Creating and testing a structured self-referral form to make it easier for veterans to access care directly.

The main outcome will be whether the AI tool makes the same referral decisions as the MDT. Other outcomes include patient satisfaction, quality of life, time taken to reach the right service, and overall costs.

The study will recruit veterans aged 18 or older who are referred to OpRESTORE with a physical health need. It will run for two years. If successful, this approach could free up clinician time, shorten waits for treatment, and improve veterans' health and wellbeing, while laying the foundations for wider use of AI-supported navigation across the NHS.

Study Overview

• Status: Not yet recruiting
• Conditions: Veteran
• Intervention / Treatment: Other: OpRESTORE healthcare navigation; Other: AI-OpRESTORE healthcare navigator

Detailed Description

BACKGROUND

Introduction OpRESTORE was established in 2016 to provide specialist multidisciplinary care for UK military veterans with service-related physical health needs. Over the past seven years, more than 1,500 patients have been referred into the service, reflecting both the high burden of complex conditions in this group and the unmet need for coordinated care pathways. OpRESTORE has demonstrated clear benefits: patients report high satisfaction, almost 60% of cases are redirected to a more appropriate pathway, and statistically significant improvements are seen in EQ-5D-5L outcomes . However, referral rates are rising by more than 30% annually threatening to exceed clinical capacity. The challenge is not a lack of expertise but one of scalability. To continue delivering high-quality, timely, and equitable care, a digital evolution of the OpRESTORE pathway is required.

Why OpRESTORE was needed OpRESTORE arose in response to systemic inefficiencies in the NHS patient pathways. Over the last decade years in good health have declined, with 15-20% of the population affected by musculoskeletal pathology, a leading cause of disability and economic inactivity and the bulk of OpRESTORE's workload. Primary care is increasingly overstretched: for the first time in two decades, patient dissatisfaction with GP services exceeds satisfaction. Access is particularly limited in deprived communities, contributing to widening health inequalities. For veterans, these challenges are compounded by fragmented medical care following transition out of the Armed Forces, difficulties navigating civilian healthcare systems, and the psychosocial impact of leaving service life. Scepticism over civilian providers ability to understanding the nuances of military-related conditions also deters veterans from accessing NHS care.

Referral pathways are a major bottleneck. 7-9% of GP appointments result in a referral, yet 18% of patients require four or more consultations before being referred. Crucially, 21% of patients fall into the "referrals black hole", where appointments are cancelled, misdirected, or lost to follow-up. These delays prolong the already long referral journey: in 2022, 25% of people in England were waiting for an appointment, test, or intervention, and 59% of patients did not receive specialist care within the 18-week target. The net result is a system in which GPs shoulder a rising workload, specialists face inappropriate or delayed referrals, and patients experience inequitable, protracted and fragmented care. Veterans, who often present with multi-morbidity, complex physical needs and often psychosocial challenges, are especially vulnerable to these failures.

Healthcare navigation as a solution

Healthcare navigation offers a proven solution to fragmented systems. The evidence base is extensive: one meta-analysis and 13 systematic reviews were consulted for this project, in addition to relevant individual publications. Collectively, these studies demonstrate that navigation programmes consistently improve access to care and enhance patient outcomes. Key findings include:

• Increased screening uptake,
• Improved attendance to appointments, follow-up adherence and treatment compliance,
• Decreased time to diagnosis and treatment,
• Reduced unplanned care including emergency visits and hospitalisations ((8,16,17) and shorter hospitalisations when they do occur,
• Improved patient satisfaction and outcomes, particularly for chronic disease and underserved groups,
• Reduced mortality.

Navigation has also been shown to deliver measurable cost savings. Examples include reductions in cancer care spending of $1,676 per patient per month and savings of $17,780 annually for patients in the NavSTAR substance misuse programmes. Systematic reviews consistently conclude that patient navigation shortens hospital stays and decreases unplanned care, both of which are major cost drivers. Financial modelling studies similarly predict significant per-patient savings in transitional care and cancer pathways.

The OpRESTORE patient navigator has proven some of these benefits. Over half of patients are re-routed to a different pathway than originally anticipated, ensuring more appropriate use of scarce specialist services. Patient Reported Outcome Measures (PROM; EQ-5D-5L) demonstrate measurable improvement in quality of life, and patient surveys report high levels of satisfaction with the service. In short, OpRESTORE has proven the concept of navigation for veterans in the UK context.

The emerging challenge: workload and scalability Despite these successes, OpRESTORE now faces an inflection point. With referral volumes growing by more than 30% annually, the workload threatens to outstrip available staff. Continuing to expand purely through human capacity is unsustainable. What is required is a digital augmentation of the pathway, working smarter, not harder. Some elements of navigation, particularly triage of straightforward cases, are amenable to automation. However, this must be achieved without sacrificing the patient-centred and "human touch" elements that underpin OpRESTORE's success. An AI-solution could streamline workflow, decrease workload and free up human resource to add value through direct patient encounters rather than bureaucratic and administrative processes.

A further dimension is the potential for self-referral models. Across Europe, healthcare systems have increasingly turned to restrictive gatekeeping, limiting direct specialist access in the hope of controlling costs amid ageing populations and rising demand. Yet such approaches risk delaying care for those who genuinely need it. AI-enhanced patient navigation offers a more balanced alternative: enabling efficient self-referral for patients who warrant specialist care, while still ensuring intelligent gatekeeping through AI-facilitated triage.

Evidence gaps and the role of AI While patient navigation is well established in cancer care and some chronic disease contexts, there is little published research on AI-enhanced healthcare navigation, particularly in the UK and across multiple specialties. Current published literature mostly consists of study protocols or future projects and opinion pieces. Reviews highlight the potential of AI to support triage, personalise patient education, improve care coordination, and optimise resource use. However, robust, real-world evaluations are lacking.

Our team is addressing this gap through a systematic review of AI in healthcare navigation (PROSPERO 2025 CRD420251019208). This will inform the design and evaluation of a digital, AI-enhanced version of the OpRESTORE pathway.

Not only are the digitalisation and intelligent automation of OpRESTORE critical for its continued growth and success, but it is also uniquely positioned to serve as a test case for pioneering AI-patient navigation in UK veterans. It is safe, with an established multidisciplinary clinical safety net. It has almost nine years of patient-level data across >1,500 cases. The case mix offers both complexity (multi-morbidity, service-related pathology) and simplicity (nearly 50% remain on the same pathway, and there is high homogeneity with musculoskeletal referrals making up 74% of cases). This balance makes OpRESTORE both a rigorous and feasible setting to pilot AI-enabled navigation.

Policy alignment Importantly, digitising OpRESTORE aligns with UK health system priorities. The Delivery Plan for Recovering Access to Primary Care (2023-25) highlights the importance of self-referral and digital transformation to relieve GP burden and improve access to healthcare. The NHS Long-Term Plan and the Darzi Review (2024) both emphasise accelerating digitalisation, harnessing AI, and shifting towards technology-enabled efficiency in patient navigation. The Tony Blair Institute (2024) has underscored AI, specifically for triage and navigation, as a critical driver for sustainable healthcare reform.

Conclusion OpRESTORE has proven the value of patient navigation for veterans in the UK but faces an unsustainable rise in demand. Digitisation, informed by robust research and AI-enhanced tools, offers a safe, scalable pathway forward. By combining evidence-based navigation with national policy priorities, the proposed research will generate insights with relevance not only for veterans, but for wider NHS and international healthcare systems.

RATIONALE FOR CURRENT STUDY This project aims to evaluate whether an AI-enhanced patient navigation tool can replicate MDT decision-making while decreasing clinician workload. This study is designed to inform the staged implementation of a digital navigation pathway, including self-referral, clinician-verified algorithmic triage, and, ultimately, selective automation for appropriate cases.

Research Question:

Can an AI-enhanced digital patient navigator safely and accurately replicate MDT decisions in the OpRESTORE pathway, while decreasing clinician workload? Can it improve system efficiency, and patient-reported experiences and outcomes?

Hypothesis:

A digital, AI-enhanced patient navigation tool will be able to guide patients to appropriate care pathways with the same reliability as MDT decision-making, while requiring less manual input. Additionally, structured self-referral will capture richer clinical data than traditional GP-led referrals, leading to improved patient experience and engagement.

2. STUDY OBJECTIVES

Primary objective:

To determine whether a digital AI-enhanced navigation algorithm is equivalent to MDT decision-making in directing patients to appropriate care pathways.

Secondary objective:

• To evaluate patient experience (PREMs) and PROMs across different referral pathways.
• To identify patient cohorts suitable for automation (defined as 80-95% initial algorithmic accuracy).
• To assess time to MDT discussion and referral issuance.
• To conduct a cost analysis comparing standard and digital workflows.

• To benchmark MDT decisions against NICE guideline-based recommendations.

  3. STUDY DESIGN

Type of study: Digital Health Intervention Development and Evaluation - A mixed-methods digital health and AI algorithm development study, involving retrospective analysis of clinical records, prospective observational validation, and a future interventional case-control trial of an AI-based healthcare navigation tool.

Duration: 2 years

Subjects: Veterans accessing the OpRESTORE patient navigation pathway.

3.1. STUDY OUTCOME MEASURES

Primary endpoint:

The concordance between algorithm-generated care-pathway recommendations and MDT decisions.

Secondary Endpoints:

1. The accuracy of referrals, as determined by independent experts, being equal to or superior to that of the MDT.
2. The increase in EQ5D-5L scores (baseline to exit) of patients under the algorithm-supported pathway being equal to or greater than those under standard pathway.
3. Patient-reported experience measures (PREMs) assessed post-referral (e.g. overall satisfaction, ease of use, accessibility) of patients under the algorithm-supported pathway being equal to or greater than those under the standard pathway.
4. Time from referral to MDT decision and referral issuance being faster in the algorithm-supported pathway.
5. Cost per referral episode being lower in the AI-supported pathways.
6. Proportion of algorithm-assisted referrals providing strong support for a treatment pathway and not requiring further input from the MDT, with a target of 40%
7. The suitability of this platform for a subgroup of veteran amputees.

• Study Type: Interventional
• Enrollment (Estimated): 1389
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Natalia L Sanchez-Thompson, MRCS MSc; Phone Number: +447791700255; Email: natalia.sanchez-thompson@nhs.net

Study Locations

• United Kingdom
  • London, United Kingdom, W2 1NY
    • OpRESTORE, Imperial College Healthcare NHS trust
    • Contact: Beth Lambert; Email: beth.lambert4@nhs.net

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Meets criteria for referral to OpRESTORE service
• Age 18 years or older.
• Capacity to consent.
• Have a physical health need (e.g. not purely mental health, or seeking social care advice)

Exclusion Criteria:

• Referrals processed outside standard MDT workflow.
• Patients lacking capacity to consent.
• Prisoners.
• Acute presentation best managed by emergency services and not appropriate for OpRESTORE.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: Standard care - MDT pathway; The patient will run in the current OpRESTORE pathway. This means manual processing of their referral by the OpRESTORE healthcare navigation team, summarising of the medical picture, discussion at a multidisciplinary team meeting and agreement on a treatment outcome.	Other: OpRESTORE healthcare navigation; This is the current standard of care which is a human healthcare navigation pathway where information is extracted manually, and cases are discussed at the opRESTORE MDT for UK veterans.
Experimental: AI-OpRESTORE - automated pathway; In this pathway data is automatically gathered from the patients themselves through self-referral, automated screening of their medical record and only where needed human input (by members of the OpRESTORE clinical team) or additional information. This information is then run through the outcome predicting algorithm which decides on a treatment outcome. The decision is consider final but for the purpose of this study is reviewed by members of the clinical team and clinical members of the research team to ensure clinical coherence and avoid harm to participants.	Other: AI-OpRESTORE healthcare navigator; An algorithm is developed as part of this study that predicts a patients best treatment pathway based on basic demographic variables, targeted clinical questions and prior clinical records. This is the first version of this algorithm in development built on UK OpRESTORE veteran data and tested on this same population.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Algorithm concordance against control	Concordance between algorithm-generated care-pathway recommendations and multidisciplinary team (MDT) decisions. MDT decisions determine the most appropriate treatment pathway and are recorded in the MDT summary document and referral tracking database. Pathways correspond to specific NHS or third-sector services grouped into categories and subcategories (e.g., orthopaedic surgery clinic by joint; ENT services subdivided into ENT clinic or audiology; pain services subdivided into NHS clinics, named consultants, or third-sector programmes). Algorithm outputs are compared with MDT decisions and classified as: full match (category and subcategory), category match only, incorrect recommendation, or referral to MDT due to low algorithm confidence.	At completion of both MDT decision and algorithm output generation (whichever occurs later), typically 8 weeks post-referral for prospective cases or after algorithm processing for retrospective data.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient Reported Outcomes (EQ5D-5L)	The increase inpatient reported outcomes (baseline to exit) of patients under the algorithm-supported pathway being equal to or greater than those under standard pathway. The standardised EuroQol EQ5D-5L questionnaire is used. A lower score equates to better quality of life. It is made up of 5 questions and results are presented descriptively as a 5 digit number (with each digit corresponding to each of the questions answers) or as a compound score with a minimum value of 5 and maximum value of 25 (all questions are weighted equally).	From recruitment to 6 months post recruitment.
Referral accuracy	The accuracy of referrals, as determined by independent experts, being equal to or superior to that of the MDT. Results fall into 3 categories similar to the primary outcomes: full match (category and subcategory), category match only, incorrect recommendation. Those referred to the MDT by the algorithm are not considered for this outcome.	At the point of outcome decision compared to expert input within 3 months of referral.
Patient reported experience measures	Patient-reported experience measures (PREMs) assessed post-referral compared between patients using self-referral versus GP referral, and between those running through the algorithm and those running through the manual MDT (4 resulting groups). PREMS is assessed using the Veteran Affairs Trust questionnaire. It is comprised of 4 questions with multiple choice answers on a 5 point Likert scale. Comparison is descriptive across the 4 questions (compound scoring is not validated).	From referral to 6 months post referral
Pathway time efficiency	Time from receipt of referral (self-referral or GP referral) to treatment pathway decision. In the control arm, this is the time from referral receipt to definitive treatment decision recorded at the MDT and documented in the patient tracker and MDT minutes. In the algorithm arm, this is the time from referral receipt to algorithm-generated treatment recommendation. If algorithm confidence is low, the case is referred to the MDT and the time to decision is recorded at completion of the MDT, as in the control arm.	From referral receipt to treatment pathway decision (algorithm-generated recommendation or MDT decision), up to 6 months.
Cost per referral episode	Cost per referral episode being lower in the AI-supported pathways.	From referral until discharge from the service (typical no longer than 6 months).
Degree of automation	Proportion of algorithm-assisted referrals providing strong support for a treatment pathway and not requiring further input from the MDT, with a target of 40%.	Assessed from referral to discharge from service (typical no longer than 6 months)

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Suitability for amputees	The suitability of this platform for a subgroup of veteran amputees.	Assessed after MDT outcome generated and for a period of 3 months after.

Collaborators and Investigators

• Sponsor: Imperial College Healthcare NHS Trust
• Investigators: Principal Investigator: Shehan Hettiaratchy, FRCS MD, Imperial College Healthcare NHS Trust

Publications and helpful links

General Publications

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• Ali-Faisal SF, Colella TJ, Medina-Jaudes N, Benz Scott L. The effectiveness of patient navigation to improve healthcare utilization outcomes: A meta-analysis of randomized controlled trials. Patient Educ Couns. 2017 Mar;100(3):436-448. doi: 10.1016/j.pec.2016.10.014. Epub 2016 Oct 17.
• Desveaux L, McBrien K, Barnieh L, Ivers NM. Mapping variation in intervention design: a systematic review to develop a program theory for patient navigator programs. Syst Rev. 2019 Jan 8;8(1):8. doi: 10.1186/s13643-018-0920-5.
• Roland KB, Milliken EL, Rohan EA, DeGroff A, White S, Melillo S, Rorie WE, Signes CC, Young PA. Use of Community Health Workers and Patient Navigators to Improve Cancer Outcomes Among Patients Served by Federally Qualified Health Centers: A Systematic Literature Review. Health Equity. 2017 May 1;1(1):61-76. doi: 10.1089/heq.2017.0001. eCollection 2017.
• Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc. 2004 May;52(5):675-84. doi: 10.1111/j.1532-5415.2004.52202.x. Erratum In: J Am Geriatr Soc. 2004 Jul;52(7):1228.
• Le Berre M, Maimon G, Sourial N, Gueriton M, Vedel I. Impact of Transitional Care Services for Chronically Ill Older Patients: A Systematic Evidence Review. J Am Geriatr Soc. 2017 Jul;65(7):1597-1608. doi: 10.1111/jgs.14828. Epub 2017 Apr 12.
• Large O, Ward D, Refsum C, Atkinson I. Preparing the NHS for the AI Era: Why Smarter Triage and Navigation Mean Better Health Care. 2025.
• FIT FOR THE FUTURE 10 Year Health Plan for England Executive Summary. 2025.
• NHS England. Delivery plan for recovering access to primary care: update and actions for 2024-25 [Internet]. 2024 [cited 2024 Nov 5]. Available from: https://www.england.nhs.uk/long-read/delivery-plan-for-recovering-access-to-primary-care-update-and-actions-for-2024-25/
• Hill A, Morrissey D, Marsh W. "It's the future, come on!": a think aloud study exploring clinicians' use of knowledge-based AI decision support. Int J Med Inform. 2025 Dec;204:106089. doi: 10.1016/j.ijmedinf.2025.106089. Epub 2025 Aug 21.
• Moser EC, Narayan G. Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits. Breast. 2020 Apr;50:25-29. doi: 10.1016/j.breast.2019.12.006. Epub 2020 Jan 21.
• D'Amours G, Clausen M, Luca S, Reble E, Kodida R, Assamad D, Bernier F, Chad L, Costain G, Dhalla I, Faghfoury H, Friedman JM, Hewson S, Jamieson T, Silver J, Shuman C, Osmond M, Carroll JC, Jobling R, Laberge AM, Aronson M, Liston E, Lerner-Ellis J, Marshall C, Brudno M, Pham Q, Rudzicz F, Cohn R, Mamdani M, Smith M, Shastri-Estrada S, Seto E, Thorpe K, Ungar W, Hayeems RZ, Bombard Y. Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations. BMJ Open. 2024 Sep 3;14(9):e090084. doi: 10.1136/bmjopen-2024-090084.
• Puthenpura V, Hunter M, Marks AM. "Techquity" in Pediatric, Adolescent, and Young Adult Oncology: Addressing Inequities Through Artificial Intelligence and Immersive Technologies. Pediatr Blood Cancer. 2025 Oct;72(10):e31909. doi: 10.1002/pbc.31909. Epub 2025 Jul 18.
• Sripa P, Hayhoe B, Garg P, Majeed A, Greenfield G. Impact of GP gatekeeping on quality of care, and health outcomes, use, and expenditure: a systematic review. Br J Gen Pract. 2019 May;69(682):e294-e303. doi: 10.3399/bjgp19X702209. Epub 2019 Mar 25.
• Ntais C, Kontodimopoulos N, Talias MA. Gatekeeping or Provider Choice for Sustainable Health Systems? A Literature Review on Their Impact on Efficiency, Access, and Quality of Services. J Mark Access Health Policy. 2024 Dec 6;12(4):378-387. doi: 10.3390/jmahp12040029. eCollection 2024 Dec.
• Rajabiun S, Cabral HJ, Chen CA, Lloyd-Travaglini C, Dugas JN, Amburgey D, Fitzgerald M, Lemon SC, Haas JS, Freund KM, Battaglia T; TRIP Consortium. Cost and activity analysis for a citywide patient navigation intervention to engage underserved patients in breast cancer treatment: Findings from the Translating Research Into Practice study. Cancer. 2025 Jan 1;131(1):e35671. doi: 10.1002/cncr.35671.
• Orme S, Zarkin GA, Dunlap LJ, Nordeck CD, Schwartz RP, Mitchell SG, Welsh C, O'Grady KE, Gryczynski J. Cost and Cost Savings of Navigation Services to Avoid Rehospitalization for a Comorbid Substance Use Disorder Population. Med Care. 2022 Aug 1;60(8):631-635. doi: 10.1097/MLR.0000000000001743. Epub 2022 Jun 10.
• Mullangi S, Worland SC, Dharmarajan K, Green RJ, Sachdeva K, Johnstone D, et al. Reduction in cancer spending due to patient navigation. JCO Oncol Pract. 2023 Nov;19(11_suppl):557-557.
• Manderson B, McMurray J, Piraino E, Stolee P. Navigation roles support chronically ill older adults through healthcare transitions: a systematic review of the literature. Health Soc Care Community. 2012 Mar;20(2):113-27. doi: 10.1111/j.1365-2524.2011.01032.x. Epub 2011 Oct 13.
• Ranaghan C, Boyle K, Meehan M, Moustapha S, Fraser P, Concert C. Effectiveness of a patient navigator on patient satisfaction in adult patients in an ambulatory care setting: a systematic review. JBI Database System Rev Implement Rep. 2016 Aug;14(8):172-218. doi: 10.11124/JBISRIR-2016-003049.
• Bush ML, Kaufman MR, Shackleford T. Adherence in the Cancer Care Setting: a Systematic Review of Patient Navigation to Traverse Barriers. J Cancer Educ. 2018 Dec;33(6):1222-1229. doi: 10.1007/s13187-017-1235-2.
• Robinson-White S, Conroy B, Slavish K, Rosenzweig M. Patient Navigation in Breast Cancer: A Systematic Review. Cancer Nurs [Internet]. 2010;33(2). Available from: http://journals.lww.com/cancernursingonline
• Genoff MC, Zaballa A, Gany F, Gonzalez J, Ramirez J, Jewell ST, Diamond LC. Navigating Language Barriers: A Systematic Review of Patient Navigators' Impact on Cancer Screening for Limited English Proficient Patients. J Gen Intern Med. 2016 Apr;31(4):426-34. doi: 10.1007/s11606-015-3572-3. Epub 2016 Jan 19.
• Budde H, Williams GA, Winkelmann J, Pfirter L, Maier CB. The role of patient navigators in ambulatory care: overview of systematic reviews. BMC Health Serv Res. 2021 Oct 28;21(1):1166. doi: 10.1186/s12913-021-07140-6.

Study record dates

Study Major Dates

• Study Start (Estimated): May 1, 2026
• Primary Completion (Estimated): May 1, 2027
• Study Completion (Estimated): November 1, 2027

Study Registration Dates

• First Submitted: March 4, 2026
• First Submitted That Met QC Criteria: March 11, 2026
• First Posted (Actual): March 16, 2026

Study Record Updates

• Last Update Posted (Actual): March 16, 2026
• Last Update Submitted That Met QC Criteria: March 11, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: healthcare navigation; veteran healthcare; clinical AI
• Other Study ID Numbers: 199335; OpRESTORE number (Other Identifier: OpRESTORE, Imperial College Healthcare NHS trust)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No