The OpRESTORE AI-Patient Navigator Study: Developing and Testing an AI-enhanced Patient Navigator for UK Veterans With Service-related Physical Health Problems
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,389
- 试验地点
- 1
- 主要终点
- Algorithm concordance against control
研究概览
简要总结
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.
详细描述
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
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Single (Participant)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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)
排除标准
- •Referrals processed outside standard MDT workflow.
- •Patients lacking capacity to consent.
- •Prisoners.
- •Acute presentation best managed by emergency services and not appropriate for OpRESTORE.
研究组 & 干预措施
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.
干预措施: OpRESTORE healthcare navigation (Other)
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.
干预措施: AI-OpRESTORE healthcare navigator (Other)
结局指标
主要结局
Algorithm concordance against control
时间窗: 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.
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.
次要结局
- Patient Reported Outcomes (EQ5D-5L)(From recruitment to 6 months post recruitment.)
- Referral accuracy(At the point of outcome decision compared to expert input within 3 months of referral.)
- Patient reported experience measures(From referral to 6 months post referral)
- Pathway time efficiency(From referral receipt to treatment pathway decision (algorithm-generated recommendation or MDT decision), up to 6 months.)
- Cost per referral episode(From referral until discharge from the service (typical no longer than 6 months).)
- Degree of automation(Assessed from referral to discharge from service (typical no longer than 6 months))