We’re delighted to present our finalists for the category of “Most promising pilot”:
Menwell Ltd: The Voy Programme – revolutionising obesity care through digital tools
Overview: Voy is a digital weight loss programme designed to complement tirzepatide. It offers personalised AI coaching, AI dietary analysis, and personalised side-effect management via an app. A 60-person pilot showed exceptional clinical outcomes, with improvements to quality of life, and a 100 percent prediabetes reversal.
What happened? Voy is a next-generation digital behaviour change weight loss solution, built to create the best possible patient outcomes in obesity care whilst prioritising patient safety when on Mounjaro. The programme takes a proactive, personalised approach, combining remote 1:1 health coaching, AI-powered tools, adaptive habit formation features, a personalised titration pathway and personalised side effects response programme. A pilot study was conducted with 60 adults aged 18-75 years (BMI: 27 kg/m2) recruited via social media over 12 months. Qualitative interviews confirmed high satisfaction with coaching, app features, and the ability to customise dosing and goals. Results included a 10.4 percent average weight loss in 12 weeks, a three percent reduction in waist-to-hip ratio, an up to 70 percent improvement in quality of life, and a 53 percent increase in weight lost at four months vs medication-only participants. The pilot also saw the reversal of prediabetes in 100 percent of prediabetic patients at six months.
Shrewsbury and Telford Hospital Trust: CDS recovery through FDP: A trust-led, multi-partner transformation and platform to excel
Overview: Faced with legacy data challenges, the trust led a first-of-type collaboration with NHSE and FDP to restore national submissions, strengthen planning capabilities, and modernise its data platform. This initiative redefined data resilience, enabled future standards adoption, and now serves as a model across the NHS.
What happened? In April 2024, the trust identified legacy challenges within its Data Warehouse. By June 2025, it took a bold decision to temporarily pause national submissions, allowing teams to focus on designing a robust, scalable, and standards-compliant data solution. In December, the trust proactively engaged with NHS England and the national FDP team to explore opportunities for strategic support. What followed was an exceptional cross-organisational effort that brought together close technical collaboration between FDP developers and the trust’s internal Data Warehouse team, with support from SMEs across key areas of the organisation. Rigorous testing cycles underpinned the delivery. The trust maintained a consistently transparent and inclusive approach. This culture of collaboration, trust, and responsiveness supported the successful on-time delivery of the solution, meeting the critical 2024/25 year-end refresh. The trust successfully reinstated its national data submissions, including CDS and SLAM, allowing it to meet statutory reporting requirements and avoid financial and compliance risks.
Holmusk UK/Mersey Care NHS Foundation Trust: MaST Inpatient Safety Index – facilitating data triangulation to support safety and quality in mental health inpatient environments
Overview: MaST Inpatient Safety Index (MaISI) provides a holistic view of metrics across mental health inpatient settings, displayed within an interactive interface. Scores from multiple data associated with quality and safety allow users to quickly spot patterns, encouraging curiosity and early identification of safety concerns.
What happened? Holmusk and Mersey Care collaborated with clinical staff, service user representatives and senior healthcare leaders to co-design a solution that delivers meaningful value. Phase I of the project focused on independent evaluation and user perception scoping. Insights directly informed the design and build of Phase II, ensuring the solution is grounded in real-world needs and frontline experience. The pilot solution centres on the MaISI, which provides a holistic view of multiple critical safety and quality metrics across mental health inpatient settings, including ward incidents, staff rostering systems, patient risk and complexity, staff and patient survey and staff appraisal data. Domains are represented in a stellar chart, which allows the user insights into the ward’s safety and quality performance by showing a relative score. Clicking into each domain reveals detailed trend charts and time-based insights. The pilot is now live and fully compliant with DCB0129. In October 2025, the pilot will expand to include two additional trusts. The results of these pilots will provide evidence for scaling the MaISI nationally via the Federated Data Platform.
Community Dermatology’s Map My Mole: 64 percent referral reduction – a patient-led breakthrough in skin cancer detection
Overview: Map My Mole is a patient-led teledermatology service that cuts diagnostic waiting times from weeks to hours. In our South West pilot, we saved 989 GP appointments. Backed in Parliament, our model proves scalable, equitable innovation in early skin cancer detection.
What happened? Map My Mole offers a radically simple but effective intervention: patients receive a dermoscopic lens, a Dyplens, and access to an app guiding them through high-quality mole photography. Images are reviewed by UK-based consultant dermatologists, often within 24-48 hours, down from weeks, reducing patient anxiety and speeding up diagnosis and treatment where needed. For those without smartphones or confidence, community alternatives (pharmacies, mobile HCAs) provide flexible, equitable access. From March – June 2024, we trialled Map My Mole across three GP sites in Devon and Cornwall. Results included 1,126 cases reviewed, 989 GP appointments saved – buying GPs back 14,835 minutes, and two-week-wait referrals reduced by 64 percent. 216 secondary care referrals were avoided. Submission to result time was one day, 23 hours; and patient capture time was under 11 minutes. 73 percent of patients were aged 55+, and 62 percent of images were captured independently at home. In feedback from patients and clinicians, 94 percent rated the service “Very Good” or “Good”, and 90 percent would use it again. 29,240 GP appointments/year could be saved if scaled to Cornwall’s population.
mOm Incubators: Improving warming and outcomes for those experiencing neonatal hypothermia: A real-world mixed-methods quality-improvement study in five NHS Trusts in England & Scotland
Overview: This pilot evaluation aimed to assess the real-world effectiveness, cost-effectiveness, and acceptability (staff and parental) of the mOm Essential incubator implemented across five NHS sites in England and Scotland. Objectives included determining impacts on clinical outcomes, health economics, and barriers and enablers.
What happened? A total of 107 infants were enrolled and managed with the mOm Essential incubator across the five sites. The cohort included a mix of term and late-preterm babies, with gestational ages ranging from 35+2 to 41+2 weeks (median 37+6 weeks). Birth weights ranged from 1.85 kg to 3.97 kg. Notably, 101 out of 107 infants (94.4 percent) were observed to be mildly hypothermic (temperature between 35.9-36.4°C) on their initial assessment post-birth or post-transfer, three infants had moderate hypothermia (temperature between 35-35.8°C) and the remaining three infants had normal temperatures but were judged high-risk (and placed in the incubator prophylactically). The device enabled rapid warming of hypothermic infants, helping to avert the well-documented dangers of neonatal cold stress, and in doing so it reduced avoidable NICU admissions and significantly reduced associated costs. The findings suggest meaningful implications for practice: maternity and neonatal units can consider incorporating this multiple-modality incubator into their standard thermoregulation protocols, thereby keeping more babies with their mothers and reserving NICU beds for those who truly need intensive care.
Northern Care Alliance: CESCNNA: Cauda Equina Syndrome detection using convolutional neural networks
Overview: Our project introduces a machine learning programme to rapidly detect cauda equina compression (CEC) from MRI scans, addressing delays in diagnosing Cauda Equina Syndrome (CES). This swift identification enables faster time to treatment, preventing permanent neurological damage and improving patient outcomes.
What happened? 467 lumbar spine mid-sagittal T2 MRI images were extracted from the PACS dataset. A 20-layer convolutional neural network with skip connections was developed and trained on 418 images. The model demonstrated an overall average accuracy of 97 percent with good discrimination between normal scans, disc bulge, disc protrusion and CES scans. Decision curve analysis demonstrated that the model provided greater clinical net benefit in correctly identifying CEC at all predicted probabilities relative to the default strategies of management of all or no patients. Calibration curve analysis demonstrated that the model was liberal in its decision making by having a low threshold of suspicion for CEC. On the same dataset, a blinded senior neurosurgical fellow achieved an accuracy of 0.686 (0.587-0.775), sensitivity of 1.00 (0.932-1.00), specificity of 0.360 (0.229-0.508), PPV of 0.619 (0.569-0.667) and NPV of 1.00 (0.815-1.00). All true CEC were correctly identified with no false negatives. However, the majority of DP scans were classified as CEC, resulting in a large number of false positives.
Inhealthcare (Resmed) and Royal United Hospitals Bath: A digital rheumatology service at RUH Bath aims to help improve patient access to rheumatology services and accelerate speed of diagnosis
Overview: With 12,000 rheumatology patients under their care, RUH Bath needed a digital solution to manage a growing caseload of patients, whilst seeing new patients needing a rapid diagnosis and treatment plan. Since September 2024, more than 5,100 patients have been enrolled on the service, across six conditions.
What happened? The platform integrates digital health tools that allow patients to track symptoms, share relevant health data with clinicians, and access valuable educational resources. This approach aims to empower patients to take an active role in managing their condition. It also allows patients on Patient Initiated Follow-Ups pathways to be regularly in contact with their clinical team. Patient responses to scheduled ePROMS are automatically stored within the patient record and presented to clinicians in visual dashboard form. Clinicians can configure patient alerts based on individual thresholds. This allows patients to be flagged and clinicians can initiate an appropriate response. Patients also have the ability to communicate directly with their care teams via an online form. The service is accessible to patients and clinicians by an easy-to-use intuitive web application. The service enables clinical teams to effectively manage patient load and allows patients to track symptoms and submit forms from home. This allows HCPs to prioritise case load and reduce waiting times for new patients. Patients can choose a non-digital option if preferred. Patient and clinician feedback has been positive.
Netcall and Leeds Teaching Hospitals NHS Trust: Leeds Teaching Hospitals NHS Trust transformed its Two Week Wait booking process using Netcall’s Clinic Utilisation Solution
Overview: Leeds Teaching Hospitals NHS Trust is looking to revolutionise patient access and booking processes with Netcall’s Clinic Utilisation Solution. As part of a national pilot, LTHT redesigned their booking process for the Two Week Wait (2WW) pathway, leveraging AI to automate outbound phone communications.
What happened? Collaborating closely with Netcall, LTHT enrolled in a pilot with NHS England. Deployed in less than a month across three specialties, including Breast Two Week Wait, the trust transformed appointment booking for these critical services. The system employs a customisable Automated Virtual Assistant (AVA) to generate outbound calls to patients on the waiting list. Once connected, AVA offers available appointment dates. When patients select their preferred date, they are seamlessly transferred to a staff member with all necessary information displayed on the booking screen. A simple text 30 minutes before a call has made all the difference. Staff can upload any short-notice capacity and rapidly fill it – maximising clinic use. Calls also enable real-time updates to patient information, improving data quality trust-wide. Patient choice booking has jumped from 70 to 95 percent. Tasks that previously took one-and-a-half days now take a single morning. Initially launched in Breast 2WW, the solution has rapidly expanded to four specialties with a view to roll out to Diabetes, ENT, Ophthalmology and Orthopaedics.
Voice-Care International Ltd: Voice-Care® Surgical Workflow – A digital platform delivering enhanced patient safety, productivity and efficiency gains with specialised voice-directed technology
Overview: The Voice-Care digital platform enables the guidance and recording of Surgical Safety Checklists with the use of voice, delivering 100 percent compliance and a significant increase in productivity, patient safety and care. Trials at Imperial College Healthcare Trust delivered benefits for patients and clinicians alike.
What happened? The solution was trialled at ICHT, with a specifically designed medical cart featuring docking stations for both a PDA and a Jabra headset, while an in-built Jabra speaker allows for directives to be heard by the entire surgical team. The solution is geared to listen out for specific responses to specific questions asked, and the team at ICHT worked alongside Voice-Care to focus on a highly used LocSSIPs, being the CVV line insertion; a procedure which involves around 40 steps. Taking clinicians through a step-by-step approach to the procedure, Voice-Care is totally hands free and voice commanded, which allows users to confirm that each step within this particular procedure has been completed. All documents are automatically uploaded to the patient record directly from the point of care. It is fully automated and collects data constantly. The solution has also enabled ICHT to capture and document items that they were previously unable to capture (e.g. real-time images of the wire insertion). The manoeuvrability of the medical cart enables usage of the solution within wards and A&E departments as well as the operating theatre. ICHT also found it extremely easy to train clinicians on the solution – around 20 minutes per user.
