We’re delighted to present our finalists for the category of “Most promising pilot”.
Easy Soft Services Ltd/CareForAll AI: CareForAll AI: A voice-enabled AI pilot for safer, smarter social care
Overview: CareForAll AI is a voice-enabled AI pilot for care homes, nursing homes and domiciliary care providers. It aims to reduce administration, improve documentation, support compliance and enhance safety through AI-assisted voice capture, structured care records and connected device-based care intelligence for frontline care teams.
What happened? The pilot is built around a simple but highly practical concept: care staff should be able to speak naturally, and the system should help convert that voice input into structured, useful and reviewable care records. The platform is being developed with a human-in-the-loop approach, meaning AI supports staff but does not replace professional judgement. Care workers and managers remain in control, reviewing and confirming information before it becomes part of the care record. The long-term vision combines AI software with connected devices and care-environment intelligence. The expected benefits are wide-ranging. For care staff, the system could reduce repetitive typing and allow more time to focus on residents and service users. For managers, it could improve oversight, reporting, compliance preparation and incident follow-up. For residents, patients and service users, the benefit is the potential for safer, more responsive and better documented care. For families and providers, the long-term benefit is increased confidence that important care information is being captured, reviewed and acted upon. The pilot also aims to help reduce incidents and accidents by improving the way information is recorded, escalated and monitored.
Fleggburgh Surgery: GPOP – General Practice Operations Portal
Overview: GPOP is an affordable, scalable primary-care operations portal for practices and PCNs, combining care navigation, workforce visibility, compliance, finance, payroll export, AI/OCR invoice reading, document management and learning into one simple cockpit, helping teams make faster, safer operational decisions.
What happened? GPOP is a digital operations platform developed from a GP practice to solve one of primary care’s most persistent problems: information is spread across too many places. GPOP brings those signals together in one clear operational cockpit. It gives practices and PCNs a practical, low-cost alternative to large enterprise systems. The care navigation module supports reception teams with structured, auditable workflows. Instead of expecting staff to choose from long pathway lists, GPOP uses plain-language symptom search and guided questions to capture duration, recurrence, red flags, key symptoms, sex, age and relevant risk factors. It produces a clear recommended action and a SystmOne-ready note. The platform connects recommendations with live operational context: who is working, what skills they have, which rooms are available, whether leave or rota gaps affect capacity, and whether a GP, registrar, ANP, nurse, HCA, pharmacist or dispenser is the appropriate next step. The platform also supports finance and management teams. Clinicians receive better-structured requests. Managers gain a live view of operational risk. Finance teams receive cleaner workforce and invoice data. PCNs gain a scalable way to coordinate shared services.
NHS England & Chelsea and Westminster NHS Foundation Trust: AI-assisted discharge summarisation (AADS): From partnership pilot to national transformation
Overview: The AI-Assisted Discharge Summarisation (AADS) tool uses generative AI to streamline discharge documentation. A UK Government AI Exemplar now scaling via the NHS Federated Data Platform, it reduces clinician burden, improves care pathways, and accelerates patient flow.
What happened? The tool is designed to transform the creation of hospital discharge summaries using generative AI. By combining NHS England’s national AI capability and infrastructure with frontline clinical expertise at Chelsea and Westminster, AADS has been developed as a clinically grounded, real-world solution to one of the NHS’s most persistent operational challenges: delays in patient discharge caused by time-intensive documentation. It uses large language models, deployed securely via the NHS Federated Data Platform, to automatically generate structured first drafts of discharge summaries by extracting key information such as diagnoses, treatments, and test results. By automating the drafting of discharge summaries, AADS reduces time spent on repetitive administrative tasks, allows clinicians to focus more on direct patient care, improves consistency and completeness of documentation, and supports workforce sustainability by reducing pressure and cognitive load. By reducing the time required to produce discharge summaries, the solution enables faster discharge, improved patient flow and bed utilisation, more timely communication with GPs and community services, and safer and more consistent transitions of care.
Sanius Health: From vaso-occlusive crisis (VOC) prediction to proactive sickle cell disease (SCD) care: Piloting a digital early-warning pathway
Overview: Sanius is translating its sickle cell vaso-occlusive crisis (VOC) prediction work into a proactive digital care pilot, combining patient-reported outcomes, wearable signals, AI risk insight, structured engagement and specialist escalation pathways to help patients stay well at home and support earlier, better-informed action before crises.
What happened? The proposed SCD proactive care pilot builds on Sanius Health’s existing SCD digital ecosystem and AI-enabled VOC prediction work. Patients use the Sanius app and wearable devices to capture daily and longitudinal signals, including pain, quality of life, fatigue, hydration, sleep, activity, heart rate, oxygen saturation, respiratory rate and VOC status. These data streams are combined with clinical context and analysed through Sanius’ AI and machine learning models to support patient-level visibility of changing risk. Patients can see patterns in their own health, supporting earlier action at home. The model is being developed with NHS partners and SCD clinical leaders as a digital front door and early-warning pathway. It is intended to support home-first care where safe, timely escalation where clinically appropriate, and better communication with specialist teams. Sanius’ first VOC prediction model used wearable data, patient-reported outcomes and selected medical record data from 186 patients and correctly identified 84 percent of self-reported VOCs. The second model used a larger 399-patient dataset, broader variables and more flexible machine learning methods, increasing prediction performance to 92 percent.
Raising Health: Raising Health charity accelerates access to life-changing mental health treatments through charitable funding
Overview: Raising Health, the registered charity supporting Leicestershire Partnership NHS Trust, funded a pilot programme enabling patients to access alternative treatments for depression. The initiative supported diverse service users, improved symptom management, restored hope and, in some cases, proved life-saving.
What happened? At Raising Health, we have supported the delivery of an innovative pilot programme enabling patients to access Flow Neuroscience’s transcranial direct current stimulation (tDCS) headsets. Flow therapy works by delivering a gentle electrical current to areas of the brain associated with mood regulation, sleep and motivation. In 2024, LPT’s crisis resolution and home treatment team piloted the use of Flow headsets, achieving results including 80 percent of patients reporting a reduction, with some patients experiencing a drop in suicidal ideation by up to 75 percent. Through a fundraising campaign, we raised £40,000 within 12 months. This enabled us to purchase 150 headsets and extend the programme into adult eating disorder and community mental health teams. Clinical outcomes show that 71 percent of patients experienced a reliable reduction in depressive symptoms within six weeks, with sustained reductions in suicidal ideation of 33 percent by week three and 66 percent by week ten. Over a quarter of patients reached full clinical remission by ten weeks. Patients also reported meaningful improvements in daily functioning, with an average 11.66-point improvement on the Work and Social Adjustment Scale.
The Dudley Group NHS Foundation Trust and Altera Digital Health: Sunrise results acknowledgement outpatient pilot
Overview: The Dudley Group NHS Foundation Trust partnered with Altera Digital Health to pilot results acknowledgement in Outpatients Respiratory and General Surgery. Replacing a paper process with an automated digital “to-do list,” the pilot tracked and acknowledged over 8,000 outpatient tests in 8 weeks.
What happened? Instead of waiting for a clinician to manually search the system or wait up to 48 hours for the paper results, the software pushes the result straight to their view in the EPR as soon as they are resulted. When multiple clinicians are involved in a patient’s care, they can all add their own notes directly to a single result line-item. The solution works by flagging the return of laboratory or radiology results automatically creating a live task for the responsible clinician. The pilot introduced two main ways for staff to manage this information: the Provider Launcher is a personalised dashboard where a clinician can see every outstanding result assigned to them in one place; and the Patient Launcher within an individual patient’s chart that shows a count of their pending results tasks at a glance. The pilot was successfully rolled out across our outpatient clinics in respiratory, breast, colorectal and general surgery. In respiratory, a total of 6,406 investigations were ordered, with 6,310 successfully closed and acknowledged by clinicians through the system. In general surgery, across three distinct tracking streams, a total of 2,117 investigations were raised. Of these, 1,135 were actively processed and within the system during the initial pilot window.
X-on Health: Delivering equitable patient access through omni-channel workflow automation
Overview: Stamford Hill Group Practice implemented X-on Health’s Omni Consultations solution. Within four months, the practice achieved a 54 percent reduction in missed calls, reduced wait times by 40 percent, automated 17 percent of inbound calls and saved an estimated 379 reception hours.
What happened? Stamford Hill faced increasing pressure from high call volumes, long waiting times and growing demand for appointments and administrative support. The practice introduced Omni Consultations as a pilot in January 2026. Unlike traditional online consultation tools, patients can submit requests online, through a voice agent over the telephone, or with support from reception staff. Regardless of how patients make contact, all structured requests are delivered into a single inbox for efficient management and triage. Rather than requiring patients to wait in lengthy call queues, the Workflow Agent can automatically gather information, categorise requests and direct patients to the most appropriate pathway. Within the first full month following implementation, the practice achieved a 54 percent reduction in missed calls compared with December 2025. This equated to 3,714 fewer missed calls and represented a significant improvement in patients’ ability to access support when needed. At the same time, average call waiting times reduced by 40 percent, improving patient experience and reducing frustration with long queues. Between January and April 2026, the practice realised an estimated 379 hours of staff time savings.
What Caused This: Building a human-centred platform for organisational learning in primary care
Overview: In partnership with Norfolk primary care organisations, we are piloting a collaborative approach to healthcare technology design and deployment. Through face-to-face learning, operational discovery and systems-based thinking, the pilot is shaping a digital environment for safer investigations and organisational learning.
What happened? Our organisation specialises in systems-based organisational learning, Root Cause Analysis (RCA) and operational understanding within high-consequence industries. Through our partnership with Norfolk Primary Care CIC, we are applying that expertise within healthcare. To date, eight medical practices across Norfolk have participated in face-to-face training, operational discovery and collaborative learning sessions. The pilot focuses on understanding how RCA, Significant Event Analysis (SEA), governance and organisational learning currently function within primary care settings. This includes identifying operational pressures, inconsistencies, barriers to effective learning and opportunities to improve systems-based investigation approaches. The pilot combines practical learning, collaborative workshops, peer-group discussion and operational discovery. The pilot also applies SEIPS-informed thinking, a human-factors and systems-based approach to understanding why incidents happen and how safer systems can be created. The pilot is creating a continuous feedback loop between frontline operational experience and platform development.
Anglotec Academy (Anglotec AI): Anglotec AI Early Access Pilot: AI-Powered OSCE preparation for international medical graduates
Overview: Anglotec AI is an AI-powered OSCE preparation platform built on 40 years of British Council-accredited clinical teaching. It provides international medical Graduates with affordable, structured access to 805+ clinician-authored scenarios across 26 specialties.
What happened? The platform provides access to 805+ clinician-authored OSCE scenarios spanning 26 medical specialties, covering core PLAB 2 station types. Each scenario is structured around real clinical encounters, enabling candidates to practise history-taking, examination, communication, and clinical reasoning in a consistent, evidence-based framework developed by experienced NHS clinicians. Candidates can practice at any time, from anywhere in the world, at a fraction of the cost of traditional preparation routes. The platform is currently in an early access phase, with a growing cohort of candidates from across the world engaging with the content ahead of our full public launch. We are targeting partnerships with 32 NHS Trusts at launch to support workforce integration pathways for newly qualified IMGs. Our roadmap includes Clinical Knowledge Review (Q3 2026), AI-powered Video Analysis for communication skills feedback (Q4 2026), and Group Discussion Rooms and Mentor Feedback Programme (Q1 2027) — features that will further close the gap between preparation and practice. By providing an affordable, institution-backed, AI-powered preparation route, we aim to help more qualified doctors enter the NHS more quickly.
SunuCare: SunuCare – AI-powered emergency detection wearable for underserved communities
Overview: SunuCare is developing an AI-powered smart wearable that detects medical emergencies and sends real-time alerts, even in low-connectivity environments. Designed for underserved communities, the solution aims to improve emergency response, reduce healthcare inequalities, and help save lives.
What happened? Across many African communities, particularly in rural and underserved areas, access to rapid medical intervention remains a major challenge. Delays in detecting emergencies such as cardiac distress, sudden health deterioration, or critical incidents often lead to avoidable complications and deaths. SunuCare was created to address this problem through accessible and intelligent digital health technology. Our solution combines a smart wearable bracelet with an intelligent alert platform capable of monitoring selected health indicators and triggering emergency notifications in real time. The wearable is being designed to function effectively even in low-connectivity environments, making it particularly relevant for regions where digital infrastructure and emergency healthcare access remain limited. By leveraging wearable technology, connected health systems, and AI-driven analysis capabilities, SunuCare aims to bridge critical gaps in healthcare accessibility. Over the next 12 months, our objectives are to finalise a functional prototype, conduct pilot testing in targeted communities, strengthen our technical and strategic team, and build partnerships with healthcare actors and innovation ecosystems.
Platnik Solutions Ltd: NHSCareOs – A DCB0129-signed, 20-module NHS pathology platform ready for shadow evaluation alongside any existing LIMS
Overview: NHSCareOs is a pilot-ready NHS pathology platform: 20 integrated modules, all six instrument protocols confirmed, DCB0129 CSO-signed (April 2026), FHIR R4 UK Core native. Shadow evaluation alongside an existing LIMS eliminates clinical risk while generating the trust reference evidence needed for NHS-wide adoption.
What happened? NHSCareOs is pilot-ready. The platform is technically complete, the clinical safety case is signed, the regulatory foundation is in place, and the financial case for any NHS trust is demonstrably stronger than any competing product. We are seeking one NHS trust laboratory as our first pilot site. In a District General Hospital laboratory pilot, this will involve full platform deployment alongside the trust’s existing LIMS in shadow evaluation mode. NHSCareOs receives the same instrument results as the existing LIMS, runs all 20 modules against real patient data (under a signed DPA), and produces comparative audit trails, QC reports, and statutory notifications in parallel. Shadow evaluation is the clinically appropriate first stage. No patient care pathway depends on NHSCareOs during this phase – the existing LIMS remains the system of record. This eliminates clinical risk while generating the evidence base required for CQC and Trust Board approval of a phased migration. The first pilot is the commercial gate for every subsequent deployment. It provides the trust with a working second system during shadow evaluation, adding redundancy and resilience at no infrastructure cost.
