Impact on all-cause mortality of a case prediction and prevention intervention designed to reduce secondary care utilisation: findings from a randomised controlled trial

Objective To investigate how an AI case-finding and clinical coaching intervention impacted mortality and how this impact varied by age, gender, and deprivation status. Design Multi-site parallel prospective two-arm Randomised Controlled Trial led by Nuffield Trust and delivered by HN (Health Navigator Ltd). Patients were randomised on a 2:1 ratio to the intervention after consent and the automated and manual screening processes. Setting Secondary care-based patient identification for a community-based intervention; Eight hospital sites across England were enrolled onto the study (York, Staffordshire, Essex, and Kent). Participants Subjects aged 18 and over, who have experienced at least one emergency attendance in the preceding six months and identified as high-risk of unplanned hospitalisation via a prediction model. Subjects were also manually screened for their suitability to intervention. Intervention One-to-one telephone-based health coaching, led by registered nurses or paramedics. Primary outcome measure 24-month mortality. Results The intervention was associated with reduced overall mortality (posterior probability: 92.2%), predominantly driven by the impact for males aged 75 and over (log-rank p-value: 0.0011, Hazard Ratio (HR) [95% CI]: 0.57 [0.37, 0.84], number needed to treat: 8). Excluding one site unable to adopt the prediction model indicated stronger impact (HR [95% CI]: 0.45 [0.26, 0.76]), suggesting a role of prediction in reducing mortality. Conclusions Early mortality, specifically in elderly males, may be prevented by predicting individuals at risk of unplanned hospitalisation and supporting them with a clear outreach, out-of-hospital nurse-led, telephone-based coaching and care model. Trial registration IRAS project ID: 173319; and clinicaltrials.gov ID: 2015–000810-23 Key messages What is already known on this topic The overcrowding of emergency departments is a major global issue that has motivated the development of alternative models of care (e.g., case management interventions) to both reduce the strain on hospitals and improve health outcomes. Existing interventions, designed to reduced unplanned secondary care and improve patient outcomes, are rarely evaluated for their impact on mortality. What this study adds A large parallel multi-site randomised controlled trial, involving 1688 patients, suggested that an AI case-finding and clinical coaching intervention, can reduce mortality rates for males aged 75 and over. Excluding one site technically unable to adopt the prediction model provided stronger impact, suggesting a role of prediction in reducing mortality. How this study might affect research, practice, or policy Predicting unplanned hospitalisation using routinely collected secondary care data, and supporting at-risk patients earlier with remote, anticipatory care could help save lives, and address gender-related health inequalities.