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

Abstract

Background: Health coaching services could help to reduce emergency healthcare utilisation for patients targeted proactively by a clinical prediction model (CPM) predicting patient likelihood of future hospitalisations. Such interventions are designed to empower patients to confidently manage their own health and effectively utilise wider resources. Using CPMs to identify patients, rather than prespecified criteria, accommodates for the dynamic hospital user population and for sufficient time to provide preventative support. However, it is unclear how this care model would negatively impact survival.

Methods: Emergency Department (ED) attenders and hospital inpatients between 2015 and 2019 were automatically screened for their risk of hospitalisation within 6 months of discharge using a locally trained CPM on routine data. Those considered at risk and screened as suitable for the intervention were contacted for consent and randomised to one-to-one telephone health coaching for 4-6 months, led by registered health professionals, or routine care with no contact after randomisation. The intervention involved motivational guidance, support for self-care, health education, and coordination of social and medical services. Co-primary outcomes were emergency hospitalisation and ED attendances, which will be reported separately. Mortality at 24 months was a safety endpoint.

Results: Analysis among 1688 consented participants (35% invitation rate from the CPM, median age 75 years, 52% female, 1139 intervention, 549 control) suggested no significant difference in overall mortality between treatment groups (HR (95% CI): 0.82 (0.62, 1.08), pr(HR<1=0.92), but did suggest a significantly lower mortality in men aged >75 years (HR (95% CI): 0.57 (0.37, 0.84), number needed to treat=8). Excluding one site unable to adopt a CPM indicated stronger impact for this patient subgroup (HR (95% CI): 0.45 (0.26, 0.76)).

Conclusions: Early mortality in men aged >75 years may be reduced by supporting individuals at risk of unplanned hospitalisation with a clear outreach, out-of-hospital nurse-led, telephone-based coaching care model.

Keywords: Machine Learning; death; patient support; urgent care; utilisation.