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

Machine Learning death patient support urgent care utilisation

Journal

Emergency medicine journal : EMJ
ISSN: 1472-0213
Titre abrégé: Emerg Med J
Pays: England
ID NLM: 100963089

Informations de publication

Date de publication:
12 Oct 2023
Historique:
received: 14 10 2022
accepted: 03 09 2023
medline: 13 10 2023
pubmed: 13 10 2023
entrez: 12 10 2023
Statut: aheadofprint

Résumé

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. 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. 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)). 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.

Sections du résumé

BACKGROUND 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 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 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 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.

Identifiants

pubmed: 37827821
pii: emermed-2022-212908
doi: 10.1136/emermed-2022-212908
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: LMB, BA and SR are employed by Health Navigator as data scientists. AN is the head of analytics at Health Navigator. JW is the founder and chair of Health Navigator. Contractual agreements were in place between Health Navigator and East Kent Hospitals University NHS Foundation Trust for the initial data extraction and analysis by TL-B. Contractual agreements were in place between Health Navigator and Nuffield Trust for the design and oversight of the RCT analysis by CS-J. MS declared no conflicts of interest.

Auteurs

Lucy M Bull (LM)

Modelling and Insights, Health Navigator, London, UK lucy.bull@hn-company.co.uk.

Bartlomiej Arendarczyk (B)

Modelling and Insights, Health Navigator, London, UK.

Sara Reis (S)

Modelling and Insights, Health Navigator, London, UK.

An Nguyen (A)

Data Science and Strategy, Health Navigator, London, UK.

Joachim Werr (J)

Founder and Chair, Health Navigator, London, UK.

Thomas Lovegrove-Bacon (T)

Strategic Development, East Kent Hospitals University NHS Foundation Trust, Canterbury, UK.

Mark Stone (M)

North Place Clinical Lead, Staffordshire and Stoke ICB, Stafford, UK.

Christopher Sherlaw-Johnson (C)

Quality and Performance, Nuffield Trust, London, UK.

Classifications MeSH