Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs.


Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
15 07 2019
Historique:
received: 14 12 2018
accepted: 30 01 2019
revised: 29 01 2019
entrez: 2 8 2019
pubmed: 2 8 2019
medline: 24 4 2020
Statut: epublish

Résumé

The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response. We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients. We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis. The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P<.001), DID analysis with the comparator site was not significant (OR 1.13, 95% CI 0.63-1.99; P=.69). There was no impact on other secondary clinical outcomes. Mean health care costs per patient were reduced by £2123 (95% CI -£4024 to -£222; P=.03), not including costs of providing the technology. The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites.

Sections du résumé

BACKGROUND
The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response.
OBJECTIVE
We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients.
METHODS
We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis.
RESULTS
The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P<.001), DID analysis with the comparator site was not significant (OR 1.13, 95% CI 0.63-1.99; P=.69). There was no impact on other secondary clinical outcomes. Mean health care costs per patient were reduced by £2123 (95% CI -£4024 to -£222; P=.03), not including costs of providing the technology.
CONCLUSIONS
The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites.

Identifiants

pubmed: 31368447
pii: v21i7e13147
doi: 10.2196/13147
pmc: PMC6693300
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e13147

Informations de copyright

©Alistair Connell, Rosalind Raine, Peter Martin, Estela Capelas Barbosa, Stephen Morris, Claire Nightingale, Omid Sadeghi-Alavijeh, Dominic King, Alan Karthikesalingam, Cían Hughes, Trevor Back, Kareem Ayoub, Mustafa Suleyman, Gareth Jones, Jennifer Cross, Sarah Stanley, Mary Emerson, Charles Merrick, Geraint Rees, Hugh Montgomery, Christopher Laing. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.07.2019.

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Auteurs

Alistair Connell (A)

Centre for Human Health and Performance, University College London, London, United Kingdom.
DeepMind Health, London, United Kingdom.

Rosalind Raine (R)

Department of Applied Health Research, University College London, London, United Kingdom.

Peter Martin (P)

Department of Applied Health Research, University College London, London, United Kingdom.

Estela Capelas Barbosa (EC)

Department of Applied Health Research, University College London, London, United Kingdom.

Stephen Morris (S)

Department of Applied Health Research, University College London, London, United Kingdom.

Claire Nightingale (C)

Department of Applied Health Research, University College London, London, United Kingdom.
Population Health Research Institute, St George's, University of London, London, United Kingdom.

Omid Sadeghi-Alavijeh (O)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Dominic King (D)

DeepMind Health, London, United Kingdom.

Alan Karthikesalingam (A)

DeepMind Health, London, United Kingdom.

Cían Hughes (C)

DeepMind Health, London, United Kingdom.

Trevor Back (T)

DeepMind Health, London, United Kingdom.

Kareem Ayoub (K)

DeepMind Health, London, United Kingdom.

Mustafa Suleyman (M)

DeepMind Health, London, United Kingdom.

Gareth Jones (G)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Jennifer Cross (J)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Sarah Stanley (S)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Mary Emerson (M)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Charles Merrick (C)

Royal Free London NHS Foundation Trust, London, United Kingdom.

Geraint Rees (G)

Faculty of Life Sciences, University College London, London, United Kingdom.

Hugh Montgomery (H)

Centre for Human Health and Performance, University College London, London, United Kingdom.

Christopher Laing (C)

Royal Free London NHS Foundation Trust, London, United Kingdom.

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Classifications MeSH