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

Connell, A; Raine, R; Martin, P; Barbosa, EC; Morris, S; Nightingale, C; Sadeghi-Alavijeh, O; King, D; Karthikesalingam, A; Hughes, C; et al. Connell, A; Raine, R; Martin, P; Barbosa, EC; Morris, S; Nightingale, C; Sadeghi-Alavijeh, O; King, D; Karthikesalingam, A; Hughes, C; Back, T; Ayoub, K; Suleyman, M; Jones, G; Cross, J; Stanley, S; Emerson, M; Merrick, C; Rees, G; Montgomery, H; Laing, C (2019) Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs. J Med Internet Res, 21 (7). e13147. ISSN 1438-8871 https://doi.org/10.2196/13147
SGUL Authors: Nightingale, Claire

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Abstract

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.

Item Type: Article
Additional Information: © 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Keywords: acute kidney injury, nephrology, 08 Information And Computing Sciences, 11 Medical And Health Sciences, 17 Psychology And Cognitive Sciences, Medical Informatics
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: J Med Internet Res
ISSN: 1438-8871
Language: eng
Dates:
DateEvent
15 July 2019Published
30 January 2019Accepted
Publisher License: Creative Commons: Attribution 4.0
PubMed ID: 31368447
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/111225
Publisher's version: https://doi.org/10.2196/13147

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