Evidence map›Paper›PMID 31368447›Full record

ArticleJournal of medical Internet research2019

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

Alistair Connell, Rosalind Raine, Peter Martin, Estela Capelas Barbosa, Stephen Morris, Claire Nightingale, Omid Sadeghi-Alavijeh, Dominic King, Alan Karthikesalingam, Cían Hughes and 11 more

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.

  1. Pooled it
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  9. Good Comes From Evil: COVID-19 and the Advent of Telemedicine in Orthopedics.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors at 5 institutions in 1 country.

Alistair ConnellCentre for Human Health and Performance, University College London, London, United Kingdom.ORCID 0000-0003-0318-1049
Rosalind RaineDepartment of Applied Health Research, University College London, London, United Kingdom.ORCID 0000-0003-0904-749X
Peter MartinDepartment of Applied Health Research, University College London, London, United Kingdom.ORCID 0000-0003-4638-0638
Estela Capelas BarbosaDepartment of Applied Health Research, University College London, London, United Kingdom.ORCID 0000-0002-7621-7957
Stephen MorrisDepartment of Applied Health Research, University College London, London, United Kingdom.ORCID 0000-0002-5828-3563
Claire NightingaleDepartment of Applied Health Research, University College London, London, United Kingdom.ORCID 0000-0002-4803-7617
Omid Sadeghi-AlavijehRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0002-7753-0662
Dominic KingDeepMind Health, London, United Kingdom.ORCID 0000-0002-1898-842X
Alan KarthikesalingamDeepMind Health, London, United Kingdom.ORCID 0000-0001-5074-898X
Cían HughesDeepMind Health, London, United Kingdom.ORCID 0000-0001-6901-0985
Trevor BackDeepMind Health, London, United Kingdom.ORCID 0000-0002-0567-8043
Kareem AyoubDeepMind Health, London, United Kingdom.ORCID 0000-0002-4147-3790
Mustafa SuleymanDeepMind Health, London, United Kingdom.ORCID 0000-0002-5415-4457
Gareth JonesRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0003-4601-1269
Jennifer CrossRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0001-9325-2773
Sarah StanleyRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0002-9427-7152
Mary EmersonRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0003-1835-7561
Charles MerrickRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0002-2122-3831
Geraint ReesFaculty of Life Sciences, University College London, London, United Kingdom.ORCID 0000-0002-9623-7007
Hugh Montgomery *Centre for Human Health and Performance, University College London, London, United Kingdom.ORCID 0000-0001-8797-5019
Christopher Laing *Royal Free London NHS Foundation Trust, London, United Kingdom.ORCID 0000-0003-2514-5823
Google DeepMind (United Kingdom) · GBRoyal Free London NHS Foundation Trust · GBUniversity College London · GBFaculty (United Kingdom) · GBSt George's, University of London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

objectiveWe 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.

methodsWe 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Delivery of Health CareFemaleHumansMaleTelemedicineTreatment Outcomeacute kidney injurynephrology

Identifiers

PMID31368447
PMCPMC6693300
OpenAlexW2989076512

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.