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

Overview of attention for article published in Journal of Medical Internet Research, July 2019
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
1 news outlet
twitter
41 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
17 Mendeley
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Title
Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs
Published in
Journal of Medical Internet Research, July 2019
DOI 10.2196/13147
Pubmed ID
Authors

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

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 41 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 24%
Other 2 12%
Student > Bachelor 2 12%
Student > Master 2 12%
Student > Ph. D. Student 2 12%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Medicine and Dentistry 3 18%
Computer Science 2 12%
Engineering 2 12%
Social Sciences 2 12%
Psychology 1 6%
Other 1 6%
Unknown 6 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 October 2019.
All research outputs
#661,167
of 16,080,504 outputs
Outputs from Journal of Medical Internet Research
#559
of 4,346 outputs
Outputs of similar age
#18,155
of 263,186 outputs
Outputs of similar age from Journal of Medical Internet Research
#1
of 1 outputs
Altmetric has tracked 16,080,504 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,346 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,186 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them