Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned.

dc.contributor.author

Woo, Myung

dc.contributor.author

Alhanti, Brooke

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Lusk, Sam

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Dunston, Felicia

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Blackwelder, Stephen

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Lytle, Kay S

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Goldstein, Benjamin A

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Bedoya, Armando

dc.date.accessioned

2022-08-26T13:25:09Z

dc.date.available

2022-08-26T13:25:09Z

dc.date.issued

2020-08-27

dc.date.updated

2022-08-26T13:25:08Z

dc.description.abstract

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

dc.identifier

jpm10030104

dc.identifier.issn

2075-4426

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2075-4426

dc.identifier.uri

https://hdl.handle.net/10161/25610

dc.language

eng

dc.publisher

MDPI AG

dc.relation.ispartof

Journal of personalized medicine

dc.relation.isversionof

10.3390/jpm10030104

dc.subject

artificial intelligence

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clinical decision support

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falls

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intervention

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machine learning

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pressure injury

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prevention

dc.title

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned.

dc.type

Journal article

duke.contributor.orcid

Alhanti, Brooke|0000-0003-4243-8062

duke.contributor.orcid

Lytle, Kay S|0000-0001-9845-1501

duke.contributor.orcid

Goldstein, Benjamin A|0000-0001-5261-3632

duke.contributor.orcid

Bedoya, Armando|0000-0001-6496-7024

pubs.begin-page

E104

pubs.issue

3

pubs.organisational-group

Duke

pubs.organisational-group

School of Medicine

pubs.organisational-group

Staff

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Basic Science Departments

pubs.organisational-group

Clinical Science Departments

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Institutes and Centers

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Biostatistics & Bioinformatics

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Medicine

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Pediatrics

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Medicine, Pulmonary, Allergy, and Critical Care Medicine

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Duke Clinical Research Institute

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Population Health Sciences

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Pediatrics, Children's Health Discovery Institute

pubs.publication-status

Published

pubs.volume

10

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