Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1186/s12904-022-01113-0
Background As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. Methods 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. Discussion This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. Trial registration Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. Protocol v0.5, dated 9/23/2020
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12904-022-01113-0
- https://bmcpalliatcare.biomedcentral.com/counter/pdf/10.1186/s12904-022-01113-0
- OA Status
- gold
- Cited By
- 8
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319081120
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319081120Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12904-022-01113-0Digital Object Identifier
- Title
-
Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trialWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-03Full publication date if available
- Authors
-
Ethan P. Heinzen, Patrick M. Wilson, Curtis B. Storlie, Gabriel Demuth, Shusaku Asai, Gavin M. Schaeferle, Mairead M. Bartley, Rachel D. HavyerList of authors in order
- Landing page
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https://doi.org/10.1186/s12904-022-01113-0Publisher landing page
- PDF URL
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https://bmcpalliatcare.biomedcentral.com/counter/pdf/10.1186/s12904-022-01113-0Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bmcpalliatcare.biomedcentral.com/counter/pdf/10.1186/s12904-022-01113-0Direct OA link when available
- Concepts
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Palliative care, Medicine, Randomized controlled trial, Population, Primary care, Curative care, Nursing, Family medicine, Ambulatory care, Health care, Internal medicine, Environmental health, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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