Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

Sharon Hewner; Erica Smith; Suzanne S. Sullivan

doi:10.1055/a-2048-7343

Applied Clinical Informatics, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(03): 408-417
DOI: 10.1055/a-2048-7343

CIC 2022

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

Sharon Hewner

¹Department of Family, Community and Health Systems Science, School of Nursing, University at Buffalo, The State University of New York, Buffalo, New York, United States

,

Erica Smith

¹Department of Family, Community and Health Systems Science, School of Nursing, University at Buffalo, The State University of New York, Buffalo, New York, United States

,

Suzanne S. Sullivan

¹Department of Family, Community and Health Systems Science, School of Nursing, University at Buffalo, The State University of New York, Buffalo, New York, United States

› Institutsangaben

Abstract

Background Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors.

Objectives This study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge.

Methods A primary care practice dataset (N = 3,438) of high-need patients defined by practice criteria was parsed to a subset population of patients with diabetes (n = 1233). Three expert nurses selected variables for k-means cluster analysis using knowledge of critical factors for care coordination. Nursing knowledge was again applied to describe the psychosocial phenotypes in four prominent clusters, aligned with social and medical care plans.

Results Four distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure.

Conclusion This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.

Keywords

social determinants of health - phenotypes - primary care - nursing - machine learning - care coordination

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Referenzen

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