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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 
1   Department 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
1   Department 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 
1   Department of Family, Community and Health Systems Science, School of Nursing, University at Buffalo, The State University of New York, Buffalo, New York, United States
› Author Affiliations Funding Research reported in this publication was supported, in part, by the Agency for Healthcare Research and Quality under Award number: R01 HS028000-01.
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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

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the University at Buffalo Institutional Review Board and the extracted data were determined to not qualify as human subject research.




Publication History

Received: 19 September 2022

Accepted: 20 February 2023

Accepted Manuscript online:
07 March 2023

Article published online:
24 May 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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