Using applied machine learning to predict healthcare utilization based on socioeconomic determinants of care
| Autor: | Soy Chen, Allison Kavanagh, John Showalter, Kelly Miller, John Frownfelter, Danielle Bergman |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Risk Georgia Adolescent Social Determinants of Health MEDLINE Decision tree Psychological intervention Machine learning computer.software_genre Affect (psychology) Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine Medicine Humans 030212 general & internal medicine Social determinants of health Child Socioeconomic status Aged Ohio business.industry Health Policy Decision Trees Infant Emergency department Middle Aged Patient Acceptance of Health Care Outreach Hospitalization Socioeconomic Factors 030220 oncology & carcinogenesis Child Preschool Alabama Female Artificial intelligence business Emergency Service Hospital computer |
| Zdroj: | The American journal of managed care. 26(1) |
| ISSN: | 1936-2692 |
| Popis: | Objectives To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. Study design The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States. Methods A hold-out methodology was used to ensure that the model's performance could be tested on a completely independent set of subjects. A proprietary decision tree methodology was used to make the predictions. Only the socioeconomic features-age group, gender, and race-were used in the prediction of a patient's risk of admission. Results The decision tree-based machine learning approach analyzed in this study was able to predict inpatient and emergency department utilization with a high degree of discrimination using only purchasable and publicly available data on SDH. Conclusions This study indicates that it is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the patient's age, gender, race, and address. The implications of this application are wide and have the potential to positively affect health systems by facilitating targeted patient outreach with specific, individualized interventions to tackle detrimental SDH at not only the individual level but also the neighborhood level. |
| Databáze: | OpenAIRE |
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