Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study
| Autor: | Gamaliel Yu Heng Tan, Adrian Yeow, Hermione Mei Niang Poh, Lydia Shu Yi Au, Kim Huat Goh, Le Wang, Joannas Jie Lin Yeow, Ke Li, Yew Yoong Ding |
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| Přispěvatelé: | Nanyang Business School |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Population Health Informatics text mining Patient Readmission General [Business] law.invention Readmission Risk Risk Factors law Health care medicine Data Mining Humans psychosocial factors education readmission risk Aged Retrospective Studies Geriatrics Original Paper education.field_of_study geriatrics business.industry Medical record Emergency department Length of Stay artificial intelligence Intensive care unit Cohort Emergency medicine business Psychosocial |
| Zdroj: | Journal of Medical Internet Research |
| ISSN: | 1438-8871 |
| DOI: | 10.2196/26486 |
| Popis: | Background Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes. Objective We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk. Methods We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission. Results The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians. Conclusions The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction. |
| Databáze: | OpenAIRE |
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