Prediction of readmission in geriatric patients from clinical notes : Retrospective text mining study

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
View graph of relations

Author(s)

  • Kim Huat Goh
  • Adrian Yong Kwang Yeow
  • Yew Yoong Ding
  • Lydia Shu Yi Au
  • Hermione Mei Niang Poh
  • Ke Li
  • Joannas Jie Lin Yeow
  • Gamaliel Yu Heng Tan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article numbere26486
Journal / PublicationJournal of Medical Internet Research
Volume23
Issue number10
Online published19 Oct 2021
Publication statusPublished - Oct 2021

Link(s)

Abstract

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.

Research Area(s)

  • Artificial intelligence, Geriatrics, Psychosocial factors, Readmission risk, Text mining

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Prediction of readmission in geriatric patients from clinical notes : Retrospective text mining study. / Goh, Kim Huat; Wang, Le; Yeow, Adrian Yong Kwang et al.

In: Journal of Medical Internet Research, Vol. 23, No. 10, e26486, 10.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

Download Statistics

No data available