The role of the electronic medical record in the assessment of health related quality of life.

Serguei V.S. Pakhomov, Nilay D. Shah, Holly K. Van Houten, Penny L. Hanson, Steven A. Smith

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

We applied a hybrid Natural Language Processing (NLP) and machine learning (ML) approach (NLP-ML) to assessment of health related quality of life (HRQOL). The approach uses text patterns extracted from HRQOL inventories and electronic medical records (EMR) as predictive features for training ML classifiers. On a cohort of 200 patients, our approach agreed with patient self-report (EQ5D) and manual audit of the EMR 65-74% of the time. In an independent cohort of 285 patients, we found no association of HRQOL (by EQ5D or NLP-ML) with quality measures of metabolic control (HbA1c, Blood Pressure, Lipids). In addition; while there was no association between patient self-report of HRQOL and cost of care, abnormalities in Usual Activities and Anxiety/Depression assessed by NLP-ML were 40-70% more likely to be associated with greater health care costs. Our method represents an efficient and scalable surrogate measure of HRQOL to predict healthcare spending in ambulatory diabetes patients.

Original languageEnglish (US)
Pages (from-to)1080-1088
Number of pages9
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2011
StatePublished - 2011

ASJC Scopus subject areas

  • Medicine(all)

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