title: The role of the electronic medical record in the assessment of health related quality of life.
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.
authors.affiliation: Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Serguei V S
authors.initials: SV
authors.lastname: Pakhomov
authors.forename: Nilay D
authors.initials: ND
authors.lastname: Shah
authors.forename: Holly K
authors.initials: HK
authors.lastname: Van Houten
authors.forename: Penny L
authors.initials: PL
authors.lastname: Hanson
authors.forename: Steven A
authors.initials: SA
authors.lastname: Smith
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010044:Outpatient Clinics, Hospital; D010363:Pattern Recognition, Automated; D011320:Primary Health Care; D011788:Quality of Life