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Development and Validation of a Predictive Algorithm to Identify Adult Asthmatics from Medical Services and Pharmacy Claims Databases
Author(s) -
Kawasumi Yuko,
Abrahamowicz Michal,
Ernst Pierre,
Tamblyn Robyn
Publication year - 2011
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2010.01235.x
Subject(s) - medical prescription , asthma , medicine , pharmacy , medical record , predictive value , gold standard (test) , database , electronic health record , positive predicative value , health care , medline , family medicine , machine learning , algorithm , data mining , computer science , law , political science , economics , pharmacology , economic growth
Objective. To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases. Study Setting. An existing electronic medical record project in Montreal, Quebec. Study Design. One thousand four hundred and thirty‐one patients with confirmed asthma status were identified from primary care physician's electronic medical record. Data Collection/Extraction Methods. Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma‐specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross‐validation evaluated the diagnostic ability of each predictive model using 50 percent of sample. Principal Findings. The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best‐fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims–specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined. Conclusions. Our algorithm using asthma‐specific markers from administrative claims databases provided moderate sensitivity and high specificity.