A data mining approach to characterizing medical code usage patterns.
This research describes a synthetic data mining approach to identifying diagnostic (ICD-9) and procedure (CPT) code usage patterns in two US. hospitals, with the goal of determining the adequacy and effectiveness of the current coding classification systems. We combine relative frequency measurements with measures of industry concentration borrowed from industrial economics in order to (1) ascertain the extent to which physicians utilize the available codes in classifying patients and (2) discover the factors that impinge on code usage. Our results partition the domain into areas for which the coding systems perform well and those areas for which the systems perform relatively poorly. The goal is to use this approach to understand how coding systems are used and to highlight areas for targeted improvement of the current coding
Spangler
William E
WE
J. F. Donahue Graduate School of Business, Duquesne University, Pittsburgh, Pennsylvania 15282, USA. spangler@duq.edu
May
Jerrold H
JH
Strum
David P
DP
Vargas
Luis G
LG
D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D003657:Decision Making; D004194:Disease / Q000145:classification*; D005161:Facility Regulation and Control; D005568:Forms and Records Control / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data*; D006302:Health Services Research; D006761:Hospitals / Q000145:classification; D006801:Humans; D007344:Insurance Claim Reporting; D008499:Medical Records / Q000145:classification*; D008511:Medicine / Q000145:classification; D015203:Reproducibility of Results; D013038:Specialization; D013812:Therapeutics / Q000145:classification*; D014481:United States