Accession Number : ADA372083


Title :   Coding Accuracy of the Ambulatory Data System: A Study of Coding Accuracy Within the General Internal Medicine Clinic, Walter Reed Army Medical Center


Descriptive Note : Final rept. Jul 97-Jul 98


Corporate Author : WALTER REED ARMY MEDICAL CENTER WASHINGTON DC


Personal Author(s) : Gall, Daniel W


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a372083.pdf


Report Date : 17 Apr 1998


Pagination or Media Count : 74


Abstract : The Military Health system (MHS) has implemented a relatively new automated information system to help capture diagnoses, procedures, and insurance data for all ambulatory encounters. This system, recently implemented at Walter Reed Army Medical Center (WRAMC), is called the Ambulatory Data System (ADS). While the current MHS metric for ADS focuses on compliance, the quality of the data has yet to be extensively measured. Hence, the purpose of this project was to statistically analyze the data quality by studying the coding accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes, Evaluation and Management (E&M) codes, and insurance indicator codes within the General Internal Medicine Clinic (GIMC) at WRAMC. Interventions to improve the data quality were identified, developed, and implemented during the course of this project. The sensitivities, positive predictive values (PPVs), and the kappa statistics determined from random samples collected before and after the interventions were compared using Chi square analysis (alpha=05). The results showed an increase in overall non-adjusted ICD-9-CM coding rates from 60% to 67% sensitivity, 66% to 73% PPV, and kappa=.18-.36, however, the differences were not significant. E&M coding improved from a poor sensitivity rate of 21% to significantly better rate of 55%. The study also identified a poor level of accuracy concerning the capture of insurance information (kappas=.18 and .16) that conservatively indicated approximately $1.35 million dollars of missed third party collections within the past year. This study provides a model to improve the quality ADS data that may enhance the organization's ability to efficiently and effectively identify clinical process improvements, make sound resource allocation decisions, increase third party collections, and conduct outcomes research.


Descriptors :   *DATA MANAGEMENT , *ARMY FACILITIES , *MILITARY MEDICINE , OUTPATIENT CLINICS , MACHINE CODING , BIOMEDICAL INFORMATION SYSTEMS , BUSINESS PROCESS REENGINEERING , HEALTH CARE MANAGEMENT


Subject Categories : Computer Systems
      Medical Facilities, Equipment and Supplies


Distribution Statement : APPROVED FOR PUBLIC RELEASE