Using administrative data to measure ambulatory mental health service provision in primary care

Med Care. 2004 Oct;42(10):960-5. doi: 10.1097/00005650-200410000-00004.

Abstract

Objective: We sought to determine the accuracy of administrative data for identifying mental health service provision in primary care.

Study design: This was a chart abstraction study measuring agreement between billing data and clinical data on the binary variable "mental health visit." Data were collected from the charts and billing records of 5 academic family practice clinics in Toronto, Ontario (1999 to 2000). Billing claims (n = 952) were selected from the billings for all visits by a stratified random sampling technique. A blinded data abstractor reviewed the clinical charts and assigned diagnostic codes for each patient visit associated with the selected claims. Any visit with at least 1 abstracted mental health diagnostic code was defined as a mental health visit. The test characteristics of 4 administrative measures of mental health service provision, based on different combinations of billing codes, were calculated.

Results: The accuracy of the administrative data was 86.8% when compared with clinical data. The sensitivity of the 4 administrative measures ranged from 22.3% to 80.7%. The specificity ranged from 97.0% to 99.5%.

Conclusions: This is the first study to establish the performance of administrative data in measuring mental health service provision in a primary care setting. In our setting, broadly defined administrative measures of mental health have excellent specificity and adequate sensitivity for exploring and understanding mental health service utilization.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Ambulatory Care*
  • Confidence Intervals
  • Databases as Topic
  • Health Services Research*
  • Humans
  • Mental Disorders / diagnosis
  • Mental Health Services / statistics & numerical data*
  • Office Visits
  • Ontario
  • Primary Health Care*
  • Sampling Studies
  • Sensitivity and Specificity