Ambulatory care groups: a categorization of diagnoses for research and management

Published: April 1, 1991
Category: Papers
Authors: Mumford L, Starfield B, Steinwachs D, Weiner J
Country: United States
Language: null
Type: Care Management
Setting: Academic

Health Serv Res 26:53-74.

Department of Health Policy and Management, Johns Hopkins School of Hygiene and Public Health, Baltimore, MD, USA

This article describes a case-mix measure for application in ambulatory populations. The method is based primarily on categorization of diagnoses according to their likelihood of persistence. Fifty-one combinations (the ambulatory care groups or ACGs) result from applying multivariate techniques to maximize variance explained in use of services and ambulatory care charges. The method is tested in four different HMOs and a large Medicaid population. The percentage of the population in each of the 51 categories is similar across the HMOs; the Medicaid population has higher burdens of morbidity as measured by more numerous types of diagnoses. Mean visit rates for individuals within each of the 51 morbidity categories are generally similar across the five facilities, but these visit rates vary markedly from one category to another, even within groupings that are similar in the number of types of diagnoses within them. Visit rates for individuals who stay in the same ACG were similar from one year to the next. The ACG system is found useful in predicting both concurrent and subsequent ambulatory care use and charges as well as subsequent morbidity. It provides a way to specify case mix in enrolled populations for research as well as administration and reimbursement for ambulatory care.

PMID: 1901841
PMCID: PMC1069810

Overall Morbidity Burden,Case Management,Diagnostic Certainty,United States,Adolescent,Adult,Aged,Baltimore,Boston,Child,Preschool,Gender,Infant,Newborn,Likelihood Functions,Los Angeles,Maryland,Medicaid/statistics & numerical data,Middle Aged,Minnesota,Morbidity,Multivariate Analysis,Predictive Value of Tests,Regression Analysis,Reproducibility of Results

Please log in/register to access.

Log in/Register

LinkedIn Facebook Twitter

© The Johns Hopkins University, The Johns Hopkins Hospital, and Johns Hopkins Health System.
All rights reserved. Terms of Use Privacy Statement

Back to top