Literature DB >> 1902278

Development and application of a population-oriented measure of ambulatory care case-mix.

J P Weiner1, B H Starfield, D M Steinwachs, L M Mumford.   

Abstract

This article describes a new case-mix methodology applicable primarily to the ambulatory care sector. The Ambulatory Care Group (ACG) system provides a conceptually simple, statistically valid, and clinically relevant measure useful in predicting the utilization of ambulatory health services within a particular population group. ACGs are based on a person's demographic characteristics and their pattern of disease over an extended period of time, such as a year. Specifically, the ACG system is driven by a person's age, sex, and ICD-9-CM diagnoses assigned during patient-provider encounters; it does not require any special data beyond those collected routinely by insurance claims systems or encounter forms. The categorization scheme does not depend on the presence of specific diagnoses that may change over time; rather it is based on broad clusters of diagnoses and conditions. The presence or absence of each disease cluster, along with age and sex, are used to classify a person into one of 51 ACG categories. The ACG system has been developed and tested using computerized encounter and claims data from more than 160,000 continuous enrollees at four large HMOs and a state's Medicaid program. The ACG system can explain more than 50% of the variance in ambulatory resource use if used retrospectively and more than 20% if applied prospectively. This compares with 6% when age and sex alone are used. In addition to describing ACG development and validation, this article also explores some potential applications of the system for provider payment, quality assurance, utilization review, and health services research, particularly as it relates to capitated settings.

Entities:  

Mesh:

Year:  1991        PMID: 1902278     DOI: 10.1097/00005650-199105000-00006

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  224 in total

1.  Risk adjusting capitation: applications in employed and disabled populations.

Authors:  C W Madden; B P Mackay; S M Skillman; M Ciol; P K Diehr
Journal:  Health Care Manag Sci       Date:  2000-02

2.  [The computerization of primary care].

Authors:  F A Alonso López; C J Cristos; A Brugos Larumbe; F García Molina; L Sánchez Perruca; A Guijarro Eguskizaga; A Ruiz Téllez; M Medina Peralta; F A Alonso López
Journal:  Aten Primaria       Date:  2000-11-15       Impact factor: 1.137

3.  Variations in primary care physician referral rates.

Authors:  P Franks; J Zwanziger; C Mooney; M Sorbero
Journal:  Health Serv Res       Date:  1999-04       Impact factor: 3.402

4.  Risk adjustment alternatives in paying for behavioral health care under Medicaid.

Authors:  S L Ettner; R G Frank; T G McGuire; R C Hermann
Journal:  Health Serv Res       Date:  2001-08       Impact factor: 3.402

Review 5.  Use of risk adjustment in setting budgets and measuring performance in primary care I: how it works.

Authors:  A Majeed; A B Bindman; J P Weiner
Journal:  BMJ       Date:  2001-09-15

6.  Assessing population health care need using a claims-based ACG morbidity measure: a validation analysis in the Province of Manitoba.

Authors:  Robert J Reid; Noralou P Roos; Leonard MacWilliam; Norman Frohlich; Charlyn Black
Journal:  Health Serv Res       Date:  2002-10       Impact factor: 3.402

7.  Impact of a high-deductible health plan on outpatient visits and associated diagnostic tests.

Authors:  Sheila R Reddy; Dennis Ross-Degnan; Alan M Zaslavsky; Stephen B Soumerai; James F Wharam
Journal:  Med Care       Date:  2014-01       Impact factor: 2.983

8.  Effect of risk adjustment method on comparisons of health care utilization between complementary and alternative medicine users and nonusers.

Authors:  Bonnie K Lind; Mary M Gerkovich; Daniel C Cherkin; Richard A Deyo; Karen J Sherman; William E Lafferty
Journal:  J Altern Complement Med       Date:  2012-10-04       Impact factor: 2.579

9.  Diagnostic cost groups (DCGs) and concurrent utilization among patients with substance abuse disorders.

Authors:  Amy K Rosen; Susan A Loveland; Jennifer J Anderson; Cheryl S Hankin; James N Breckenridge; Dan R Berlowitz
Journal:  Health Serv Res       Date:  2002-08       Impact factor: 3.402

10.  [Use of resources and costs profile in patients with fibromyalgia or generalized anxiety disorder in primary care settings].

Authors:  Antoni Sicras-Mainar; Milagrosa Blanca-Tamayo; Ruth Navarro-Artieda; Javier Rejas-Gutiérrez
Journal:  Aten Primaria       Date:  2009-02-03       Impact factor: 1.137

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