Literature DB >> 14713742

Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management.

John S Hughes1, Richard F Averill, Jon Eisenhandler, Norbert I Goldfield, John Muldoon, John M Neff, James C Gay.   

Abstract

OBJECTIVE: To develop Clinical Risk Groups (CRGs), a claims-based classification system for risk adjustment that assigns each individual to a single mutually exclusive risk group based on historical clinical and demographic characteristics to predict future use of healthcare resources. STUDY DESIGN/DATA SOURCES: We developed CRGs through a highly iterative process of extensive clinical hypothesis generation followed by evaluation and verification with computerized claims-based databases containing inpatient and ambulatory information from 3 sources: a 5% sample of Medicare enrollees for years 1991-1994, a privately insured population enrolled during the same time period, and a Medicaid population with 2 years of data.
RESULTS: We created a system of 269 hierarchically ranked, mutually exclusive base-risk groups (Base CRGs) based on the presence of chronic diseases and combinations of chronic diseases. We subdivided Base CRGs by levels of severity of illness to yield a total of 1075 groups. We evaluated the predictive performance of the full CRG model with R2 calculations and obtained values of 11.88 for a Medicare validation data set without adjusting predicted payments for persons who died in the prediction year, and 10.88 with a death adjustment. A concurrent analysis, using diagnostic information from the same year as expenditures, yielded an R2 of 42.75 for 1994.
CONCLUSION: CRGs performance is comparable to other risk adjustment systems. CRGs have the potential to provide risk adjustment for capitated payment systems and management systems that support care pathways and case management.

Entities:  

Mesh:

Year:  2004        PMID: 14713742     DOI: 10.1097/01.mlr.0000102367.93252.70

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


  88 in total

1.  Estimates of patient costs related with population morbidity: can indirect costs affect the results?

Authors:  M Carreras; M García-Goñi; P Ibern; J Coderch; L Vall-Llosera; J M Inoriza
Journal:  Eur J Health Econ       Date:  2010-03-20

2.  Overpaying morbidity adjusters in risk equalization models.

Authors:  R C van Kleef; R C J A van Vliet; W P M M van de Ven
Journal:  Eur J Health Econ       Date:  2015-09-29

Review 3.  Condition based payment: improving care of chronic illness.

Authors:  Albert DiPiero; David G Sanders
Journal:  BMJ       Date:  2005-03-19

4.  Timing of first dental checkup for newly Medicaid-enrolled children with an intellectual or developmental disability.

Authors:  Donald L Chi; Elizabeth T Momany; Michael P Jones; Raymond Kuthy; Peter C Damiano
Journal:  Intellect Dev Disabil       Date:  2012-02

5.  The EQ-5D-5L Is Superior to the -3L Version in Measuring Health-related Quality of Life in Patients Awaiting THA or TKA.

Authors:  Xuejing Jin; Fatima Al Sayah; Arto Ohinmaa; Deborah A Marshall; Christopher Smith; Jeffrey A Johnson
Journal:  Clin Orthop Relat Res       Date:  2019-07       Impact factor: 4.176

6.  Predicting Changes in Pediatric Medical Complexity using Large Longitudinal Health Records.

Authors:  Yanbo Xu; Mohammad Taha Bahadori; Elizabeth Searles; Michael Thompson; Tejedor-Sojo Javier; Jimeng Sun
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

7.  Identifying Subgroups of Adult Superutilizers in an Urban Safety-Net System Using Latent Class Analysis: Implications for Clinical Practice.

Authors:  Deborah J Rinehart; Carlos Oronce; Michael J Durfee; Krista W Ranby; Holly A Batal; Rebecca Hanratty; Jody Vogel; Tracy L Johnson
Journal:  Med Care       Date:  2018-01       Impact factor: 2.983

8.  Regression tree boosting to adjust health care cost predictions for diagnostic mix.

Authors:  John W Robinson
Journal:  Health Serv Res       Date:  2008-04       Impact factor: 3.402

9.  Visually guided classification trees for analyzing chronic patients.

Authors:  Cristina Soguero-Ruiz; Inmaculada Mora-Jiménez; Miguel A Mohedano-Munoz; Manuel Rubio-Sanchez; Pablo de Miguel-Bohoyo; Alberto Sanchez
Journal:  BMC Bioinformatics       Date:  2020-03-11       Impact factor: 3.169

10.  Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.

Authors:  Tamara D Simon; Mary Lawrence Cawthon; Susan Stanford; Jean Popalisky; Dorothy Lyons; Peter Woodcox; Margaret Hood; Alex Y Chen; Rita Mangione-Smith
Journal:  Pediatrics       Date:  2014-05-12       Impact factor: 7.124

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.