Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-27T07:22:14.154Z Has data issue: false hasContentIssue false
  • English
  • Français

A RUG-III Case-Mix System for Home Care

Published online by Cambridge University Press:  29 November 2010

Magnus A. Björkgren ,
Brant E. Fries  and
Lisa R. Shugarman
Magnus A. Björkgren
Affiliation:
Health Services Research Unit, Helsinki and Jyväskylä University
Brant E. Fries
Affiliation:
The University of Michigan and Ann Arbor VA Medical Center
Lisa R. Shugarman
Affiliation:
RAND Corporation
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The nursing home case-mix classification system, Resource Utilization Groups Version III (RUG-III), has been tested and refined for long-term home care clients. The study sample included 804 individuals seeking home care through the Michigan Care Management Program or the Home and Community Based Waiver for the Elderly and Disabled. Clients were classified, and RUG-III models were derived using the Minimum Data Set for Home Care (MDS-HC). A refined home care model, RUG-III/HC, was developed incorporating Instrumental Activities of Daily Living (IADLs) to the nursing home RUG-III classification. The model explained 33.7 per cent of the variance of per diem cost, using cost weighted formal and informal care as the dependent variable. Resource use within groups was relatively homogeneous. The case-mix index (CMI) of weighted formal and informal care time spanned an eight-fold range. Further analysis is suggested regarding the inclusion of informal care as a cost in case-mix classification for long-term home care clients.

Résumé

RÉSUMÉ

Le système de classification de la composition de la clientèle des maisons de soins infirmiers, Resource Utilization Groups Version III (RUG-III), a été éprouvé et raffiné pour les bénéficiaires de soins de longue durée à domicile. Lapos;échantillonnage étudié regroupe 804 personnes recevant des soins à domicile par l'entremise du Michigan Care Management Program ou du Home and Community Based Waiver for the Elderly and Disabled. On a catégorisé les clients et établi des modèles de RUG-III à partir du Minimum Data Set for Home Care (MDS-HC). On a établi un modèle raffiné de soins à domicile, RUG-III/HC, en incorporant les activités instrumentales de la vie quotidienne (AIVQ) à la classification RUG-EH des établissements de soins. Le modèle explique 33,7 pour cent de la variance des coûts quotidiens, à partir de la variable dépendante du coût pondéré des soins structurés ou non. L'utilisation des ressources à l'égard des différents groupes est relativement homogène. Le CMI (case-mix index) du temps pondéré des soins structurés ou non couvre une échelle de 8. Il faudra songer à effectuer des analyses plus poussées du coût de l'inclusion des soins non structurés à l'égard des patients recevant des soins à domicile de longue durée.

Keywords

Case-MixPatient ClassificationHome CareLong-Term CareResource UsePayment SystemsClientèleclassification des patientssoins à domicilesoins de longue duréeutilisation des ressourcessystèmes de paiement
Type
Research Article
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 19 , supplement S2 , Fall/Automne 2000 , pp. 106 - 125
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
Copyright © Canadian Association on Gerontology 2000

Footnotes

1

Institute of Gerontology, 300 N. Ingalls, Ann Arbor, MI, 48109-2009, (bfries@umich.edu)

*

Supported in part by a grant from the Michigan Public Health Institute #33-33000-189-18 to the University of Michigan (B. Pries, Principal Investigator). The findings represent the opinions of the authors, and do not represent the official policy of the State of Michigan or the Michigan Public Health Institute. Magnus Björkgren was supported in part by grants from the Yrjö Jahnsson Foundation, The University of Kuopio, and The Jyväskylä University Chydenius Institute. The authors wish to thank John Hirdes of the University of Waterloo, Department of Health Studies and Gerontology, and Mary James, of the Michigan Department of Community Health, for their valuable comments on the paper.

References

1. Fetter, RB, Brand, DA, Gammache, D. DRGs: Their Design and Development. Ann Arbor, MI: Health Administration Press, 1991.Google Scholar
2. Fries, BE, Schneider, DP, Foley, WJ, Gavazzi, M, Burke, R, Cornelius, E. Refining a case mix measure for nursing homes: Resource Utilization Groups (RUG-III). Medical Care 1994; 32(7):668–85.CrossRefGoogle Scholar
3. Foley, W, Schneider, D, Dowling, M, Fries, BE, et al. Development of a survey, case mix measurement system, and assessment instrument to rationalize the long-term care home care system. Final report, Troy, NY: Rensselaer Polytechnic Institute, School of Management, Home Care Classification Project, 1986.Google Scholar
4. Goldberg, HB, Schmitz, RJ. Contemplating home health PPS: Current patterns of medicare service use. Health Care Financing Review, Fall 1994;16(1):109–30.Google ScholarPubMed
5. Branch, LG, Goldberg, HB. A preliminary case-mix classification system for medicare home health clients. Medical Care 1993; 31(4):309–21.CrossRefGoogle ScholarPubMed
6. Coughlin, TA, McBride, TD, Perozek, M, Liu K Home care for the disabled elderly: Predictors and expected costs. Health Services Research, October 1992; 27(4): 453–79.Google ScholarPubMed
7. Phillips, BR, Brown, RS, Schore, JL et al. Home health prospective payment demonstration: Case-mix analysis using demonstration data. Report to the Health Care Financing Administration. Mathematica Policy Research, December 1992.Google Scholar
8. Saba, VK, Zuckerman, AW. Home care classification project. Final report, submitted to HCFA, Georgetown University, February 1991.Google Scholar
9. Irvine, A, Phillips, EK, Cloonan, P, Torner, JC, Fisher, ME, Chase, GA. Impact of medicare payment policy on home health resources utilization. Health Care Financing Review, Winter 1991;13(2):13–8.Google ScholarPubMed
10. Williams, BC, Phillips, EK, Torner, JC, Irvine, AA. Predicting utilization of home health resources: Important data from routinely collected information. Medical Care 1990;28:379–91.CrossRefGoogle ScholarPubMed
11. Manton, KG, Hausner, T. A multidimensional approach to case mix for home health services. Health Care Financing Review, Summer 1987;8(4):37–54.Google ScholarPubMed
12. Hirdes, John, Professor, University of Waterloo, Department of Health Studies and Gerontology, Canada, Personal correspondence, 1998.Google Scholar
13. Vladeck, BC. Statement on reforming medicare home health benefit before the House Commerce Committee Subcommittee on Health and Environment, March 5, 1997.Google Scholar
14. Welch, HG, Wennberg, DE, Welch, WP. The use of medicare home health care services. The New England Journal of Medicine, August 1 1996;335(5):324–9.CrossRefGoogle ScholarPubMed
15.Health Care Financing Administration (HCFA), Mary Duckett, Personal correspondence, 1998.Google Scholar
16. Morris, JN, Hawes, C, Fries, BE, Phillips, CD, Mor, V, Katz, S, Murphy, K, Drugovich, ML, Friedlob, AS. Designing the National Resident Assessment Instrument for Nursing Homes. Gerontologist 1990;30(3):293–307.CrossRefGoogle ScholarPubMed
17. Hawes, C, Morris, JN, Phillips, CD, Mor, V, Fries, BE, Nonemaker, S. Reliability estimates for the Minimum Data Set for Nursing Home Resident Assessment and Care Screening (MDS). The Gerontologist 1995;35(2):172–8.CrossRefGoogle Scholar
18. Sgadari, A, Morris, JN, Fries BE et al. Efforts to establish the reliability of the resident assessment instrument. Age and Ageing, 26 Suppl. 1997;2:27–31.CrossRefGoogle Scholar
19. Morris, JN, Fries, BE, Mehr, DR et al. MDS Cognitive Performance Scale. Journal of Gerontology 1994;49(3):M174–82.CrossRefGoogle ScholarPubMed
20. Morris, JN, Nonemaker, S, Murphy, K et al. A commitment to change: Revision of HCFA's RAI. J. Am. Geriatric Society 1997;45(8):1011–6.CrossRefGoogle ScholarPubMed
21. Morris, JN, Fries, BE, Steel, K. et al. Comprehensive clinical assessment in community setting: Applicability of the MDS-HC. J. Am. Geriatric Soc. 1997; 45(8):1017–24.CrossRefGoogle ScholarPubMed
22. Mor, V, Branco, K, Fleishman, J et al. The structure of social engagement among nursing home residents. J. Gerontology - Psychological Science, January 1995;50B(1):P1–8.CrossRefGoogle ScholarPubMed
23. Williams, BC, Li, Y, Fries, BE, Warren, R. Predicting patient scores between the functional independence measure and the minimum data set — Development and performance of a FIM-MDS “Crosswalk”. Archives of Physical Medicine and Rehabilitation 199;78:48–54.CrossRefGoogle Scholar
24. Blaum, CS, Fries, BE, Fiatarone, MA. Factors associated with low body mass index and weight loss in nursing home residents. J. Gerontology: Medical Sciences, May 1995;50A(3):M162–8.Google ScholarPubMed
25. Morris, JN, Fries, BE, Barnebei, R. et al. RAI Home Care (RAI-HC) Assessment Manual; Primer on use of the Minimum Data Set-Home Care (MDS-HC) and the Client Assessment Protocols (CAPs). Hebrew Rehabilitation Center for Aged, Boston, MA, 1996.Google Scholar
26. Frijters, D, Van der Kooij, C. Resource utilization groups for nursing home patients in the Netherlands. Utrecht: SIG, Dutch Centre for Health Care Information, 1991.Google Scholar
27. Ljunggren, G, Fries, BE, Winblad, U. International validation and reliability testing of a patient classification system for long-term care. European Journal of Gerontology 1992;1(6):48–59.Google Scholar
28. Ikegami, N, Fries, BE, Takagi, Y, Ikeda, S, Ibe, T. Applying RUG-III in Japanese long-term care facilities. Gerontologist 1994;34:628–39.CrossRefGoogle ScholarPubMed
29. Carpenter, IG, Main, A, Turner, G.F. Casemixfor the elderly inpatient: Resource Utilization Groups (RUGs) validation project. Age and Ageing 1995;24:5–13.CrossRefGoogle ScholarPubMed
30. Carrillo, E, Garcia-Altes, A, Peiro, S, Portella, E, et al. System for the classification of patients in mid and long-term care facilities: Resource Utilization Groups, version III. Validation in Spain, (in Spanish). Revista de Gerontologia 1996; 6(4):276–84.Google Scholar
31. Hirdes, J, Botz, CA, Kozak, J, Lepp, V. Identifying an appropriate case mix measure for chronic care: evidence from an Ontario pilot study. Health Care Management Forum, Spring 1996;9(1):40–6.CrossRefGoogle ScholarPubMed
32. Björkgren, MA, Häkkinen, U, Finne-Soveri, UH, Fries, BE. Validity and reliability of Resource Utilization Groups (RUG-III) in Finnish long-term care facilities. Scandinavian Journal of Public Health 1999;27:228–34.CrossRefGoogle ScholarPubMed
33.Michigan Home Health Association. Private Duty Field Staff Survey, November, 1997.Google Scholar
34.SAS Institute. Release 6.12. Cary, NC, USA, 1997.Google Scholar
35.Austin Data Management. PC-Group. Austin, TX; Austin Data Management, 1993.Google Scholar
36. Morgan, JN, Sonquist, JA. Problems in the analysis of survey data and a proposal. J. Am. Statist. Assoc 1963;58(415).CrossRefGoogle Scholar
37. Fetter, RB, Shin, Y, Freeman, JL et al. Case mix definition by diagnosis-related groups. Medical Care, Feb. 1980;18(2)Supp.:1–53.Google ScholarPubMed