Explaining the diffusion of medicaid home care waiver programs using VPRS decision rules

Health Care Management Science

Volumn 7, Issue 3, 2004, Pages 237-244

  Kitchener, Martin ^a   Beynon, Malcolm ^b   Harrington, Charlene ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

decision rules; long term care; Medicaid home and community based waivers; variable precision rough sets theory

Indexed keywords

ACCURACY; ARTICLE; CALCULATION; CLASSIFICATION; DATA ANALYSIS; DECISION MAKING; HEALTH CARE POLICY; HEALTH PROGRAM; HOME CARE; MEDICAID; MEDICAL RESEARCH; PREDICTION; REGRESSION ANALYSIS; STATISTICAL MODEL; VALIDATION PROCESS;

COMMUNITY HEALTH SERVICES; DATA INTERPRETATION, STATISTICAL; DECISION SUPPORT TECHNIQUES; HEALTH POLICY; HEALTH SERVICES ACCESSIBILITY; HEALTH SERVICES RESEARCH; HOME CARE SERVICES; LONG-TERM CARE; MEDICAID; PROGRAM EVALUATION; REGRESSION ANALYSIS; UNCERTAINTY;

EID: 4444364045     PISSN: 13869620     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:HCMS.0000039386.59886.dd     Document Type: Article

References (30)

1. B. Burwell, Medicaid Long Term Care Expenditures in FY 2002 (The Medstat Group, Boston, MA, 2003).
2. M. Kitchener and C. Harrington, U.S. Long-Term Care: A Dialectic Analysis of Institutional Dynamics (University of California, San Francisco, CA, 2003).
3. N. Miller, S. Ramsland, E. Goldstein and C. Harrington, Use of Medicaid 1915(c) home- and community-based care waivers to reconfigure state long-term care systems, Medical Care Research and Review 58 (2001) 100-119.
4. M. Kitchener, T. Ng and C. Harrington, Medicaid Home and Community-Based Services: Program Data, 1992-2001 (Kaiser Commission on Medicaid and the Uninsured, Washington, DC, 2003).
5. R.L. Kane, R.A. Kane, R. Ladd and W. Nielson, Variations in state spending for long term care: Factors associated with more balanced systems, Journal of Health Politics, Policy and Law 23 (1998) 363-390.
6. M. Kitchener, M. Beynon and C. Harrington, QCA and public services research: Lessons from an early application, Public Management Review 4 (2002) 485-504.
7. C. Harrington, H. Carrillo, V. Wellin, N. Miller and A. LeBlanc, Predicting state Medicaid home and community based waiver participants and participants, The Gerontologist 40 (2000) 673-686.
8. P. Domingos, The role of Occam's Razor in knowledge discovery, Data Mining and Knowledge Discovery 3 (1999) 409-425.
9. L. Breiman, Statistical modeling: The two cultures, Statistical Science 16 (2001) 199-231.
10. W. Ziarko, Variable precision rough set model, Journal of Computer and System Sciences 46 (1993) 39-59.
11. W. Ziarko, Analysis of uncertain information in the framework of variable precision rough sets, Foundations of Computing Decision Sciences 18 (1993) 381-396.
12. Z. Pawlak, Rough Sets - Theoretical Aspects of Reasoning about Data (Kluwer Academic, London, 1991).
13. Z. Pawlak, K. Slowinski and R. Slowinski, Rough classification of patients after highly selective vagotomy for duodenal ulcer, International Journal of Man-Machine Studies 24 (1986) 413-433.
14. M. Beynon and M. Peel, Variable precision rough set theory and data discrimination: An application to corporate failure prediction, OMEGA 29 (2001) 561-576.
15. M. Beynon, B. Curry and P. Morgan, Classification and rule induction using rough set theory, Expert Systems 17 (2000) 136-148.
16. Y. Yao and S. Wong, A decision theoretic framework for approximating concepts, International Journal of Man-Machine Studies 37 (1992) 793-809.
17. A. An, N. Shan, C. Chan, N. Gereone and W. Ziarko, Discovering rules for water demand prediction: An enhanced rough-set approach, Engineering Applications of Artificial Intelligence 9 (1996) 645-653.
18. M. Beynon, Reducts within the variable precision rough sets model: A further investigation, European Journal of Operational Research 134 (2001) 134-147.
19. J. Cromwell, K. Adamache, R. Khandker and C. Ammering, Programmatic, economic, and demographic forces underlying Medicaid enrollment trends, Medical Care Research and Review 45 (1997) 150-159.
20. C. Barrilleaux and M. Miller, The political economy of state Medicaid policy, American Political Science Review 82 (1988) 1089-1107.
21. S. Schneider, Examining the relationship between public policies: AFDC and Medicaid, Public Administration Review 53 (1993) 756-763.
22. S. Schneider and W. Jacoby, Influences on bureaucratic policy initiatives in the American states, Journal of Public Administration Research and Theory 6 (1996) 495-522.
23. N. Miller, S. Ramsland and C. Harrington, Trends and issues in the Medicaid 1915c waiver program, Health Care Financing Review 20 (1999) 139-150.
24. M. Chmielewski and J. Grzymala-Busse, Global discretization of continuous attributes as preprocessing for machine learning, International Journal of Approximate Reasoning 15 (1996) 319-331.
25. U. Fayyad and K. Irani, On the handling of continuous-valued attributes in decision tree generation, Machine Learning 8 (1992) 87-102.
26. S. Weiss and C. Kalikowski, Computer Systems that Learn (Morgan Kaufman, San Mateo, 1991).
27. A. Dimitras, R. Slowinski, R. Susmaga and C. Zopounidis, Business failure using rough sets, European Journal of Operational Research 114 (1999) 263-280.
28. R. Slowinski and J. Stefanowski, RoughDAS and RoughClass software implementations of the rough sets approach, in: Intelligent Decision Support. Applications and Advances of the Rough Sets Theory, ed. R. Slowinski (Kluwer Academic, London, 1992).
29. C. Browne, I. Duntsch and G. Gediga, IRIS revisited: A comparison of discriminant and enhanced rough set data analysis, in: Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, eds. L. Polkowski and A. Skowron (Physica, Heidelberg, 1998).
30. A. Getis, Some thoughts on the impact of large data sets on regional science, The Annals of Regional Science 33 (1999) 145-150.