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A Consumer Preference Approach to the Planning of Rural Primary Health-Care Facilities

Author

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  • Barnett R. Parker

    (Oakland University, Rochester, Michigan)

  • V. Srinivasan

    (Stanford University, Stanford, California)

Abstract

A problem in planning the expansion of a rural primary health-care delivery system is to determine the set of facilities to be added to an existing system so as to maximize the incremental benefit to the community subject to a cost constraint. The proposed approach involves the following five steps: (1) identification of facility attributes relevant to patients in their choice of health-care facilities, (2) modeling of an individual's overall preference for alternate facilities as a weighted linear function of these facility attributes, (3) transformation of each consumer's preference model into a benefit function expressing the individual's benefit in dollars/year for an existing or potential facility, (4) provision of a method for determining the total incremental benefit to the community from a set of proposed health-care facilities, and (5) use of a heuristic procedure to determine a set of facilities that yields near-optimum total incremental benefit subject to the cost constraint. A practical application of the proposed approach reveals that the consumer preference model has substantial reliability and predictive validity.

Suggested Citation

  • Barnett R. Parker & V. Srinivasan, 1976. "A Consumer Preference Approach to the Planning of Rural Primary Health-Care Facilities," Operations Research, INFORMS, vol. 24(5), pages 991-1025, October.
    • Handle: RePEc:inm:oropre:v:24:y:1976:i:5:p:991-1025
    DOI: 10.1287/opre.24.5.991
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    Citations

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    Cited by:

    1. Parker, Barnett R., 1995. "Ensuring a responsive health care planning function in developing regions of the world," Evaluation and Program Planning, Elsevier, vol. 18(3), pages 281-293.
    2. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    3. Stirling Bryan & Lisa Gold & Rob Sheldon & Martin Buxton, 2000. "Preference measurement using conjoint methods: an empirical investigation of reliability," Health Economics, John Wiley & Sons, Ltd., vol. 9(5), pages 385-395, July.
    4. Leonard Greenhalg & Scott A. Neslin, 1981. "Conjoint Analysis of Negotiator Preferences," Journal of Conflict Resolution, Peace Science Society (International), vol. 25(2), pages 301-327, June.
    5. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    6. Milena Pavlova & Wim Groot & Godefridus Merode, 2005. "An Application of Rating Conjoint Analysis to Study the Importance of Quality-, Access- and Price-attributes to Health Care Consumers," Economic Change and Restructuring, Springer, vol. 37(3), pages 267-286, September.
    7. Soheil Davari & Kemal Kilic & Gurdal Ertek, 2015. "Fuzzy bi-objective preventive health care network design," Health Care Management Science, Springer, vol. 18(3), pages 303-317, September.
    8. Mandy Ryan, 1996. "Using Consumer Preferences in Health Care Decision Making: The Application of Conjoint Analysis," Monograph 000420, Office of Health Economics.
    9. Teodor Pevec & Aleksandra Pisnik, 2017. "The Development And Validation Of An Evaluation System For Patients’ Assessment Of Health Services," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 26(2), pages 611-635, december.
    10. J. S. Armstrong & R. Brodie & S. McIntyre, 2005. "Forecasting Methods for Marketing:* Review of Empirical Research," General Economics and Teaching 0502023, University Library of Munich, Germany.
    11. Zhu, Wei & Timmermans, Harry, 2010. "Modeling simplifying information processing strategies in conjoint experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 764-780, July.
    12. Mandy Ryan & Jenny Hughes, 1997. "Using Conjoint Analysis to Assess Women's Preferences for Miscarriage Management," Health Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 261-273, May.
    13. Sarang Deo & Milind Sohoni, 2015. "Optimal Decentralization of Early Infant Diagnosis of HIV in Resource-Limited Settings," Manufacturing & Service Operations Management, INFORMS, vol. 17(2), pages 191-207, May.
    14. Mandy Ryan & Emma McIntosh & Phil Shackley, 1998. "Methodological issues in the application of conjoint analysis in health care," Health Economics, John Wiley & Sons, Ltd., vol. 7(4), pages 373-378, June.
    15. Zhang, Yue & Berman, Oded & Verter, Vedat, 2009. "Incorporating congestion in preventive healthcare facility network design," European Journal of Operational Research, Elsevier, vol. 198(3), pages 922-935, November.
    16. Klein, Michael G. & Verter, Vedat & Moses, Brian G., 2020. "Designing a rural network of dialysis facilities," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1088-1100.

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