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A Comparison of Methods for Capturing Patient Preferences for Delivery of Mental Health Services to Low-Income Hispanics Engaged in Primary Care

Author

Listed:
  • Patricia M. Herman

    (RAND Corporation)

  • Maia Ingram

    (University of Arizona, Zuckerman College of Public Health)

  • Charles E. Cunningham

    (McMaster University)

  • Heather Rimas

    (McMaster University)

  • Lucy Murrieta

    (Sunset Community Health Center)

  • Kenneth Schachter

    (University of Arizona, Zuckerman College of Public Health)

  • Jill Guernsey Zapien

    (University of Arizona, Zuckerman College of Public Health)

  • Scott C. Carvajal

    (University of Arizona, Zuckerman College of Public Health)

Abstract

Background Consideration of patient preferences regarding delivery of mental health services within primary care may greatly improve access and quality of care for the many who could benefit from those services. Objectives This project evaluated the feasibility and usefulness of adding a consumer-products design method to qualitative methods implemented within a community-based participatory research (CBPR) framework. Research Design Discrete-choice conjoint experiment (DCE) added to systematic focus group data collection and analysis. Subjects Focus group data were collected from 64 patients of a Federally-Qualified Health Center (FQHC) serving a predominantly low-income Hispanic population. A total of 604 patients in the waiting rooms of the FQHC responded to the DCE. Measures The DCE contained 15 choice tasks that each asked respondents to choose between three mental health services options described by the levels of two (of eight) attributes based on themes that emerged from focus group data. Results The addition of the DCE was found to be feasible and useful in providing distinct information on relative patient preferences compared with the focus group analyses alone. According to market simulations, the package of mental health services guided by the results of the DCE was preferred by patients. Conclusions Unique patterns of patient preferences were uncovered by the DCE and these findings were useful in identifying pragmatic solutions to better address the mental health service needs of this population. However, for this resource-intensive method to be adopted more broadly, the scale of the primary care setting and/or scope of the issue addressed have to be relatively large.

Suggested Citation

  • Patricia M. Herman & Maia Ingram & Charles E. Cunningham & Heather Rimas & Lucy Murrieta & Kenneth Schachter & Jill Guernsey Zapien & Scott C. Carvajal, 2016. "A Comparison of Methods for Capturing Patient Preferences for Delivery of Mental Health Services to Low-Income Hispanics Engaged in Primary Care," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 9(4), pages 293-301, August.
  • Handle: RePEc:spr:patien:v:9:y:2016:i:4:d:10.1007_s40271-015-0155-7
    DOI: 10.1007/s40271-015-0155-7
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    References listed on IDEAS

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    1. Joel Huber & Bryan Orme & Richard Miller, 2007. "Dealing with Product Similarity in Conjoint Simulations," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 17, pages 347-362, Springer.
    2. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
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