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Predicting dentists decisions: a choice-based conjoint analysis of Medicaid participation

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RES_88_JOURNAL_paper_4.pdf (173.6Kb)
Date
2015-11-17
Author
Kateeb, Elham T.
McKernan, Susan C.
Gaeth, Gary J.
Kuthy, Raymond A.
Adrianse, Nancy B.
Damiano, Peter C.
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Abstract
Objectives: Private practice dentists are the major source of care for the dental safety net; however, the proportion of dentists who participate in state Medicaid programs is low, often due to poor perceptions of the program’s administration and patient population. Using a discrete choice experiment and a series of hypothetical scenarios, this study evaluated trade-offs dentists make when deciding to accept Medicaid patients. Methods: An online choice-based conjoint survey was sent to 272 general dentists in Iowa. Hypothetical scenarios presented factors at systematically varied levels. The primary determination was whether dentists would accept a new Medicaid patient in each scenario. Using an ecological model of behavior, determining factors were selected from the categories of policy, administration, community, and patient population to estimate dentists’ relative preferences. Results: 62 percent of general dentists responded to the survey. The probability of accepting a new Medicaid patient was highest (81 percent) when reimbursement rates were 85 percent of the dentist’s fees, patients never missed appointments, claims were approved on first submission, and no other practices in the area accepted Medicaid. Although dentists preferred higher reimbursement rates, 56 percent would still accept a new Medicaid patient when reimbursement decreased to 55 percent if they were told that the patient would never miss appointments and claims would be approved on initial submission. Conclusions: This study revealed trade-offs that dentists make when deciding to participate in Medicaid. Findings indicate that states can potentially improve Medicaid participation without changing reimbursement rates by making improvements in claims processing and care coordination to reduce missed appointments.
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https://dspace.alquds.edu/handle/20.500.12213/834
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