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Abstract
Identification of competitive healthcare providers is an important issue for the successful operation of a bundled payment reimbursement program. We develop a healthcare provider selection framework via data envelopment analysis (DEA) and combinatorial auction (CA). Our goal is to cover target regions with adequate numbers of healthcare providers to optimally deploy a bundled payment program across these regions. Our methodology balances bid prices and performance of applicants to cover the entire regions in an equitable manner, allows for provider preferences in selecting the bundle of services, and determines winners considering service quality, efficiency and the price of the bundles. Our work provides a practical and systematic selection procedure for payers compared to the extant subjective selection methods.
Recommended Citation
Youn, Seokjun; Agrawal, Anupam; Kumar, Subodha; and Sriskandarajah, Chelliah, "Provider Selection Framework for Bundled Payments in Healthcare Services" (2020). AMCIS 2020 Proceedings. 38.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/38
Provider Selection Framework for Bundled Payments in Healthcare Services
Identification of competitive healthcare providers is an important issue for the successful operation of a bundled payment reimbursement program. We develop a healthcare provider selection framework via data envelopment analysis (DEA) and combinatorial auction (CA). Our goal is to cover target regions with adequate numbers of healthcare providers to optimally deploy a bundled payment program across these regions. Our methodology balances bid prices and performance of applicants to cover the entire regions in an equitable manner, allows for provider preferences in selecting the bundle of services, and determines winners considering service quality, efficiency and the price of the bundles. Our work provides a practical and systematic selection procedure for payers compared to the extant subjective selection methods.
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