A Data Scientific Approach to Measure Hospital Productivity

Babak Daneshvar Rouyendegh (B. Erdebilli), Asil Oztekin (), Joseph Ekong and Ali Dag
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Babak Daneshvar Rouyendegh (B. Erdebilli): Ankara Yıldırım Beyazıt University
Asil Oztekin: University of Massachusetts Lowell
Joseph Ekong: Ohio Northern University
Ali Dag: Creighton University

Chapter Chapter 12 in Data Science and Productivity Analytics, 2020, pp 337-358 from Springer

Abstract: Abstract This study is aimed at developing a holistic data analytic approach to measure and improve hospital productivity. It is achieved by proposing a fuzzy logic-based multi-criteria decision-making model so as to enhance business performance. Data Envelopment Analysis is utilized to analyze the productivity and then it is hybridized with the Fuzzy Analytic Hierarchy Process to formulate the decision-making model. The simultaneous hybrid use of these two methods is utilized to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid methodology presents uniqueness in that it helps make the most suitable decision with the consideration of the weights determined by the data from the hybrid model.

Keywords: Data Envelopment Analysis (DEA); Data science; Analytics; Analytic Hierarchy Process (AHP); Fuzzy logic; Hospital efficiency (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-43384-0_12

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DOI: 10.1007/978-3-030-43384-0_12

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