Journal of the Operations Research Society of China ›› 2023, Vol. 11 ›› Issue (1): 29-50.doi: 10.1007/s40305-021-00377-2

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Strategic Admission Behavior and Its Implications: Evidence from a Cardiac Surgery Department

Yan-Ying Zhao1, Pei-Wen Yu2, Jian-Qiang Hu1   

  1. 1 School of Management, Fudan University, Shanghai 200433, China;
    2 School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
  • Received:2020-12-20 Revised:2021-10-06 Online:2023-03-30 Published:2023-02-28
  • Contact: Jian-Qiang Hu, Yan-Ying Zhao, Pei-Wen Yu E-mail:hujq@fudan.edu.cn;14110690021@fudan.edu.cn;ypw@cqu.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Nos. 71720107003, 72033003 and 71722008).

Abstract: This paper examines a decentralized admission control system with partial capacity sharing in a hospital setting. The admission decision is made by each physician who is assigned a number of dedicated inpatient beds. A physician can "borrow" beds from other physicians if his dedicated beds are all occupied. We seek to understand the impact of the "borrowing cost" on physicians' admission behavior.We find that(i) If the borrowing cost is low, a physician tends to admit lower-risk patients when either his or others' capacity utilization is higher; (ii) If the borrowing cost is moderate, a physician tends to admit higher (lower)-risk patients when his (others') capacity utilization is higher; and (iii) If the borrowing cost is high, a physician tends to admit higher-risk patients when either his or others' capacity utilization is higher. We then empirically test and validate these findings. Our work demonstrates that when designing strategic admission control systems, it is important to quantify and perhaps then influence the magnitude of the borrowing cost to induce a proper level of competition without sacrificing the benefit of resource pooling.

Key words: Decentralized control system, Admission policy, Beds sharing, Strategic behavior, Dynamic programming, Empirical study

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