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A decision‐making model for performance evaluation and profit sharing in a diagnostic laboratory network
Author(s) -
Ghafari Someh Niloufar,
Pishvaee Mir Saman,
Sadjadi Seyed Jafar,
Soltani Roya
Publication year - 2020
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.13336
Subject(s) - profit sharing , profit (economics) , sample (material) , business , actuarial science , marketing , operations management , computer science , microeconomics , economics , finance , chemistry , chromatography
Rationale, aims, and objectives Creating networked business models is one of the innovative approaches that have the ability and potential for meeting market needs. The purpose of this study is to provide a decision‐making model for a fair profit sharing among the members of a diagnostic laboratory network while providing a distinctive value for the patients. Methods To identify the members of the network of laboratories, a suitable approach to calculate members' efficiency scores is proposed. Then, the network members are classified into three groups based on their performance scores. The three groups help administrators identify eligible members, members who need to improve their performance in order to meet the minimum requirements, and members who do not qualify for admission to the network. Since the performance of the members should play a significant role in the fair profit‐sharing mechanism, the fair allocation of profits among network members is done by the use of Shapely value based on the efficiency scores of members. Results The results show that for such a fair mechanism, the efficiency and sample size (the number of samples [blood and urine] taken from the patients by the laboratories), as the two effective factors, have a decisive role in the share of profit of laboratory units of the network. In the Laboratory Services Network, members receive a number of samples according to their performance. As a result, the sample size received has a direct impact on the net income of each member. Conclusion In conclusion, it is evident that the use of Shapely value may help managers in the process of sharing profits among network members in a fair way, thereby improving network performance. In this way, incentive strategies may be created for the members of the network, and long‐term survival of the network may be achieved.