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Model for allocation of medical specialists in a hospital network

Authors Suppapitnarm N, Pongpirul K

Received 28 February 2018

Accepted for publication 30 May 2018

Published 6 September 2018 Volume 2018:10 Pages 45—53

DOI https://doi.org/10.2147/JHL.S166944

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 3

Editor who approved publication: Professor Russell Taichman


Nantana Suppapitnarm,1,2 Krit Pongpirul1,3,4

1Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; 2Medical Affairs Office, Bangkok Dusit Medical Services Public Company Limited, Bangkok, Thailand; 3Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 4Thailand Research Center for Health Services System (TRC-HS), Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

Introduction: As human diseases are becoming increasingly complex, the need for medical specialist consultation is more pronounced, and innovative ways to allocate medical specialists in hospital networks are essential. This study aimed to construct allocation models using a multi-objective programming approach in a large private hospital network in Thailand.
Methods: Our study included 13 medical specialist types in four main disease groups of the Bangkok Dusit Medical Services network. Mixed-integer linear programming models were developed using inputs from a modified Delphi survey of executives, the Physician Engagement Survey, and the Physician Registry (PR) databases and featuring three objectives: 1) minimizing travel expense, 2) optimizing physician engagement, and 3) maximizing the chance of direct patient encounters with respective medical specialists who were formally qualified for the clinical complexity of the patients, as measured by the case mix index (CMI).
Results: The constructed models included the core components but varied by a combination of whether part-time medical specialists are included or not (noPT) and whether CMI is included (CMI) or not (noCMI). Because the noPT + CMI model had the highest capability to solve for specialist allocation, it was further improved for some specialist types in terms of flexibility for sensitivity analysis of the variables. Moreover, to assess the feasibility and practicality of the models, a web-based system incorporating the final model was developed to support the central executives’ decision to allocate medical specialists to the network, especially for finding the most optimal and timely solution for widespread shortages.
Conclusion: The linear programming models that accommodate critical components for allocating medical specialists in the hospital network were feasible and practical for the central executives’ timely decision making. The models could be further tested for their application in hospitals in the public sector or other private hospital networks.

Keywords: medical specialist, allocation, linear programming, human resource planning

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