From Doctors to Teams: Expanding Lean ILP Models for Smarter Outpatient Capacity Planning

Abstract

Effective capacity planning in outpatient departments faces several challenges, particularly long patient waiting times and insufficient working time due to the increasing workload of senior doctors. Lean thinking, a methodology that improves efficiency by eliminating waste, has been widely adopted in healthcare operations. A recent approach integrated lean thinking with integer linear programming to improve capacity planning by optimizing resource allocation. Particularly, it reduces workload by transferring patients from senior to associate senior doctors. However, the existing model involves only doctors while neglecting other medical staff, and lacks a guarantee of balanced workload distribution after patient transfers. Therefore, incorporating additional medical staff and rebalancing the workload are required to reduce maximum working time and achieve a balanced workload among staff, respectively. In this paper, we propose two enhanced ILP-based lean thinking-enabled models, OCPlean1 and OCPlean2, to further improve planning efficiency and workload balance. OCPlean1 incorporates nurses into the model to reduce the maximum working time for the doctors by delegating certain examination services from associate senior doctors to nurses. In addition to that, OCPlean2 introduces an adaptive workload balancing strategy aimed at achieving a more equitable distribution of workload after patient transfers. Experimental results demonstrate that OCPlean1 significantly reduces maximum workload under varying conditions, including different numbers of doctors and patients, and fluctuating diagnosis and examination times. On the other hand, OCPlean2 achieves a more balanced workload distribution. These findings contribute to more efficient and sustainable outpatient scheduling models and support better utilization of healthcare personnel.