Optimization model for waiting list management and service continuous improvement
Optimization model for waiting list management and service continuous improvement
Índice
Purpose – This study aims to assess the number of hours*doctor required per day in each week, in a time horizon of 52 weeks, so that it is possible to gradually and controllably reduce the waiting list and the response time for the triage process that precedes the scheduling of an hospital appointment of orthopaedics speciality. A national decree law requires a response time equal or less than 5 days for the triage process, but currently, in the hospital under study, with a waiting list of 1244 users, the response time is, on average, 66 days for the speciality of orthopaedics.
Design / methodology / approach – With a team of orthopaedists (constituted with the objective of improving access to orthopaedics speciality appointments), the current status of the waiting list was analysed and possibilities for improvement were discussed. Based on the professional’s expertise, several parameters were defined as the more relevant to manage the waiting list for the triage process, allowing the development of an optimization model which aims to minimize the number of hours*doctor per day per week required to achieve the defined objectives.
Findings – The model is able to define an optimal number of hours*doctor per day per week meeting all the process constraints. Thus, it is required 1400 hours*doctor to reduce and maintain the waiting list between the boundaries defined as acceptable, as well as reduce the waiting time to 9 days. The model is also capable of orienting the professionals to search alternative optimal solutions that for specific contexts may better fill the hospital needs.
Originality / value – This study presents a tool that can support waiting lists management across any service provided by health organizations. The model ease of use allows for fast parameterization and results achievement in continuous improvement meetings.
See paper
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Operational Research, Hospitals, Continuous Improvement, Optimization Model
