ArticleHealth care management science2021
A robust optimization model for tactical capacity planning in an outpatient setting.
Article in Health care management science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing patient flow logistics: strategic challenges, tactical solutions, and future directions.BMC health services research · 2025Pooled it
- Data-driven identification of outpatient-suitable procedures: a machine learning approach.Health care management science · 2026Article
- A Scheduling Optimization Approach to Reduce Outpatient Waiting Times for Specialists.Healthcare (Basel, Switzerland) · 2025Article
- Optimizing neurosurgery clinic operations: a comparative study of interventions in Finland's public healthcare system.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
- Research on outpatient capacity planning combining lean thinking and integer linear programming.BMC medical informatics and decision making · 2023Article
- Mind the gap: a review of optimisation in mental healthcare service delivery.Health systems (Basingstoke, England) · 2023Review
- A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources.Health care management science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tactical capacity planning is a key element of planning and control decisions in healthcare settings, focusing on the medium-term allocation of a clinic's resources to appointments of different types. One of the most scarce resources in healthcare is physician time. Due to uncertainty in demand for appointments, it is difficult to provide an exact match between the planned physician availability and appointment requests. Our study uses cardinality-constrained robust optimization to develop tactical capacity plans which are robust against uncertainty, providing a feasible allocation of capacity for all realizations of demand to the extent allowed by the budget of uncertainty. The outpatient setting we consider sees first-visit patients and re-visit patients, and both patient types have access time targets. We experimentally evaluate our robust model and its practical implications under different levels of conservatism. We show that we can guarantee 100% feasibility of the robust tactical capacity plan while not being fully conservative, which will lead to the clinic saving money while being able to meet demand despite uncertainty. We also show how the robust model helps us to identify the critical time periods leading to worst case physician peak load, which could be valuable to decision-makers. Throughout the experiments, we find that the step of translating available data into an uncertainty set can influence the true conservatism of a solution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.