ArticleProduction and operations management2019
Integrated Scheduling and Capacity Planning with Considerations for Patients' Length-of-Stays.
Article in Production and operations management, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 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
- An optimisation framework for resource allocation in palliative and end-of-life care.Scientific reports · 2026Article
- Analytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring.Healthcare (Basel, Switzerland) · 2024Article
- Article
- Cope with the COVID-19 pandemic: Dynamic bed allocation and patient subsidization in a public healthcare system.International journal of production economics · 2022Article
- Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations.Annals of operations research · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Motivated by the shortcoming of current hospital scheduling and capacity planning methods which often model different units in isolation, we introduce the first dynamic multi-day scheduling model that integrates information about capacity usage at more than one location in a hospital. In particular, we analyze the first dynamic model that accounts for patients' length-of-stay and downstream census in scheduling decisions. Via a simple and innovative variable transformation, we show that the optimal number of patients to be allowed in the system is increasing in the state of the system and in the downstream capacity. Moreover, the total system cost exhibits decreasing marginal returns as the capacity increases at any location independently of another location. Through numerical experiments on realistic data, we show that there is substantial value in making integrated scheduling decisions. In contrast, localized decision rules that only focus on a single location of a hospital can result in up to 60% higher expenses.
Identifiers
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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.