ArticleHeliyon2023
Health care overbooking cost minimization model.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
0 citing papers in PubMed.
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Challenges in the health care industry have confounded the provision of quality services for patients. Among many relevant concerns, the drive for cost-effectiveness and efficient care have placed considerable pressure on public health care systems and insurance coverage, amid existing barriers to restructuring entrenched systems. This study closely examines the factors impacting disruptions in health care scheduling systems using a structured case study in the health care industry. The study introduces a novel model to identify optimal overbooking capacity and minimize no-show costs. To address the complexity of this issue on a smaller scale, the model is implemented using a private hospital clinical platform in Jeddah, Saudi Arabia, instituting an overbooking reservation and queuing system in 14 departments to investigate the factors that influence disruptions and inefficacy of service provision, while also introducing a strategy for covering costs. The results identify the maximum amount of overbooking that can be made for each clinic. The cost-saving plan developed is expected to save each clinic a considerable sum, as opposed to randomly overbooking without any cost assumptions. Overall, if the clinics studied implemented this strategy, a total loss of no more than SAR. 2, 408 would be incurred from overbooking, in contrast to the exponentially growing amount of SAR. 10,000 that is currently lost on scheduling errors per year. The loss model developed has practical application as a tool for decision-making that includes no-show cost minimization variables.
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Registered trials
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