Health care management science
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Health Care Manag Sci · Sep 2020
Advancing evidence-based healthcare facility design: a systematic literature review.
Healthcare facility design is a complex process that brings together diverse stakeholders and ideally aligns operational, environmental, experiential, clinical, and organizational objectives. The challenges inherent in facility design arise from the dynamic and complex nature of healthcare itself, and the growing accountability to the quadruple aims of enhancing patient experience, improving population health, reducing costs, and improving staff work life. Many healthcare systems and design practitioners are adopting an evidence-based approach to facility design, defined broadly as basing decisions about the built environment on credible and rigorous research and linking facility design to quality outcomes. ⋯ The review identifies gaps in the existing literature and proposes solutions to advance evidence-based healthcare facility design. This work is the first of its kind to review the facility design literature across the disciplines of evidence-based healthcare design research, healthcare systems engineering, and operations research/operations management. The review suggests areas for future study that will enhance evidence-based healthcare facility designs through the integration of operations research and management science methods.
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Health Care Manag Sci · Sep 2020
COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.
Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. ⋯ Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.
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Health Care Manag Sci · Sep 2020
Prediction of emergency department patient disposition decision for proactive resource allocation for admission.
We investigate the capability of information from electronic health records of an emergency department (ED) to predict patient disposition decisions for reducing "boarding" delays through the proactive initiation of admission processes (e.g., inpatient bed requests, transport, etc.). We model the process of ED disposition decision prediction as a hierarchical multiclass classification while dealing with the progressive accrual of clinical information throughout the ED caregiving process. Multinomial logistic regression as well as machine learning models are built for carrying out the predictions. ⋯ Moreover, patients destined to intensive care unit present a more drastic increment in prediction quality at triage than others. Disposition decision classification models can provide more actionable information than a binary admission vs. discharge prediction model for the proactive initiation of admission processes for ED patients. Observing the distinct trajectories of information accrual and prediction quality evolvement for ED patients destined to different types of units, proactive coordination strategies should be tailored accordingly for each destination unit.