Health care management science
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Health Care Manag Sci · Dec 2019
Improving the efficiency of the operating room environment with an optimization and machine learning model.
The operating room is a major cost and revenue center for most hospitals. Thus, more effective operating room management and scheduling can provide significant benefits. In many hospitals, the post-anesthesia care unit (PACU), where patients recover after their surgical procedures, is a bottleneck. ⋯ Specifically, we use machine learning to estimate the required PACU time for each type of surgical procedure, we develop and solve two integer programming models to schedule procedures in the operating rooms to minimize maximum PACU occupancy, and we use discrete event simulation to compare our optimized schedule to the existing schedule. Using data from Lucile Packard Children's Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital.
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Health Care Manag Sci · Mar 2018
Comparative StudyComparison of emergency department crowding scores: a discrete-event simulation approach.
According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. ⋯ We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.
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Health Care Manag Sci · Sep 2017
Demographic factors influencing nonurgent emergency department utilization among a Medicaid population.
To use administrative medical encounter data to examine nonurgent emergency department (ED) utilization as it relates to member characteristics (i.e., age, gender, race/ethnicity, urbanicity and federal poverty level (FPL)). This 1 year cross-sectional study used medical claims from a managed care organization for Medicaid members enrolled from October 1, 2010 - September 30, 2011. ED encounters occurring during the study period were classified as either urgent or nonurgent using ICD-9 diagnosis codes obtained from medical claims. ⋯ This study supports the need to determine factors associated with misuse of ED services for nonurgent care. Demographic factors significantly impacting nonurgent ED utilization include gender, age, race/ethnicity, urbanicity and percent of the FPL. Results may be useful in ED utilization management efforts.
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In many hospitals there are patients who receive surgery later than what is medically indicated. In one of Europe's largest hospitals, the University Hospital Leuven, this is the case for approximately every third patient. Serving patients late cannot always be avoided as a highly utilized OR department will sometimes suffer capacity shortage, occasionally leading to unavoidable delays in patient care. ⋯ This is the case as those scheduling strategies that ensure that OR capacity is efficiently used will also result in a high number of patients served within their medically indicated time limit. An efficient use of OR capacity can be achieved, for instance, by serving patients first come, first served. As applying first come, first served might not always be possible in a real setting, we found it is important to allow for patient replanning.
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Health Care Manag Sci · Jun 2017
A stochastic model of acute-care decisions based on patient and provider heterogeneity.
The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. ⋯ Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.