Timing it Right: Balancing Inpatient Congestion Versus Readmission Risk at Discharge

In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.

Section: Neural Network Model For Prediction Of Icu Bed Occupancymentioning

confidence: 94%

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

Schiele

Koperna

Brunner

2020

“…In particular, the health economics literature has established a tradeoff between the patient's LOS and readmission rates: for the same patient, a greater LOS is associated with a lower readmission rate, and vice versa (Carey 2015). 8 Broadly aligned with this literature, a number of recent healthcare operations management papers (e.g., Guo et al 2019, Shi et al 2019 explicitly assume increasing the LOS will lead to a reduction in readmissions.…”

Section: Research Questionsmentioning

confidence: 99%

Equity in genetic newborn screening

Hajj

Bish

2019

“…In particular, the health economics literature has established a tradeoff between the patient's LOS and readmission rates: for the same patient, a greater LOS is associated with a lower readmission rate, and vice versa (Carey, 2015). Broadly aligned with this literature, a number of recent healthcare operations management papers (eg, Guo, Tang, Wang, & Zhao, 2019; Shi, Helm, Deglise‐Hawkinson, & Pan, 2019) explicitly assume increasing the LOS will lead to a reduction in readmissions.…”

Section: Heart Failurementioning

confidence: 99%

Heart analytics: Analytical modeling of cardiovascular care

Dai

Gleason

Hwang

et al. 2019

Cardiovascular diseases (CVDs) are the leading cause of death and disability both in the United States and worldwide. Despite high morbidity, mortality, and cost in the United States and global healthcare systems, cardiovascular care has been understudied in the healthcare operations management literature. In this paper, we identify research opportunities for healthcare operations management scholars to aid in improving cardiovascular care. We focus on three burdensome conditions, including (1) coronary artery disease, (2) stroke, and (3) heart failure, which, collectively, lead to the vast majority of CVD‐caused mortality and disability. For each condition, we characterize a typical patient's journey in receiving cardiovascular care, elucidating key challenges in improving care and outlining research questions for healthcare operations management scholars. We close with a reference to new research opportunities that emerge as artificial intelligence is likely to transform much of cardiovascular care.