No abstract

Hospital observation units (OUs) are meant to host patients for relatively short periods of time, during which healthcare providers can observe a patient (usually following an emergency department encounter) and assess the need for an inpatient hospital admission. The initial placement of a patient in a unit (inpatient or observation) can have consequences for the care the patient receives and the patient's length of stay in the hospital, as well as financial implications for both the patient and the hospital. We propose a practical real-time optimization algorithm that balances the need to place a patient in the OU with considerations for available capacity. Through computational experiments with inputs derived from real data, we study the benefit of using the algorithm for placing the right type of patients in the OU. We discuss the role of data-driven decision support tools like the proposed algorithm for anchoring the decision-making process in a hospital setting and for incorporating the viewpoints of multiple stakeholders by defining appropriate "rewards" for available alternatives.

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