Person:

Saghafian, Soroush

Various hospitals in the U.S. and around the world suffer from the well-known problem of Emergency Department (ED) overcrowding, which prevents them from serving their patients in effective and efficient ways. An important contributor to this problem, which became even more dire after the COVID-19 pandemic, is prolonged boarding of patients who are admitted to inpatient units through the ED. Patients admitted through the ED constitute about 50% of all non-obstetrical hospital admissions in the U.S., and may be boarded in the ED for long hours with the hope of finding an available bed in their primary inpatient unit. In this chapter, we shed light on effective ways of reducing ED boarding times by considering the trade-off between keeping patients in the ED and assigning them to a secondary inpatient unit. The former can increase the risk of adverse events and also cause congestion in the ED (which, in turn, prevents from serving new ED patients in a timely manner), whereas the latter may adversely impact the quality of care post ED service. Further complicating this calculus is the fact that a secondary inpatient unit for a currently boarded ED patient can be the primary unit for a future arriving patient; assignments, therefore, should be made in an orchestrated way. Developing a queueing-based Markov decision process, we demonstrate that patience in transferring patients is a virtue, but only up to a point. We also find that, contrary to the prevalent perception, idling inpatient beds in hospitals can be beneficial (under some circumstances). Since the optimal policy for dynamically assigning patients to their primary and secondary inpatient units is complex and hard to implement in hospitals, we develop a simple policy which we term penalty-adjusted Largest Expected Workload Cost (LEWC-p). Using simulation analyses calibrated with hospital data, we find that implementing this policy could significantly help hospitals to improve their patient safety by reducing boarding times while controlling the overflow of patients to secondary units. Using data analyses and various simulation experiments, we also help hospital administrators by generating insights into hospital conditions under which achievable improvements are significant.

As a major public health crisis, the opioid epidemic caused over 556,000 deaths in the U.S. between 2000 and 2020. To control the epidemic, the Centers for Disease Control and Prevention (CDC) has developed some general guidelines, encouraging physicians to use opioid medications only when their benefits outweigh their risks. The CDC’s 2016 guidelines mainly left it to physicians to decide when the benefits outweigh the risks. A few years later (in 2022), the CDC made some modifications to make its recommendations a bit less reliant on each individual physician’s perception of benefits versus risks. In complex and high stake decision-making environments such as those pertaining the use of opioid medications, it is not clear whether and how human-based perceptions might differ from algorithmic-based ones. In this study, we first develop some longitudinal machine learning algorithms (e.g., historical random forest, recurrent neural networks, and long short-term memory networks) and train them on clinical evidence of more than 3 million patients. We then feed the best machine learning algorithm to a mathematical model that enables determining cost-effective treatments for each patient in a personalized manner. Through extensive numerical experiments, we compare the treatment options and recommendations from our algorithmic-based approach with human-based ones that are currently followed in the medical practice. Compared to the human-based approach, our results show that the average saving in quality-adjusted life years and costs obtained by following our algorithmic-based treatments are about 2.82 days and $461.46 per patient per year. Finally, we make use of our findings and generate insights for policymakers as well as individual physicians into better ways of managing opioid prescriptions (and hence, the opioid epidemic) by incorporating and interacting with our algorithmic-based approach.