Machine learning based forecast for the prediction of inpatient bed demand.

BACKGROUND: Overcrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events. OBJECTIVE: The purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization. METHODS: The data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR). RESULTS: The performance obtained by the K-SVR strategy in the retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results. CONCLUSIONS: The results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.

Optimizing Bed Utilization for COVID-19 Patient Flow Protocols in a Behavioral Health Inpatient Facility using Discrete Event Simulation.

In this paper, we propose utilizing a discrete event simulation model as a decision-support tool to optimize bed capacity and configuration of Geisinger's inpatient drug and alcohol treatment facility. During the COVID-19 pandemic patient flows and processes needed to adapt to new safety protocols. The existing bed configurations are not designed for social distancing and other COVID protocols. The data for this study was collected post implementation of COVID-19 protocols on patient arrivals, and process flows by level of care. The baseline model was validated and verified against retrospective data to ensure the model assumptions were reasonable. The model showed that current bed capacity can be reduced by approximately 14% and bed configurations can be modified without impacting patient flow and wait times. These results help stakeholders make data-driven decisions to reduce redundancies and realize efficiency gains while improving their ability to plan for the growth of the facility.

Gaining Insights Into Patient Satisfaction Through Interpretable Machine Learning.

Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursement. To discover the major factors that affect patient experiences is considered as an effective way to formulate corrective actions. A patient during his/her healthcare journey interacts with multiple health professionals across different service units. The health-related data generated at each step of the journey is a valuable resource for extracting actionable insights. In particular, self-reported satisfaction survey and the associated patient electronic health records play an important role in the hospital-patient interaction analysis. In this paper, we propose an interpretable machine learning framework to formulate the patient satisfaction problem as a supervised learning task and utilize a mixed-integer programming model to identify the most influential factors. The proposed framework transforms heterogeneous data into human-understandable features and integrates feature transformation, variable selection, and coefficient learning into the optimization process. Therefore, it can achieve desirable model performance while maintaining excellent model interpretability, which paves the way for successful real-world applications.