Patient Flow Management - Combining Analytical and Observational Data to Uncover Flow Patterns

Background: Hospitals must improve patient flow to achieve better efficiency and improve patients’ outcomes. Recent advancements in real time monitoring have provided immediate feedback for clinicians to address any bottlenecks. However, root causes of delays remain embedded in the details of clinicians’ activities. This work presents an observational study of a clinical pathway within a heart unit at a community hospital in North America. Observational data is correlated with multiple sources to uncover flow patterns. Materials and Methods: We observe heart patients as they arrive in at the heart unit and throughout their care up until their discharge. Data is correlated with electronic healthcare records and paper trails to enhance data reliability and accuracy. Results: Single data source alone is not sufficient to uncover process patterns. In our study, we discovered a negative correlation between the number of patients arriving at the hospital, and the total wait time each patient has experienced. We also identified key inefficiencies in the first and last hours of work shifts. Conclusion: Correlating multiple data sources can provide insights into details of process activities and uncover patterns and inefficiencies.