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A ‘Small’ Application of Big Data Analytics in Healthcare


Chris DeRienzo, MD, Chief Patient Safety Officer, Mission Health System
David Tanaka, MD, Professor of Pediatrics, Duke University Medical Center
Emily Lada, PhD, Senior Operations Research Specialist, Advanced Analytics Division, SAS,
Phil Meanor, Senior Manager, Advanced Analytics Division, SAS

Patient safety in a neonatal intensive care unit (NICU) is critically dependent on appropriate staffing. This project used JMP and SAS Simulation Studio to create a discrete-event simulation model of a specific NICU that can be used to predict the number of nurses needed per shift. The model incorporates the complexities inherent in determining staffing needs, including variations in patient acuity, referral patterns and length of stay (LOS). The group used JMP to estimate probability distributions for the number and type of patients admitted each day to the unit. Using both internal and published data, the team estimated distributions for various NICU-specific patient morbidities, including type and timing of each morbidity event and its temporal effect on a patient's acuity. The simulation model samples from these input distributions and simulates the flow of individual patients through the NICU over a one-year period. The model provides clinicians and administrators a tool to rigorously and quantitatively support staffing decisions. It can also be used to assess the relative significance of the factors selected to be included in the model on LOS. With additional refinements, the use of the model over time can provide significant benefits in both patient safety and operational efficiency.

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