With nearly half of its expenses coming from managing inpatient care, US hospitals wrestle with a never-ending challenge: find new ways to lower costs while maintaining a high standard of care. Master of Science in Analytics (MSiA) graduate students Jamie Green and Kapil Bhatt believe a solution could lie in the data.
Winners of the MSiA’s third annual Hackathon on Thursday, May 5, Green and Bhatt sought to gain insights into the dilemma by using analytics software to study inpatient discharge data from Texas hospitals over a six-month period in 2007. The duo created a linear model that codified and analyzed variables like length of stay, reason for admittance, and patient demographics.
The team determined that the patients’ admitting diagnosis was the most accurate predictor of their length of stay. Patients grouped with “mental health-related illness” as an initial diagnosis required the longest hospital stay. Conversely, diagnoses categorized by “pregnancy and child-birth” reflected relatively short visits, likely due to the brief nature of ultrasound and check-up screenings.
“To be able to predict length of stay based on patient data could help staff more accurately set schedules, order and maintain supplies, and prepare for new patients,” said Green. “The cost savings from those changes could eventually be passed on to the consumer.”