Leveraging Digital Infrastructure for Data Analysis: An Example in Bariatric Surgery.

BACKGROUND: The digitalization of healthcare information provides hospitals with the ability to gain insight into patterns and associations pertaining to disease and management. Using bariatric patient data as an example provided an opportunity to explore the potential of electronic medical record (EMR) data to generate insights. OBJECTIVE: The aim of this study was to extract EMR data pertaining to bariatric patient information as a means to explore predictive factors of weight loss post-bariatric surgery. METHODS: We conducted a retrospective cohort study of patients undergoing bariatric surgery between January 1, 2018, and April 30, 2019, at Humber River Hospital. Multiple linear regression was used to examine whether age, pre-surgery body mass index (BMI), comorbidities and mental health disorders predicted higher weight loss 6 months following bariatric surgery. RESULTS: A total of 502 patients were included in the final analysis. Age (ss = 0.04 [95% CI 0.01, 0.06], p = 0.005), baseline BMI (ss = -0.16 [95% CI -0.19, -0.13], p = <0.0001) and diabetes (ss = 0.82 [95% CI 0.23, 1.42], p = 0.007) were associated with weight loss six months post-bariatric surgery. CONCLUSION: EMRs are a rich source of data with the potential to generate insights that can lead to improved care.