A regional hospital system serving Kentucky and Indiana engaged EdjAnalytics to improve the targeting logic for marketing campaigns around elective procedures which are strong contributors to the hospital’s profitability. The elective procedures of interest were Hip Replacement, Knee Replacement, and Bariatric Surgeries. The previous targeting logic relied primarily on age for Hip and Knee procedures and BMI for Bariatrics. Using the complete medical history and demographics information from the hospital’s Electronic Medical Records system offered the opportunity to identify the specific individuals most likely to require each procedure, allowing the hospital to reach the audience most likely to convert and to reduce wasteful marketing spend.
Edj received data covering 7.7 million total procedures executed over five years as well as information on demographics, labs, vitals, encounters, diagnoses, and medications used to identify each patient’s surgical risk. The data science team tested various machine learning techniques and achieved the strongest results with an Extreme Gradient Boosting method which creates a series of decision trees in order to assess likelihood of a specific outcome. The outputs of the models are predictions based on probability calculated after passing observations through all the decision trees. Patients are then classified as High, Medium, or Low Risk based on their probability scores. The High Risk group captures between 70 and 78% of the executed procedures.