Identifying Patients at High Risk for Falls using an AI/ML model

<h3>Context:</h3> Patient Safety <h3>Objective:</h3> Identify Patients at High Risk for Falls using an AI/ML model. Falls among elderly patients are costly and potentially preventable. Approximately $50 billion dollars are spent on medical costs related to falls in US older adults every year and fatal falls are on the rise. Overtreatment of hypertension has been identified as a major risk factor for putting patients at risk for falls in patients with low blood pressures. Historically, most fall risk mitigation efforts occur after the first fall. Evidence-based interventions have been aimed at this population. Given the prevalence of fall risk, it is not feasible for a primary care practice to implement an evidence-based risk reduction intervention on every patient before they fall. A risk assessment tool that proactively identifies a smaller cohort of patients at the highest fall risk of fall could provide primary care practices with the ability to implement meaningful fall mitigation efforts. <h3>Study Design:</h3> Secondary data analysis created a machine learning model that identified high risk patients potentially over treated for hypertension placing them at higher fall risk. We utilized Amazon Sagemaker Canvas to design our machine learning model. <h3>Model design Variables included:</h3> blood pressure values, medications, chronic conditions and history of fractures. <h3>Setting/Dataset:</h3> Database containing over 20 million patients. <h3>Population Studied:</h3> Patients over the age of 75 with hypertension and most recent systolic blood pressures below 100 mmHg systolic and diastolic 70mmHg. <h3>Intervention:</h3> Amazon Sagemaker Canvas Outcome Measures: Patients with a calculated fall risk score of greater than 80%. <h3>Results:</h3> Initial cohort included 264,634 people 75 and older; 18,465 had diagnosis of hypertension and at least one low blood pressure. Of the 18,465 patients, 2,739 were identified by the model of having a potential fall outcome (14%). Of these identified, 329 patients were deemed highest risk with a fall risk score greater than 80%. Fall rate of the entire population was 2.2% over 24 months. The odds ratio of a fall for the high-risk cohort was 5.1 (CI 3.4 – 7.5) Model performance metrics: area under the curve (AUC) 0.89, precision of 87.9 and accuracy of 88.2. <h3>Conclusion:</h3> We designed a well performing, machine learning algorithm that identifies an addressable cohort of patients at high-risk for falls on whom an evidence-base intervention may be implemented.