How to Develop a Risk Prediction Smartphone App

Abstract

Purpose. Powered by big data, predictive models provide individualized risk stratification to inform clinical decision-making and mitigate long-term morbidity. We describe how to transform a large institutional dataset into a real-time, interactive clinical decision support mobile user interface for risk prediction. Methods. A clinical decision point ideal for risk stratification and modification was identified. Demographics, medical comorbidities, and operative characteristics were abstracted from the electronic medical record (EMR) using ICD-9 codes. Surgery-specific predictive models were generated using regression modeling and corroborated with internal validation. A clinical support interface was designed in partnership with an app developer, followed by subsequent beta testing and clinical implementation of the final tool. Results. Individual, specialty-specific, and preoperatively actionable models incorporating clustered procedural codes were created. Using longitudinal inpatient, outpatient, and office-based data from a large multicenter health system, all patient and operative variables were weighted according to ß-coefficients. The individual risk model parameters were incorporated into specialty-specific modules and implemented into an accessible iOS/Android compatible mobile application. Conclusions. As proof of concept, we provide a framework for developing a clinical decision support mobile user interface, through the use of clinical and administrative longitudinal data. Point-of-care applications, particularly ones designed with implementation and actionability in mind, have the potential to aid clinicians in identifying and optimizing risk factors that impact the outcome of interest’s occurrence, thereby enabling clinicians to take targeted risk-reduction actions. In addition, such applications may help facilitate counseling, informed consent, and shared decision-making, leading to improved patient-centered care.