EVALUATING A DIGITAL POPULATION HEALTH MANAGEMENT PROJECT: AUTOMATED SOLUTIONS ASSISTING PRIORITY POPULATIONS (ASAPP)

Abstract

Population segmentation of complex patients at higher risk of poor outcomes can facilitate improved resource allocation and health outcomes. The Automated Solutions Assisting Priority Populations (ASAPP) project used robotic process automation (RPA) to standardize data in electronic medical records (EMR), and predictive algorithms, as well as neighbourhood-level social determinants of health (SDOH), to identify complex patients at high-risk of hospitalization. The ASAPP data were shared with primary care clinicians and system-level leaders to support the proactive care coordination for complex patients. The purpose of this study was to evaluate the adoption, effectiveness, and value of the pilot ASAPP project to help inform recommendations for future digital population health projects. A summative evaluation with a convergent parallel mixed methods design was conducted, combining data from three semi-structured interviews with four stakeholders, including three clinicians and one system-level leader, and quantitative data on adoption and use. A reflexive thematic analysis was conducted and descriptive statistics and recommendations for improvement were generated. Six sites across three health regions engaged in ASAPP, involving 26 clinicians and 34,710 patients. RPA coded 2,240 additional conditions across five sites; 1,790 complex patients were identified using the predictive algorithms; 220 patients were found to be living in high SDOH complex areas. Four overarching themes were generated: (1) perceived value and unrealized potential of population health management (PHM), (2) effectiveness and limitations, (3) barriers and facilitators, and (4) recommendations. ASAPP demonstrated potential to support PHM, but its value was not fully realized due to technology limitations, and adoption barriers including resource constraints. Stakeholder recommendations included early engagement, clinician champions, and transparent communication. Although the small qualitative sample size limits the transferability of findings to settings beyond early-adopters, the evaluation highlights the need for more strategic and user-centric development and adoption, prioritizing stakeholder engagement and system-level support.