Over the last two decades, a growing proportion of primary health care organizations have implemented electronic medical records (EMR). This data holds the potential to help providers and organizations proactively identify patients who are at risk of unnecessary and unexpected health service use and poor health outcomes. Research to date on using EMR data to predict health outcomes or health service use has mostly focused on using in-patient EMR data to predict re-admission. In this systematic review, we will identify studies that use primary health care EMR data to predict emergency department visits, hospitalizations and mortality.
Through this systematic review, we will be able to identify the current published studies that use primary care EMR data to predict emergency department visits, hospitalizations, and mortality. We will also evaluate the comparative power of traditional statistical methods and artificial intelligence tools in processing and making sense of primary care EMR data. Together, our findings will help us to understand the potential for primary care EMR data to improve healthcare.