Using electronic health records to monitor, augment and evaluate patient care in Singapore

Abstract

Abstract The advent of electronic health records (eHRs) has opened up many opportunities for researchers and practitioners in healthcare to improve human lives. The opportunity to monitor and evaluate events or processes in real time on a large scale is feasible with big data, thereby facilitating timely information and the practice of proactive care. Predictive analytics allows the re-engineering of processes by augmenting them with predictive scores to identify individuals at high risk of adverse events for early intervention. However, to seize these opportunities to improve patient care, alleviating the challenges faced when utilizing eHRs to generate actionable insights is important to unleash the full potential of eHRs. With better access to eHRs and relevant domain experts, it facilitates the appropriate use of eHRs and analytical approaches. To illustrate some of these opportunities and challenges, the following two case studies will be discussed during this presentation: (i) programs developed at an academic health system in Singapore that aim to improve the experience and outcomes of inpatients, and (ii) a nationwide Predictive Model for Admission Prevention in Singapore that identifies suitable discharged inpatients for enrolment into a community-centric program.