Development and implementation of a computerised clinical decision support system for hospital‐in‐the‐home patient identification

BACKGROUND: Identification of appropriate patients for hospital-in-the-home (HITH) remains a significant challenge. We hypothesise that a human-centred design (HCD) approach can assist in developing a computerised clinical decision support system (CDSS) to address this issue via leveraging data within the electronic medical record (EMR). AIMS: We describe the design, development, validation and implementation outcomes of the HITH Eligibility Identifier (HEI), a CDSS for identifying inpatients suitable for HITH in Melbourne, Australia. METHODS: Approaches include: (a) describing digital algorithmic design and workflow development via the iterative HCD process; (b) validation of the HEI algorithm, interface and performance using Message Understanding Conference and Miettinen-Nurminen analyses; and (c) evaluation of system impact and outcomes post-implementation. RESULTS: The HEI CDSS integrates algorithmic decision support with clinician judgement through an interactive user interface embedded within the EMR. It ranks hospitalised patients from most to least likely eligible for HITH using administrative data, clinical information and markers of illness severity. Validation of HEI performance found that high-ranking patients were twice as likely to be eligible for HITH (improvement in eligibility probability of 46%; 95% confidence interval: 38%-54%). Post-implementation metrics show improvements in HITH utilisation, service delivery and efficiency. CONCLUSIONS: The HEI CDSS illustrates the importance of an HCD approach in ensuring local relevancy and clinical adoption of digital healthcare projects. Principles from its design and implementation can improve patient identification for HITH programs and be adapted for other CDSSs relevant to hospitalised patient cohorts.