CMC-ID: identifying children and youth with medical complexity using an electronic health record.

Abstract

We aimed to develop and evaluate the performance of CMC-ID, an algorithm designed to identify children and youth with medical complexity (CMC) from electronic health record (EHR) data for recruitment to a transition to adult care programme, and then tested it in younger patients. CMC-ID was developed iteratively at a paediatric tertiary care hospital (SickKids) in Toronto, Canada, using a data repository derived from an Epic System EHR. The algorithm captured standard clinical criteria for CMC used throughout Ontario, Canada, including (1) Complexity, (2) Chronicity, (3) Fragility and (4) Technology dependence. We refined the CMC-ID iteratively to maximise positive predictive value (PPV). In phase I, we retrospectively identified youth aged 17 years to <18 years and conducted chart reviews to confirm CMC status, and then repeated the search in a younger cohort (1 year to <17 years) in phase II. Among 280 unique patients identified in phase I, the last of six iterations of the algorithm identified CMC with a PPV of 85.5% (95% CI 73.3% to 93.5%). When applied to a younger cohort in phase II, 947 CMC were identified, and the algorithm performed similarly with a PPV of 89.1% (95% CI 86.9% to 91.0%). CMC-ID identified youth for recruitment to an intervention focused on transition to adult care with high PPV, and the algorithm performed similarly when applied to younger patients. CMC-ID is a pragmatic, high-alert index to support recruitment to clinical programmes and other interventions aimed at improving CMC outcomes.