Objectives Develop a digital phenotyping algorithm (PheIndex) using electronic medical records (EMR) data to identify children aged 0-3 who have been diagnosed with genetic disorders or present with illness with an increased risk for genetic disorders from a mother-child cohort. Methods We established 13 criteria for the algorithm where two metrics — a quantified score and a classification — were derived. The criteria and the classification were validated by chart review from a pediatrician and clinical geneticist. To demonstrate the utility of our algorithm in real-world evidence applications, we examined the association between size of carrier screening panel (small/≤4 genes [CS-S] vs large/≥100genes [CS-L]) undertaken by mothers prior to delivery, and children classified as presenting with illness with an increased risk for genetic disorders by our algorithm. Results The PheIndex algorithm identified 1,088 such children out of 93,154 live births and achieved 90% sensitivity, 97% specificity, and 94% accuracy by chart review. We found that children whose mothers received CS-L were less likely to be classified as presenting with illness with an increased risk for genetic disorders and a decreased need to have multiple specialist visits and multiple ER visits, compared to children whose mothers received CS-S. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, and has the potential to improve healthcare delivery by alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist or other specialists.

We characterize the clinical utility and economic benefits of a comprehensive pan-ethnic carrier screening panel that spans 282 monogenic disease conditions in a large, diverse population of 397,540 reproductive health patients. For 142,049 of these patients, we were able to accurately estimate genetic ancestries across 7 major population groups. We examined individual carrier and at-risk carrier couple (ARCC) rates with respect to self-reported and genetic ancestries across ancestry-specific and pan-ethnic panels. Our results show that this comprehensive panel identified >10-times the ARCCs compared with a two-gene pan-ethnic panel and provided a substantial benefit over ancestry-specific screening panels across the major population groups. Finally, we generated a universal cost-of-care model across the monogenic disease conditions represented on the comprehensive pan-ethnic carrier screening panel to demonstrate potential healthcare savings in addition to the demonstrated clinical benefits that could be realized adopting this type of panel as standard of care for all.