Objective This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. Materials and Methods We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital’s COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. Results Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. Conclusion Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.

Objective The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions’ coronavirus disease 2019 (COVID-19) response. Materials and Methods We designed a survey to capture key domains of health informatics and perceptions regarding fellows’ application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series. Results Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were “able to apply clinical informatics training and interest to the COVID-19 response.” The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows. Discussion Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones. Conclusions CI fellows contributed meaningfully and rapidly to their institutions’ response to the COVID-19 pandemic.

Introduction Deployment-limiting medical conditions are the primary reason why service members are not medically ready. Service-specific standards guide clinicians in what conditions are restrictive for duty, fitness, and/or deployment requirements. The Air Force (AF) codifies most standards in the Medical Standards Directory (MSD). Providers manually search this document, among others, to determine if any standards are violated, a tedious and error-prone process. Digitized, standards-based decision-support tools for providers would ease this workflow. This study digitized and mapped all AF occupations to MSD occupational classes and all MSD standards to diagnosis codes and created and validated a readiness decision support system (RDSS) around this mapping. Materials and Methods A medical coder mapped all standards within the May 2018 v2 MSD to 2018 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. For the publication of new MSDs, we devised an automated update process using Amazon Web Service’s Comprehend Medical and the Unified Medical Language System’s Metathesaurus. We mapped Air Force Specialty Codes to occupational classes using the MSD and AF classification directories. We uploaded this mapping to a cloud-based MySQL (v5.7.23) database and built a web application to interface with it using R (v3.5+). For validation, we compared the RDSS to the record review of two subject-matter experts (SMEs) for 200 outpatient encounters in calendar year 2018. We performed four separate analyses: (1) SME vs. RDSS for any restriction; (2) SME interrater reliability for any restriction; (3) SME vs. RDSS for specific restriction(s); and (4) SME interrater reliability for categorical restriction(s). This study was approved as “Not Human Subjects Research” by the Air Force Research Laboratory (FWR20190100N) and Boston Children’s Hospital (IRB-P00031397) review boards. Results Of the 709 current medical standards in the September 2019 MSD, 631 (89.0%) were mapped to ICD-10-CM codes. These 631 standards mapped to 42,810 unique ICD codes (59.5% of all active 2019 codes) and covered 72.3% (7,823/10,821) of the diagnoses listed on AF profiles and 92.8% of profile days (90.7/97.8 million) between February 1, 2007 and January 31, 2017. The RDSS identified diagnoses warranting any restrictions with 90.8% and 90.0% sensitivity compared to SME A and B. For specific restrictions, the sensitivity was 85.0% and 44.8%. The specificity was poor for any restrictions (20.5%–43.4%) and near perfect for specific restrictions (99.5+%). The interrater reliability between SMEs for all comparisons ranged from minimal to moderate (κ = 0.33–0.61). Conclusion This study demonstrated key pilot steps to digitizing and mapping AF readiness standards to existing terminologies. The RDSS showed one potential application. The sensitivity between the SMEs and RDSS demonstrated its viability as a screening tool with further refinement and study. However, its performance was not evenly distributed by special duty status or for the indication of specific restrictions. With machine consumable medical standards integrated within existing digital infrastructure and clinical workflows, RDSSs would remove a significant administrative burden from providers and likely improve the accuracy of readiness metrics.