ABSTRACT
Background. Infectious disease exposure investigations in the hospital are labor-intensive for the infection prevention and control (IPC) team and vulnerable to healthcare personnel (HCP) recall bias. We developed an electronic health record (EHR) algorithm to identify and rank patient-HCP interactions based on the likelihood and intensity of exposure. Methods. We compared conventional and EHR-based findings from seven exposure investigations conducted between November 1, 2020 and February 1, 2022 at The Johns Hopkins Hospital (JHH), a 1095 bed academic tertiary center in Baltimore, MD. Conventional exposure investigations were conducted for hospitalized patients who tested positive for SARS-CoV-2 while not in COVID-19 isolation precautions. IPC contacted department managers to identify and report potentially exposed HCPs to occupational health. The EHR-based method identified HCP-patient interactions based on clinical data such as documentation in the flowsheet, medication administration, etc. A score was calculated for each HCP based on the estimated duration and intensity of the contact. Genomic sequencing of available samples was performed to investigate transmission events. Results. Overall, the EHR-based system identified 75% (59/79) of the HCPs identified by conventional exposure investigations and 100% of those who document in the EHR. In contrast, it was unable to identify any potentially exposed individuals who do not document in the EHR (Table 1). All patient-HCP COVID-19 transmissions identified by conventional investigation and confirmed through genomic sequencing were identified by the EHR-based system, and all had high-intensity scores (i.e., top quartile of the list of exposed individuals). Conclusion. We found clinical EHR data was highly sensitive and specific in identifying potentially exposed HCPs compared with conventional exposure investigations. The inability to detect interactions with support staff or others who do not document in the EHR was a limitation and suggests that EHR data can augment but not replace conventional exposure investigations. The system's speed, ease, and lower resource requirements make it a promising tool to more efficiently complete exposure investigations in healthcare settings.