Healthcare Personnel Interactive Pathogen Exposure Response System

Abstract

Exposure investigations are labor-intensive and vulnerable to recall bias. We developed an algorithm to identify healthcare personnel (HCP) interactions from the electronic health record (EHR) and evaluated its accuracy against conventional exposure investigations. The EHR algorithm identified every known transmission and used ranking to produce a manageable contact list.

Background

Exposure investigations are regularly conducted in the hospital setting for many diseases, including tuberculosis and more recently SARS-CoV-2. However, conventional exposure investigations are time-consuming, prone to recall bias, and labor-intensive for the infection prevention and control (IPC) team tasked with ascertaining patient and healthcare personnel interactions.¹

Timely and effective exposure investigations and notification of possible exposures are essential to prevent onward transmission. The electronic health record (EHR) serves as a chronicle of healthcare personnel (HCP) and patient interactions and can aid more effective exposure investigations.^2–5 Using methods to analyze clinical EHR data that previously demonstrated the importance of HCP-patient contacts in transmission of vancomycin-resistant enterococcus⁶, we developed an algorithm to both identify index patient-HCP interactions and rank those interactions based on the likelihood of exposure. We retrospectively applied this EHR algorithm to findings from real-world COVID-19 exposure investigations conducted in our hospital to evaluate the potential of integrating these algorithms into IPC practice.

Methods

We compared EHR-based findings to seven conventional exposure investigations conducted between November 2020, and February 1, 2022, at The Johns Hopkins Hospital (JHH), a 1095-bed academic tertiary center in Baltimore, Maryland. Exposure investigations were conducted on all hospitalized patients who tested positive for SARS-CoV-2 and who were not already appropriately isolated.⁷ All admitted patients were tested on admission for SARS-CoV-2 and every 7 days while hospitalized or through provider discretion.⁸ To identify potentially exposed HCP, information on the index patient, including the infectious period and exposure definition, was sent by email to the managers of HCP who may have interacted with the patient. Exposure time frame was defined as 48 hours before symptom onset or positive test if asymptomatic. Managers were responsible for identifying potentially exposed HCP within their team. HCP were defined as exposed if their interaction with the index patient included: (a) performing an airborne-generating procedure without respirator and eye protection, (b) being within 6 feet of an unmasked patient for >15 minutes without a respirator, or (3) being within 6 feet of a patient for >15 minutes without a mask or eye protection. If HCP tested positive after an exposure, genomic sequencing was performed, if samples were available, to confirm transmission.

JHH uses the Epic EHR system (Epic Systems Corporation), and the algorithm uses data from the clinical reporting database. Potentially exposed HCP were detected based on both time-stamped data that is highly likely to be associated with an actual physical interaction between a patient and HCP (e.g., medication administration or laboratory specimen collection⁶ ) as well as non-time-stamped EHR records (e.g., care team assignment). For time-stamped data, events close in time (<15 mins) were concatenated to estimate time spent with patients with increasing time given a higher “contact score”. For non-time-stamped events, each was mapped to a contact type and assigned a point if the contact type was more likely to be associated with a physical interaction (e.g., transport) than ones less likely (e.g., care team assignment). The sum of each events contact score was used to rank the potential HCP exposure, with higher scores suggesting increased likelihood of exposure (See supplementary material for full algorithm description).

Statistical Analysis

To compare findings from conventional and EHR-based exposure investigation we used descriptive statistics including total, median and range of HCP identified through traditional and EHR-based methods. Percent agreement was calculated by determining the number of exposed employees identified through traditional methods that were also identified through the EHR algorithm.

Results

Seven conventional exposure investigations were included that occurred between November 1, 2020 and February 1, 2022. The investigations were all COVID-19 exposures in patients initially negative at admission that were later found to be positive. Through conventional exposure investigation methods, a median of 10 (Range 4–23) exposed HCP were identified, while the EHR-based method identified a median of 82 (Range 50 −119) HCP possibly at risk (Table 1). The EHR-based contact scores had high specificity for identifying HCP at risk: while the median contact score for all HCP was one (Range 0–58), the median contact score for HCP also identified through conventional exposure investigation was seven (Range 0–58). Additionally, every known HCP identified through conventional methods who tested positive after a patient exposure was identified in the EHR-based list. A total of 20 HCP were identified through conventional methods that were not found through use of clinical EHR data, however, none of the individuals tested positive for COVID-19.

Table 1.

Exposure investigation	# exposed HCP identified by traditional investigations	# exposed HCP identified by EHR	% of HCP identified through traditional methods also identified by EHR	# exposed HCP with subsequent positive tests	Comments/findings
#1	12	74	91.6	0	Lead Clinical RN not identified through EHR
#2	6	82	16	0	All HCP not identified through EHR were EVS
#3	10	82	80	0	Student, RN not identified through EHR
#4	15	55	73	0	Unit Associate, PA Resident, Customer Service Representative not identified through EHR
#5	23	98	65	7	Unit Associate, RN, Nutrition, Medical Coordinator, and Transport not identified through EHR
#6	9	119	100	3	All HCP identified
#7	4	50	100	0	All HCP identified

Abbreviations: HCP, healthcare personnel; EHR, electronic health record; RN, nurse; EVS, environmental services; PA, physician assistant.

Two of the seven infection clusters were confirmed by genomic sequencing, and all positive HCP were identified by the algorithm as at risk of exposure (Figure 1). The median contact score of HCP with a confirmed transmission was 14 (Range 3 −33), and they all appeared above the median contact score. In contrast, HCP who were identified as potentially at risk of exposure but did not have a documented COVID-19 infection in these clusters had a median contact score of 4 (Range 0–47).

Figure 1.

HCP Contact Scores in Exposure Investigations. These boxplots show the spread of contact scores for each exposure investigation that was performed. Only exposed HCP identified through EHR are included. The red dots represent HCP who tested positive for SARS-CoV-2 and all appear above the median contact score for these exposure investigations. The grey dots represent exposed HCP who did not have a recorded positive test.

Discussion

Clinical EHR data is comprehensive and, for certain events, highly time-specific, making it ideal for conducting IPC exposure investigations. Our results found that EHR data was highly sensitive and specific in identifying HCP that were at high risk of exposure. All HCP-patient COVID-19 transmissions confirmed through conventional methods were identified by the EHR algorithm, and HCP with a documented transmission had higher contact scores than those who tested negative.

The use of clinical data reduces the need for HCP to remember at-risk interactions, but does not assess adherence to PPE. As a result, the median list length of HCP identified through clinical data was significantly larger than conventional processes (82 vs. 10). To combat the potential problem of over-notification, which has been noted in other EHR-based exposure investigations⁹, we created a “contact score” that estimated the risk of exposure based on time and type of activity. Our comparison to conventional exposure investigations found that all HCP who tested positive were above the median of contact scores (Figure 1). Thus, depending upon the infections, cutoffs can be set for notifiying HCP to ensure only those at greatest risk are contacted.

The limitation of EHR-based algorithms is that even though 100% of HCP who could reasonably be expected to have charted information about a patient were captured, overall only 75% percent of all HCP identified through conventional measures were identified. The majority of those missed by the EHR algorithm were HCP that are unlikely to enter data into the EHR, such as Food/Environmental Service staff and students. None of the missed individuals tested positive for COVID-19. Thus, while EHR-based methods are not a direct substitute for traditional exposure investigations, they can augment traditional methods by more rapidly and accurately identifying HCP at highest risk of exposure. This technique of identifying HCW-patient interactions through EHR can be generalized to other transmissible infectious diseases in healthcare settings.