Hostname: page-component-5d59c44645-7l5rh Total loading time: 0 Render date: 2024-03-01T09:01:41.661Z Has data issue: false hasContentIssue false

Healthcare personnel interactive pathogen exposure response system

Published online by Cambridge University Press:  28 April 2023

Leigh L. Smith*
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Susan A. Fallon
Affiliation:
The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Zunaira Q. Virk
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland
Alejandra B. Salinas
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland
Melanie S. Curless
Affiliation:
The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Sara E. Cosgrove
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Lisa L. Maragakis
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Clare Rock
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland The Johns Hopkins Hospital Department of Hospital Epidemiology and Infection Control, Baltimore, Maryland
Eili Y. Klein
Affiliation:
Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland Center for Disease Dynamics, Economics & Policy, Washington, DC
*
Author for correspondence: Leigh L. Smith, E-mail: lsmit213@jh.edu or smith.laurenleigh@gmail.com
Rights & Permissions [Opens in a new window]

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 we evaluated its accuracy against conventional exposure investigations. The EHR algorithm identified every known transmission and used ranking to produce a manageable contact list.

Type
Concise Communication
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Exposure investigations are regularly conducted in the hospital setting for many diseases, including tuberculosis and more recently severe acute respiratory coronavirus virus 2 (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. Reference Breeher, Boon, Hainy, Murad, Wittich and Swift1

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. Reference Curtis, Hlady, Kanade, Pemmaraju, Polgreen and Segre2Reference Venkataraman, Poon and Siau5 Using methods to analyze clinical EHR data that previously demonstrated the importance of HCP–patient contacts in transmission of vancomycin-resistant Enterococcus, Reference Klein, Tseng and Hinson6 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 coronavirus disease 2019 (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 7 conventional exposure investigations conducted between November 1, 2020, and February 1, 2022, at The Johns Hopkins Hospital (JHH), a 1,095–bed, academic, tertiary-care 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. Reference Smith, Morris and Jibowu7 All admitted patients were tested on admission for SARS-CoV-2 and every 7 days while hospitalized or through provider discretion. Reference Smith, Pau and Fallon8 To identify potentially exposed HCP, information on the index patient, including the infectious period and exposure definition, was sent by e-mail to the managers of HCP who may have interacted with the patient. The 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 the following: (1) performing an airborne-generating procedure without respirator and eye protection, (2) being within 2 m (6 feet) of an unmasked patient for >15 minutes without a respirator, or (3) being within 2 m (6 feet) of a patient for >15 minutes without a mask or eye protection. If an HCP tested positive after an exposure, genomic sequencing was performed, if samples were available, to confirm transmission.

The JHH uses the Epic EHR system (Epic Systems, Verona, WI), and the algorithm uses data from the clinical reporting database. Potentially exposed HCP were detected based on both time-stamped data that are highly likely to be associated with an actual physical interaction between a patient and an HCP (eg, medication administration or laboratory specimen collection Reference Klein, Tseng and Hinson6 ) and non–time-stamped EHR records (eg, care team assignment). For time-stamped data, events close in time (<15 minutes) 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 (eg, transport) than ones less likely (eg, 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 online for full algorithm description).

Statistical analysis

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

Results

In total, 7 conventional exposure investigations that occurred between November 1, 2020, and February 1, 2022, were included in this study. The investigations were all COVID-19 exposures in patients initially negative at admission who were later found to be positive. Through conventional exposure investigation methods, a median of 10 exposed HCP (range, 4–23) were identified, whereas the EHR-based method identified a median of 82 HCP (range, 50–119) possibly at risk (Table 1). The EHR-based contact scores had high specificity for identifying HCP at risk. The median contact score for all HCP was 1 (range, 0–58), and the median contact score for HCP also identified through conventional exposure investigation was 7 (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. In total, 20 HCP were identified through conventional methods who were not identified through use of clinical EHR data; however, none of the individuals tested positive for SARS-CoV-2.

Table 1. Comparison of Exposure Investigation Methods

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

Of the 7 infection clusters, 2 were confirmed by genomic sequencing, and all positive HCP were identified by the algorithm as at risk of exposure (Fig. 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).

Fig. 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 the 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 are comprehensive and, for certain events, highly time specific, making them ideal for conducting IPC exposure investigations. In our study, EHR data were 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 overnotification, which has been noted in other EHR-based exposure investigations, Reference Hong, Herigon and Uptegraft9 we created a “contact score” that estimated the risk of exposure based on time and type of activity. Our comparison to conventional exposure investigations showed that all HCP who tested positive were above the median of contact scores (Fig. 1). Thus, depending upon the infections, cutoffs can be set for notifiying HCP to ensure that only those at greatest risk are contacted.

EHR-based algorithms have limitations. Although 100% of HCP who could reasonably be expected to have charted information about a patient were captured, overall, only 75% of all HCP identified through conventional measures were identified. Most of those missed by the EHR algorithm were HCP who were unlikely to enter data into the EHR, such as food and environmental service staff and students. None of the missed individuals tested positive for SARS-COV-2. Thus, although 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.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2022.261

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official view of the funding agency.

Financial support

This work was funded by the Centers for Disease Control and Prevention’s Prevention Epicenters Program (grant no. 1 U54CK000617-01-00).

Conflicts of interest

All authors report no conflicts of interest relevant to this article.

References

Breeher, L, Boon, A, Hainy, C, Murad, MH, Wittich, C, Swift, M. A framework for sustainable contact tracing and exposure investigation for large health systems. Mayo Clin Proc 2020;95:14321444.CrossRefGoogle ScholarPubMed
Curtis, DE, Hlady, CS, Kanade, G, Pemmaraju, SV, Polgreen, PM, Segre, AM. Healthcare worker contact networks and the prevention of hospital-acquired infections. PLoS One 2013;8:e79906.CrossRefGoogle ScholarPubMed
Cusumano-Towner, M, Li, DY, Tuo, S, Krishnan, G, Maslove, DM. A social network of hospital acquired infection built from electronic medical record data. J Am Med Inform Assoc 2013;20:427434.CrossRefGoogle ScholarPubMed
Usiak, SC, Romero, FA, Schwegman, P, et al. Utilization of electronic health record events to conduct a tuberculosis contact investigation in a high-risk oncology unit. Infect Control Hosp Epidemiol 2017;38:12351239.CrossRefGoogle Scholar
Venkataraman, N, Poon, BH, Siau, C. Innovative use of health informatics to augment contact tracing during the COVID-19 pandemic in an acute hospital. J Am Med Inform Assoc 2020;27:19641967.CrossRefGoogle Scholar
Klein, EY, Tseng, KK, Hinson, J, et al. The role of healthcare worker–mediated contact networks in the transmission of vancomycin-resistant enterococci. Open Forum Infect Dis 2020;7:ofaa056.CrossRefGoogle ScholarPubMed
Smith, L, Morris, CP, Jibowu, MH, et al. SARS-CoV-2 exposure investigations using genomic sequencing among healthcare workers and patients in a large academic center. Infect Control Hosp Epidemiol 2022. doi: 10.1017/ice.2022.37.CrossRefGoogle Scholar
Smith, L, Pau, S, Fallon, S, et al. Impact of weekly asymptomatic testing for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in inpatients at an academic hospital. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2021.384.CrossRefGoogle Scholar
Hong, P, Herigon, JC, Uptegraft, C, et al. Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic. J Am Med Inform Assoc 2021;29:142148.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Comparison of Exposure Investigation Methods

Figure 1

Fig. 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 the 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.

Supplementary material: File

Smith et al. supplementary material

Smith et al. supplementary material

Download Smith et al. supplementary material(File)
File 18 KB