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      Electronic case reporting (eCR) of COVID-19 to public health: implementation perspectives from the Minnesota Department of Health

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          Abstract

          Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites), and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20 000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the 6 reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues, and expanding to other reportable conditions.

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          Most cited references25

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          Public Health 3.0: A Call to Action for Public Health to Meet the Challenges of the 21st Century

          Public health is what we do together as a society to ensure the conditions in which everyone can be healthy. Although many sectors play key roles, governmental public health is an essential component. Recent stressors on public health are driving many local governments to pioneer a new Public Health 3.0 model in which leaders serve as Chief Health Strategists, partnering across multiple sectors and leveraging data and resources to address social, environmental, and economic conditions that affect health and health equity. In 2016, the US Department of Health and Human Services launched the Public Health 3.0 initiative and hosted listening sessions across the country. Local leaders and community members shared successes and provided insight on actions that would ensure a more supportive policy and resource environment to spread and scale this model. This article summarizes the key findings from those listening sessions and recommendations to achieve Public Health 3.0.
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            Public Health COVID-19 Impact Assessment: Lessons Learned and Compelling Needs

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              Electronic health information quality challenges and interventions to improve public health surveillance data and practice.

              We examined completeness, an attribute of data quality, in the context of electronic laboratory reporting (ELR) of notifiable disease information to public health agencies.
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                Author and article information

                Contributors
                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                29 July 2022
                29 July 2022
                : ocac133
                Affiliations
                Informatics Program, School of Nursing, University of Minnesota , Minneapolis, Minnesota, USA
                Institute for Health Informatics, University of Minnesota , Minneapolis, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Minnesota Department of Health , Saint Paul, Minnesota, USA
                Author notes
                Corresponding Author: Sripriya Rajamani, MBBS, PhD, MPH, FAMIA, Informatics Program, School of Nursing, University of Minnesota, 308 Harvard St SE, Minneapolis, MN 55455, USA; sripriya@ 123456umn.edu
                Author information
                https://orcid.org/0000-0002-5941-7500
                Article
                ocac133
                10.1093/jamia/ocac133
                9384568
                35904765
                caf7460b-c0ea-442e-8fe3-4f2399dbd72d
                © The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 16 May 2022
                : 05 July 2022
                : 19 July 2022
                : 26 July 2022
                : 13 August 2022
                Page count
                Pages: 9
                Funding
                Funded by: CDC, DOI 10.13039/100000030;
                Categories
                Case Report
                AcademicSubjects/MED00580
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01530
                Custom metadata
                corrected-proof
                PAP

                Bioinformatics & Computational biology
                public health informatics,public health reporting,covid-19,electronic case reporting,standards,interoperability

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