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Cited by 88Data-Driven Disease SurveillanceCOVID-19 epidemiological studiesElectronic Health Records Systems

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

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Dixon, Brian E, Grannis, Shaun J, McAndrews, Connor, Broyles, Andrea A, Mikels-Carrasco, Waldo, Wiensch, Ashley, Williams, Jennifer L, Tachinardi, Umberto +1 more·2021·Journal of the American Medical Informatics Association
DOI 10.1093/jamia/ocab004PMC PMC7928924
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Abstract

Objective: We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. Materials and Methods: We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. Results: Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. Discussion: Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. Conclusions: The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

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  • CoRDaCo enables easier participation in national collaborations such as the NCATS N3C initiative.

    “Important secondary uses of CoRDaCo include easier participation in national collaborations, such as the NCATS N3C initiative [49, 52, 53, 54], which will greatly expand the pool of individuals who can directly access this data.”

    From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [49]

    The Discussion (page unavailable) says standardized INPC data make retrieval efficient and scalable, but it does not address CoRDaCo or participation in national collaborations. Searches for CoRDaCo, NCATS N3C, and national research collaboration found no passage supporting the attributed claim. (LLM, conf=0.74)

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Research works printed but not linked: 28

  1. Menachemi N, Yiannoutsos CT, Dixon BE, et al. Population point preva- lence of SARS-CoV-2 infection based on a statewide random sample -In- diana, April 25-29, 2020. MMWR Morb Mortal Wkly Rep 2020; 69 (29): 960-4.
  2. Yoon P, Pollock D, Foldy S. National public health informatics, United States. In: Magnuson JA, Dixon BE, eds. Public Health Informatics and Information Systems. Cham, Switzerland: Springer International; 2020: 439-58.
  3. Meigs M, Merrick R, Hinrichs S. Public health laboratories. In: Magnuson JA, Dixon BE, eds. Public Health Informatics and Information Systems. 3rd ed. Cham, Switzerland: Springer Nature; 2020: 259-68.
  4. Dixon BE Rahurkar, S. Public health informatics. In: Hoyt RE, Hersh WR, eds. Health Informatics: Practical Guide. 7th ed. Bethesda, MD: American Medical Informatics Association; 2018: 373-95.
  5. Magnuson JA, Hopkins R, McFarlane TD. Informatics in disease preven- tion and epidemiology. In: Magnuson JA, Dixon BE, eds. Public Health Informatics and Information Systems. 3rd ed. Cham, Switzerland: Springer Nature; 2020: 239-58.
  6. Sittig DF, Singh H. COVID-19 and the need for a national health informa- tion technology infrastructure. JAMA 2020; 323 (23): 2373-4.
  7. Dixon BE, Caine VA, Halverson PK. Deficient response to COVID-19 makes the case for evolving the public health system. Am J Prev Med 2020; 59 (6): 887-91.
  8. Reeves JJ, Hollandsworth HM, Torriani FJ, et al. Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc 2020; 27 (6): 853-9.
  9. Dixon BE, Grannis SJ. Information infrastructure to support public health. In: Magnuson JA, Dixon BE, eds. Public Health Informatics and Information Systems. 3rd ed. Cham, Switzerland: Springer Nature; 2020: 85-104.
  10. Overhage JM. The Indiana Health Information Exchange. In: Dixon BE, ed. Health Information Exchange: Navigating and Managing a Network of Health Information Systems. 1st ed. Waltham, MA: Academic Press, 2016: 267-79.
  11. Dixon BE, Tao G, Wang J, et al. An integrated surveillance system to ex- amine testing, services, and outcomes for sexually transmitted diseases. Stud Health Technol Inform 2017; 245: 361-5.
  12. Rahurkar S, McFarlane TD, Wang J, et al. Leveraging health information exchange to construct a registry for traumatic brain injury, spinal cord in- jury and stroke in Indiana. AMIA Annu Symp Proc 2017; 2017: 1440-9.
  13. Whipple EC, Dixon BE, McGowan JJ. Linking health information tech- nology to patient safety and quality outcomes: a bibliometric analysis and review. Inform Health Soc Care 2013; 38 (1): 1-14. doi: 10.3109/ 17538157.2012.678451[published Online First: Epub Date]j. · DOI
  14. Madhavan S, Bastarache L, Brown JS, et al. Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers. J Am Med Inform Assoc 2021; 28 (2): 393-401.
  15. Fidahussein M, Friedlin J, Grannis S. Practical challenges in the secondary use of real-world data: the notifiable condition detector. AMIA Annu Symp Proc 2011; 2011: 402-8.
  16. Overhage JM, Dexter PR, Perkins SM, et al. A randomized, controlled trial of clinical information shared from another institution. Ann Emerg Med 2002; 39 (1): 14-23.
  17. McDonald CJ, Overhage JM, Barnes M, et al.; the INPC Management Committee. The Indiana network for patient care: a working local health information infrastructure. An example of a working infrastructure col- laboration that links data from five health systems and hundreds of mil- lions of entries. Health Affairs (Millwood) 2005; 24 (5): 1214-20.
  18. Dixon BE, Zou J, Comer KF, Rosenman M, Craig JL, Gibson PJ. Using electronic health record data to improve community health assessment. Front Public Health Serv Sys Res 2016; 5 (5): 50-6.
  19. Chiolero A, Buckeridge D. Glossary for public health surveillance in the age of data science. J Epidemiol Commun Health 2020; jech-2018-211654. doi: 10.1136/jech-2018-211654[published Online First: Epub Date]j. · DOI
  20. Dixon BE, Vreeman DJ, Grannis SJ. The long road to semantic interopera- bility in support of public health: experiences from two states. J Biomed Inform 2014; 49: 3-8.
  21. Hosseini M, Meade J, Schnitzius J, Dixon BE. Consolidating CCDs from multiple data sources: a modular approach. J Am Med Inform Assoc 2016; 23 (2): 317-23.
  22. Dixon BE, Taylor DE, Choi M, Riley M, Schneider T, Duke J. Integration of FHIR to facilitate electronic case reporting: results from a pilot study. Stud Health Technol Inform 2019; 264: 940-4.
  23. Hosseini M, Ahmadi M, Dixon BE. A service oriented architecture ap- proach to achieve interoperability between immunization information sys- tems in Iran. AMIA Annu Symp Proc 2014; 2014: 1797-805.
  24. Dixon BE, Zhang Z, Lai PTS, et al. Completeness and timeliness of notifi- able disease reporting: a comparison of laboratory and provider reports submitted to a large county health department. BMC Med Inform Decis Mak 2017; 17 (1): 87.
  25. DeSalvo KB, O'Carroll PW, Koo D, Auerbach JM, Monroe JA. Public health 3.0: time for an upgrade. Am J Public Health 2016; 106 (4): 621-2.
  26. Vest JR, Menachemi N, Grannis SJ, et al. Impact of risk stratification on referrals and uptake of wraparound services that address social determi- nants: a stepped wedged trial. Am J Prev Med 2019; 56 (4): e125-33.
  27. Polubriaginof FCG, Ryan P, Salmasian H, et al. Challenges with quality of race and ethnicity data in observational databases. J Am Med Inform Assoc 2019; 26 (8-9): 730-6.
  28. Lee SJ, Grobe JE, Tiro JA. Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals. J Am Med Inform Assoc 2016; 23 (3): 627-34.

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Authors
Dixon, B. E., Grannis, S. J., McAndrews, C., Broyles, A. A., Mikels-Carrasco, W., Wiensch, A., Williams, J. L., Tachinardi, U., & Embi, P. J.
Affiliations
  • Regenstrief Institute (US)
  • Indiana Health Information Exchange (US)
  • Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
Corresponding author
Dixon, Brian E
ORCID
Venue
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Published
2021-02-24
Volume
28
Issue
7
Pages
1363-1373
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
DOI
10.1093/jamia/ocab004
PMC
PMC7928924
OpenAlex
W3124660266
Cited by
88
References
2
Citations by year
2026: 2 · 2025: 16 · 2024: 15 · 2023: 17 · 2022: 22 · 2021: 16
FWCI
12.79
Type
journal article
Language
en
Open access
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations
  • Journal of the American Medical Informatics Association (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion)
  • Author eBooks
Funders
  • Indiana State Department of Health to the Regenstrief Institute (R21HS025502)
  • U.S. Agency for Healthcare Research and Quality
  • Agency for Healthcare Research and Quality
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
COVID-19 surveillance, health information exchange, data visualization, dashboards, public health informatics, pandemic response, hospitalization trends, public health emergency preparedness, Indiana
MeSH terms
Data Visualization, COVID-19, Humans, Indiana, United States, Public Health Informatics, Public Health Surveillance, Health Information Exchange

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