National Public Health Informatics, United States. In: Magnuson J, Dixon BE, editors. Public Health Informatics and Information Systems. Cham: Springer International Publishing; 2020. pp. 439-58. [DOI: 10.1007/978-3-030-41215-9_24]

Electronic Case Reporting Development, Implementation, and Expansion in the United States

INTRODUCTION

The COVID-19 pandemic highlighted the need for a nationwide health information technology solution that could improve upon manual case reporting and decrease the clinical and administrative burden on the US health care system. We describe the development, implementation, and nationwide expansion of electronic case reporting (eCR), including its effect on public health surveillance and pandemic readiness.

METHODS

Multidisciplinary teams developed and implemented a standards-based, shared, scalable, and interoperable eCR infrastructure during 2014-2020. From January 27, 2020, to January 7, 2023, the team conducted a nationwide scale-up effort and determined the number of eCR-capable electronic health record (EHR) products, the number of reportable conditions available within the infrastructure, and technical connections of health care organizations (HCOs) and jurisdictional public health agencies (PHAs) to the eCR infrastructure. The team also conducted data quality studies to determine whether HCOs were discontinuing manual case reporting and early results of eCR timeliness.

RESULTS

During the study period, the number of eCR-capable EHR products developed or in development increased 11-fold (from 3 to 33), the number of reportable conditions available increased 28-fold (from 6 to 173), the number of HCOs connected to the eCR infrastructure increased 143-fold (from 153 to 22 000), and the number of jurisdictional PHAs connected to the eCR infrastructure increased 2.75-fold (from 24 to 66). Data quality reviews with PHAs resulted in select HCOs discontinuing manual case reporting and using eCR-exclusive case reporting in 13 PHA jurisdictions. The timeliness of eCR was <1 minute.

PRACTICE IMPLICATIONS

The growth of eCR can revolutionize public health case surveillance by producing data that are more timely and complete than manual case reporting while reducing reporting burden.

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

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.

COVID-19 Case Surveillance: Trends in Person-Level Case Data Completeness, United States, April 5-September 30, 2020

OBJECTIVES

To obtain timely and detailed data on COVID-19 cases in the United States, the Centers for Disease Control and Prevention (CDC) uses 2 data sources: (1) aggregate counts for daily situational awareness and (2) person-level data for each case (case surveillance). The objective of this study was to describe the sensitivity of case ascertainment and the completeness of person-level data received by CDC through national COVID-19 case surveillance.

METHODS

We compared case and death counts from case surveillance data with aggregate counts received by CDC during April 5-September 30, 2020. We analyzed case surveillance data to describe geographic and temporal trends in data completeness for selected variables, including demographic characteristics, underlying medical conditions, and outcomes.

RESULTS

As of November 18, 2020, national COVID-19 case surveillance data received by CDC during April 5-September 30, 2020, included 4 990 629 cases and 141 935 deaths, representing 72.7% of the volume of cases (n = 6 863 251) and 71.8% of the volume of deaths (n = 197 756) in aggregate counts. Nationally, completeness in case surveillance records was highest for age (99.9%) and sex (98.8%). Data on race/ethnicity were complete for 56.9% of cases; completeness varied by region. Data completeness for each underlying medical condition assessed was <25% and generally declined during the study period. About half of case records had complete data on hospitalization and death status.

CONCLUSIONS

Incompleteness in national COVID-19 case surveillance data might limit their usefulness. Streamlining and automating surveillance processes would decrease reporting burdens on jurisdictions and likely improve completeness of national COVID-19 case surveillance data.