A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study

  1. Bala Hota
  2. Paul Casey
  3. Anne F McIntyre
  4. Jawad Khan
  5. Shafiq Rab
  6. Aneesh Chopra
  7. Omar Lateef
  8. Jennifer E Layden

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Abstract

Disease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards.

Objective

We sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources.

Methods

Following a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems—a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds.

Results

Data from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability.

Conclusions

We described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

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  1. Version published to 10.2196/35973
  2. ScreenIT

    SciScore for 10.1101/2020.08.12.20173559: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Additionally, a third dataset of NHSN patient safety and hospital capacity was included, which hospitals were asked to either enter into a REDCap database or send electronically to the Azure tenant.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Significant limitations in case reporting have identified during the COVID-19 pandemic, including limited data on key variables such as age, race/ethnicity, hospitalization, and ICU status(22). Laboratory reporting does not provide all of the information needed for adequate case investigation, however. As our data shows, demographic and risk factor information may not be complete in the HL7 feeds for ELR, and case report forms continue to have a critical role in the work of public health practice. Additionally, co-morbid conditions, a significant predictor of disease outcome, are not captured. We found that CCDA data had a broader set of clinical fields, and have the advantage of providing valuable comorbidity information. While only small improvements in completeness were achieved, a high match rate to ELR data makes CCDA a compelling addition to ELR to improve the analytic power of public health data sets. Initiatives to standardize and automate case report form completion have been developed (23) and piloted (24), which have shown promise at reducing the time to complete reporting. Similar to our results, others have found that health information exchanges show value in prepopulating key elements for reporting through automated matching and searches in the patient record(25). The use of FHIR(26) may provide a viable path for automatic of public health case reporting and reduce administrative burden: when combined with an ELR based trigger for a case (in this example, sexua...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.08.12.20173559v1 on medRxiv