JMIR Publications

ABSTRACT

Background:

Background:

Disease surveillance is a critical function of public health, provides essential information about disease burden, 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 the past two decades, key national informatics initiatives have provided support for public health functions. One such initiative is the meaningful use program that empowered hospitals to implement electronic health records (EHR) through multiple incentives. Syndromic surveillance, electronic laboratory reporting (ELR) in the meaningful use program and the growth of the National Healthcare Safety Network (NHSN) create linkages between hospitals, commercial laboratories, and public health that can collect and organize data, often through EHR and order workflows, to improve the timeliness and completeness of reporting. In theory, the standard data formats and exchange methods provided by EHR meaningful use should enable rapid healthcare data exchange in the setting of disruptive healthcare events like a pandemic. In reality, access to data remains challenging, and, even if available, often lacks conformity to regulated standards.

Objective:

Objective:

The ultimate goal of this effort was to reduce the administrative burden of reporting for stakeholders that could be replicated by other public health agencies in the United States. As such, 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.

Methods:

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. We measured the completeness of each feed and the match rate between feeds.

Results:

Results:

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. Data from 88,906 persons from consolidated clinical data architecture (CCDA) records among 14 facilities, and 408,741 persons from ELR records among 88 facilities, were submitted. Most (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:

Conclusions:

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