JMIR Publications

ABSTRACT

Background:

Background:

The COVID-19 pandemic has exacerbated the challenge of meaningful healthcare digitization. The need for rapid yet validated decision making requires robust data infrastructure. Organizations with a Learning Healthcare (LHC) systems focus tend to adapt better to rapidly evolving data needs. The literature lacks examples of successful implementation of data digitization principles in an LHC context across healthcare systems during the COVID-19 pandemic.

Objective:

Objective:

We share our experience and provide a framework for assembling and organizing multi-disciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of healthcare digitization, in the context of LHC across a complex healthcare organization.

Methods:

Methods:

Houston Methodist (HM) comprises eight tertiary care hospitals, and an expansive primary care network across Greater Houston – one of the most populous and diverse U.S. regions. Early in the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and establish the retrospective research task force (RRTF). We provide an account of structure, functioning and productivity of RRTF. We further elucidate the technical and structural details of a comprehensive data repository – the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard healthcare digitization principles in the LHC context.

Results:

Results:

The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research, and to date has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR which in itself was IRB approved in April 2020. CURATOR is a relational Structured Query Language database that is directly populated with data from electronic health records, via largely automated extract, transform and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 patients and controls before and after COVID-19 testing. CURATOR has been set up following the single source of truth (SSoT) principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. Currently hosting deeply phenotyped socio-demographic, clinical and outcomes data on approximately 200,000 COVID-19 tested individuals, CURATOR supports more than 30 IRB approved protocols across several clinical domains and has generated a track record of publications from its core and associated data sources.

Conclusions:

Conclusions:

A data-driven decision-making mindset is paramount to the success of healthcare organizations. Investment in cross-disciplinary expertise, healthcare technology and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future healthcare catastrophes by providing timely and validated decision support.