Creating and implementing a COVID-19 recruitment Data Mart

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review

OBJECTIVES

The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research.

MATERIALS AND METHODS

We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities.

RESULTS

Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself.

DISCUSSION

Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities.

CONCLUSION

The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing

OBJECTIVE

Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology.

MATERIALS AND METHODS

We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs.

RESULTS

Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities.

DISCUSSION

Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research.

CONCLUSION

Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.

Comparing automated vs. manual data collection for COVID-specific medications from electronic health records

INTRODUCTION

Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).

MATERIALS AND METHODS

For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.

RESULTS

For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).

CONCLUSION

Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

The RIC COVID-19 Recruitment & Retention Toolkit: A community-informed resource of recruitment tools and strategies for clinical trial investigators

The Recruitment Innovation Center (RIC) has created a toolkit of novel strategies to engage potential participants in response to recruitment and retention challenges associated with COVID-19 studies. The toolkit contains pragmatic, generalizable resources to help research teams increase awareness of clinical trials and opportunities to participate; produce culturally sensitive and engaging recruitment materials; improve consent and return of results processes; and enhance recruitment of individuals from populations disproportionately impacted by COVID-19. This resource, the “RIC COVID-19 Recruitment and Retention Toolkit,” is available free online. We describe the toolkit and the community feedback used to author and curate this resource.

The Recruitment Innovation Center: Developing novel, person-centered strategies for clinical trial recruitment and retention

Clinical trials continue to face significant challenges in participant recruitment and retention. The Recruitment Innovation Center (RIC), part of the Trial Innovation Network (TIN), has been funded by the National Center for Advancing Translational Sciences of the National Institutes of Health to develop innovative strategies and technologies to enhance participant engagement in all stages of multicenter clinical trials. In collaboration with investigator teams and liaisons at Clinical and Translational Science Award institutions, the RIC is charged with the mission to design, field-test, and refine novel resources in the context of individual clinical trials. These innovations are disseminated via newsletters, publications, a virtual toolbox on the TIN website, and RIC-hosted collaboration webinars. The RIC has designed, implemented, and promised customized recruitment support for 173 studies across many diverse disease areas. This support has incorporated site feasibility assessments, community input sessions, recruitment materials recommendations, social media campaigns, and an array of study-specific suggestions. The RIC’s goal is to evaluate the efficacy of these resources and provide access to all investigating teams, so that more trials can be completed on time, within budget, with diverse participation, and with enough accrual to power statistical analyses and make substantive contributions to the advancement of healthcare.

Patient Perceptions of Receiving COVID-19 Test Results via an Online Patient Portal: An Open Results Survey

BACKGROUND

In the United States, attention has been focused on "open notes" and "open results" since the Office of the National Coordinator for Health Information Technology implemented the 21st Century Cures Act Final Rule on information blocking. Open notes is an established best practice, but open results remains controversial, especially for diseases associated with stigma, morbidity, and mortality. Coronavirus disease 2019 (COVID-19) is associated with all three of these effects and represents an ideal disease for the study of open results for sensitive test results.

OBJECTIVES

This study evaluates patient perspectives related to receiving COVID-19 test results via an online patient portal prior to discussion with a clinician.

METHODS

We surveyed adults who underwent COVID-19 testing between March 1, 2020 and October 21, 2020 who agreed to be directly contacted about COVID-19-related research about their perspectives on receiving test results via a patient portal. We evaluated user roles (i.e., patient vs. care partner), demographic information, ease of use, impact of immediate release, notification of results, impact of viewing results on health management, and importance of sharing results with others.

RESULTS

Users were mostly patients themselves. Users found the portal easy to use but expressed mixed preferences about the means of notification of result availability (e.g., email, text, or phone call). Users found immediate access to results useful for managing their health, employment, and family/childcare. Many users shared their results and encouraged others to get tested. Our cohort consisted mostly of non-Hispanic white, highly educated, English-speaking patients.

CONCLUSION

Overall, patients found open results useful for COVID-19 testing and few expressed increased worries from receiving their results via the patient portal. The demographics of our cohort highlight the need for further research in patient portal equity in the age of open results.

REDCap on FHIR: Clinical Data Interoperability Services

BACKGROUND

Despite widespread use of electronic data capture (EDC) systems for research and electronic health records (EHR), most transfer of data between EHR and EDC systems is manual and error prone. Increased adoption of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) application programming interfaces (APIs) in recent years by EHR systems has increased the availability of patient data for external applications such as REDCap.

OBJECTIVE

Describe the development of the REDCap Clinical Data Interoperability Services (CDIS) module that provides seamless data exchange between the REDCap research EDC and any EHR system with a FHIR API. CDIS enables end users to independently set up their data collection projects, map EHR data to fields, and adjudicate data transfer without project-by-project involvement from Health Information Technology staff.

METHODS

We identified two use cases for EHR data transfer into REDCap. Clinical Data Pull (CDP) automatically pulls EHR data into user-defined REDCap fields and replaces the workflow of having to transcribe or copy and paste data from the EHR. Clinical Data Mart (CDM) collects all specified data for a patient over a given time period and replaces the process of importing EHR data for registries from research databases. With an iterative process, we designed our access control, authentication, variable selection, and mapping interfaces in such a way that end users could easily set up and use CDIS.

RESULTS

Since its release, the REDCap CDIS has been used to pull over 19.5 million data points for 82 projects at Vanderbilt University Medical Center. Software and documentation are available through the REDCap Consortium.

CONCLUSIONS

The new REDCap Clinical Data and Interoperability Services (CDIS) module leverages the FHIR standard to enable real-time and direct data extraction from the EHR. Researchers can self-service the mapping and adjudication of EHR data into REDCap. The uptake of CDIS at VUMC and other REDCap consortium sites is improving the accuracy and efficiency of EHR data collection by reducing the need for manual transcription and flat file uploads.