Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research

Garza, Maryam Y.; Rutherford, Michael; Adagarla, Bhargav; Eisenstein, Eric L.; Kumar, Karan; Zimmerman, Kanecia O.; Topaloğlu, Ümit; Zozus, Meredith N.

doi:10.3233/shti210188

Cited by 17 publications

(24 citation statements)

References 6 publications

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“…As we look back at the results from the literature, the variability and the magnitude of error rates reported should encourage additional evaluation of the impact of new technology and processes on data accuracy and subsequent decisions regarding whether the accuracy of the data is acceptable for the intended use. Research opportunities exist (1) to understand the types of errors that perpetuate in MRA data collection, and (2) in the areas of data and process standardization [20][21][22] to aid in streamlining data collection for clinical research studies. Our ongoing work aims to address these issues as we develop solutions to streamline data collection processes in clinical research.…”

Section: Discussionmentioning

confidence: 99%

Measuring and Controlling Medical Record Abstraction (MRA) Error Rates in an Observational Study

Garza

Ounpraseuth

et al. 2022

Preprint

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Background: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical studies. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and control of MRA. The objective of this work is to determine the effects of formalized MRA training and continuous quality control (QC) processes on data quality over time. Methods: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald’s method) and adjusted (generalized estimating equation) error rates over time. Comparison of error rates with estimates derived from meta-analysis of the literature on the topic were conducted. We calculated error rates using the number of errors divided by total fields (“all-field” error rate) and populated fields (“populated-field” error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively. Results: On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative), 3-5 percentage points less than the observed rate from the literature. Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted.Conclusions: Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study.

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Section: Discussionmentioning

confidence: 99%

Measuring and Controlling Medical Record Abstraction (MRA) Error Rates in an Observational Study

Garza

Ounpraseuth

et al. 2022

Preprint

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“…Of the 49 studies, the majority were conducted in Germany (47%, n=23) [12,26,[28][29][30][31]34,35,[40][41][42][45][46][47]52,53,[56][57][58]60,62,63,69], the United States (27%, n=13) [22,25,36,44,[48][49][50]61,[64][65][66]68,70], and Australia (6%, n=3) [1,43,67]. The remaining studies were performed in Austria (2%, n=1) [32], Canada (2%, n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan (2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55], Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and the United Kingdom (2%, n=1)…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…In terms of medical specialty, most (55%, 27/49) of the studies [24,[27][28][29][30][31][32]34,[36][37][38][39][40][41][42]44,46,48,49,53,56,[60][61][62][63]67,70] were using a generic approach-implementable in any kind of specialty (Table 2). Of the remaining studies, 16% (8/49) use cases focused on infectious disease [1,22,23,26,45,47,50,59], whereas 12% (6/49) focused on oncology [25,55,57,58,65,66] and 8% (4/49) on genomics [43,52,68,69].…”

Section: Study Objectivesmentioning

confidence: 99%

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Vorisek¹,

Lehne²,

Klopfenstein³

et al. 2022

JMIR Med Inform

105

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Background:The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective:The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods:We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results:We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions:Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such...

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“…Phase I of the OneSource demonstration project leveraged the HL7 ® CCD ® , Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM), and IHE RFD standards for the capture and transmission of clinical research data. The primary objective of Phase I was to "assess the utility of the standards-based technology" 26 through EHR-to-eCRF data element mapping using Clinical Data Acquisition Standards Harmonization (CDASH) terminology (similar to the data standards work done by Garza and colleagues [44][45][46] and to the RADaptor implementation work by Nordo and colleagues 18 ). As part of Phase I, Rocca and colleagues 26 performed a gap analysis to identify the availability of CRF data elements within the EHR.…”

Section: Single-site Single-ehr Implementationsmentioning

confidence: 99%

“…Additionally, the standards implemented as part of Phase I -primarily HL7 ® CCD ® and IHE RFD -have been identified in earlier works as outdated and/or lacking in content coverage. 11,18,46 Phase II was discussed briefly as proposed future work to extend OneSource by incorporating additional standards (i.e., HL7 ® FHIR ® ) and functionality (i.e., visualization systems integration); although, a timeline for this work was not provided.…”

Section: Single-site Single-ehr Implementationsmentioning

confidence: 99%

eSource for Standardized Health Information Exchange in Clinical Research: A Systematic Review of Progress in the Last Year

Garza¹,

Myneni

Fenton

et al. 2021

Journal of the Society for Clinical Data Management

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To identify studies conductedusing the direct, electronic extraction of electronic health record (EHR) datato electronic data capture (EDC) systems, also known as eSource, and toidentify any gaps or limitations present for promoting standardized healthinformation exchange in clinical research.Materials and Methods:Articleswere included only if the solution described (1) utilized eSource to directlyexchange data electronically from EHR-to-EDC and (2) was relevant to aprospective clinical study use case.Results:Intotal, 20 relevant articles were identified, describing a total of 15 uniqueeSource interventions. Of the 15interventions, 12 were single-site, single-EHR (SS-SE) implementations and 3were multi-site, multi-EHR (MS-ME) implementations. All 15 implementationsmentioned the use of standards, but nearly all referenced older data exchangestandards.  Discussion:Following the trajectory of work towardsdirect EHR-to-EDC, eSource data collection, we appear to have arrived at thepoint where information systems leveraging data standards can offer efficiencyand increased quality in clinical research. However, these methods need to betested for effectiveness and acceptance in the context of real multicenterclinical trials. Several early studies using a single source of data forresearch and patient care appeared over a decade ago. Since that time,implementations and evaluations have been scarce and almost always confined tosingle-EHR, single-EDC, single-institution implementations.Conclusion:These results only further emphasize the observation thatthe clinical trial use case continues to be the most difficult and leastdemonstrated eSource-related initiative. Thus, additional work is criticallyneeded in this area to address the gaps identified from the literature.

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Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research

Cited by 17 publications

References 6 publications

Measuring and Controlling Medical Record Abstraction (MRA) Error Rates in an Observational Study

Measuring and Controlling Medical Record Abstraction (MRA) Error Rates in an Observational Study

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

eSource for Standardized Health Information Exchange in Clinical Research: A Systematic Review of Progress in the Last Year

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