Automating Electronic Clinical Data Capture for Quality Improvement and Research:
            The CERTAIN Validation Project of Real World Evidence

Devine, Emily Beth; Eaton, Erik van; Zadworny, Megan E.; Symons, Rebecca; Devlin, Allison; Yanez, David; Yetisgen, Meliha; Keyloun, Katelyn R.; Capurro, Daniel; Alfonso-Cristancho, Rafael; Flum, David R.; Tarczy‐Hornoch, Peter

doi:10.5334/egems.211

Cited by 12 publications

(17 citation statements)

References 16 publications

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“…Devine et al , 8 present an evaluation of data management at the hospitals of the Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) network engaged in the Comparative Effectiveness Research and Translation Network (CERTAIN). It aims at reusing EHRs for quality improvement and research.…”

Section: Discussionmentioning

confidence: 99%

Clinical Research Informatics: Contributions from 2018

Daniel

2019

Yearb Med Inform

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Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results: Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions: Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

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Section: Discussionmentioning

confidence: 99%

Clinical Research Informatics: Contributions from 2018

Daniel

2019

Yearb Med Inform

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“…This resulted in the identification of six additional articles. [24][25][26][27][28][29] Thus, the final set was comprised of 20 total articles (Figure 1).…”

Section: Methodsmentioning

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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“…• Encourage investigation of Natural Language Processing technology: Improvements in sharing of common data elements and automated data collection will never completely remove the need for identification of complex information within clinical data. Research has shown 38,39 that Natural Language Processing can provide reasonable automated extraction to both find and encode information only in free-text, but also help support the documentation process.…”

Section: Harmonization Beyond Measure Artifactsmentioning

confidence: 99%

Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR

McClure¹,

Macumber²,

Skapik

et al. 2020

Appl Clin Inform

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Background Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems. Objective In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets. Methods The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs. Results In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance. Conclusion Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.

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Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence

Cited by 12 publications

References 16 publications

Clinical Research Informatics: Contributions from 2018

Clinical Research Informatics: Contributions from 2018

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

Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR

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