Best Paper Selection

Appendix: Content Summaries of Selected Best Papers for the 2024 IMIA Yearbook, Section Clinical Research Informatics

Gierend K, Waltemath D, Ganslandt T, Siegel F.

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study.

JMIR Form Res 2023;7:e50027.

doi: 10.2196/50027

The study aimed to enhance the reusability of clinical routine data within a medical data integration center (DIC) for secondary use in clinical research and to establish traceable provenance information to ensure data integrity, reliability, and trust. This was achieved by developing a proof-of-concept provenance class, which integrated provenance traces at the data element level using the W3C international standard. The study employed a customized roadmap for a provenance framework, aligning records with healthcare standards such as FHIR (Fast Healthcare Interoperability Resources) and conducted a comprehensive assessment of provenance trace measurements. The results demonstrated successful implementation of traceable provenance information within a German medical DIC, marking the first such integration. The study showcased effective data management practices enhanced by provenance metadata, with commendable execution times and accuracy in processing clinical routine data. Provenance traces allowed for a detailed and reliable presentation of data transformations and their lineage, thereby supporting secondary use and research. The study concluded that the innovative method of integrating provenance information into clinical data promotes effective and reliable data management. This approach enhances trust and accountability in clinical data used for research, with potential applications beyond the medical sector. The traceable provenance information significantly improves the quality and reliability of data. The system mitigates risks by ensuring that data analysis is informed by knowledge of the origin and quality of all data elements, thus preventing ineffective analyses based on compromised data. These principles, although developed for the medical DIC use case, can be universally applied throughout the scientific domain, thereby enhancing the reliability and safety of quality-assured patient data for secondary use.

Hamidi B, Flume PA, Simpson KN, Alekseyenko AV.

Not all phenotypes are created equal: covariates of success in e-phenotype specification.

J Am Med Inform Assoc. 2023 Jan 18;30(2):213-221.

doi: 10.1093/jamia/ocac157.

The goal of the study was to understand the factors contributing to the successful creation of electronic phenotypes (e-phenotypes) and identify covariates associated with success rates in e-phenotype validation. Specifically, it aimed to compare the performance of “computer scientists” and “noninformaticists” in this task. Noninformaticist experts (n=21) created e-phenotypes using the i2b2 platform with support from a data broker and a project coordinator. Validation involved re-identifying patient and visit sets and selecting a random sample of charts for experts to review, assessing their match to the intended e-phenotypes. The study focused on characteristics of the queries and the experts themselves.

Results showed significant variability in validation rates for e-phenotypes, largely influenced by the domain of expertise and query characteristics. Domains such as infectious diseases, rheumatic conditions, neonatal issues, and cancers demonstrated better performance. Challenges included distinguishing between patient characteristics and clinical events and configuring temporal constraints. Besides, inpatient-focused domains, which collect more comprehensive data in electronic health records, had higher match rates compared to outpatient-focused domains. The study highlighted that expert domain knowledge and the design of queries are crucial for the success of e-phenotypes. It emphasized the importance of specialized support in phenotype design to ensure high-quality, reliable e-phenotype creation for diverse clinical and research applications. The collaborative process between clinical domain experts and data brokers, referred to as “biomedical query mediation,” played a significant role in achieving successful phenotyping.

Ozonze O, Scott PJ, Hopgood AA.

Automating Electronic Health Record Data Quality Assessment.

J Med Syst. 2023 Feb 13;47(1):23.

doi: 10.1007/s10916-022-01892-2.

The topic of data quality has gained importance across the clinical and research spectrum, and fuelled important research in recent years that has also been reflected in some CRI best papers. However, to scale the process of assessing data quality, the definition of dimensions and data element specific rules need to be progressively standardised, and automated tooling is required. These aspects of standardisation and automation are still emerging, and this paper was selected as one of the best papers because it provides an up-to-date review of that automation process. The authors have focused attention on three recognised dimensions of data quality: completeness, correctness and currency and drawn attention to the need to assess both univariate and multivariate data quality issues. Their review focuses on 23 articles reporting data quality assessment implementations over the past eight years. Their assessment includes which dimensions of data quality were capable of assessment, the technical nature of the tool that was produced, and the points in the data quality assessment life cycle the tools and any accompanying rules can be used. The paper proposes a concept model for data quality assessments that is comprehensive and may serve the developers of future data quality tools to position their work consistently. The authors examined the functional characteristics of the reported tools: the extent to which the tool could be configured for different clinical domains, usability, performance and information security. They conclude that there is still a lack of clarity about the positioning of data quality assessment tools within an assessment workflow, and the need for the tooling to be better able to handle the heterogeneity of EHR data repositories. They also note that there is no current culture of quality assessing the tools used for data quality assessment. Maturity in this field of data quality assessment methodologies and tools is vitally needed to scale up the routine assessment of data quality and to propel the data quality improvement momentum.

Die Autoren geben an, dass kein Interessenkonflikt besteht.

Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025