Leveraging Interoperable Electronic Health Record (EHR) Data for Distributed Analyses in Clinical Research: Technical Implementation Report of the HELP Study

Abstract

Background: The Medical Informatics Initiative (MII) Germany established 38 data integration centers (DIC) in university hospitals to improve health care and biomedical research through the use of electronic health record (EHR) data. To showcase the value of these DIC, the HELP (Hospital-wide Electronic Medical Record Evaluated Computerized Decision Support System to Improve Outcomes of Patients with Staphylococcal Bloodstream Infection) study was initiated as a use case. This study is a clinical trial designed to assess the impact of a computerized decision support system for managing staphylococcal bacteremia.

Objective: In this paper, we present the lessons learned during the use case from a technical perspective. This paper outlines the challenges encountered and solutions developed during our initial implementation of this infrastructure, providing insights applicable to other research platforms using EHR data. These insights are organized into 3 key areas: study-specific data definition and modeling, interoperable data integration and transformation, and distributed data extraction and analysis.

Methods: An interdisciplinary team of clinicians, computer scientists, and statisticians created a catalog of items to identify data elements necessary for the study's evaluation and developed a domain-specific information model. DIC developed extract-transform-load pipelines to collect the disparate, site-specific EHR data and to transform it into a common data format. Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) and the MII's core dataset profiles were adopted for consistent data representation across sites. Additionally, data not present in EHRs was gathered using structured electronic case report forms. Analysis scripts were then distributed to the sites to preprocess the data locally, followed by a central analysis of the preprocessed data to generate the final overall results.

Our analysis revealed significant heterogeneity in data quality and implementation of interoperability standards, requiring substantial harmonization efforts. The development of analysis scripts and data extraction processes demanded multiple iterative cycles and close collaboration with local data experts. Despite these challenges, the successful implementation demonstrated the feasibility of distributed EHR analyses while highlighting the importance of thorough data quality assessment, realistic timeline planning, and multidisciplinary expertise.

Conclusions: The HELP study highlights challenges and opportunities in leveraging EHR data for clinical research, particularly in the absence of mandatory data standards and resource-intensive data harmonization efforts. Despite limitations in data availability and quality, progress in digitization and interoperability frameworks offers hope for future improvements. Lessons learned from this study can inform the development of standardized methodologies and infrastructures for sustainable EHR data integration in research.

Keywords: clinical decision support system; data collection methods; electronic health records; health information interoperability; software design.

Figure 1.. Overall data flow in the HELP (Hospital-wide Electronic Medical Record Evaluated Computerized Decision Support System to Improve Outcomes of Patients with Staphylococcal Bloodstream Infection) study, based on interoperable formats created in data integration centers (DIC). EHR: electronic health record; eCRF: electronic case report form; FHIR: Fast Healthcare Interoperability Resources; MII CDS: core dataset of the Medical Informatics Initiative.

Figure 2.. Illustration of modules of the Medical Informatics Initiative (MII) core dataset. The HELP (Hospital-wide Electronic Medical Record Evaluated Computerized Decision Support System to Improve Outcomes of Patients with Staphylococcal Bloodstream Infection) study used the basic modules “person,” “treatment case,” “diagnosis,” “procedures,” “lab results,” “medication” [15], and the extension module “microbiology” [16].

Figure 3.. One table was created per resource type; afterwards, tables were joined based on references.

Figure 4.. Examples for units of measurement for alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in German laboratory results, captured from a PDF.

Figure 5.. Two exemplary FHIR (Fast Healthcare Interoperability Resources) resources, illustrating potential differences despite adhering to the same FHIR profile. Elements that exist in both resources but contain different codes are highlighted in yellow, while elements present in only 1 resource are marked in red. The resources have been shortened and anonymized.

Figure 6.. Iterative data extraction and analysis process in the HELP (Hospital-wide Electronic Medical Record Evaluated Computerized Decision Support System to Improve Outcomes of Patients with Staphylococcal Bloodstream Infection) study. FHIR: Fast Healthcare Interoperability Resources; DIC: data integration center; ADT: admission, discharge, and transfer data; LIS: laboratory information system; eCRF: electronic case report form; ETL: extract-transform-load.