Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs

Development of a Core Critical Care Data Dictionary With Common Data Elements to Characterize Critical Illness and Injuries Using a Modified Delphi Method

OBJECTIVES

To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries.

DESIGN

Group consensus process using modified Delphi approach.

SETTING

Electronic surveys and in-person meetings.

SUBJECTS

A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured.

INTERVENTIONS

The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset.

MEASUREMENTS AND MAIN RESULTS

The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients.

CONCLUSIONS

The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.

Converting Health Level 7 Clinical Document Architecture (CDA) documents to Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) by leveraging CDA Template definitions

Objectives

This work aims to develop a methodology for transforming Health Level 7 (HL7) Clinical Document Architecture (CDA) documents into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The described method seeks to improve the Extract, Transform, Load (ETL) design process by using HL7 CDA Template definitions and the CDA Refined Message Information Model (CDA R-MIM).

Material and Methods

Our approach utilizes HL7 CDA Templates to define structural and semantic mappings. Supported by the CDA R-MIM for semantic alignment with the OMOP CDM, we developed a tool named CDA Rabbit that enables the generation of Rabbit-In-a-Hat project files from HL7 CDA Template definitions and could be successfully integrated into the existing toolchain around OMOP.

Results

We tested our approach using 13 CDA Templates from the Austrian national EHR System (ELGA) and 430 anonymized CDA test documents that were mapped to 10 OMOP CDM tables. The data quality assessment, using OMOP's DataQualityDashboard, showed a 99% pass rate, indicating a robust and accurate data transformation.

Conclusion

This study presents a novel framework for transforming HL7 CDA documents into OMOP CDM using template definitions and CDA R-MIM. The methodology improves semantic interoperability, mapping reusability, and ETL design efficiency. Future work should focus on automating code generation, improving data profiling, and addressing cyclic dependencies within CDA templates. The presented approach supports improved secondary use of health data and research while adhering to standardized data models and semantics.

Discussion

Using CDA Templates for ETL design addresses common ETL challenges, such as data accessibility during ETL design, by decoupling the process from the actual CDA instances. Future work could focus on extending this approach to automatically generate boilerplate code, address cyclic dependencies within CDA Templates, and adapt the method for the use with FHIR profiles.

Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

A common longitudinal intensive care unit data format (CLIF) for critical illness research

RATIONALE

Critical illness threatens millions of lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness.

OBJECTIVES

Overcome the data management, security, and standardization barriers to large-scale critical illness EHR studies.

METHODS

We developed a Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format to harmonize EHR data necessary to study critical illness. We conducted proof-of-concept studies with a federated research architecture: (1) an external validation of an in-hospital mortality prediction model for critically ill patients and (2) an assessment of 72-h temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models.

MEASUREMENTS AND MAIN RESULTS

We converted longitudinal data from 111,440 critically ill patient admissions from 2020 to 2021 (mean age 60.7 years [standard deviation 17.1], 28% Black, 7% Hispanic, 44% female) across 9 health systems and 39 hospitals into CLIF databases. The in-hospital mortality prediction model had varying performance across CLIF consortium sites (AUCs: 0.73-0.81, Brier scores: 0.06-0.10) with degradation in performance relative to the derivation site. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.

CONCLUSIONS

CLIF enables transparent, efficient, and reproducible critical care research across diverse health systems. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational artificial intelligence (AI) models of critical illness, and real-time critical care quality dashboards.

Guiding Principles for Data Sharing and Harmonization: Results of a Systematic Review and Modified Delphi From the Society of Critical Care Medicine Data Science Campaign

OBJECTIVES

This study aimed to establish a set of guiding principles for data sharing and harmonization in critical care, focusing on the use of real-world data (RWD) and real-world evidence (RWE) to improve patient outcomes and research efficacy. The principles were developed through a systematic literature review and a modified Delphi process, with the goal of enhancing data accessibility, standardization, and interoperability across critical care settings.

DATA SOURCES

Data sources included a comprehensive search of peer-reviewed literature, specifically studies related to the use of RWD and RWE in healthcare, guidelines, best practices, and recommendations on data sharing and harmonization. A total of 8150 articles were initially identified through databases such as MEDLINE and Web of Science, with 257 studies meeting inclusion criteria.

STUDY SELECTION

Inclusion criteria focused on publications discussing health-related informatics, recommendations for RWD/RWE usage, data sharing, and harmonization principles. Exclusion criteria ruled out non-human studies, case studies, conference abstracts, and articles published before 2013, as well as those not available in English.

DATA EXTRACTION

From the 257 selected studies, 322 statements were extracted. After removing irrelevant definitions and off-topic content, 232 statements underwent content validation and thematic analysis. These statements were then consolidated into 24 candidate guiding principles after rigorous review and consensus-building among the expert panel.

DATA SYNTHESIS

A three-phase modified Delphi process was employed, involving a conceptualization group, a review group, and a Delphi group. In phase 1, experts identified key themes and search terms for the systematic review. Phase 2 involved validating and refining the prospective guiding principles, while phase 3 employed a Delphi panel to rate principles on acceptability, importance, and feasibility. This process resulted in 24 guiding principles, with high consensus achieved in rounds 2 and 3 on their relevance and applicability.

CONCLUSIONS

The systematic review and Delphi process resulted in 24 guiding principles to improve data sharing and harmonization in critical care. These principles address challenges across the data lifecycle, including generation, storage, access, and usage of RWD and RWE. This framework is designed to promote more effective and equitable data practices, with relevance for the development of artificial intelligence-based decision support tools and clinical research. The principles are intended to guide the responsible use of data science in critical care, with emphasis on ethics and equity, while acknowledging the variability of resources across settings.

Critical Data for Critical Care: A Primer on Leveraging Electronic Health Record Data for Research From Society of Critical Care Medicine's Panel on Data Sharing and Harmonization

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research

Background

Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.

Methods

A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models.

Results

We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.

Conclusions

CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

Objectives

The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Materials and Methods

The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database.

Results

Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed.

Discussion

OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data.

Conclusion

MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

Objective

The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Materials and Methods

The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database.

Results

Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed.

Discussion

OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data.

Conclusion

MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.