Published May 7, 2025 | Version v1
Dataset Open

Addressing the Challenges of Health Data Standard Adoption and Usage: A Systematic Review - Data Extraction

Creators

  • 1. ROR icon University of Bologna
  • 2. Alma Mater Studiorum - University of Bologna
  • 3. IRCCS Istituto Ortopedico Rizzoli

Contributors

  • 1. ROR icon University of Bologna
  • 2. Alma Mater Studiorum - University of Bologna
  • 3. IRCCS Istituto Ortopedico Rizzoli

Description

This table presents the data extraction from the 99 studies included according to the criteria outlined in the main manuscript. It is provided as supplementary material to enhance the readability of the paper while ensuring that all relevant information is preserved and accessible without loss of detail.

The names of the variables and their descriptions are provided in the attached file, along with the following details:

Variable Description
Ref. The citation in the format: First author et al. [Year] (e.g., AuthorA et al. [2022]). This identifies the study's primary citation for easy reference.
Title The title of the paper
Standard The healthcare data standard used in the study. Possible values are: OMOP, OpenEHR, FHIR.
Study Location The country where the study was conducted.
Objective for using the standard Detailed The comprehensive explanation of the specific objective of using the standard in the study, describing how it supports the study’s goals.
Short The primary purpose for applying the healthcare standard. Possible values are: Secondary data reuse, Data exchange, Clinical decision support, Vocabulary definition, EHR system design,
Application domain Type The application domain type that represents the healthcare standard. Possible solution are:  Clinical: Studies with a direct impact on clinical practice, applying established tools or methods in healthcare settings (e.g., predicting in-hospital mortality for heart attack patients) and  Research: Studies proposing innovative tools, methodologies, or frameworks still in the design/testing phase, not yet clinically implemented.
Healthcare Area The relevant healthcare domain for the study, such as Cardiovascular, Intensive Care Unit, Emergency Department, Oncology, Biology, etc.
Cluster The healthcare domain clusterized for easier readability. Possible values include: Clinical Medicine, Clinical Services and Diagnostics, Public Health, Health Information Management and Biomedical Sciences
Use This report if the results of the paper serving a Primary use (direct care) or a Secondary use (repurposing existing data or tools for new objectives).
Scale The scale of the study. Possible values are: Single center (one hospital/clinic), Multi-center (multiple institutions), Regional (specific region), National level (countrywide).
Dataset magnitude in patients The magnitude of the dataset expressed in chars. Possible values are: A (<10 to 99), B (100 to 9,999), C (10,000 to 999,999) and D (1,000,000 and above).
N° Elements The number of variables of input in the process of standardization.
Percentuage of mapped variables The percentage of successful data standardisation.
Coverage of the standard The methodology of standardisation wheter it was adapted or not.
ETL Tools Data cleaning & extraction The tools adopted for supporting data cleaning and extraction.
Mapping The tools adopted for the mapping of the variables.
Validation The tools adopted for the validation of the standardization process.
Database The database adopted for storing the result of the healthcare data standardization.
Process efficiency and Economic assessment The information about the economic impact if the consequences are concrete and measured by the authors (e.g., actual cost savings, resource usage reductions). If the authors did not measure the economic impact, this field remains blank.
Comments by authors Limitations The significant limitations or challenges faced during the study about the standard adopted, such as issues with data compatibility, scalability, or the need for customization.
Advantages The benefits of applying the standard model, such as improved data consistency, enhanced clinical outcomes, better interoperability, or more efficient workflows.

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Additional details

Dates

Created
2025-05-07