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Review

Use of Metadata-Driven Approaches for Data Harmonization in
the Medical Domain: Scoping Review
Yuan Peng1, MSc; Franziska Bathelt2, Dr Rer Nat; Richard Gebler1, MSc; Robert Gött3, Dipl.-Ing.; Andreas
Heidenreich4, Dipl.-Biol., Dr Rer Nat; Elisa Henke1, MSc; Dennis Kadioglu4,5, MSc; Stephan Lorenz1, MSc; Abishaa
Vengadeswaran5, MSc; Martin Sedlmayr1, Dr Rer Nat, Prof Dr
1

Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany

2

Thiem-Research GmbH, Cottbus, Germany

3

Core Unit Datenintegrationszentrum, University Medicine Greifswald, Greifswald, Germany

4

Department for Information and Communication Technology (DICT), Data Integration Center (DIC), Goethe University Frankfurt, University Hospital,Frankfurt am Main, Germany
5

Institute for Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany

Corresponding Author:
Yuan Peng, MSc
Institute for Medical Informatics and Biometry
Carl Gustav Carus Faculty of Medicine
Technische Universität Dresden
Fetscherstraße 74
Dresden, 01307
Germany
Phone: 49 3514583648
Fax: 49 3514585738
Email: yuan.peng@tu-dresden.de

Abstract
Background: Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to theheterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation ofExtract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, inorder to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable.
A promising way to ease this is the generalization of ETL/ELT processes.
Objective: In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly,
we focus on approaches with low development complexity by using descriptive metadata and structural metadata.
Methods: We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews andMeta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center)to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence
Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis andvisualization.
Results: Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications
were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology,
and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different
thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases.
Conclusions: Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process canserve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process
by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational
Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to
implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizationsof the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in thishttps://medinform.jmir.org/2024/1/e52967

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study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This
could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.
(JMIR Med Inform 2024;12:e52967) doi: 10.2196/52967
KEYWORDS
ETL; ELT; Extract-Load-Transform; Extract-Transform-Load; interoperability; metadata-driven; medical domain; data
harmonization

Introduction
Multisite clinical studies are increasingly using real-world data
to gain real-world evidence, especially during the COVID-19
pandemic [1]. However, not all clinics use the same hospital
information system, resulting in heterogeneity of data produced
by different hospital information systems. These heterogeneous
data are not semantically and syntactically interoperable.
Therefore, it is difficult to analyze such data in a unified way
across sites. For this, the heterogeneous data need to beharmonized and standardized, for example, by using a common
data model (CDM) [2]. For example, the European Medical
Agency [3] set up the DARWIN EU (Data Analysis and RealWorld Interrogation Network European Union) [4] to provide
real-world evidence on use and adverse events of medicines
across the European Union. DARWIN EU uses theObservational Medical Outcomes Partnership (OMOP) CDM[5] as the base model, which is provided by the Observational
Health Data Sciences and Informatics [6] community. To
participate in such networks, a transformation of local data isneeded. A common approach is to develop an
Extract-Transform-Load (ETL) or Extract-Load-Transform(ELT) process. Both are used to harmonize heterogeneous data
into the target systems. The only difference between them isthe order of processing data. ETL transforms the data before
loading them into the target systems, while ELT loads the data
into the target systems first, and then transforms the data. Dueto the different data formats and source systems, multiple
ETL/ELT processes have to be implemented [7-10]. This work
is time-consuming and hard to maintain [11].
Using a standard data exchange format can reduce thecomplexity of transforming heterogeneous data into CDMs. An
example is the Fast Healthcare Interoperability Resources(FHIR) [12] format. FHIR is a communication standard and isprovided by the Health Level 7 (HL7) [13]. In Germany, theMedical Informatics Initiative (MII) [14] provides a Core Data
Set (CDS) [15] in FHIR format for enabling the interoperabilityof data across all university hospitals. Another German
association “the National Association of Statutory Health
Insurance Physicians” (KBV, German: Kassenärztliche
Bundesvereinigung) [16] also provides a KBV CDS in FHIR
format, which provides a stable foundation for the developmentof the medical information objects [17] (eg, immunization
records and maternity records). Although both MII CDS andKBV CDS are based on the German HL7 Basis Profiles [18],the FHIR profiles defined in the 2 CDSs are not identical [19].
This is due to the different requirements of MII and KBV. For
example, codes indicating departments within a clinic (eg, 0100
for internal medicine department) are defined in different
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complicates the implementation and maintenance of ETL/ELT
processes.
Furthermore, most countries try to standardize their electronic
health records (EHR) data for research and to improve theinteroperability of the data. Consequently, country-specific
FHIR profiles are developed, for example, German HL7 Basis
Profiles [18] and the US CDS [20]. Due to different languages(ie, German vs English), different structure definitions (eg,extensions and cardinality) and different coding systems (eg,system URL for International Classification of Diseases, 10,
Revision: German Modification [21] vs system URL for
International Classification of Diseases, 10, Clinical
Modification [22]) used in the FHIR profiles, different ETL
processes need to be implemented [8,23]. Although these arejust a few examples, it is conceivable that with the expansionof supported use cases, the time required for implementing an
ETL/ELT process increases massively, while the maintainability
decreases. Therefore, the implementation of a generic ETL/ELT
process for harmonizing local health data can guarantee thesemantic and syntactic interoperability of research data acrosssites and countries.
Using metadata for the implementation of ETL/ELT processes
is a promising approach, as stated by David Loshin [24]: “inorder to organize data for analytical purposes, it will need to be
extracted from the original source (source metadata),transformed into a representation that is consistent with thewarehouse (target metadata) in a way that does not lose
information due to differences in format and precision (structure
metadata) and is aligned in a meaningful way (semantic
metadata).” A very broad definition of metadata is “data aboutother data” [25]. Depending on the specific context of use,
metadata can be classified into 3 types [26]:•••Descriptive metadata: the metadata is used for discovery
and identification purposes, for example metadata for source
and target data.
Structural metadata: the metadata is used for managing
data in information systems, for example, column names
and table names in a database.
Administrative metadata: the metadata exists within adatabase that provides additional information, for example,
the name of a person, who has changed the data in a
database.

Metadata can be represented by metadata languages (eg,Resource Description Framework and Notation3) [27]. Suchlanguages are also called ontology languages. For enabling theinteroperability of data from different source and target systems,
rule languages (eg, Rule Markup Language and Semantic Web
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Rule Language) can be used to define the transformation rules
between them [27]. Therefore, the use of metadata is expected
to improve the development and maintenance for transforming
FHIR resources to OMOP CDM.

program (Corporation for Digital Scholarship) [29] and the
duplicates were manually removed. To better categorize thepublications to be excluded, we defined 8 exclusion criteria(Textbox 2).

As a side note, we understand any (descriptive and structural)
metadata-based approach used for developing ETL/ELT
processes as metadata-driven (MDD) approach. This work
focuses on providing an overview of the types of MDD
approaches and their use in different thematic categories. The
overview aims to identify a suitable MDD approach to enhancethe data transformation from FHIR to OMOP CDM. This will
be achieved by answering the following questions:This review was a 2-fold process consisting ofTitle-Abstract-Screening (TAS) and full-text screening (FTS).
Both screening processes used the same exclusion criteria listed
in Textbox 2. The unique publications were divided into 2
groups based on their publication dates and uploaded to a
research collaboration platform, Rayyan (Qatar Computing
Research Institute and Cochrane Bahrain) [30], as 2 separate
projects. Each publication group was assigned with 4 reviewers.
The corresponding author reviewed all publications. The TAS
was performed under the blind-modus, so that each reviewer
could label the publication independently. The blind-modus
was turned off after all publications were tagged and theconflicts were discussed and resolved. After that, all included
publications were randomly divided into 2 groups and reloaded
into Rayyan as a new project for FTS. Similar to TAS, 4
reviewers were assigned to each publication group and thecorresponding author reviewed all publications. The FTS was
also conducted under the blind-modus and followed the same
review process as the TAS.

•
•
•
•

Q1: What are the themes of application for MDD
approaches?
Q2: What types of MDD approaches exist in the literature?
Q3: What are the reasons for the usage of MDD
approaches?
Q4: What tool was used to implement the MDD approach?

Methods
To answer our 4 research questions, we conducted a literature
review. To ensure the transparency of the review process, wefollowed the PRISMA (Preferred Reporting Items for Systematic
Reviews and Meta-Analyses) guidelines [28]. We used 4
publication databases (ie, PubMed, IEEE Explore, Web of
Science, and Biomed Center) to search for relevant publications
from 2012 to 2022 written in German or English (Textbox 1).
The first search was performed on August 11, 2022, and thesecond one was on March 15, 2023, which in turn completed
the search through December 31, 2022. The collectedpublications were loaded into the Zotero Citation Management

We extracted the content of all included publications based on
the categories listed in Textbox 3. The extraction of publication
content was done by the corresponding author and validated by
4 coauthors. The extracted content was stored in a spreadsheet
for further analysis and visualization.
The result of the literature review was visualized using an
R-based tool, which was developed based on PRISMA 2020[31].

Textbox 1. Search string and publication databases.
Search string
PubMed•((meta data) OR (meta-data) OR (metadata) OR (ontology) OR (rules)) AND ((extract transform load) OR (ETL) OR (extract load transform)
OR (ELT))IEEE Explore•((“All Metadata”:metadata) OR (“All Metadata”:meta-data) OR (“All Metadata”:meta data) OR (“All Metadata”:ontology) OR (“AllMetadata”:rules)) AND ((“All Metadata”:ETL) OR (“All Metadata”:extract transform load) OR (“All Metadata”:ELT) OR (“All Metadata”:extract
load transform))

Web of Science•(ALL=(metadata) OR ALL=(meta-data) OR ALL=(“meta data”) OR ALL=(ontology) OR ALL=(rules)) AND (ALL=(ETL) OR ALL=(“extract
transform load”) OR ALL=(ELT) OR ALL=(“extract load transform”))

Biomed Center (BMC)•(“meta data” OR meta-data OR metadata OR ontology OR rules) AND (“extract transform load” OR ETL OR “extract load transform” OR ELT)

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Textbox 2. Labels and descriptions of exclusion criteria.
Wrong_abbreviation•

Publication does not contain Extract-Transform-Load (ETL) as “Extract-Transform-Load.”•

Publication does not contain Extract-Load-Transform (ELT) as “Extract-Load-Transform.”Wrong_definition•

Publication does not use metadata in the context of “metadata of data in source or target.”•Publication does not use rules in the context of “rules for data transformation.”

Only_etl_elt•

Publication describes only ETL/ELT.

Only_metadata•

Publication describes only metadata.

Wrong_focus•

Publication mentioned metadata and ETL/ELT, but the focus is not about data harmonization

Wrong_type•

Publication is not a conference paper or a journal publication

Foreign_language•

Publication is written in other languages than English and GermanWrong_content•

Publication does not mention ETL/ELT or metadata

Textbox 3. Categories for data extraction.
Theme•

The main theme of the work.

Metadata-driven method•

The used metadata-driven method in the work.

Metadata-driven method tool•

Tool which was used to conduct the metadata-driven method.

Purpose•The purpose of using the metadata-driven method.

Results
Literature Search
The literature search resulted in 538 publications. Afterremoving 85 duplicates, 453 publications were screened during
the TAS phase. By using the exclusion criteria defined in

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Textbox 2 and excluding the publications, which have nofull-text, 64 publications were included for FTS. Finally, we
included 33 publications in this work. The screening process
and results are structured using the PRISMA flow diagram 2020
(Figure 1). A complete list of included publications is available
in Multimedia Appendix 1.

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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Generated using an R-based tool (reproduced
from Haddaway et al [31], with permission from Neal R Haddaway).

Distribution of Publications
In order to gain an overview of the potential application focusesof MDDs (Q1) and thus an indication of where the approaches
have proven beneficial, the focused theme of application was
first evaluated. According to the extracted data, the focuses of
all included publications are classified into 7 different categories,
namely medicine (n=9) [10,32-39], data warehouse (n=13)[40-52], big data (n=4) [53-56], industry (n=4) [57-60],
geoinformatics (n=1) [61], archaeology (n=1) [62], and military(n=1) [63]. This shows that data warehouse and medicine are
the 2 categories that use the MDD approach the most.

MDD Approaches Used for Various Thematic
Categories
Different types of MDD approaches were used across thethematic categories. To gain knowledge about the use of thesehttps://medinform.jmir.org/2024/1/e52967

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types of MDD approaches in each category (Q2), the distributionof MDD approaches was investigated. Figure 2 shows theapplication of different types of MDD approaches in different
thematic categories. The most frequently used type of MDD
approach was ontology-based, where the ontology (using forexample, resource description framework) of the source or target
was applied in the ETL/ELT process. This approach was usedin 6 categories, particularly in the categories of data warehouse[45-48,50,52] and medicine [10,32,35,37-39]. Anotherfrequently used type of MDD approach was rule-based, whichapplied transformation rules generated based on the source andtarget to the ETL/ELT process. The rule-based approach was
also widely used in the categories of data warehouse [40-43,49]
and medicine [33,34,37,39]. All other MDD approaches besides
the ontology-based and rule-based approaches were categorized
as “other” (Table 1).

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Figure 2. Metadata-driven approaches used in each thematic category.

Table 1. MDDa approaches that are categorized as “other.”
MDD approach type and publication Example
UMLb-based
Dhaouadi et al [46]

UML class diagram is used for modeling the transformation process

Graphic-based
Dhaouadi et al [46]BPMNc standard is used for modeling an ETLd process

Ad hoc formalisms-based
Dhaouadi et al [46]Entity Mapping Diagram is used for representing ETL tasks

MDAe-based
Dhaouadi et al [46]MDA is a multilayered framework with multiple submodules for separation of the specification of a functionality from its implementation

Message-based
Novak et al [51]“Normal message” contains information of mapping and transformation; “command message” configures the(execution) system

Template-based
McCarthy et al [58]A transformation template for each data source that manages the complex transformation process

Binding et al [62]A template contains the mapping patterns which is then used for querying in database

Metadata-basedfaOzyurt and Grethe [36]Implementing a generic data transformation language to transform heterogeneous data from multiple sources
to a common format

Tomingas et al [44]Metadata of the source and target stored in a knowledge and metadata repository

Suleykin and Panfilov [60]Metadata of the mapping path stored in a metadata management framework

MDD: metadata-driven.

b

UML: unified modeling language.

c

BPMN: Business Process Model Notation.

d

ETL: Extract-Transform-Load.

e

MDA: Model Driven Architecture.

f

Metadata-based approach: approach uses metadata without any specification.

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Purposes of Using MDD Method for Data
Harmonization
The purpose of using MDD approaches in each use case wasthen investigated to clarify the reasons why MDD approaches
were used (Q3). Figure 3 shows different purposes of using
MDD approaches in developing ETL/ELT processes based on
the extracted data. The majority of publications describe theuse of MDD approaches to develop an ETL/ELT process. This
purpose can be divided into three detailed categories: (1) toautomate the development of the ETL/ELT process[35,38,42,46,48-51,60], (2) to develop a generic ETL/ELT
process [39,47,52], and (3) to develop a new ETL/ELT process

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without any further technical specifications [40,45,46,55,57,61].
Additionally, the transformation part of the ETL/ELT process
could also be automated by applying an MDD approach[34,37,41,44,58,63]. For example, Chen and Zhao [41] described
an MDD approach for the automatic generation of SQL scripts
for data transformation. Moreover, using MDD approaches canalso help to improve the performance of ETL/ELT processes[43,46] or to partially or fully reuse the ETL/ELT process[10,33,43,62]. Other goals (categorized as “Others” in Figure3), such as simplifying the maintenance of the transformation
process [37] and reducing the complexity of the extraction
process [53], can also be realized by using MDD approaches in
ETL/ELT processes.

Figure 3. Purposes of using MDD approaches in ETL/ELT process. ELT: Extract-Load-Transform; ETL: Extract-Transform-Load; i2b2: Informatics
for Integrating Biology and the Bedside; MDD: metadata-driven.

Relationship Between Use Case and Used MDD
Approach
As shown in the previous section, different MDD approaches
were applied for different purposes. To further elucidate thereasons for choosing MDD approaches (Q3), the relationshipbetween them was investigated. Table 2 lists the number ofpublications, which used a type of MDD approach to achievea specific purpose. The ontology-based approach was used toachieve purposes (1) and (2), and (4)-(7). For example, Huang
et al [63] created both local ontology (ontology based on thesource data) and global ontology (ontology for the query
processing) for the data transformation process, so that the datatransformation from local ontology to global can be automatedby applying ontology learning, ontology mapping, and ontology

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rules. Additionally, the ontology-based approach was also used
to achieve other goals, such as controlling the ETL process toInformatics for Integrating Biology and the Bedside [32] andreducing the complexity of the extraction process [53]. Similar
to the ontology-based approach, the rule-based approach wasused to achieve the purposes of (1)-(3) and (5)-(7). Due to thereusability of the transformation rules, it was also possible tosimplify the maintenance of the ETL/ELT process by applying
rules in the process [37]. Other MDD approaches such as
template-based [58,62], message-based [51], and metadata-based[41,44,48] were used to achieve the goals of (1)-(3) and (5)-(7).
A metadata-based approach (eg, metadata management
framework) can be used to develop the ETL tasks automatically[60]. The detailed information of Table 2 is available in theMultimedia Appendix 1.

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Table 2. Relationships between purposes and MDDa approaches used.

aPurposes

MDD approaches

Number Description

Ontology-based, n/N (%) Rule-based, n/N (%)

Other, n/N (%)(1)

To automate the data transformation within an ETLb/ELTc process

2/6 (33)

3/6 (50)

1/6 (17)(2)

To reuse an ETL/ELT process (partially or completely)

1/4 (25)

2/4 (50)

1/4 (25)(3)

To improve the performance of an ETL/ELT process

0/2 (0)1/2 (50)

1/2 (50)(4)

To develop a generic ETL/ELT process

3/3 (100)

0/3 (0)0/3 (0)(5)

To develop an ETL/ELT process automatically

5/9 (56)

2/9 (22)

2/9 (22)(6)

To develop a new ETL/ELT process (without any other specificpurposes)

4/6 (67)

1/6 (17)

1/6 (17)

(7)

Other

5/11 (45)

2/11 (18)

4/11 (36)

MDD: metadata-driven.

b

ETL: Extract-Transform-Load.

c

ELT: Extract-Load-Transform.

Tools Used for Implementing MDD Approaches
Finally, we focused on the tools used to implemented MDD
approaches (Q4). For achieving various purposes as shown in
the previous section, different tools were used. As shown in
Figure 4, each type of MDD approach can be implemented byusing either an existing tool or a use case specific tool. Basedon the included publications, the ontology-base approaches
were mostly implemented using Protégé (Stanford Center for
Biomedical Informatics Research) [64]. Protégé is an ontology
editor, as well as OntoEdit (Institute AIFB, University ofKarlsruhe and Ontoprise GmbH) [65]. The main reason forusing an ontology editor is its ease of use and maintenance, as
well as the various plug-ins. The use of case specific tools, suchas ontology generator introduced by Kamil et al [45], generated
ontologies based on the data definition language of the relational
database. Both types of tools were used for creating andmaintaining the ontology, which was then used to establish a

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generic mapping logic in the ETL/ELT process[32,50,52,54,55,61]. Another type of frequently used MDD
approach is rule-based, which is used for phrasing and storing
the transformation rules. The transformation rules can be stored
in a mapping sheet [49], a CSV file [34], a YAML (YAML
Ain’t Markup Language) file [33] or a table within a database[43], which were implemented manually. Afterwards, thetransformation rules could be used in the ETL/ELT process, forexample, to enable the automatic transformation. Other types
of MDD approaches can also be implemented by using existing
tools (eg, knowledge and metadata repository [66]) or use casespecific tools (eg, metadata repository [41] and metadatamanagement framework [60]). For example, Ozyurt and Grethe[36] implemented a generic transformation language using thebioCADDIE Data Tag Suite (bioCADDIE Project) [67] (ametadata schema) to align heterogeneous data from multiple
sources, which provided a basis for further analytic queries.

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Figure 4. Tools used for developing the metadata-driven approach. MMF: metadata management framework; OWL: Web Ontology Language; YAML:YAML Ain’t Markup Language.

Discussion
Principal Findings
Our literature review on the topic “metadata-driven ETL/ELT”
includes all publications listed on PubMed, IEEE Explore, Web
of Science, and Biomed Center on MDD ETL/ELT processfrom 2012 to 2022. In some context, the use of metadata isrepresented specifically using “ontology” or “rules.” Therefore,
we added “ontology” and “rules” into the search string to expand
the search range.
With the review process presented, we were able to provide an
overview of the thematic categories to which the MDD
ETL/ELT processes were applied (Q1), the types of MDD
approaches used in the ETL/ELT processes (Q2), the purposes
of using MDD approaches (Q3), as well as the tools used to
implement the MDD approaches (Q4).
Across all thematic categories, ontology-based and rule-based
approaches are the most used approaches in the data warehouseand the medical thematic categories. In some cases, more than
one MDD approach was used in the ETL/ELT process. For
example, Del Carmen Legaz-García et al [39] used both
ontology-based and rule-based approaches. Therefore, such
publications were categorized as both MDD approach types.
Various tools can be used to implement MDD approaches.
Unfortunately, we were not able to extract this information fromhttps://medinform.jmir.org/2024/1/e52967

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all included publications. The reason for that is that somepublications used proprietary or nontransferable approaches(eg, data-specific ontologies [39,62] and rules from Data Vault[DataVaultAlliance] [42]). Some other publications did not
explicitly mention or describe the tools they used. Therefore,
these publications were not included in the analysis of MDD
tools used.
The results indicate that it is promising to implement a generic
ETL/ELT process to transform different FHIR profiles to OMOPCDM automatically by applying MDD approaches. However,
the results do not provide a trivial solution for this. For example,
Huang et al [63] used an ontology-based approach to be able toautomate the data transformation in an ETL/ELT process, while
Ong et al [34] used a rule-based approach to achieve the same
purpose. In some cases, more than one MDD approach wereused as complements in order to accomplish the datatransformation. For example, Pacaci et al [37] chose an
ontology-based approach to automate the data transformation
and a rule-based to simplify the maintenance of thetransformation process in case of changes in data sources. By
applying these 2 approaches in combination, the authors wereable to transform EHR data from heterogeneous EHR systems
into OMOP CDM. Therefore, determining an appropriate MDD
approach and tool to implement a generic ETL/ELT process totransform FHIR to OMOP CDM automatically remains achallenge.
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JMIR MEDICAL INFORMATICSThis work aimed to provide an overview of different types of
MDD approaches and their tools. Consequently, this review
lacks an analysis of detailing the specific traits of each MDD
approach. This gap underscores the importance of providing acomprehensive insight into the characterizations of the MDD
approaches presented in this study. This analysis will be
conducted in the future to provide solid evidence for selecting
the most suitable MDD approach and tool, or for considering
using multiple MDD approaches in combination to implement
the generic ETL/ELT process for transforming FHIR to OMOP
CDM.

Conclusions
Our literature review shows that using MDD approaches todevelop an ETL/ELT process can serve different purposes inPeng et al
different focus groups (ie, medicine, data warehouse, big data,industry, geoinformatics, archaeology, and military). The results
show that it is promising to implement an ETL/ELT process byapplying MDD approach for automating the data transformationfrom FHIR to OMOP CDM. However, the determination of an
appropriate MDD approach and tool to implement such an
ETL/ELT process remains a challenge. This is due to the lackof comprehensive insight into the characterizations of the MDD
approaches presented in this study. Therefore, our next step isto evaluate the MDD approaches presented in this study and to
determine the most appropriate MDD approaches and the wayof integrating them into the MII CDS FHIR to OMOP CDM
ETL process [8]. This could verify the ability of using MDD
approaches to generalize the ETL process for harmonizing
medical data [11].

Acknowledgments
This publication was partially funded by the German Federal Ministry of Education and Research (BMBF) Network of University
Medicine 2.0: “NUM 2.0”, Grant No. 01KX2121, Project: NUM-Data integration center – NUM-DIZ. The Article Processing
Charge was funded by the joint publication funds of the Technische Universität, Dresden, including the Carl Gustav Carus Facultyof Medicine, and the Sächsische Landesbibliothek—Staats- und Universitätsbibliothek, Dresden, as well as the Open AccessPublication Funding of the Deutsche Forschungsgemeinschaft.

Authors' Contributions
All authors contributed substantially to this work. YP did the search string definition and publications for the review-process
preparation. YP, FB, Robert G, AH, EH, DK, SL, and AV: screened the title and abstract. YP, FB, Richard G, Robert G, AH,
EH, DK, SL, and AV screened the full text. YP did the data extraction. FB, DK, Robert G, and SL performed the data extraction
validation. YP wrote the original draft. YP, FB, Richard G, Robert G, AH, EH, DK, SL, AV, and MS reviewed and edited thewriting. MS handled the resources. All authors have read and agreed to the current version of the paper and take responsibility
for the scientific integrity of the work.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Excel tables for extracted data from included publications.
[XLSX File (Microsoft Excel File), 462 KB-Multimedia Appendix 1]

Multimedia Appendix 2
PRISMA-ScR checklist.
[DOCX File , 85 KB-Multimedia Appendix 2]

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Abbreviations
CDM: Common Data Model
CDS: Core Data Set
DARWIN EU: Data Analysis and Real World Interrogation Network European Union
EHR: electronic health record
ELT: Extract-Load-Transform
ETL: Extract-Transform-Load
FHIR: Fast Healthcare Interoperability Resources
FTS: full-text screening
HL7: Health Level 7
KBV: The National Association of Statutory Health Insurance Physicians (German: Kassenärztliche
Bundesvereinigung)
MDD: metadata-driven
MII: Medical Informatics Initiative
OMOP: Observational Medical Outcomes Partnership
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
TAS: Title-Abstract-Screening
YAML: YAML Ain’t Markup Language

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Edited by C Lovis; submitted 20.09.23; peer-reviewed by M Löbe, W Xu; comments to author 24.10.23; revised version received
01.12.23; accepted 03.12.23; published 14.02.24
Please cite as:Peng Y, Bathelt F, Gebler R, Gött R, Heidenreich A, Henke E, Kadioglu D, Lorenz S, Vengadeswaran A, Sedlmayr M
Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review
JMIR Med Inform 2024;12:e52967
URL: https://medinform.jmir.org/2024/1/e52967
doi: 10.2196/52967
PMID: 38354027©Yuan Peng, Franziska Bathelt, Richard Gebler, Robert Gött, Andreas Heidenreich, Elisa Henke, Dennis Kadioglu, Stephan
Lorenz, Abishaa Vengadeswaran, Martin Sedlmayr. Originally published in JMIR Medical Informatics (https://medinform.jmir.org),
14.02.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

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