# Clinical Data Integration Strategies for Multicenter Studies

José Luís Oliveira, João Rafael Almeida, Alejandro Pazos

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José Luís Oliveira, João Rafael Almeida, Alejandro Pazos. Clinical Data Integration Strategies for Multicenter Studies. 14th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jul 2023, Monte Da Caparica, Portugal. pp.175-190, 10.1007/978-3-031-36007-7_13 . hal-05473067

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# Clinical Data Integration Strategies
for Multicenter Studies

João Rafael Almeida¹,²(✉), Alejandro Pazos², and José Luís Oliveira¹

¹ DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal
{joao.rafael.almeida,jlo}@ua.pt

² Department of Computation, University of A Coruña, A Coruña, Spain
apazos@udc.es

**Abstract.** Multicenter health studies are important to enrich the outcomes of medical research findings due to the number of subjects that they can engage. To simplify the execution of these studies, the data-sharing process should be effort-less, for instance, using interoperable databases. However, achieving this interoperability is still an ongoing research topic. In the first stage of this work, we propose methodologies to optimize the harmonization pipelines of health databases, considering the OMOP CDM as the destination schema. In the following stage, aiming to enrich the information stored in OMOP CDM databases, we have investigated solutions to extract clinical concepts from unstructured narratives. In the final stage, we aimed to simplify the protocol execution of multicenter studies, by proposing novel solutions for facilitating the discovery of databases. The developed solutions are currently being used in European projects aiming to create federated networks of health databases across Europe.

**Keywords:** Health Data · Database Profiling · Data Integration · Text Mining · OMOP CDM

## 1 Introduction

The efforts made to increase health treatments have encouraged numerous medical research investigations, including different types of clinical studies (observation and trial) [1]. The clinical trials are conducted by splitting patients into groups, in which some perform an active protocol while others are taking a placebo. This strategy aims to study the efficacy of the treatments in specific clinical conditions [2]. In these studies, health professionals have a direct intervention with the patients, which may not be always possible [3]. Another type of study, in which researchers do not perform any active intervention with patients is the observational study. In this case, the exposure occurs naturally or through other factors, i.e., a group of patients suffer from a specific condition while others with similar conditions are healthy [1]. The research is made by documenting the relationship between the exposure and the outcome of the study by analyzing data [2].
Observational studies employ diverse strategies and are established by defining a set of inclusion and exclusion criteria for subjects participating in the study. These studies also observe several features that are identified and monitored over a period of time [4]. Certain initiatives aim to reuse data that was already collected by health professionals during follow-up visits to carry out observational studies. This approach not only saves time but also allows for a pre-verification of the number of subjects before beginning the analysis [5]. In case of certain diseases, it becomes necessary to gather more data about the selected subjects. For those situations the data is recorded based on the study guidelines, and various solutions can be employed for data storage, such as the institutional Electronic Health Record (EHR) system [6]. The dependence on technical teams is a major barrier when there is a need to extract data for analysis. Many other ethical and technical issues are raised when one wants to combine datasets from distinct organizations. This is the case of multicenter studies with the goal of increasing the population size, that may impact the power of the statistical evidence, and thereby the study's findings [7].

The process of integrating data sources is more than a technological issue. There are some Extraction, Transformation and Load (ETL) tools capable of performing this task using large amounts of data. The non-technical problem of this aggregation is the data domain, i.e., identifying the concepts in the data and combine correctly the information associated with them that was extracted from multiple sources. Healthcare databases belong to one of the domains in which this is a concerning problem, due to the variety of concepts to represent similar procedures and medical terms. Solving these problems is helpful to optimize studies, but sharing patient-level data still raises some issues, specifically related with the subjects' privacy [8]. The data contains very sensitive information, and the disruption of this privacy has consequences for several entities involved in the process [9, 10]. The legislation of the different countries raises more challenges regarding the analysis of multiple data sources without exposing sensitive data.

Researchers are driven by the potential impact of multicenter studies to look for more reliable and reusable solutions to combine knowledge from distributed health datasets [1]. Organizations and methodologies were established to explore clinical databases by reusing existent data [5]. One of these efforts aims to create a strategy to reuse EHR databases using a homogeneous schema, to facilitate the interoperability between databases. This integration is currently possible using open-source frameworks that help support the whole process [1].

The research objectives of this work can be paraphrased into several questions focused in addressing specific problems. We recognize that multicenter medical studies may raise additional issues that will not be considered in this work, mainly due to the data types used on these. For instance, research studies based on biomarkers that are cor- related with DNA information, or studies primarily focused on using medical images. Therefore, to achieve a scenario capable of supporting distributed health studies using multiple data sources from distinct institutions, we limited the scope to EHR databases. This objective will be accomplished by answering the following research questions:

1. *How to execute a database query over a network of heterogeneous health databases?*

The lack of interoperability is the main problem in this scenario. An ecosystem with
heterogeneous databases may not share the same data schema, which invalidates the query-sharing. Besides this problem, the healthcare domain contains huge amounts of medical concepts, that may differ between institutions, at the national or international level. A methodology to harmonize such databases is required, which converts them into an interoperable format. This process may have different stages and components, and some of them will be automatized aiming to reduce the cost and time of executing this procedure. This may result in a new software solution.

2. *How to enrich patients' health history using information available in clinical notes?*

The free format of this type of information raises several challenges in terms of named-entity recognition and normalization. Currently, the research lines focused on NLP, may lead to new solutions that can enrich this field. In this work, we aim to investigate a solution capable of improving the quality of information stored in the relational databases. This solution will be a software solution capable of processing clinical free text and storing this information in a relational database, following the harmonization principles defined in the ETL procedures for EHR databases.

3. *How to select the most adequate health databases for a specific research study?*

This question can be addressed from different perspectives. However, by correlating it with the previous statements, we identified that the main problems medical researchers meet are: i) the discovery of databases of interest; and ii) the access to those databases without violating privacy policies and ethical regulations. The solution to these problems is too complex to be solved by a software application alone. To answer this question, it is necessary to create an ecosystem of tools and methodologies, in which data owners can feel confident in sharing characteristics about their databases, while researchers can have enough information to select the databases that fit better their needs. Therefore, the solution proposed for this problem will be a portal that integrates: i) a web catalogue of database characteristics; ii) tools to visualize and compare these characteristics; and iii) tools for orchestrating distributed studies.

In this manuscript, we describe an extended overview of a PhD thesis focused on enriching information extraction pipelines in clinical decision support systems. The main goal of this work was to investigate a new strategy to use medical data from distributed databases to conduct multicenter health studies.

## 2 Contribution to Technological Innovation for Connected Cyber Physical Spaces

One of the most important parts of a medical study is the patients' information correlated to the study scope. The patient's characteristics are crucial to the success of medical studies since it is estimated that up to 50% of trials are not completed due to insufficient enrollment [11]. When collecting these characteristics, the procedure is achieved to a specific goal, resulting in distinct data types. For instance, distinct medical tests can be performed on the subjects, such as medical imaging, blood analysis and electrocardiogram, to identify any health issue. The information extracted from these sources can contribute to the clinical history of each person, which can be a valuable insight for making more precise diagnostics [12]. However, the digital data formats for those records can be different.
To address the challenge of managing different digital data formats for medical records, technological innovation for connected cyber-physical spaces can play a crucial role. By integrating several medical data sources, healthcare researchers can collect or questioning different institutions based on a study scope. This has the potential of increas- ing the number of subjects in the study, improving its findings. Overall, technological innovation for connected cyber-physical spaces can support the integration and analysis of patient data, leading to improved healthcare outcomes and better understanding of human health.

# 3 Secondary Use of Clinical Data

The secondary use of medical data to conduct research studies has become a common practice. These studies have provided complementary support to generate new insights and knowledge, namely in pragmatic trials using records collected from routine clinical care visits, comparative effectiveness studies or patient-centered outcomes research [13]. These data were not primarily generated to support research or secondary analysis. This concept refers to the use of data for purposes other than those that it was originally col- lected [14]. However, over the last few years, the clinical research community recognized that recruiting patients to record their medical characteristics over time is challenging. Although this practice is required in some types of studies, medical research is currently not limited to them. Therefore, this section presents an overview of the most relevant data types in the medical domain.

## 3.1 Electronic Health Records

An EHR is a digital version of the data collected about a patient. Hospitals usually have an EHR system to make the information available in real-time to the health professionals of the institution [15]. Besides patient data, there is an amount of additional information regarding patients’ medical conditions that are also stored in such systems. EHR aims to simplify the data management and data exchange within the institution, between different services, resulting in higher quality and safer care for patients.

Some of the data stored in these systems follow a tabular structure, following the principles of relational databases [16]. Although there are some efforts in having interoperable databases supporting the EHR systems, each vendor has its own data schema. Over the last years, several EHR standards were developed, namely the CEN ISO 13606 [17], OpenEHR [18], OMOP CDM from OHDSI [5, 19] and HL7 standards (CIMI, HL7-CDA HL7-FHIR) [16]. The HL7 standards were proposed to simplify the processes of exchanging of data between software applications provided by distinct vendors that are used by different healthcare providers. They define guidelines and methodologies to support the communication between various healthcare systems [20].

Utilizing the data from the EHR system to answer healthcare questions differs from the traditional approach based on collecting data after defining a question [15]. The tabular data can help medical researchers conduct different types of studies, namely by identifying patient populations with specific healthcare interventions and outcomes, e.g., related to drug exposure, procedures, and conditions, among others. The parameters
available to characterize these patient populations are various, including demographic information, morbidities, utilization and cost, healthcare delivery, treatments, and disease natural history.

Although EHR has been used for many years, as well as the idea of secondary use of this data, the process of reutilizing its data still raises some challenges. These challenges include limitations of processing ability [21, 22], interoperability [23, 24], inability to extract the required information [25, 26], and security and privacy concerns [27].

EHR can be a strategy for federating biomedical data using a particular data view. Some information may not be possible to represent in this format, such as medical images or omics data. However, this tabular format already contains valuable information to be used for conducting medical studies.

## 3.2 Clinical Notes

EHR systems can have repositories of non-tabular patients' information, e.g., clinical notes. The text data contained in these notes is typically subdivided into main categories, depending on whether they are structured or not. The structured notes, as the name indicates, integrate some structured format, for instance, a form. Examples of this data are the diagnosis forms or the laboratory analysis results. Alternatively, unstructured notes refer to notes that contain free-text, for instance, some of the physician's notes transcripts [28]. Free text notes are characterized by their vast variability, especially due to their heterogeneity. EHR contains agglomerates of different types of medical narratives (for progress, admission, operative, primary care, and discharge, among others), with different dimensions (from very short to very long) [29]. Structuring the information available in medical narratives is challenging due to this reason, but also because those are often ungrammatical. They contain short telegraphic sentences, plenty of misspellings, and are filled with abbreviations. In some cases, these abbreviations refer to local dialectal shorthand expressions, which may overload the use of acronyms, i.e., the same group of letters with different meanings [28].

One strategy to introduce some kind of structure in these notes is making use of pseudo-templates or integrating tabular data into the narratives, for instance, the laboratory results. This pseudo-structure is not generalized, and nothing ensures that this is used in all narratives present in the system. Besides, the adoption can vary between physicians, services, or institutions [28]. A different attempt to increase the readability of free-text notes was through the adoption of standard lexicons to encode the information present in the narratives [30].

Despite clinical text being of significant interest, extracting pertinent information from it has traditionally relied on clinical experts manually reviewing clinical notes. However, various approaches have been developed to automate this process and extract relevant information. There are challenges associated to these processes since that cannot scale with the growing rate of generation of medical data [31], much research has been made during the past years in domains such as clinical NLP to create systems for automatically extract data from clinical notes [32].

The use of unprocessed narratives for conducting multicenter studies raises several challenges, namely regarding patients' privacy and data interoperability. Mapping the clinical concepts to their standard definition can solve the latter issue but raises other
challenges since this task can become time-consuming. Additionally, it is acknowledged
that the challenging nature of the free text can difficult the development of solutions for
retrieving data from medical [33].

# 4 Semi-automatic Translation of Data Sources into a Common Schema

The challenges to define and implement ETL workflows collaboratively can be more
complex in specific application domains. In medical scenarios that handle with sensitive
data and requires the harmonization of the data into a common data schema, it requires the
collaboration between technical teams and medical specialists [34]. This collaboration
can be necessary in different stages, namely design, implementation, and validation. In
each stage, there are some challenges that we addressed in this work.

## 4.1 Methodology for Cohort Harmonization

We proposed a methodology that is based on OHDSI ETL principles. During the extraction stage, the chosen data source is obtained from one or multiple data sources using a dedicated process. This strategy aims to get the data from the source systems without interfering with their usual performance since in health databases, the medical system should not be overloaded with tasks that can be scheduled. Therefore, the data is exported to a tabular format.

Out of all the stages, the transformation stage is the most complex component. This
stage requires the mapping of the source database into the target schema, as well as the
harmonization of the content. For a data source, this procedure requires full mapping,
which is time-consuming and requires specialized entities to validate the mappings.
Custom operations on the data may be necessary, depending on the source of the data.
Clinical databases contain a broad range of clinical concepts that require harmonization
using standard vocabularies. Although certain parts of this stage have been automated,
we rely on manual approval by a specialized medical entity, ensuring that all mapped
data is accurate.

The loading stage completes the process by inserting the data into the OMOP CDM
database. These databases are loaded with data that is pseudo-anonymized to protect the
patient's privacy rights. Furthermore, when data is migrated to this schema, the original
raw data can also be validated, and issues can be detected. The pipeline includes quality
mechanisms that verify whether loaded data adhere to the attributes for each concept.

## 4.2 Proposed Tools

The technical components of the proposed ETL methodology were first implemented in a
Python-based tool [1, 35]. This tool considers all the three stages of the ETL operations,
i.e. the operations from the original source data into the OMOP CDM database, as
presented in Fig. 1. In this implementation, we split these stages to enable their execution
in an isolated manner. We also adopted some open-source tools to support some of
the stages of this workflow. WhiteRabbit and Usagi are tools from the OHDSI ETL
ecosystem. WhiteRabbit is responsible for collecting information about the data sources'
structure, namely columns and attributes. While Usagi is used to map concepts into their
standard definition. This last offers an intuitive GUI that helps the medical specialist to
validate these mappings.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The proposed methodology, that also includes some of the ETL OHDSI tools, facilitating the migration workflow of raw data to the OMOP CDM structure. This process comprises three primary stages, with two processes running in parallel (indicated by a red box). The initial stage involves the extraction of cohort information and its subsequent loading into the system. Next, the transformation stage employs mappings combined with ontology rules to perform all the necessary operations on the raw data. Finally, the loading stage inserts the processed data into the database, generating a migration report highlighting any issues with the original data [1]. (Color figure online)</figcaption></figure>

The implementation of some components raised some challenges due to the data
sensibility of the proposed research application. Dealing with health data demands a
deep understanding of the data source to perform accurate harmonization. Besides, there
is another challenge is the task to define custom transformation on each data source. This
is because the data collection strategy was not standardized, making it difficult to work
with the original data. This lack of interoperability when recording the data complicated
the implementation of the migration workflow.

The data quality is another advantage of using this workflow. When an ETL procedure
is finished, a report is generated which includes statistical data related to the migrated
data, i.e., typing errors when recording the raw data.

BIcenter is a web based ETL tool capable of covering some of the existent limita-
tions currently found in multi-institution environments that require the collaboration for
building and managing ETL workflows [36]. This application can simplify the devel-
opment of these workflows due to its intuitive GUI, namely users without technical
expertise. BIcenter replicates the Kettle features in a web environment, which creates a
more user-friendly environment.

The use of BIcenter leveraged the proposed methodology to new possibilities, leading to a collaborative and multi-institutional environment. This tool was initially proposed to simplify the management of the distinct roles assigned to different institutions. However, the system can be installed locally and used internally with invited users to support
the definition of these pipelines. The RBAC mechanisms of this tool can support the definition of rules associated with the different features of this tool.

BIcenter-AD was proposed to be an extension of BIcenter applied to Alzheimer's disease datasets [37]. This tool provided a collaborative environment that was focused in the ETL Task Editor. With this component, a workspace is created that allows the implementation of the ETL pipelines with all components required to harmonize Alzheimer's disease cohorts. The users with the role capable of editing ETL tasks can work collaboratively using a shared workspace.

# 5 From Unstructured Text to Ontology-Based Registers

In the previous section, we proposed different strategies to migrate heterogeneous data into a common data schema. Following this research direction, we identified some gaps in these ETL procedures regarding non-structured medical information.

## 5.1 Extract and Harmonize Drug Mentions

The first proposal for extracting medical information from non-structured data was a two-stage workflow, denominated DrAC [38]. The initial stage involves extracting pre-scriptions found in patients' narratives. Subsequently, the extracted information is harmonized into standard definitions during the second stage, before being stored in a shared database schema known as the OMOP CDM.

To implement the reader component, a factory programming pattern was used. This simplifies the adoption of this tool a new dataset of clinical notes is used. Once the clinical notes have been read, the annotator is employed to extract the medication concepts present in each narrative. The annotations are then stored and subject to post-processing. The tool used for this was Neji, an open-source and modular framework designed for processing text and retrieving medical annotation [39].

Neji was configured as a medication annotator using three drug-related medical terminologies from the UMLS Metathesaurus [40]: AOD, DrugBank and RxNorm. These terminologies encompass several semantic types and groups. Therefore, we narrowed the content of these dictionaries by only retaining entries from the “Chemicals & Drugs” semantic group. After cleaning these dictionaries, they were imported to an instance of the Neji server and a new service was configured. Once all of these notes were loaded into the proposed system, it uses the Neji endpoints to annotate the medication entities and the extracted information was stored in a matrix structured by patient, drug and values for this relation.

Therefore, each cell contains the strength, dosage, and route for each drug annotated and associated with the patient. This particular format was adopted since it is similar to the one used in one of the stages of the ETL workflows, proposed for harmonizing cohort studies into an OMOP CDM database.
## 5.2 Multi-language Concept Normalization

One of the challenges posed by ETL procedures is the definition of the transformation rules. The task for mapping original concepts into their standard definitions is time-consuming and requires specialized teams. Although there are various automatic mapping solutions available to assist this process, these are more complex when working with multilingual databases, resulting in a substantial manual effort for translation and mapping. The proposed component defines a strategy that combines language detection techniques with text mining algorithms to optimize the ETL workflows [41]. This component is intended to be integrated into existing harmonization pipelines.

The proposed component employs two open-source tools: i) a web collaborative platform capable of simplifying the management of the ontologies used in this proposal; and ii) a tool with a user interface to support the mapping validation. Usagi was used as the interface for validating the mappings. This tool provides a straightforward and user-friendly interface to validate mappings using word similarity and suggestive associations. While Usagi's suggestions only compare concepts with the standard vocabulary, resulting in some incorrect mappings that require manual modification, the user interface is user-friendly and can be reused for our proposed approach. Currently, it is used in several migration workflows, including those proposed in the previous section.

# 6 Scalable Database Profiling for Multicenter Studies

One of the challenges when reusing health databases for research is the correct selection of the data sources. This is a complex problem since it requires strategies to characterize data sources without revealing their content, and platforms for disseminating the databases' characteristics [42, 43]. For the database characterization issue, there are already some guidelines when dealing with this type of data. Depending on the project or institution's policies, the data owners can share aggregated information about their data. This can provide a summarization of the patients in the databases. Other characteristics can also be provided, namely data governance policies and contact details. These summarization guidelines are not standard and may differ depending on the context. For instance, a community focused on studying Alzheimer's Disease would have datasets with different characteristics compared with a more generic domain [44].

Profiling databases (or fingerprinting) is the action of representing a database using a set of characteristics that combined can create a singular conception of the database. Defining these characteristics raises some issues that vary depending on the project scope. While these issues have complex solutions, we propose a different strategy to help the discovery of medical databases. It aims to provide enough information about the databases, that can characterize them at a deeper level, without sharing sensitive information. Figure 2 represents the main idea of the concept of this summarization, which is defined as fingerprinting.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The concept of fingerprinting databases focuses on extracting characterizes from databases of the same type.</figcaption></figure>

## 6.1 Framework for Profiling Databases

The MONTRA 2 framework was developed as a solution for enabling biomedical data sharing through the creation of web-based environments for research purposes. The database catalogue can be considered one of the core features of MONTRA 2. In this catalogue, it is represented each database through the concept of fingerprinting, as was already described. Therefore, the data owners can define the catalogue structure that better fits their needs in that scope, and the system generates the web catalogue based on that file. The skeleton structure is flexible and contains fields (questions) to be filled by the data owners. Several questions can be aggregated in a “QuestionSet”, creating a hierarchical data representation. Each question can store different types of data, for instance, dates, numbers, strings, multiple-choice values, geographic location, among others. These fields, which represent the metadata about the health databases in the catalogue, are used for free text search, advanced search, dataset comparison, and other features of the catalogue.

MONTRA 2 was implemented to also support the creation of an environment to integrate distinct tools in a centralized platform. The goal of this paradigm was to provide the researchers with a workplace with all required tools to: i) compare and identify the databases of interest for clinical studies; ii) streamline a study over the network; and iii) retrieve the results and aggregate them. All these tools are protected under a federated SSO mechanism with profile verification. MONTRA 2 is currently used to support different other projects. The system has three instances in production, to support different platforms, namely the EHDEN Portal, EMIF Catalogue and MSDA Portal.

## 6.2 Exploring Distributed Patient-Level Databases

The proposed component with the goal of streamlining multicenter studies is based on MONTRA 2. To accomplish this, we developed an additional tool that was integrated in MONTRA 2 as a plugin [45]. It aims to simplify the execution of health studies as
well as centralize and coordinate the operations between all the entities involved. The proposed system, designated as Study Manager, adopted the same technologies used in MONTRA 2, namely Django in its core. To simplify the integration between systems, this tool was implemented to be compliant with the MONTRA SDK, following a MVC software pattern. This pattern segregates the application logic into three main elements: i) the model, responsible for handling the data storage; ii) the view, that generates the data representation for the client; and iii) the controller, which contains the business layer.

With all the proposed solutions in the previous sections, including MONTRA 2 framework, the process of conducting multicenter medical studies is currently a reality. Medical researchers have identified some challenges and opportunities when sharing biomedical data. The component proposed in this part of this work aims to empower these opportunities, which may increase the impact of the outcome of such medical studies. Therefore, the proposed strategies aim to facilitate the exploration of patient-level databases, while minimizing the risk of violating the patient's privacy.

# 7 Results and Discussion

Enriching information extraction pipelines in clinical decision support systems is a research topic that can be addressed from different points of view. In this work, we tried to enrich these pipelines starting by working on the foundations of clinical decision support systems. We recognised that to increase the quality of the treatments, researchers need to study the impact of new drugs, or the efficiency of current treatments. These findings can originate new treatment protocols that can be integrated into the decision-support systems of healthcare institutions. Therefore, in this work, we focused on creating methodologies and tools to help medical researchers conduct more impactful findings, to improve the source of these systems.

We started by specifying the scope of this work, based on the biomedical data formats that we could use. Motivated by EHDEN project, we focused this work on EHR relational data, that we tried to supplement with data extracted from medical narratives. Then, in the later stage, after defining strategies to have an interoperable network of data sources, we proposed solutions to support research using these data sources. In short, we presented some software solutions to integrate medical data sources, and the final product is a platform to simplify the data analysis across distributed databases [46].

The first hypothesis addressed the lack of interoperability between health databases. However, as we find during this work, the problem was not the lack of standard solutions to interconnect these databases. Instead, the problem was the effort required to adopt one of these standards. To answer this problem, we proposed solutions to simplify the migration of EHR data to one of the standard data schemas currently used in medical studies. We validated these solutions using heterogeneous cohorts of patients' data suffering from Alzheimer's disease. After harmonizing these datasets, the data of 6,669 subjects were combined considering the information of 172 clinical concepts. The interoperability was ensured by converting data sources to the OMOP CDM data schema [1].

The second hypothesis was about enriching the information stored in the databases, using unstructured data present in clinical narratives. For this, we proposed a solution
capable of extracting medical concepts and storing them in an OMOP CDM database. Part of this solution is supported by the work done to answer the first hypothesis. We validated the proposed NLP strategies using scientific challenges, namely organized by n2c2 organization [38].

Finally, the third hypothesis was focused on finding the most adequate health databases for specific research studies. To answer this question, we have collaborated during this doctoral program with the EHDEN partners aiming to propose and adjust a solution based on real needs. The result was a flexible framework capable of being extended to support complementary tools. This work was validated in the context of the EHDEN project [46]. This portal currently contains information about 93 EHR databases, which are publicly accessible to all the 750 registered. Additionally, it also replaced old technologies that have supported the EMIF project in the past. This tool was validated with thousands of users. Overall, all these platforms have a real impact on medical environments since researchers can easily identify databases of interest to conduct multicenter studies.

## 8 Conclusions and Future Work

Enriching information extraction pipelines in clinical decision support systems is a research topic that can be addressed from different points of view. In this work, we tried to enrich these pipelines starting by working on the foundations of clinical decision support systems. We recognized that to increase the quality of the treatments, researchers need to study the impact of new drugs, or the efficiency of current treatments. These findings can originate new treatment protocols that can be integrated into the decision-support systems of healthcare institutions. Therefore, in this work, we focused on creating methodologies and tools to help medical researchers conduct more impactful findings, to improve the source of these systems.

We started by specifying the scope of this work, based on the biomedical data formats that we could use. Motivated by EHDEN project, we focused this work on EHR relational data, that we tried to supplement with data extracted from medical narratives. Then, in the later stage, after defining strategies to have an interoperable network of data sources, we proposed solutions to support research using these data sources. In short, we presented several software solutions to integrate medical data sources, and the final product is a platform to support the analysis of biomedical data across distributed databases.

With this research, we identified some future work and research directions, namely by analyzing some of the limitations of the proposed solutions. Herein, we present and discuss some of the possible research lines for future work:

1. Standardizing a fingerprinting schema: A lot of efforts have been conducted to ensure interoperability between data sources, as well as to publish their metadata to facilitate discovery. This resulted in several health database catalogues that cannot communicate and exchange information between them. There are already some initiatives to create federated catalogues in specific domains, however, this is only the beginning. Standard schemas and ontologies to federate this communication is a possible research direction to optimize the creation of health database catalogues.
2. Automatic definition of ETL workflows: Automatically establishing the mappings between the initial data schema to the target is an open research direction that can be applied beyond the health domain. This can be simplified and focused on the medical domain, by using OMOP CDM as the target data schema. In this work, we proposed semi-automatic methodologies, but this proposal can be optimized at different levels.

3. Extending OMOP CDM to incorporate other data types: Over the years some initiatives tried to extend the OMOP CDM to incorporate more information. The adoption of these initiatives at a large scale fails due to several issues (ensuring data privacy in complex data formats, breaking the schema interoperability, and raising issues when sharing results, among others). Investing in this direction may leverage medical research to new levels, namely by allowing distributed studies using DICOM images, or genomic data.

4. Secure FAIR data: The objective of FAIR principles is to optimize the reutilization of data. The principles highlight the importance of machine-actionability, which refers to the ability of computational systems to independently discover, access, interoperate, and reuse data with minimal or without manual intervention [47]. However, we identified a research line in this topic, by combining it with security, i.e. applying the FAIR principles following secure guidelines to ensure safe machine-to-machine communication.

Considering the increasing impact of technology in healthcare, along with the rapid developments in this field, we firmly believe in the importance of the presented research topics.

**Acknowledgments.** This work has received support from the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. JRA has been funded by FCT (Foundation for Science and Technology) under the grant SFRH/BD/147837/2019.

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