Making EHRs Reusable: A Common Framework of Data Operations

Pedrera, Miguel; García, Noelia; Rubio, Paula; Cruz, Juan Luis de la; Bernal, José Luis; Serrano, Pablo E.

doi:10.3233/shti210831

Cited by 3 publications

(7 citation statements)

References 6 publications

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“…The benefits of standardizing EHR data are not limited to primary use. The reuse of clinical data for secondary purposes, such as investigation in both single- and multicenter studies, requires formal information models in order to make data unequivocally understandable and reproducible [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Successful Integration of EN/ISO 13606–Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a Methodology

Frid¹,

Expósito²,

Grau-Corral³

et al. 2022

JMIR Med Inform

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Background There is an increasing need to integrate patient-generated health data (PGHD) into health information systems (HISs). The use of health information standards based on the dual model allows the achievement of semantic interoperability among systems. Although there is evidence in the use of the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) framework for standardized communication between mobile apps and electronic health records (EHRs), the use of European Norm/International Organization for Standardization (EN/ISO) 13606 has not been explored yet, despite some advantages over FHIR in terms of modeling and formalization of clinical knowledge, as well as flexibility in the creation of new concepts. Objective This study aims to design and implement a methodology based on the dual-model paradigm to communicate clinical information between a patient mobile app (Xemio Research) and an institutional ontology-based clinical repository (OntoCR) without loss of meaning. Methods This paper is framed within Artificial intelligence Supporting CAncer Patients across Europe (ASCAPE), a project that aims to use artificial intelligence (AI)/machine learning (ML) mechanisms to support cancer patients’ health status and quality of life (QoL). First, the variables “side effect” and “daily steps” were defined and represented with EN/ISO 13606 archetypes. Next, ontologies that model archetyped concepts and map them to the standard were created and uploaded to OntoCR, where they were ready to receive instantiated patient data. Xemio Research used a conversion module in the ASCAPE Local Edge to transform data entered into the app to create EN/ISO 13606 extracts, which were sent to an Application Programming Interface (API) in OntoCR that maps each element in the normalized XML files to its corresponding location in the ontology. This way, instantiated data of patients are stored in the clinical repository. Results Between December 22, 2020, and April 4, 2022, 1100 extracts of 47 patients were successfully communicated (234/1100, 21.3%, extracts of side effects and 866/1100, 78.7%, extracts of daily activity). Furthermore, the creation of EN/ISO 13606–standardized archetypes allows the reuse of clinical information regarding daily activity and side effects, while with the creation of ontologies, we extended the knowledge representation of our clinical repository. Conclusions Health information interoperability is one of the requirements for continuity of health care. The dual model allows the separation of knowledge and information in HISs. EN/ISO 13606 was chosen for this project because of the operational mechanisms it offers for data exchange, as well as its flexibility for modeling knowledge and creating new concepts. To the best of our knowledge, this is the first experience reported in the literature of effective communication of EN/ISO 13606 EHR extracts between a patient mobile app and an institutional clinical repository using a scalable standard-agnostic methodology that can be applied to other projects, data sources, and institutions.

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Section: Discussionmentioning

confidence: 99%

Successful Integration of EN/ISO 13606–Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a Methodology

Frid¹,

Expósito²,

Grau-Corral³

et al. 2022

JMIR Med Inform

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“…20 However, it is necessary to continue its development to improve each of its component phases. Thus, in this work the third phase of this methodology is expanded, with the aim of making the ETL processes for EHR reuse understandable, auditable, and reproducible, 16 a fundamental requirement for relying on studies that use nonmanually collected data in a controlled and audited manner into study-specific information systems. 15 It is important to highlight that, although guidelines are given for the suitability of EHR for this methodology, data quality aspects such as completeness, correctness, or timeliness 7 are outside the scope of this study for obtaining automated transparent ETL processes.…”

Section: Discussionmentioning

confidence: 99%

“…5 Specifically, it is focused on the third phase of that process, which covers the definition and implementation of the extraction and transformation operations on EHR data for its reuse in a multipurpose and transparent way, and without changing its original meaning. 16 This extended process comprises four stages:…”

Section: Methodsmentioning

confidence: 99%

“…Data operations for EHR reuse constitute the core of the methodology, providing a transparent process for extraction and transformation of health care data into specific models for the development of research studies. 16 They are health condition-agnostic by design, and flexible to adapt to the data sources and constraints of the secondary use data model, as well as to the requirements set by the regulatory authorities. Furthermore, these operations can be implemented within the health care organization's infrastructure according to the information security policies in place, for example, by pseudonymizing the data at the source on which the extraction operations are applied.…”

Section: Methodsmentioning

confidence: 99%

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TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse

et al. 2022

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Background During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable. Objectives This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization. Methods The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML. Results First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined. Conclusions This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.

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“…In addition, it can be applied to other purposes, known as secondary uses, including clinical research and public health [2]. To achieve this, recent studies have designed methodologies based on health information standards for allowing the effective reuse of EHRs, which is essential for obtaining data in an agile, flexible and efficient way [3,4]. Traditionally, data for research have been manually collected in purpose-built and controlled databases.…”

Section: Introductionmentioning

confidence: 99%

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research

Pedrera-Jiménez

Barrio

Rubio-Mayo

et al. 2022

Studies in Health Technology and Informatics

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One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.

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Making EHRs Reusable: A Common Framework of Data Operations

Cited by 3 publications

References 6 publications

Successful Integration of EN/ISO 13606–Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a Methodology

Successful Integration of EN/ISO 13606–Standardized Extracts From a Patient Mobile App Into an Electronic Health Record: Description of a Methodology

TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research

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