scholarly journals A patient screening tool for clinical research based on Electronic Health Records Using openEHR (Preprint)

10.2196/33192 ◽  
2021 ◽  
Author(s):  
Mengyang Li ◽  
Hailing Cai ◽  
Shan Nan ◽  
Jialin Li ◽  
Xudong Lu ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Screening Tool ◽  
Health Records ◽  
Patient Screening ◽  
Electronic Health

A patient screening tool for clinical research based on Electronic Health Records Using openEHR (Preprint)

2021 ◽  
Author(s):  
Mengyang Li ◽  
Hailing Cai ◽  
Shan Nan ◽  
Jialin Li ◽  
Xudong Lu ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Screening Tool ◽  
Eligibility Criteria ◽  
Health Records ◽  
Query Performance ◽  
Secondary Use ◽  
Patient Screening ◽  
Information Models ◽  
High Level

BACKGROUND The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHR is a challenging task. The concepts in eligibility criteria are not completely matched with EHR, especially for “derived concepts”. The lack of high-level expression of SQL makes it difficult and time-consuming to express them. The openEHR Expression language (openEHR EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria. OBJECTIVE The study aims to develop a patient screening tool based on EHR for clinical research using openEHR to solve concepts mismatch and improve query performance. METHODS A patient screening tool based on EHRs using openEHR is proposed. It utilizes the advantages of information models and expression language in openEHR to provide high-level expressions and improve query performance. Firstly, openEHR archetypes and templates were chosen to define concepts called “simple concepts” from EHR directly. After, openEHR EL was utilized to generate “derived concepts” by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch was generated to improve query performance for subqueries and join queries related to “derived concepts”. Finally, on top of these works, we realized a patient screening tool for clinical research. RESULTS 500 sentences randomly selected from 4691 eligibility criteria in total 389 clinical trials about stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a Grade A tertiary hospital in China was considered as an experimental environment. Based on them, 589 medical concepts were found in these sentences. Among all of them, 513(87.1%) concepts can be represented, and the others cannot be represented because of a lack of information models and coarse-grained requirements. Also, our case study on 6 queries demonstrates our tool shows better query performance among 4 cases (66.67%). CONCLUSIONS We develop a patient screening tool using openEHR. It not only helps solve concepts mismatch but also improves the query performance to reduce the burden on researchers. Also, we demonstrate the promising solution for secondary use of EHR data using openEHR which can be referenced by other researchers.

Piloting the EHR4CR Feasibility Platform across Europe

2014 ◽  
Vol 53 (04) ◽  
pp. 264-268 ◽  
Author(s):  
R. Bache ◽  
M. McGilchrist ◽  
C. Daniel ◽  
M. Dugas ◽  
F. Fritz ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Test Data ◽  
Data Access ◽  
Data Warehouses ◽  
University Hospitals ◽  
Local Networks ◽  
Health Records ◽  
Electronic Health ◽  
Set Up

SummaryBackground: Pharmaceutical clinical trials are primarily conducted across many countries, yet recruitment numbers are frequently not met in time. Electronic health records store large amounts of potentially useful data that could aid in this process. The EHR4CR project aims at re-using EHR data for clinical research purposes.Objective: To evaluate whether the protocol feasibility platform produced by the Electronic Health Records for Clinical Research (EHR4CR) project can be installed and set up in accordance with local technical and governance requirements to execute protocol feasibility queries uniformly across national borders.Methods: We installed specifically engineered software and warehouses at local sites. Approvals for data access and usage of the platform were acquired and terminology mapping of local site codes to central platform codes were performed. A test data set, or real EHR data where approvals were in place, were loaded into data warehouses. Test feasibility queries were created on a central component of the platform and sent to the local components at eleven university hospitals.Results: To use real, de-identified EHR data we obtained permissions and approvals from ‘data controllers‘ and ethics committees. Through the platform we were able to create feasibility queries, distribute them to eleven university hospitals and retrieve aggregated patient counts of both test data and de-identified EHR data.Conclusion: It is possible to install a uniform piece of software in different university hospitals in five European countries and configure it to the requirements of the local networks, while complying with local data protection regulations. We were also able set up ETL processes and data warehouses, to reuse EHR data for feasibility queries distributed over the EHR4CR platform.

scholarly journals Clinical Research Informatics: Contributions from 2017

2018 ◽  
Vol 27 (01) ◽  
pp. 177-183 ◽  
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Association Studies ◽  
Bias Reduction ◽  
Lessons Learned ◽  
Editorial Team ◽  
Private Industry ◽  
Health Records ◽  
Clinical Research Informatics ◽  
Electronic Health

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2017. Method: A bibliographic search using a combination of MeSH descriptors and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 741 returned papers published in 2017 in the various areas of CRI, the full review process selected five best papers. The first best paper reports on the implementation of consent management considering patient preferences for the use of de-identified data of electronic health records for research. The second best paper describes an approach using natural language processing to extract symptoms of severe mental illness from clinical text. The authors of the third best paper describe the challenges and lessons learned when leveraging the EHR4CR platform to support patient inclusion in academic studies in the context of an important collaboration between private industry and public health institutions. The fourth best paper describes a method and an interactive tool for case-crossover analyses of electronic medical records for patient safety. The last best paper proposes a new method for bias reduction in association studies using electronic health records data. Conclusions: Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.

scholarly journals Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e029314 ◽  
Author(s):  
Kaiwen Ni ◽  
Hongling Chu ◽  
Lin Zeng ◽  
Nan Li ◽  
Yiming Zhao
Keyword(s):  
Qualitative Study ◽  
Electronic Health Records ◽  
Clinical Research ◽  
Data Quality ◽  
Healthcare Professionals ◽  
Barriers And Facilitators ◽  
Health Records ◽  
Improve Data Quality ◽  
Electronic Health

ObjectivesThere is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals’ experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.SettingFour tertiary hospitals in Beijing, China.ParticipantsNineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.MethodsA qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.ResultsThe main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.ConclusionsThese results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

scholarly journals Intérêt des dossiers de santé électroniques hospitaliers pour la recherche clinique

2018 ◽  
Vol 34 (11) ◽  
pp. 972-977 ◽  
Author(s):  
Danielle Dupont ◽  
Ariel Beresniak ◽  
Dipak Kalra ◽  
Pascal Coorevits ◽  
Georges De Moor
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Qualité Des Soins ◽  
Health Records ◽  
Recherche Clinique ◽  
Electronic Health

Les dossiers de santé électroniques hospitaliers contribuent à l’amélioration de la qualité des soins en permettant une meilleure gestion des informations cliniques. Les bases de données numériques ainsi constituées facilitent l’échange des informations de santé avec les prestataires de soins et optimisent la coordination multidisciplinaire pour de meilleurs résultats thérapeutiques. Le projet européen EHR4CR (electronic health records for clinical research) a développé une plateforme pilote innovante permettant de réutiliser ces données numériques pour la recherche clinique. En améliorant et en accélérant les procédures de recherche clinique, cette approche permet d’envisager la réalisation d’études cliniques de manière plus efficiente, plus rapide et plus économique.

scholarly journals Cost-Benefit Assessment of the Electronic Health Records for Clinical Research (EHR4CR) European Project

2014 ◽  
Vol 17 (7) ◽  
pp. A630 ◽  
Author(s):  
A. Beresniak ◽  
A. Schmidt ◽  
J. Proeve ◽  
E. Bolanos ◽  
N. Patel ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Cost Benefit ◽  
Benefit Assessment ◽  
Health Records ◽  
European Project ◽  
Electronic Health

scholarly journals The need for Electronic Health Records (EHR) based Clinical Decision Support (CDS) in developing countries like India

2019 ◽  
Vol 7 (4) ◽  
pp. 36-39
Author(s):  
Rengalakshmi Balasubramanian
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Electronic Health Records ◽  
Clinical Research ◽  
Clinical Decision Support ◽  
Clinical Studies ◽  
Clinical Decision ◽  
Health Records ◽  
Electronic Health ◽  
Research Studies

Electronic health records (EHRs), an integrated approach is gaining importance not only within healthcare organizations and with their related stakeholders but also with the clinical research studies. EHR-based clinical decision support (CDS) systems in clinical studies have been proven successful in many clinical studies. The World Health Organization has emphasised the necessity to revolutionise the healthcare system in all levels, from primary health care and community service to health care research and this started with the implementation of EHR services extensively in various health care systems. Further, American and European healthcare systems are keen in improving EHR-based measurements to enable interoperability for future integrations of multiple data sources, which in turn would prove beneficial in the global platform for not only to the provider, patient, and other healthcare stakeholders but also for clinical research studies. This paper discusses the EHR in clinical research, the challenges in the EHR, and the need for developing nations to adopt and develop EHR.

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