scholarly journals Enrollment into a multi-center clinical cohort linking electronic health records from five health systems: The PaTH Clinical Research Network (Preprint)

10.2196/24003 ◽  
2020 ◽  
Author(s):  
Wendy Bennett ◽  
Carolyn Bramante ◽  
Scott Rothenberger ◽  
Jennifer Kraschnewski ◽  
Sharon Herring ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Health Systems ◽  
Research Network ◽  
Health Records ◽  
Clinical Research Network ◽  
Clinical Cohort ◽  
Electronic Health

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

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
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