scholarly journals A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation

2017 ◽  
Vol 5 (1) ◽  
pp. 10 ◽  
Author(s):  
Toan C. Ong ◽  
Rosina Pradhananga ◽  
Erin G. Holve ◽  
Michael G. Kahn
Keyword(s):  
Data Extraction ◽  
Health Data ◽  
Data Network ◽  
Electronic Health

scholarly journals Design and operation of a distributed health data network

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Jeffrey Brown
Keyword(s):  
Data Quality ◽  
Real World ◽  
Health Data ◽  
Data Networks ◽  
Common Data Model ◽  
Administrative Claims ◽  
Data Network ◽  
Operational Design ◽  
Level Data ◽  
Electronic Health

IntroductionSeveral large health data networks such as FDA Sentinel, PCORnet, and the Canadian Network of Observational Drug Effect Studies (CNODES) facilitate multi-site research using real-world electronic health data such administrative claims data, electronic health record data and registries. Experience in operation of mutliple health data networks will described. Objectives and ApproachOver the past 15 years substantial progress has been made in developing the optimal network operational design, governance, and technical architecture to facilitate the creation and operation of large-scale distributed health data networks. The design, architecture, and operation of a sustainable health data network requires balancing the needs of the network stakeholders such as funders, data sources, investigators, and regulatory bodies while enabling rapid and efficient use of data to support evidence generation and decision making. Important topics include protection of patient privacy, security, data autonomy, distributed analytics, data quality, and protection of confidential information. ResultsThe design and architecture of existing distributed health data networks provides guidance regarding the potential operational model for new networks and identifies areas of research to improve network functionality and capabilities. Most health data network adopt a common data model approach to facilitate multi-site querying and data quality assessment. This approach is coupled with distributed querying in which data partners maintain physical and operational control of their data. This design maximizes protection of confidential and proprietary information and minimizes the need to share patient-level data. Privacy-preserving distributed regression approaches and methods that obviate the need to share person-level data while generating robust results help to ensure network participation. Strong security and governance structures are also necessary for effective operation of a distributed network. Conclusion/ImplicationsDistributed health data networks offer the opportunity to use real-world data for public health surveillance and comparative safety and effectiveness research across large populations. The operational design, technical and analytic architecture, and governance models of networks drive their acceptance and success.

Integrating Exosomal MicroRNA and Electronic Health Data Improves Tuberculosis Diagnosis

2018 ◽  
Author(s):  
Xuejiao Hu ◽  
Shun Liao ◽  
Hao Bai ◽  
Lijuan Wu ◽  
Minjin Wang ◽  
...  
Keyword(s):  
Health Data ◽  
Tuberculosis Diagnosis ◽  
Electronic Health ◽  
Exosomal Microrna

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

Identification and Validation of Anaphylaxis Using Electronic Health Data in a Population-based Setting

Epidemiology ◽  
2021 ◽  
Vol 32 (3) ◽  
pp. 439-443
Author(s):  
Maralyssa A. Bann ◽  
David S. Carrell ◽  
Susan Gruber ◽  
Mayura Shinde ◽  
Robert Ball ◽  
...  
Keyword(s):  
Population Based ◽  
Health Data ◽  
Electronic Health

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

scholarly journals Cancer Screening Results and Follow-up Using Routinely Collected Electronic Health Data: Estimates for Breast, Colon, and Cervical Cancer Screenings

2018 ◽  
Vol 34 (3) ◽  
pp. 341-343 ◽  
Author(s):  
Sudha R. Raman ◽  
Jeffrey S. Brown ◽  
Lesley H. Curtis ◽  
Kevin Haynes ◽  
James Marshall ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Health Data ◽  
Cancer Screenings ◽  
Electronic Health

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

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