scholarly journals Unlocking the Potential of Electronic Health Records for Translational Research

2012 ◽  
Vol 21 (01) ◽  
pp. 135-138 ◽  
Author(s):  
Y. L. Yip ◽  
Keyword(s):  
Electronic Health Records ◽  
Translational Research ◽  
Large Scale ◽  
Association Studies ◽  
System Level ◽  
Biomedical Knowledge ◽  
Health Records ◽  
Excellent Research ◽  
Electronic Health ◽  
Translational Informatics

SummaryTo review current excellent research and trend in the field of bioinformatics and translational informatics with direct application in the medical domain.Synopsis of the articles selected for the IMIA Yearbook 2012.Six excellent articles were selected in this Yearbook’s section on Bioinformatics and Translational Informatics. They exemplify current key advances in the use of patient information for translational research and health surveillance. First, two proof-of-concept studies demonstrated the cross-institutional and -geographic use of Electronic Health Records (EHR) for clinical trial subjects identification and drug safety signals detection. These reports pave ways to global large-scale population monitoring. Second, there is further evidence on the importance of coupling phenotypic information in EHR with genotypic information (either in biobank or in gene association studies) for new biomedical knowledge discovery. Third, patient data gathered via social media and self-reporting was found to be comparable to existent data and less labor intensive. This alternative means could potentially overcome data collection challenge in cohort and prospective studies. Finally, it can be noted that metagenomic studies are gaining momentum in bioinformatics and system-level analysis of human microbiome sheds important light on certain human diseases.The current literature showed that the traditional bench to bedside translational research is increasing being complemented by the reverse approach, in which bedside information can be used to provide novel biomedical insights.

scholarly journals A Modeling Framework for Exploring Sampling and Observation Process Biases in Genome and Phenome-wide Association Studies using Electronic Health Records

2018 ◽  
Author(s):  
Lauren J. Beesley ◽  
Lars G. Fritsche ◽  
Bhramar Mukherjee
Keyword(s):  
Sensitivity Analysis ◽  
Electronic Health Records ◽  
Large Scale ◽  
Association Studies ◽  
Modeling Framework ◽  
Health Records ◽  
Association Analyses ◽  
Observation Process ◽  
Special Cases ◽  
Electronic Health

AbstractLarge-scale agnostic association analyses based on existing observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, particular challenges of non-probability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. In general, the extent of the bias that may be introduced by ignoring these factors is unknown. In this paper, we develop a statistical framework for characterizing the degree of bias expected in association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis type approach, this framework can be used to obtain plausible values for parameters of interest given results obtained from standard naive analysis methods under varying degree of misclassification and sampling biases. We develop an online tool for performing this sensitivity analysis in some special cases that occur frequently. Simulations demonstrate promising properties of the proposed way of characterizing biases. We apply our approach to study bias in genetic association studies using data from the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine.

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.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

scholarly journals An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

2021 ◽  
Author(s):  
Sergiusz Wesolowski ◽  
Gordon Howard Lemmon ◽  
Edgar J Hernandez ◽  
Alex Ryan Henrie ◽  
Thomas A Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Large Scale ◽  
Cardiovascular Health ◽  
Cardiovascular Outcomes ◽  
Health Records ◽  
University Of Utah ◽  
Explainable Artificial Intelligence ◽  
Electronic Health ◽  
Discovery Method

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2017 ◽  
pp. 528-542
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

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