The adolescence of electronic health records: Status and perspectives for large scale implementation

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

Clinical Trials ◽

10.1177/1740774520931211 ◽

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.

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

10.1101/2021.07.21.21260474 ◽

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.

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

Healthcare Ethics and Training ◽

10.4018/978-1-5225-2237-9.ch023 ◽

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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Large-Scale Identification of Aortic Stenosis and its Severity Using Natural Language Processing on Electronic Health Records

Cardiovascular Digital Health Journal ◽

10.1016/j.cvdhj.2021.03.003 ◽

2021 ◽

Author(s):

Matthew D. Solomon ◽

Grace Tabada ◽

Amanda Allen ◽

Sue Hee Sung ◽

Alan S. Go

Keyword(s):

Natural Language Processing ◽

Aortic Stenosis ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Large Scale ◽

Health Records ◽

Electronic Health

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Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

npj Digital Medicine ◽

10.1038/s41746-021-00455-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Laila Rasmy ◽

Yang Xiang ◽

Ziqian Xie ◽

Cui Tao ◽

Degui Zhi

Keyword(s):

Deep Learning ◽

Electronic Health Records ◽

Large Scale ◽

Training Data ◽

Fine Tuning ◽

Disease Prediction ◽

Operating Characteristics ◽

Health Records ◽

Clinical Databases ◽

Electronic Health

AbstractDeep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed for the text domain to the structured EHR domain. Med-BERT is a contextualized embedding model pretrained on a structured EHR dataset of 28,490,650 patients. Fine-tuning experiments showed that Med-BERT substantially improves the prediction accuracy, boosting the area under the receiver operating characteristics curve (AUC) by 1.21–6.14% in two disease prediction tasks from two clinical databases. In particular, pretrained Med-BERT obtains promising performances on tasks with small fine-tuning training sets and can boost the AUC by more than 20% or obtain an AUC as high as a model trained on a training set ten times larger, compared with deep learning models without Med-BERT. We believe that Med-BERT will benefit disease prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.

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The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential Opportunities

10.20944/preprints201809.0388.v1 ◽

2018 ◽

Cited By ~ 3

Author(s):

Lauren Beesley ◽

Maxwell Salvatore ◽

Lars Fritsche ◽

Anita Pandit ◽

Arvind Rao ◽

...

Keyword(s):

Electronic Health Records ◽

Large Scale ◽

Sampling Strategy ◽

Data Access ◽

Open Problems ◽

Health Records ◽

Data Resource ◽

Electronic Health ◽

Rich Data ◽

Scientific Questions

Biobanks linked to electronic health records provide a rich data resource for health-related research. With the establishment of large-scale infrastructure, the availability and utility of data from biobanks has dramatically increased over time. As more researchers become interested in using biobank data to explore a diverse spectrum of scientific questions, resources guiding the data access, design, and analysis of biobank-based studies will be crucial.  The first aim of this review is to characterize the types of biobanks that are discussed in the recent literature and provide detailed descriptions of specific biobanks including their location, size, data access, data linkages and more. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, new discoveries, and hypothesis-generating studies of disease-treatment, disease-exposure and disease-gene associations. Rather than spending time and money designing and implementing a single study with pre-defined objectives, researchers can use biobanks’ existing data-rich resources to answer scientific questions as quickly as they can analyze them. While the data are becoming increasingly available, additional thought is needed to address issues related to the design of such studies and analysis of these data. In the second aim of this review, we discuss statistical issues related to biobank research in general including study design, sampling strategy, phenotype identification, and missing data. These issues are illustrated using data from the Michigan Genomics Initiative, UK Biobank, and Genes for Good. We summarize the current body of statistical literature aimed at addressing some of these challenges and discuss some of the standing open problems in this area. This work serves to complement and extend recent reviews about biobank-based research and aims to provide a resource catalog with statistical and practical guidance to researchers pursuing biobank-based research.

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Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records

BMJ Open ◽

10.1136/bmjopen-2019-033174 ◽

2019 ◽

Vol 9 (12) ◽

pp. e033174 ◽

Cited By ~ 3

Author(s):

Antonio Gimeno-Miguel ◽

Anyuli Gracia Gutiérrez ◽

Beatriz Poblador-Plou ◽

Carlos Coscollar-Santaliestra ◽

J Ignacio Pérez-Calvo ◽

...

Keyword(s):

Heart Failure ◽

Electronic Health Records ◽

Health Outcomes ◽

Chronic Diseases ◽

Large Scale ◽

Health Records ◽

Men And Women ◽

Patients With Heart Failure ◽

Electronic Health ◽

The Impact

ObjectivesTo characterise the comorbidities of heart failure (HF) in men and women, to explore their clustering into multimorbidity patterns, and to measure the impact of such patterns on the risk of hospitalisation and mortality.DesignObservational retrospective population study based on electronic health records.SettingEpiChron Cohort (Aragón, Spain).ParticipantsAll the primary and hospital care patients of the EpiChron Cohort with a diagnosis of HF on 1 January 2011 (ie, 8488 women and 6182 men). We analysed all the chronic diseases registered in patients’ electronic health records until 31 December 2011.Primary outcomeWe performed an exploratory factor analysis to identify the multimorbidity patterns in men and women, and logistic and Cox proportional-hazards regressions to investigate the association between the patterns and the risk of hospitalisation in 2012, and of 3-year mortality.ResultsAlmost all HF patients (98%) had multimorbidity, with an average of 7.8 chronic diseases per patient. We identified six different multimorbidity patterns, named cardiovascular, neurovascular, coronary, metabolic, degenerative and respiratory. The most prevalent were the degenerative (64.0%) and cardiovascular (29.9%) patterns in women, and the metabolic (49.3%) and cardiovascular (43.2%) patterns in men. Every pattern was associated with higher hospitalisation risks; and the cardiovascular, neurovascular and respiratory patterns significantly increased the likelihood of 3-year mortality.ConclusionsMultimorbidity is the norm rather than the exception in patients with heart failure, whose comorbidities tend to cluster together beyond simple chance in the form of multimorbidity patterns that have different impact on health outcomes. This knowledge could be useful to better understand common pathophysiological pathways underlying this condition and its comorbidities, and the factors influencing the prognosis of men and women with HF. Further large scale longitudinal studies are encouraged to confirm the existence of these patterns as well as their differential impact on health outcomes.

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Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research

Medical Decision Making ◽

10.1177/0272989x20954403 ◽

2020 ◽

pp. 0272989X2095440

Author(s):

Glen B. Taksler ◽

Jarrod E. Dalton ◽

Adam T. Perzynski ◽

Michael B. Rothberg ◽

Alex Milinovich ◽

...

Keyword(s):

Health Services Research ◽

Health Services ◽

Electronic Health Records ◽

Large Scale ◽

Data Access ◽

Data Sets ◽

Services Research ◽

Health Records ◽

Electronic Health ◽

Derived Data

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

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