The Role of Electronic Health Records in Advancing Genomic Medicine

Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 22 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Negotiating stigma in health care: disclosure and the role of electronic health records

Health Sociology Review ◽

10.1080/14461242.2015.1078218 ◽

2015 ◽

Vol 24 (3) ◽

pp. 227-241 ◽

Cited By ~ 9

Author(s):

Timothy Stablein ◽

Joseph Lorenzo Hall ◽

Chauna Pervis ◽

Denise L. Anthony

Keyword(s):

Health Care ◽

Electronic Health Records ◽

Health Records ◽

Electronic Health

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The futility of genomic counseling: essential role of electronic health records

Genome Medicine ◽

10.1186/gm48 ◽

2009 ◽

Vol 1 (5) ◽

pp. 48 ◽

Cited By ~ 15

Author(s):

John Belmont ◽

Amy L McGuire

Keyword(s):

Electronic Health Records ◽

Essential Role ◽

Health Records ◽

Genomic Counseling ◽

Electronic Health

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Clinical Research Informatics: Contributions from 2017

Yearbook of Medical Informatics ◽

10.1055/s-0038-1641220 ◽

2018 ◽

Vol 27 (01) ◽

pp. 177-183 ◽

Cited By ~ 1

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.

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The Impact of the Global, Extensible Electronic Health Record

Managing Healthcare Information Systems with Web-Enabled Technologies ◽

10.4018/978-1-878289-65-0.ch001 ◽

2011 ◽

pp. 3-13 ◽

Cited By ~ 2

Author(s):

David W. Forslund ◽

David G. Kilman

Keyword(s):

Electronic Health Records ◽

Negative Impact ◽

Global Scale ◽

Global Perspective ◽

Health Records ◽

Technological Advances ◽

The World ◽

Electronic Health ◽

The Impact

With the arrival of the “World Wide Web,” we have witnessed a transition toward a truly global perspective with respect to electronic health records. In recent years, much more discussion has focused on the potential for international virtual electronic health records and what is required for them to become a reality in the world today (Kilman & Forslund, 1997). As the Internet becomes more ubiquitous and Web-enabled, we see access to electronic health records using these technologies becoming more commonplace. Even so, these Web-enabled health records still remain technologically isolated from other medical records in the distributed continuum of care; much of the standardization challenge still stands before us. We have witnessed startling technological advances, but we still face considerable obstacles to the goal of having globally standardized electronic health records. In this chapter we describe some of the issues associated with Web-enabled health records, the role of standards in the evolution of Web-enabled health records, and some of the barriers to the development of globally accessible electronic health records. We discuss possible ways to overcome these barriers and the kinds of benefits and opportunities that global health records will help provide. The global scale perspective makes more evident the very real and potentially tragic consequences of prolonged and unnecessary delays in deploying these technologies. Therefore, in an effort to promote a fuller consciousness of health safety, the chapter concludes with a comparative look at the negative impact of impediments in the movement toward global extensible electronic health records.

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The Role of Perceived Usefulness and Attitude on Electronic Health Record Acceptance

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2014100107 ◽

2014 ◽

Vol 5 (4) ◽

pp. 108-119 ◽

Cited By ~ 3

Author(s):

Randike Gajanayake ◽

Tony Sahama ◽

Renato Iannella

Keyword(s):

Electronic Health Records ◽

Healthcare Professionals ◽

Polynomial Regression ◽

Perceived Usefulness ◽

Intention To Use ◽

Information And Communications Technologies ◽

Health Records ◽

Communications Technologies ◽

Electronic Health

Information and communications technologies are a significant component of the healthcare domain, and electronic health records play a major role in it. Therefore, it is important that they are accepted en masse by healthcare professionals. How healthcare professionals perceive the usefulness of electronic health records and their attitudes towards them have been shown to have significant effects on the overall acceptance in many healthcare systems around the world. This paper investigates the role of perceived usefulness and attitude on the intention to use electronic health records by future healthcare professionals using polynomial regression with response surface analysis. Results show that the relationships between these variables are more complex than predicted in prior research. The paper concludes that the properties of the above determinants must be further investigated to clearly understand: (i) their role in predicting the intention to use electronic health records; and (ii) in designing systems that are better adopted by healthcare professionals of the future.

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Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification

Biometrics ◽

10.1111/biom.13400 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lauren J. Beesley ◽

Bhramar Mukherjee

Keyword(s):

Electronic Health Records ◽

Statistical Inference ◽

Selection Bias ◽

Association Studies ◽

Health Records ◽

Electronic Health

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Determinants and extent of weight recording in UK primary care: an analysis of 5 million adults’ electronic health records from 2000 to 2017

BMC Medicine ◽

10.1186/s12916-019-1446-y ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 5

Author(s):

B. D. Nicholson ◽

P. Aveyard ◽

C. R. Bankhead ◽

W. Hamilton ◽

F. D. R. Hobbs ◽

...

Keyword(s):

Primary Care ◽

Electronic Health Records ◽

Negative Binomial ◽

Cox Regression ◽

Clinical Care ◽

Negative Binomial Regression ◽

Routine Activity ◽

Health Records ◽

Clinical Events ◽

Electronic Health

Abstract Background Excess weight and unexpected weight loss are associated with multiple disease states and increased morbidity and mortality, but weight measurement is not routine in many primary care settings. The aim of this study was to characterise who has had their weight recorded in UK primary care, how frequently, by whom and in relation to which clinical events, symptoms and diagnoses. Methods A longitudinal analysis of UK primary care electronic health records (EHR) data from 2000 to 2017. Descriptive statistics were used to summarise weight recording in terms of patient sociodemographic characteristics, health professional encounters, clinical events, symptoms and diagnoses. Negative binomial regression was used to model the likelihood of having a weight record each year, and Cox regression to the likelihood of repeated weight recording. Results A total of 14,049,871 weight records were identified in the EHR of 4,918,746 patients during the study period, representing 26,998,591 person-years of observation. Around a third of patients had a weight record each year. Forty-nine percent of weight records were repeated within a year with an average time to a repeat weight record of 1.92 years. Weight records were most often taken by nursing staff (38–42%) and GPs (37–39%) as part of a routine clinical care, such as chronic disease reviews (16%), medication reviews (6–8%) and health checks (6–7%), or were associated with consultations for contraception (5–8%), respiratory disease (5%) and obesity (1%). Patient characteristics independently associated with an increased likelihood of weight recording were as follows: female sex, younger and older adults, non-drinkers, ex-smokers, low or high BMI, being more deprived, diagnosed with a greater number of comorbidities and consulting more frequently. The effect of policy-level incentives to record weight did not appear to be sustained after they were removed. Conclusion Weight recording is not a routine activity in UK primary care. It is recorded for around a third of patients each year and is repeated on average every 2 years for these patients. It is more common in females with higher BMI and in those with comorbidity. Incentive payments and their removal appear to be associated with increases and decreases in weight recording.

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