scholarly journals Increased depression genetic liability associates with inflammatory markers in multiple electronic health record systems

2020 ◽  
Author(s):  
Lea Davis ◽  
Julia Sealock ◽  
Younga Lee ◽  
Arden Moscati ◽  
Sanan Venkatesh ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Health Care Systems ◽  
Treatment Options ◽  
Cell Types ◽  
Biological Processes ◽  
Health Records ◽  
Clinical Biomarkers ◽  
Care Systems ◽  
Electronic Health ◽  
The Relationship

Abstract Although depression is a common disorder, its underlying biological basis remains poorly understood. The results of clinical lab tests available for research in electronic health records can be used to identify biomarkers that may reveal biological processes involved in the development of depression or may be markers of physiological changes due to depression. Here, we leveraged clinical laboratory tests and integrated biobank data to evaluate the relationship between genetic risk for depression and 315 routinely collected quantitative lab measures. Analyses across four health care systems (N = 382,452) robustly implicate increased white blood cell count as both a risk factor and consequence of depression diagnosis and revealed neutrophils, lymphocytes, and monocytes as the primary cell types responsible for this association. Our results highlight the importance of the immune system in the etiology of depression and motivate future development of clinical biomarkers and targeted treatment options for depression and its systemic effects.

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

scholarly journals Biases introduced by filtering electronic health records for patients with “complete data”

2017 ◽  
Vol 24 (6) ◽  
pp. 1134-1141 ◽  
Author(s):  
Griffin M Weber ◽  
William G Adams ◽  
Elmer V Bernstam ◽  
Jonathan P Bickel ◽  
Kathe P Fox ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Large Scale ◽  
Health Care Systems ◽  
Data Types ◽  
Health Records ◽  
Data Completeness ◽  
Number Of Patients ◽  
Care Systems ◽  
Electronic Health

Abstract Objective One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data “completeness” affect the number of patients in the resulting cohort and introduce potential biases. Materials and Methods We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members. Results EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete. Discussion and Conclusion As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.

The relationship between usage intention and adoption of electronic health records at primary care clinics

2013 ◽  
Vol 112 (3) ◽  
pp. 731-737 ◽  
Author(s):  
Usman Iqbal ◽  
Cheng-Hsun Ho ◽  
Yu-Chuan(Jack) Li ◽  
Phung-Anh Nguyen ◽  
Wen-Shan Jian ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Primary Care Clinics ◽  
Electronic Health ◽  
The Relationship

Electronic Health Records

2010 ◽  
pp. 287-299
Author(s):  
Eike-Henner W. Kluge
Keyword(s):  
Electronic Health Records ◽  
Professional Education ◽  
Legal Status ◽  
Healthcare Decision ◽  
Technical Standards ◽  
Health Records ◽  
Healthcare Decision Making ◽  
Electronic Health ◽  
Privacy And Confidentiality ◽  
The Relationship

The development of electronic health records marked a fundamental change in the ethical and legal status of health records and in the relationship between the subjects of the records, the records themselves and health information and healthcare professionals—changes that are not fully captured by traditional privacy and confidentiality considerations. The chapter begins with a sketch of the nature of this evolution and places it into the epistemic framework of healthcare decision-making. It then outlines why EHRs are special, what the implications of this special status are both ethically and juridically, and what this means for professionals and institutions. An attempt is made to link these considerations to the development of secure e-health, which requires not only the interoperability of technical standards but also the harmonization of professional education, institutional protocols and of laws and regulations.

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