scholarly journals The next-generation electronic health record: perspectives of key leaders from the US Department of Veterans Affairs

2013 ◽  
Vol 20 (e1) ◽  
pp. e175-e177 ◽  
Author(s):  
Jason J Saleem ◽  
Mindy E Flanagan ◽  
Nancy R Wilck ◽  
Jim Demetriades ◽  
Bradley N Doebbeling
Keyword(s):  
Electronic Health Record ◽  
Veterans Affairs ◽  
Health Record ◽  
Department Of Veterans Affairs ◽  
Next Generation ◽  
The Us ◽  
Electronic Health

Professional fission in medical routines: medical scribes and physicians in two US hospital departments

2020 ◽  
Author(s):  
Ann L Bryan ◽  
John C Lammers
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Community Hospital ◽  
Health Record ◽  
Health Departments ◽  
Health Records ◽  
The Us ◽  
Electronic Health ◽  
Medical Scribes ◽  
Digestive Health

Abstract In this study we argue that professionalism imposed from above can result in a type of fission, leading to the ambiguous emergence of new occupations. Our case focuses on the US’ federally mandated use of electronic health records and the increased use of medical scribes. Data include observations of 571 patient encounters across 48 scribe shifts, and 12 interviews with medical scribes and physicians in the ophthalmology and digestive health departments of a community hospital. We found substantial differences in scribes’ roles based on the pre-existing routines within each department, and that scribes developed agency in the interface between the electronic health record and the physicians’ work. Our study contributes to work on occupations as negotiated orders by drawing attention to external influences, the importance of considering differences across professional task routines, and the personal interactions between professional and technical workers.

scholarly journals Desiderata for the development of next-generation electronic health record phenotype libraries

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Martin Chapman ◽  
Shahzad Mumtaz ◽  
Luke V Rasmussen ◽  
Andreas Karwath ◽  
Georgios V Gkoutos ◽  
...  
Keyword(s):  
Best Practices ◽  
Electronic Health Record ◽  
Health Record ◽  
Next Generation ◽  
Electronic Health Record Data ◽  
High Quality ◽  
Record Data ◽  
Electronic Health ◽  
Further Development

Abstract Background High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. Methods A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.

scholarly journals De-identification of electronic health record using neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tanbir Ahmed ◽  
Md Momin Al Aziz ◽  
Noman Mohammed
Keyword(s):  
Neural Network ◽  
Electronic Health Record ◽  
Health Record ◽  
Sensitive Information ◽  
Protective Measures ◽  
Textual Data ◽  
The Us ◽  
Electronic Health ◽  
Utility Metrics

Abstract According to a recent study, around 99% of hospitals across the US now use electronic health record systems (EHRs). One of the most common types of EHR is the unstructured textual data, and unlocking hidden details from this data is critical for improving current medical practices and research endeavors. However, these textual data contain sensitive information, which could compromise our privacy. Therefore, medical textual data cannot be released publicly without undergoing any privacy-protective measures. De-identification is a process of detecting and removing all sensitive information present in EHRs, and it is a necessary step towards privacy-preserving EHR data sharing. Over the last decade, there have been several proposals to de-identify textual data using manual, rule-based, and machine learning methods. In this article, we propose new methods to de-identify textual data based on the self-attention mechanism and stacked Recurrent Neural Network. To the best of our knowledge, we are the first to employ these techniques. Experimental results on three different datasets show that our model performs better than all state-of-the-art mechanism irrespective of the dataset. Additionally, our proposed method is significantly faster than the existing techniques. Finally, we introduced three utility metrics to judge the quality of the de-identified data.

scholarly journals Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record

2017 ◽  
Vol 08 (04) ◽  
pp. 1159-1172 ◽  
Author(s):  
Timothy Kennell ◽  
James Willig ◽  
James Cimino
Keyword(s):  
Electronic Health Record ◽  
Biomedical Informatics ◽  
Research Result ◽  
Clinical Informatics ◽  
Quality Data ◽  
Health Record ◽  
Next Generation ◽  
Patient Reported ◽  
Data Content ◽  
Electronic Health

Objective Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR. Materials and Methods We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations. Results Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping. Discussion These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics. Conclusion Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

scholarly journals Pharmacists Without Access to the EHR: Practicing with One Hand Tied Behind Our Backs

2021 ◽  
Vol 12 (3) ◽  
pp. 16
Author(s):  
Deeatra S. Craddock ◽  
Ronald G. Hall
Keyword(s):  
Electronic Health Record ◽  
Healthcare Providers ◽  
Monitoring Program ◽  
Veterans Affairs ◽  
Health Record ◽  
National Data ◽  
Controlled Substances ◽  
The Public ◽  
Lack Of Information ◽  
Electronic Health

Pharmacists are the most accessible healthcare professionals to the public, yet have the least amount of information from the electronic health record available to them. This lack of information makes ensuring that patients are receiving proper medications and monitoring for efficacy and safety a challenge, if not impossible in some situations. Having access to a national electronic health record would provide pharmacists with this needed information to truly engage with prescribers as fellow clinical experts in the field. Sharing prescription information for non-controlled substances would also decrease the likelihood of a patient receiving duplicative therapy from two prescribers or pharmacies that may not know what the other is doing. There are already examples of successful national data sharing including the Prescription drug Monitoring Program for controlled substances as well as the Veterans Affairs healthcare system. Therefore, our profession needs to push for nationwide access to patient electronic health records, which includes all healthcare providers. This will facilitate the inclusion of pharmacists in the optimization of the care of patients who need our expertise in managing their medication regimens as well as build better relationships with prescribing providers.

Abstract P346: Cohort Identification to Harness the Electronic Health Record for Cardiovascular Disease Prediction

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Sheila M Manemann ◽  
Jennifer St Sauver ◽  
Janet E Olson ◽  
Nicholas B Larson ◽  
Paul Y Takahashi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Health Record ◽  
Next Generation ◽  
Cvd Risk ◽  
Electronic Health

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

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