scholarly journals Association of the Usability of Electronic Health Records With Cognitive Workload and Performance Levels Among Physicians

2019 ◽  
Vol 2 (4) ◽  
pp. e191709 ◽  
Author(s):  
Lukasz M. Mazur ◽  
Prithima R. Mosaly ◽  
Carlton Moore ◽  
Lawrence Marks
Keyword(s):  
Electronic Health Records ◽  
Cognitive Workload ◽  
Health Records ◽  
Performance Levels ◽  
Electronic Health ◽  
And Performance

scholarly journals Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

2016 ◽  
Vol 24 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Benjamin A Goldstein ◽  
Ann Marie Navar ◽  
Michael J Pencina ◽  
John P A Ioannidis
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Model Building ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Health Records ◽  
Clinical Risk ◽  
Electronic Health ◽  
And Performance

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

scholarly journals Visualizing Semantic Structure of a Clinical Text Document

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Jonah Kenei ◽  
Elisha Opiyo ◽  
Robert Oboko
Keyword(s):  
Electronic Health Records ◽  
Clinical Data ◽  
Semantic Structure ◽  
Cognitive Workload ◽  
Health Records ◽  
Clinical Text ◽  
Clinical Notes ◽  
Healthcare Data ◽  
Text Document ◽  
Electronic Health

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

Which electronic health record system should we use? - a systematic review (Preprint)

2020 ◽  
Author(s):  
Vanash Patel ◽  
George Garas ◽  
James Hollingshead ◽  
Drostan Cheetham ◽  
Thanos Athanasiou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Research And Development ◽  
User Satisfaction ◽  
Electronic Records ◽  
Published Data ◽  
Health Records ◽  
Electronic Health ◽  
And Performance ◽  
The Uk

BACKGROUND Electronic health records are digital records of a patient’s health and care. At present in the UK, patients may have several paper and electronic records stored in various settings. The UK government, via NHS England, intends to introduce a comprehensive system of electronic health records in England by 2020. These electronic records will run across primary, secondary and social care linking all data in a single digital platform. OBJECTIVE This is the first systematic review to look at all published data on EHRs to determine which systems are advantageous. METHODS Design: A systematic review was performed by searching EMBASE and Ovid MEDLINE between 1974 and November 2019. Participants: All original studies that appraised EHR systems were included. Main outcome measures: EHR system comparison, implementation, user satisfaction, efficiency and performance, documentation, and research and development. RESULTS The search strategy identified 701 studies, which were filtered down to 46 relevant studies. Level of evidence ranged from 1 to 4 according to the Oxford Centre for Evidence-based Medicine. The majority of the studies were performed in the USA (n = 44). N=6 studies compared more than one EHR, and Epic followed by Cerner were the most favourable through direct comparison. N=17 studies evaluated implementation which highlighted that it was challenging, and productivity dipped in the early phase. N=5 studies reflected on user satisfaction, with women demonstrating higher satisfaction than men. Efficiency and performance issues were the driving force behind user dissatisfaction. N=26 studies addressed efficiency and performance, which improved with long-term use and familiarity. N=18 studies considered documentation and showed that EHRs had a positive impact with basic and speciality tasks. N=29 studies assessed research and development which revealed vast capabilities and positive implications. CONCLUSIONS Epic is the most studied EHR system and the most commonly used vendor on the market. There is limited comparative data between EHR vendors, so it is difficult to assess which is the most advantageous system.

scholarly journals Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

2020 ◽  
Vol 9 (5) ◽  
pp. 2331-2336
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Neglected Tropical Diseases ◽  
Support Vector ◽  
Health Records ◽  
Clinical Text ◽  
Clinical Notes ◽  
Leprosy Patients ◽  
Electronic Health ◽  
And Performance

Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis.Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.

scholarly journals Enhancing Transaction Security for Handling Accountability in Electronic Health Records

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chian Techapanupreed ◽  
Werasak Kurutach
Keyword(s):  
Electronic Health Records ◽  
Mutual Authentication ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Security Protocol ◽  
Health Records ◽  
Authentication And Authorization ◽  
Electronic Health ◽  
And Performance ◽  
Security Property

Electronic healthcare systems have received extensive attention during the last decade due to the advancement of digital technology. Using these systems in the healthcare industry can improve the quality of healthcare services tremendously. However, a major issue that needs to be concerned, when utilizing this kind of system, is accountability. Employments of electronic health records, the core of the systems, without accountability can be a big risk to both patients and service personals and, consequently, to the entire society. Accountability in electronic health records is essential to creating trust among parties. Many researchers have been introduced to the accountability protocol. However, most of them still lack some essential security property that is mutual authentication. This leads to both information traceability and nonrepudiation which are necessary for resolving any conflict that may arise. In this paper, we propose accountability protocol for electronic health records; the protocol employs both asymmetric and symmetric encryptions to ensure that the electronic health records are having confidentiality, integrity, authentication, and authorization. The accountability analysis and performance analysis show that the proposed protocol is more capable and effective than others. The novel aspect of this idea lies in the inclusion of certain forms of security that are necessary to protect the patient’s electronic health records. To the best of our knowledge, the proposed protocol consumes less cost, energy, and time compared with the existing protocols. A proof of concept of our protocol is also presented in this paper by using BAN logic, an automated security protocol proof tool named Scyther, and AVISPA

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