Electronic Health Record Content Research Articles

Private blockchain-based encryption framework using computational intelligence approach

Electronic Health monitoring system has performed an essential role in managing healthcare monitoring. E-health can provide effective and valuable facilities for the patients to monitor. Though, there are protection disputes in the current E-Health system. The current e-health system, on the other hand, has security issues. Malevolent doctors may work together with cloud Storage Service Providers (CSPs) to interfere with patients' electronic health records (EHRs) or promptly leak EHR matter to other enemies for income. (EHRs). The malevolent doctors may conspire with the Patient Healthcare Monitoring Service Provider (PHMSP) to manipulate with the patients'. For profit, EHRs or directly divulge the EHR content of EHRs to other opponents. Block-chain has recently appeared as one of the most powerful methods in the protection and secrecy fields. It is assumed to be the promised security approach that will eventually replace the security challenges in existing e-health monitoring systems. Encryption in blockchain refers to technical methods that make accessing encrypted data difficult for unauthorized resources. This research proposed a blockchain-based encryption framework to provide security-based solutions using a computational intelligence methodology. The proposed approach provides better results in terms of 0.93 in the training phase and 0.91 in the validation accuracy.

Provider documentation of electronic nicotine delivery systems use among patients prescribed contraception at an academic health center in the Southeastern United States

PurposeWomen on combined hormonal contraception (CHC) who use electronic nicotine delivery systems (ENDS) may be vulnerable to adverse cardiovascular events. To date, no study has examined whether clinicians screen for ENDS use when prescribing CHC. Therefore, we investigated documentation of ENDS screening and counseling in the electronic health record (EHR) of women prescribed CHC. MethodsWe conducted a retrospective EHR review and content analysis at an academic health center in the Southeastern United States. We randomly selected 500 records of female patients 12 years and older who had been prescribed contraception and had ENDS documented in their records identified via keyword match. Records prior to July 2020 were reviewed between June-September 2020. ResultsOf the 500 patients, 245 (49%) were ENDS users and 227 (45.4%) were non-ENDS users. Among ENDS users, there were 82 contraception-related encounters with ENDS documentation. In 55 (67.1%) of these encounters, only ENDS use status was documented. The provider counseled against ENDS use in 17 (20.7%) records. Six (7.3%) notes documented provision of patient education materials instructing patients on contraception to refrain from using ENDS. Among non-ENDS users, there were 43 contraception-related encounters with ENDS documentation; 35 (81.4%) documented the patient did not use ENDS and 3 (7%) documented provision of patient education materials. ConclusionENDS use is under-documented in contraception-related encounters. Improvements in documentation may help assess long-term effects of concurrent ENDS and CHC use. These results illustrate the need to clarify EHR prompts and increase provider awareness to improve ENDS documentation.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions.

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed. We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges. Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals. EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Status Update on Data Required to Build a Learning Health System.

Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed "real-world" data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.

Performance Evaluation of Clinical Decision Support Systems (CDSS): Developing a Business Intelligence (BI) Dashboard.

This document describes the development of a Business Intelligence (BI) dashboard for tracking the drug-drug interaction (DDI) alerts implemented as Clinical Decision Support Systems (CDSS) in Electronic Health Records (EHR). CDSS are known for their potential to reduce medical error. The use of requirements in the development of BI dashboards is crucial to obtain successful software. In this work, the requirements were analysed using a score methodology, considering the relevance of the indicators and visualization methods. CDSS effectiveness and acceptance have been questioned, so it is fundamental to monitor their behaviour and performance. The dashboard was designed in order to satisfy the needed indicators. Using BI as a tool for monitoring the CDSS performance made it possible to operationalize the EHR content repository, maximizing the understanding in relation to the override and, by inference, to optimize the CDSS system by opening new lines of work.

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health Records (EHRs) using Natural Language Processing (NLP). This exploratory study rated presence or absence and frequency of words in a forensic EHR dataset, comparing four reference dictionaries. Seven machine learning algorithms and different time periods of EHR analysis were used to probe which dictionary and which time period were most predictive of risk assessment scores on validated instruments. The EHR dataset comprised de-identified forensic inpatient notes from the Wilfred Lopes Centre in Tasmania. The data comprised unstructured free-text case note entries and serial ratings of three risk assessment scales: Historical Clinical Risk Management-20 (HCR-20), Short-Term Assessment of Risk and Treatability (START) and Dynamic Appraisal of Situational Aggression (DASA). Four NLP dictionary word lists were selected: 6865 mental health symptom words from the Unified Medical Language System (UMLS), 455 DSM-IV diagnoses from UMLS repository, 6790 English positive and negative sentiment words, and 1837 high frequency words from the Corpus of Contemporary American English (COCA). Seven machine learning methods Bagging, J48, Jrip, Logistic Model Trees (LMT), Logistic Regression, Linear Regression and Support Vector Machine (SVM) were used to identify the combination of dictionaries and algorithms that best predicted risk assessment scores. The most accurate prediction was attained on the DASA dataset using the sentiment dictionary and the LMT and SVM algorithms. NLP, used in conjunction with NLP dictionaries and machine learning, predicted risk ratings on the HCR-20, START, and DASA, based on EHR content. Further research is required to ascertain the utility of NLP approaches in predicting endpoints of actual self-harm, harm to others or victimisation.

Rates of PD-L1 expression testing in U.S. community-based oncology practices (USCPs) for patients with metastatic non-small cell lung cancer (mNSCLC) receiving nivolumab (N) or pembrolizumab (P).

11596 Background: FDA approved N for previously treated squamous mNSCLC in 3/2015 and non-squamous mNSCLC in 10/2015 regardless of PD-L1 expression. P was approved for previously treated mNSCLC in 10/2015 for patients whose tumors express PD-L1 as determined by an FDA-approved companion diagnostic (CoDx). Post-FDA approval treatment patterns of N and P, including PD-L1 testing patterns, remain unclear. Methods: We conducted a retrospective analysis using de-identified patient-level electronic health record (EHR) data aggregated from USCPs by Flatiron Health in accordance to an institutional review board approved protocol. Data was abstracted using both structured and unstructured EHR content with 10% duplicate abstraction to confirm data quality. All patients in the database with clinically-confirmed mNSCLC diagnosed from 1/1/11 to 3/31/16 and documented order or administration of N or P were included in the analyses. PD-L1 expression testing results were abstracted from unstructured data in EHR documents. Results: Within the first year of FDA approval, 1,362 mNSCLC patients received N or P (96.2% N; 3.3% P, 0.5% N+P). The median duration of treatment was 113 days. Overall, 11.3% of patients were tested for PD-L1 expression, 42.2% of whom tested positive. PD-L1 expression testing increased quarterly (Q) from 3.0% in Q2 2015 to 12.8% in Q1 2016. An FDA-approved CoDx was used 30.5% of the time. Among patients who received N, 90.5% were not tested for PD-L1 expression, 3.2% tested positive, and 4.7% tested negative for PD-L1. Among patients who received P, 32.7% were not tested for PD-L1 expression, 50.0% tested positive, and 1.9% tested negative. Conclusions: We observed low rates of PD-L1 expression testing in USCPs for patients with mNSCLC receiving N or P. Testing rates increased quarterly following FDA-approval. The majority of patients were not tested using FDA-approved CoDx. Most patients who were not tested for PD-L1 expression or tested negative received N. Clinician education will be important as data on optimal PD-L1 testing strategies guiding immunotherapy treatment decisions accumulates.

A Model-Driven Based Methodology for the Generation of Context-Aware Medical Interfaces from Open EHR Archetypes

Purpose: Different medical computer science standards, such as ISO\EN 13606, openEHR and HL7 CDA, use the dual model approach to support interoperability between medical information systems. However, use of these standards encounters some obstacles: (i) lack methodological tools to facilitate design of Electronic Health Record (EHR) systems in accordance to two conceptual levels, (ii) Lack of methodologies to perform semantic activities on EHR content in the semantic web environment, and (iii) Absence of models to adapt medical knowledge, modelled by means of archetypes, in different contexts of use. This paper presents a model-driven based methodology for the generation of context-aware medical interfaces. This methodology, entitled OpenEHR modelling Methodology: OpenEHR-MM includes concepts, models, rules, processes and tools aimed to assist EHR designer in the generation process of context-aware medical interfaces. Methods: OpenEHR-MM is based on the use of the model driven architecture technological space, and it includes the following four steps: 1. Analysis and specification of requirements: This first step aims to guide EHR interface designer in the definition of users’ needs. 2. Conceptualization: This step aims to define tools, concepts and approaches that assist EHR designer in the two conceptual levels modelling process. At this step, we define an approach to provide the context information modelling. 3. Ontologization: This step aims to assist the designer to integrate the semantic dimension in the EHR systems modeled in accordance to the dual model approach. 4. Implementation: Finally, OpenEHR-MM provides an adaptive approach to enable medical interface configuration with respect to contexts settings. Results: A model driven-based methodology for context-aware medical interfaces generation process. The experimentation of this methodology is performed on the EHR related to patients affected by cerebral palsy.

Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications.

Electronic health record content is created by clinicians and is driven largely by intermittent and brief encounters with patients. Collecting data directly from patients in the form of patient-generated data (PGD) provides an unprecedented opportunity to capture personal, contextual patient information that can supplement clinical data and enhance patients' self-care. The US Department of Veterans Affairs (VA) is striving to implement the enterprise-wide capability to collect and use PGD in order to partner with patients in their care, improve the patient healthcare experience, and promote shared decision making. Through knowledge gained from Veterans' and healthcare teams' perspectives, VA created a taxonomy and an evolving framework on which to design and develop applications that capture and help physicians utilize PGD. Ten recommendations for effectively collecting and integrating PGD into patient care are discussed, addressing health system culture, data value, architecture, policy, data standards, clinical workflow, data visualization, and analytics and population reach.

Dermatology in an age of fully transparent electronic medical records.

More than 4 decades have passed since the call for “giving patients their medical records” was first proposed to increase patient engagement in health care delivery.1Today, this vision—once considered radical—is quickly becoming reality, with millions of Americans routinely accessing their medical records through web-based patient portals.1 The electronic health record content that patients can access online is expanding to include physicians’ documentation of patient visits. Recent studies evaluating OpenNotes, a patient-centered initiative enabling online access to providers’ clinical notes, have demonstrated high levels of patient utilization and improved self-reported understanding of care planning and medication adherence, resulting in patients “feeling more in control of their health care.”1 One year after implementation of OpenNotes in 3 diverse health care environments in 2010 (Beth Israel Deaconess Medical Center in Boston, Massachusetts; Geisinger Health System in northeastern and central Pennsylvania; and Harborview Medical Center in Seattle, Washington), more than 99% of patients wished to have ongoing online access to clinicians’ notes, paralleled by similar rates of primary care provider satisfaction.1 Open-access notes have since become standard of care at several large health care systems,2 including the Veterans Health Administration; as of January 2014, more than 1.4 million veterans have registered for full online access to their clinical notes.3 However, medical specialties may face distinct challenges consequent to adoption of open-access medical records. As more patients seek—and demand—comprehensive online availability of open-access notes, what will this mean for the field of dermatology? We believe the following issues merit particular attention among dermatologists in the era of medical record transparency.

A Standardized SOA Based Solution to Guarantee the Secure Access to EHR

Abstract Continued advances in science and technology and general improvements in environmental and social conditions is extending the population's life expectancy with the consequence that a person can undergo many episodes of healthcare during lifetime. In this context, the Electronic Health Record (EHR) represents a fundamental tool to support treatment continuity, education and research. The economic restrictions in healthcare and the need to increase efficiency in term of cost/effectiveness ration could lead institutional organizations to choose cloud solutions to host the EHR. In this paper, a cloud infrastructure architecture, focus on the EHR and based on SOA (Service Oriented Architecture) paradigm, which is able both to completely support technical, semantic and process interoperability, and to guarantee security, is proposed. In order to achieve this goal, the indications and the standards proposed by Healthcare Services Specification Project (HSSP) was adopted. Different situations can be managed by the proposed architecture and are described: the user access to an encrypted resource in EHR, the availability of EHR content for external Decision Support Systems, the update of EHR content, the management of semantic of clinical data exchanged among distributed healthcare organizations. Finally, the authors propose a discussion on the proposed solution.

A Proposed Electronic Health Record Content Structure Based on Clinical Organizations Survey

Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. Several electronic health records systems were implemented in different states with different clinical data structures that prevent data exchange between systems even in the same state. This leads to the important barrier in implementing EHR system which is the lack of standards of EHR clinical data structure. In this paper we made a survey on several in international and Egyptian medical organization for implementing electronic health record systems for finding the best electronic health record clinical data structure that contains all patient’s medical data. We proposed an electronic health record system with a standard clinical data structure based on the international and Egyptian medical organization survey and with avoiding the limitations in the other electronic health record that exists in the survey.

Implementing electronic health records in hospitals: a systematic literature review.

BackgroundThe literature on implementing Electronic Health Records (EHR) in hospitals is very diverse. The objective of this study is to create an overview of the existing literature on EHR implementation in hospitals and to identify generally applicable findings and lessons for implementers.MethodsA systematic literature review of empirical research on EHR implementation was conducted. Databases used included Web of Knowledge, EBSCO, and Cochrane Library. Relevant references in the selected articles were also analyzed. Search terms included Electronic Health Record (and synonyms), implementation, and hospital (and synonyms). Articles had to meet the following requirements: (1) written in English, (2) full text available online, (3) based on primary empirical data, (4) focused on hospital-wide EHR implementation, and (5) satisfying established quality criteria.ResultsOf the 364 initially identified articles, this study analyzes the 21 articles that met the requirements. From these articles, 19 interventions were identified that are generally applicable and these were placed in a framework consisting of the following three interacting dimensions: (1) EHR context, (2) EHR content, and (3) EHR implementation process.ConclusionsAlthough EHR systems are anticipated as having positive effects on the performance of hospitals, their implementation is a complex undertaking. This systematic review reveals reasons for this complexity and presents a framework of 19 interventions that can help overcome typical problems in EHR implementation. This framework can function as a reference for implementers in developing effective EHR implementation strategies for hospitals.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials.Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed.Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches.Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department

ObjectiveTo evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).MethodsWe analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision.ResultsThe system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit.ConclusionsAutomated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.

P012 The EHR

Guidelines must adhere to a rigorous, transparent, evidence-based methodology and contain explicit, actionable recommendations to ensure acceptance and facilitate implementation by clinicians and health care delivery systems. In addition to...

Electronic Health Records and US Public Health: Current Realities and Future Promise

Electronic health records (EHRs) could contribute to improving population health in the United States. Realizing this potential will require understanding what EHRs can realistically offer to efforts to improve population health, the requirements for obtaining useful information from EHRs, and a plan for addressing these requirements. Potential contributions of EHRs to improving population health include better understanding of the level and distribution of disease, function, and well-being within populations. Requirements are improved population coverage of EHRs, standardized EHR content and reporting methods, and adequate legal authority for using EHRs, particularly for population health. A collaborative national effort to address the most pressing prerequisites for and barriers to the use of EHRs for improving population health is needed to realize the EHR's potential.

Applying representational state transfer (REST) architecture to archetype-based electronic health record systems

BackgroundThe openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content.The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored.ResultsThe main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. The modular design makes it possible to prototype, test, replicate, distribute, cache, and load-balance the system using ordinary web technology. Other contributions are approaches to query and retrieval of the EHR content that takes caching, logging, and distribution into account. Triggering on EHR change events is also explored.A final contribution is an open source openEHR implementation using the above-mentioned approaches to create LiU EEE, an educational EHR environment intended to help newcomers and developers experiment with and learn about the archetype-based EHR approach and enable rapid prototyping.ConclusionsUsing REST addressed many architectural concerns in a successful way, but an additional messaging component was needed to address some architectural aspects. Many of our approaches are likely of value to other archetype-based EHR implementations and may contribute to associated service model specifications.

Applicability of Business Intelligence in Electronic Health Record

Clinical datasets provide an excellent environment in which combined analyses of both structured and unstructured datasets can prove fruitful and useful. With an increasing need to deploy Electronic Health Record (EHR) and Electronic Medical Record (EMR), there is also a corresponding need to apply data mining technologies to extract quality data and inference rules from the information stored in those electronic records so as to provide real-time decision supports and evidence-based practice to clinicians and healthcare providers. Business Intelligence (BI) has emerged as a technology that has the potential to operationalize the repository content of EHR in supporting evidence-based practice and improving the quality of healthcare delivery. In this paper, a literature review was used to explore the key benefits, challenges, and obstacles of incorporating the BI technology into EHR so as to improve the quality and safety of healthcare delivery.

Providing Nutrition Support in the Electronic Health Record Era

Data are not available on the quantity or quality of nutrition support content in the currently available electronic health record (EHR) systems. A survey study of the American Society for Parenteral and Enteral Nutrition membership regarding the safety and efficacy of nutrition documentation and the ordering process for oral diets, oral supplements, tube feedings, and parenteral nutrition in currently used EHRs was conducted. The responses were converted to a rating of excellent, good, fair, poor, or unacceptable for each component. The survey responders rated all 5 of the above EHR nutrition components only as fair. There were no differences between disciplines regarding how they rated these nutrition components. The responders who had been using their EHR for more than 10 years rated these components as fair to good, which was significantly better than those responders who have been using their EHR for less than a year, who rated these components from poor to fair. There was some variation in ratings given to the 7 different EHR vendors used by the responders, with 2 vendors having significantly poorer ratings than the other vendors. However, even the top-rated vendors received ratings of only fair. This study should be a wake-up call for EHR developers/vendors, healthcare systems, and clinicians that the nutrition and nutrition support content of the current EHRs needs significant improvements. Nutrition support clinicians need to be actively involved in optimizing this EHR content.