Actual Electronic Health Record Research Articles

Exploring the impact of cybersecurity on using electronic health records and their performance among healthcare professionals: A multi-analytical SEM-ANN approach

Cybersecurity is critical in safeguarding sensitive information against evolving threats, especially in the healthcare sector, where Electronic Health Records (EHR) are central to the digital transformation of healthcare. This study takes a unique approach by investigating the influence of cybersecurity on healthcare professionals' use of EHR systems and its impact on their performance. It goes beyond the traditional adoption theories by integrating the Technology Acceptance Model (TAM) with cybersecurity and emotional trust and focuses on actual EHR system usage and its performance impact. The data from 459 healthcare professionals, analyzed using a Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) approach, provides a comprehensive view of the dynamics involved. The findings suggest that cybersecurity's impact on the system's ease of use may vary by context. Ease of use was linked to trust but not directly to perceived usefulness. Importantly, systems perceived as efficient and beneficial are used more, leading to improved healthcare service quality. The study underscores the importance of robust cybersecurity measures and trust in effective EHR integration, offering valuable insights for enhancing EHR system acceptance and healthcare outcomes in Jordan.

Development and pilot evaluation of an electronic health record usability and safety self-assessment tool

ObjectivePoor electronic health record (EHR) usability contributes to clinician burnout and poses patent safety risks. Site-specific customization and configuration of EHRs require individual EHR system usability and safety testing which is resource intensive. We developed and pilot-tested a self-administered EHR usability and safety assessment tool, focused on computerized provider order entry (CPOE), which can be used by any facility to identify specific issues. In addition, the tool provides recommendations for improvement.Materials and MethodsAn assessment tool consisting of 104 questions was developed and pilot-tested at 2 hospitals, one using a Cerner EHR and the other using Epic. Five physicians at each site participated in and completed the assessment. Participant response accuracy compared to actual EHR interactions, consistency across participants, and usability issues identified through the tool were measured at each site.ResultsAcross sites, participants answered an average of 46 questions in 23 min with 89.9% of responses either correct or partially correct. The tool identified 8 usability and safety issues at one site and 7 at the other site across medication, laboratory, and radiology CPOE functions.DiscussionThe tool shows promise as a method to rapidly evaluate EHR usability and safety and provide guidance on specific areas for improvement. Important improvements to the evaluation tool were identified including the need to clarify certain questions and provide definitions for usability terminology.ConclusionA self-administered usability and safety assessment tool can serve to identify specific usability and safety issues in the EHR and provide guidance for improvements.

Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar.

The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.

A Module in Medication Safety Involving Remote Access and Review of Veterans’ Health Records

Objective. To describe and assess the effectiveness of an ambulatory care introductory pharmacy practice experience (IPPE) module in medication safety monitoring that was conducted remotely for first- and second-year pharmacy students in collaboration with a Department of Veterans Affairs (VA) health care system.Methods. A module in medication safety was developed and piloted as part of a required, one-unit IPPE in ambulatory care. The module encompassed three sessions of the 15-week course. Working remotely at the school under the direct supervision of VA clinical pharmacists, students accessed the electronic health records (EHRs) of veterans for whom methotrexate, sulfasalazine, or mineralocorticoid receptor antagonists had been prescribed as such patients require ongoing laboratory monitoring to screen for adverse drug effects. After reviewing the record, students ordered laboratory tests under the prescriber's name, documented notes in the patient's EHR, and sent laboratory reminder letters to patients. Students completed a brief survey at the beginning and end of the course. Differences in survey responses were compared using the Wilcoxon signed rank test.Results. Fifty-eight students reviewed 148 patient records, including non-VA electronic records, and ordered laboratory tests for 79 patients. On the follow-up survey at course completion, students reported significant changes in their ability to monitor laboratory tests, use an actual (non-simulated) EHR to perform patient care, and explain the role of a clinical dashboard to conduct population health activities.Conclusion. Remotely conducting an ambulatory care IPPE course in which pharmacy students used an actual EHR to participate in medication safety monitoring for ambulatory patients expanded the pharmacy school's ability to provide crucial practice experience. Other pharmacy schools struggling with logistical challenges and limited resources should explore this approach to fulfilling the experiential education requirements for pharmacy students.

ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care.

BackgroundWith increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.ObjectiveOur goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.MethodsWe propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.ResultsIn collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.ConclusionsWe studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.

The application of unsupervised deep learning in predictive models using electronic health records

BackgroundThe main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks.MethodsWe compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals.ResultsOn simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper.ConclusionsWe conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and model training.

Validation and Refinement of a Pain Information Model from EHR Flowsheet Data.

Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

Unit-Based Simulation for Training Residents on a New Electronic Health Record

Objective: To determine if a unit-based simulated experience provides a meaningful learning experience for residents prior to the implementation of a new electronic health record (EHR). Methods: IRB exemption was obtained. Faculty (OB, Family Medicine, CNM), residents (OB, Family Med), and nurses participated in 64 simulations over a 4week period. A simulation scenario, utilizing birthing simulators was designed taking a patient from admission through labor, delivery, postpartum hemorrhage, and discharge. Participants performed actual clinical actions and charted all items in a training environment of the actual electronic health record. Nurses in parallel completed their charting, notes and order completion on the same patient at the same time. Each session lasted for 2-hours including a 10-minute brief, 80-minutes of simulation, and 30minutes of team debrief. Participants completed a survey regarding their experience.

On-Unit Obstetrics Team Simulation Enhances Implementation of a New Electronic Health Record [345

INTRODUCTION: Implementation of a new electronic health record must address the needs of multiple team members and various workflows. Patient safety can be compromised if users are underprepared. This study evaluated the effectiveness of a simulation training curriculum based on pairing physicians and nurses together to learn a new electronic health record. Rapid-cycle improvement was used to identify and fix workflow issues. METHODS: Faculty and residents from obstetrics and family medicine, midwives, and nurses participated in 64 simulations over 4 weeks. Physician–nurse teams admitted, delivered, and treated a postpartum hemorrhage on a birthing simulator while documenting in the actual electronic health record. Learners completed presurveys and postsurveys regarding their satisfaction with this experience. Each session lasted 2 hours, including a 30-minute debrief. Data collection included workflow optimizations reported to the electronic health record build teams for rapid-cycle improvement. RESULTS: A total of 188 participants (faculty: 47, certified nurse–midwives: 23, residents: 31, burses: 87) completed training. The intervention was perceived as useful, created the desire to participate in more simulations, and decreased anxiety. Debrief session observations resulted in a 20-item FAQ distributed to increase uniform use of the electronic health record. CONCLUSION: This study demonstrates that simulation provides a useful platform for preparing a large health care team for a new electronic health record. Participants found the training useful, believed it improved understanding of workflow, and found it reduced anxiety. Workflow problems and conflicts were identified and resolved before rollout.

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

ObjectiveModels of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems. MethodWe designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: (1) Coding & Charge Entry and (2) Medical Information Services and two clinical areas: (3) Anesthesiology and (4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient's record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models. ResultsThe findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas’ associations were more distinguishable than others. ConclusionsThese results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.

Electronic Health Records and the Increasing Complexity of Medical Practice: “It Never Gets Easier, You Just Go Faster”

Electronic Health Records and the Increasing Complexity of Medical Practice: “It Never Gets Easier, You Just Go Faster”

Community-Based, Interdisciplinary Geriatric Care Team Satisfaction With an Electronic Health Record

This multimethod study measured the impact of an electronic health record (EHR) on clinician satisfaction with clinical process. Subjects were 39 clinicians at a Program of All-inclusive Care for Elders (PACE) site in Philadelphia utilizing an EHR. Methods included the evidence-based evaluation framework, Health Information Technology Research-Based Evaluation Framework, which guided assessment of clinician satisfaction with surveys, observations, follow-up interviews, and actual EHR use at two points in time. Mixed-methods analysis of findings provided context for interpretation and improved validity. The study found that clinicians were satisfied with the EHR; however, satisfaction declined between time periods. Use of EHR was universal and wide and was differentiated by clinical role. Between time periods, EHR use increased in volume, with increased timeliness and decreased efficiency. As the first EHR evaluation at a PACE site from the perspective of clinicians who use the system, this study provides insights into EHR use in the care of older people in community-based healthcare settings.

Development and Validation of an Electronic Health Record–Based Chronic Kidney Disease Registry

Chronic kidney disease (CKD) is increasing, and outcomes-related research from diverse health care settings is needed to target appropriate efforts and interventions. We developed an electronic health record (EHR)-based CKD registry at the Cleveland Clinic and validated comorbid conditions. Patients who had at least one face-to-face outpatient encounter with a Cleveland Clinic health care provider and (1) had two estimated GFR values <60 ml/min per 1.73 m(2) >90 days apart as of January 1, 2005 and/or (2) were patients with International Classification of Diseases-9 (ICD-9) diagnosis codes for kidney disease were included. Our registry includes 57,276 patients (53,399 patients met estimated GFR criteria and 3877 patients met ICD-9 diagnosis code criteria) as of March 2010. Mean age was 69.5 ± 13.4 years, with 55% women and 12% African Americans. Medicare is the primary insurer for more than one half of the study cohort. The κ statistics to assess the extent of agreement between the administrative dataset extracted from the EHR and actual EHR chart review showed substantial agreement (>0.80) for all conditions except for coronary artery disease and hypertension, which had moderate agreement (<0.60). Development of an EHR-based CKD registry is feasible in a large health system, and the comorbid conditions included in the registry are reliable. In addition to conducting research studies, such a registry could help to improve the quality of care delivered to CKD patients and complement the ongoing nationwide efforts to develop a CKD surveillance project.

A phenomenological perspective on clinical communication and interaction

PurposeThe purpose of this paper is to investigate how people communicate in organisations.Design/methodology/approachThe approach in doing this is based on two pillars. One being a philosophical phenomenological approach dealing mainly ethical questions concerning communication and interaction in designing and implementing electronic health records (EHR). The other is videoobservation of work procedures in hospitals because appropriate for unveiling tacit knowledge in an organisation.FindingsThe paper discusses the inappropriate design and implementation of actual EHR's in the Danish hospital system. Where the technology is based on hierarchical and economic structured managerialist thinking and doing, that eliminates the everyday knowledge of nursing staff and the patient.Originality/valueThe value of this paper that it lays forward the inappropriate paradigmatic thinking of hospital systems concerning communication and interaction in designing and implementing EHRs.