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Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.

Background Hospitals rely on their electronic health record (EHR) systems to assist with the provision of safe, high quality, and efficient health care. However, EHR systems have been found to disrupt clinical workflows and may lead to unintended consequences associated with patient safety and health care professionals’ perceptions of and burden with EHR usability and interoperability. This study sought to explore the differences in staff perceptions of the usability and safety of their hospital EHR system by staff position and tenure. Methods We used data from the AHRQ Surveys on Patient Safety Culture ® (SOPS ® ) Hospital Survey Version 1.0 Database and the SOPS Health Information Technology Patient Safety Supplemental Items (“Health IT item set”) collected from 44 hospitals and 8,880 staff in 2017. We used regression modeling to examine perceptions of EHR system training, EHR support & communication, EHR-related workflow, satisfaction with the EHR system, and the frequency of EHR-related patient safety and quality issues by staff position and tenure, while controlling for hospital ownership type and bed-size. Results In comparison to RNs, pharmacists had significantly lower (unfavorable) scores for EHR system training (regression coefficient = -0.07; p  = 0.047), and physicians, hospital management, and the IT staff were significantly more likely to report high frequency of inaccurate EHR information (ORs = 2.03, 1.34, 1.72, respectively). Compared to staff with 11 or more years of hospital tenure, new staff (less than 1 year at the hospital) had significantly lower scores for EHR system training, but higher scores for EHR support & communication ( p  < 0.0001). Dissatisfaction of the EHR system was highest among physicians and among staff with 11 or more years tenure at the hospital. Conclusions There were significant differences in the Health IT item set’s results across staff positions and hospital tenure. Hospitals can implement the SOPS Health IT Patient Safety Supplemental Items as a valuable tool for identifying incongruity in the perceptions of EHR usability and satisfaction across staff groups to inform targeted investment in EHR system training and support.

Recent health behavior interventions combine social determinants of health (SDOH) and biosocial perspectives, refocusing from the individual to broader societal contexts under the SDOH approach. Targeting modifiable health behaviors can significantly reduce disease risk and save up to 30% of health care costs. Screening tools individual and societal factors are being increasingly integrated into electronic health record (EHR) systems. Epic Systems is a leading, most adopted EHRs worldwide, with modules on SDOH and modifiable risk factors. Literature on integration and use of screening tools for SDOH and modifiable risk factors is lacking. This review aimed to (1) summarize evidence integrating screening and referral tools for SDOH and modifiable risk factors including tobacco/alcohol use and physical inactivity in the Epic EHR; (2) synthesize findings on implementation methods, processes, clinical workflow modifications, and outcomes from integrating SDOH screening and referral tools in EHR systems; and (3) capture the major barriers, facilitators, and lessons learned across the included implementation studies. We followed Joanna Briggs Institute's guidelines, Arksey and O'Malley's framework, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We included 3 peer-reviewed databases, 2 gray literature sources, and citation chaining from related reviews and articles. All included studies (n=43) were from 24 US states; 26 reported quantitative methods, 12 reported mixed methods, and 6 were qualitative studies across various health settings. Most studies focused on adults, with the top 3 SDOH domains being housing, food and transportation, while physical activity, alcohol and tobacco were the most common modifiable risk factors. The top 3 SDOH domains were housing, food, and transportation, while physical activity, alcohol, and tobacco use were the most common risk factors targeted. Various screening tools were used, with the Protocol for Responding to & Assessing Patients' Assets, Risks, and Experiences (PRAPARE) being used the most across 6 studies. Most integrations used enhanced support or optimized workflows, with MyChart and Best Practice Advisories being the most used Epic modules and functions. MyChart was the most patient-accepted module. Screening and referral patient outcomes varied, with many studies presenting a significant impact. The most important integration facilitators included leadership support, dedicated clinical champions, and well-defined roles; barriers included clinician time, inefficient workflows, and the availability of devices and staff to ensure integrated tools' usage. Integration of SDOH and modifiable risk factors in the Epic EHR is being increasingly adopted to capture and target equitable health services. While Epic is among the most globally adopted EHRs, studies are primarily from the United States. Epic's SDOH wheel module is insufficient in capturing context-based SDOH and behavioral domains. Need for contextual standardization of SDOH and modifiable risk factor domains and EHR tools is being increasingly felt. Future research is needed for enhanced learning, improvement and use of built-in and customized tools, standardization, and processes for integrating targeted patient-centered interventions.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems.

BackgroundThe electronic health record (EHR) has been widely implemented internationally as a tool to improve health and healthcare delivery. However, EHR implementation has been comparatively slow amongst hospitals in the Arabian Gulf countries. This gradual uptake may be linked to prevailing opinions amongst medical practitioners. Until now, no systematic review has been conducted to identify the impact of EHRs on doctor-patient relationships and attitudes in the Arabian Gulf countries.ObjectiveTo understand the impact of EHR use on patient-doctor relationships and communication in the Arabian Gulf countries.DesignA systematic review of English language publications was performed using PRISMA chart guidelines between 1990 and 2023.MethodsElectronic database search (Ovid MEDLINE, Global Health, HMIC, EMRIM, and PsycINFO) and reference searching restricted to the six Arabian Gulf countries only. MeSH terms and keywords related to electronic health records, doctor-patient communication, and relationship were used. Newcastle-Ottawa Scale (NOS) quality assessment was performed.Results18 studies fulfilled the criteria to be included in the systematic review. They were published between 1992 and 2023. Overall, a positive impact of EHR uptake was reported within the Gulf countries studied. This included improvement in the quality and performance of physicians, as well as improved accuracy in monitoring patient health. On the other hand, a notable negative impact was a general perception of physician attention shifted away from the patients themselves and towards data entry tasks (e.g., details of the patients and their education at the time of the consultation).ConclusionThe implementation of EHR systems is beneficial for effective care delivery by doctors in Gulf countries despite some patients' perception of decreased attention. The use of EHR assists doctors with recording patient details, including medication and treatment procedures, as well as their outcomes. Based on this study, the authors conclude that widespread EHR implementation is highly recommended, yet specific training should be provided, and the subsequent effect on adoption rates by all users must be evaluated (particularly physicians). The COVID-19 Pandemic showed the great value of EHR in accessing information and consulting patients remotely.

Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable. This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs). Using a dataset from the EHR systems of more than 50 health care provider organizations in the United States from 2016 to 2021, we extracted all clinical terms that appeared in at least 1000 records of individuals admitted to the ED for a mental health-related problem from a source population of over 6 million ED episodes. Two experienced mental health clinicians (one medically trained psychiatrist and one clinical psychologist) reached consensus on the classification of EHR terms and diagnostic codes into categories. We evaluated an LLM's agreement with clinical judgment across three classification tasks as follows: (1) classify terms into \"mental health\" or \"physical health\", (2) classify mental health terms into 1 of 42 prespecified categories, and (3) classify physical health terms into 1 of 19 prespecified broad categories. There was high agreement between the LLM and clinical experts when categorizing 4553 terms as \"mental health\" or \"physical health\" (κ=0.77, 95% CI 0.75-0.80). However, there was still considerable variability in LLM-clinician agreement on the classification of mental health terms (κ=0.62, 95% CI 0.59-0.66) and physical health terms (κ=0.69, 95% CI 0.67-0.70). The LLM displayed high agreement with clinical experts when classifying EHR terms into certain mental health or physical health term categories. However, agreement with clinical experts varied considerably within both sets of mental and physical health term categories. Importantly, the use of LLMs presents an alternative to manual human coding, presenting great potential to create interpretable features for prediction models.

Determinants of health - like physical activity levels and living conditions - have traditionally been the concern of public health and have not been linked closely to clinical practice. However, if standardized social and behavioral data can be incorporated into patient electronic health records (EHRs), those data can provide crucial information about factors that influence health and the effectiveness of treatment. Such information is useful for diagnosis, treatment choices, policy, health care system design, and innovations to improve health outcomes and reduce health care costs. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 identifies domains and measures that capture the social determinants of health to inform the development of recommendations for the meaningful use of EHRs. This report is the second part of a two-part study. The Phase 1 report identified 17 domains for inclusion in EHRs. This report pinpoints 12 measures related to 11 of the initial domains and considers the implications of incorporating them into all EHRs. This book includes three chapters from the Phase 1 report in addition to the new Phase 2 material. Standardized use of EHRs that include social and behavioral domains could provide better patient care, improve population health, and enable more informative research. The recommendations of Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 will provide valuable information on which to base problem identification, clinical diagnoses, patient treatment, outcomes assessment, and population health measurement.

Electronic Health Records (EHRs) are secure private lifetime records that can be shared by using interoperability standards between different organizations and units. These records are created by the productive system that is called EHR system. Implementing EHR systems has a number of advantages such as facilitating access to medical records, supporting patient care, and improving the quality of care and health care decisions. The project of electronic health record system in Iran, which is the goal of this study, is called SEPAS. With respect to the importance of EHR and EHR systems the researchers investigated the project from two perspectives: determining the coordinates of the project and how it evolved, and incorporating the coordinates of EHR system in this project. In this study two evaluation tools, a checklist and a questionnaire, were developed based on texts and reliable documentation. The questionnaire and the checklist were validated using content validity by receiving the experts' comments and the questionnaire's reliability was estimated through Test-retest(r =87%). Data were collected through study, observation, and interviews with experts and specialists of SEPAS project. This research showed that SEPAS project, like any other project, could be evaluated. It has some aims; steps, operational phases and certain start and end time, but all the resources and required facilities for the project have not been considered. Therefore it could not satisfy its specified objective and the useful and unique changes which are the other characteristics of any project have not been achieved. In addition, the findings of EHR system coordinates can be determined in 4 categories as Standards and rules, Telecommunication-Communication facilities, Computer equipment and facilities and Stakeholders. The findings indicated that SEPAS has the ability to use all standards of medical terminology and health classification systems in the case of Maksa approval (The reference health coding of Iran). ISO13606 was used as the main standard in this project. Regarding the telecommunication-communication facilities of the project, the findings showed that its link is restricted to health care centers which does not cover other institutions and organizations involved in public health. The final result showed that SEPAS is in the early stages of execution. And the full implementation of EHR needs the provision of the infrastructure of the National Health Information Network that is the same as EHR system.

This third edition presents and dissects a wide variety of HIT failures so that the reader can understand in each case what went wrong and why and how to avoid such problems, without focusing on the involvement of specific people, organizations, or vendors. The lessons may be applied to future and existing projects, or used to understand why a previous project failed. The reader also learns how common causes of failure affect different kinds of HIT projects and with different results. Cases are organized by the type of focus (hospital care, ambulatory care, and community). Each case provides analysis by an author who was involved in the project plus the insight of an HIT expert. This book presents a model to discuss HIT failures in a safe and protected manner, providing an opportunity to focus on the lessons offered by a failed initiative as opposed to worrying about potential retribution for exposing a project as having failed. Access expert insight into key obstacles that must be overcome to leverage IT and transform healthcare. Each de-identified case study includes an analysis by a group of industry experts along with a counter analysis. Cases include a list of key words and are categorized by project (e.g. CPOE, business intelligence). Each case study concludes with a lesson learned section. Thought provoking commentary chapters add additional context to the challenges faced during HIT projects, from social and organizational to legal and contractual.