Search

Background: California's relatively low smoking rate (10.1% in 2019–2020) (About CHIS, UCLA Center for Health Policy Research, 2024) masks deep disparities among low-income populations, where smoking rates are nearly double that of their middle- to upper-income peers. Low-income smokers report a similar desire to quit and similar rates of recent quit attempts as smokers from other groups; yet, they often face barriers in accessing effective resources to facilitate successful cessation. Methods: Our team will conduct a pragmatic stepped-wedge cluster, randomized controlled trial of Enhanced Multicomponent Proactive Navigator-Assisted Cessation of Tobacco Use (EMPACT-Us), a suite of tobacco cessation services supported by patient navigators, designed in close partnership with patients, providers, and community stakeholders. The study will take place at Family Health Centers of San Diego (FHCSD), the largest federally qualified health center (FQHC) in San Diego. Eight primary care clinics are included, where 70% (n = 13,496) of smokers at FHCSD receive care. Discussion: We hypothesize that multiple points of engagement and integration of navigation services into the workflow of existing staff will improve utilization and cessation success. This study will examine if the enhanced suite of services offers insights on how to best integrate evidence-based tobacco treatment services into usual care. Trial registration: ClinicalTrials.gov, NCT05750537, Registered on March 1, 2023. https://clinicaltrials.gov/study/NCT05750537. [ABSTRACT FROM AUTHOR]

When medication administration record (MAR) "hold" capability is enabled in the electronic health record (EHR) during patient transfers, medication doses appear as "held" rather than due. We sought to quantify the incidence of delayed and missed doses of critical medications during MAR hold periods and to implement and evaluate interdisciplinary efforts and technical interventions to reduce missed medication doses during these periods. A list of critical medications was identified. MAR data were collected in patients with at least 1 critical medication dose due during the MAR hold period. MAR times were used to determine if delayed doses or missed doses occurred. Our interventions included: (1) implementation of a patient list indicator to retrospectively identify recently "held" medication doses, and (2) a report for operating room pharmacists to prospectively identify upcoming doses and ensure they were administered on time. Pre- and post-intervention period data were compared using a chi-squared test. During the pre-intervention study period, there were 1044 instances of delayed or missed doses during MAR hold. Most MAR times evaluated were on MAR hold during perioperative patient transfers. Delayed, missed, and multiple missed doses were defined in accordance with internal medication administration policies. There was no significant difference in the incidence of delayed and missed doses (69% vs 66%, P =.31), however, there was a significant reduction in the number of critical medication doses missed multiple times (0.8% vs 6.7%, P <.001) and all missed doses (35% vs 42%, P =.05) between the pre- and post-intervention period. As demonstrated across in both the pre- and post-intervention period of our study, MAR hold is commonly associated with dose delays and missed doses, which has potential negative consequences on patient outcomes. Future considerations will include implementation of a best practice alert (BPA) that directs users to a MAR tab highlighting doses held during transfers. [ABSTRACT FROM AUTHOR]

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed. [ABSTRACT FROM AUTHOR]

Healthcare integrated biobanking describes the annotation and collection of residual samples from hospitalized patients for research purposes. The central idea of the current work is to establish an automated workflow for sample annotation, selection and storage for diabetes mellitus. This is challenging due to incomplete data at the time of sample selection. The study evaluates a machine learning (ML) and natural language processing (NLP) based two-step procedure for timely and precise sample annotation for diabetes mellitus. Electronic health record data of 785 persons were extracted from the hospital information system. In the first step, a conditional inference forest (CIF) model was trained and tested based on laboratory values from the first 72 h of the hospital stay using test- (n = 550) and training data sets (n = 235). Performance was compared with a simple laboratory cut-off classifier (LCC) and a logistic regression (LR) model. Algorithms based on laboratory values, ICD-10 codes or information from discharge summaries extracted by a natural language processing software (NLP-DS) were evaluated as a second (review) step designed to increase the precision of annotations. For the first step, recall/precision/F1-score/accuracy were 71 %/86 %/0.78/0.82 for CIF and 77 %/70 %/0.74/0.75 for LR compared to 73 %/68 %/0.70/0.72 for LCC. NLP-DS was the best-performing second (review) step (93 %/100 %/0.97/0.97). Combining first-step models with NLP-DS increased precision to 100 % for all procedures (66 %/100 %/0.80/0.85 for CIF&NLP-DS, 72 %/100 %/0.84/87.2 for LR&NLP-DS and 66 %/100 %/0.80/0.85 for LCC&NLP-DS). The number of samples removed by NLP-DS was higher for LR&NLP-DS and LCC&NLP-DS (removal rate 35 % and 38 % of initially selected samples) compared to CIF&NLP-DS (removal rate of 20 %). The developed two-step procedure is an efficient implementable method for timely and precise annotation of samples from diabetic hospitalized patients.

The transition from an Electronic Medical Record (EMR) to an Electronic Health Record (EHR) based system has allowed for resource savings and effective data transactions between patients and doctors. However, this transition poses threats to security, privacy, and validation. The proposed research explores integrating Aadhaar and blockchain technology to create an interoperable EHR system that ensures patient data’s secure preservation and transmission, prioritizing data integrity, privacy, and accessibility. Implementing the proposed system in solidity has allowed to incorporate of the two-factor authentication based on unique key generation, empowering patients to control access and distribution of their health records over time. The Aadhaar acts as an infallible verifier, ensuring precise record linkage through Verifiable Credentials within the blockchain-based EHR structure tested over Ethereum. Through the synergy of cryptography and blockchain, our vision aims for a future where healthcare data is decentralized and impenetrable, providing individuals and healthcare providers with tools to enhance data authority and work without any middleware or third parties, thus directly linking doctors and patients. This seamless connection between doctors and patients offers a promising vision for the future of healthcare information management. [ABSTRACT FROM AUTHOR]

Abstract Background Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. Study design and methods A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. Results The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). Interpretation We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management.

Ensuring security of online health records and patients' perceptions of security are concerns in adolescent healthcare. Little is known about adolescents' perceptions about healthcare's ability to protect online health records. This article explores adolescents' perspectives on security and privacy of their online health records, potential differences based on gender and health, attitudes to sharing information, and perceptions of what constitutes sensitive information. This study included a subset of items from a national online patient survey conducted in Sweden (January-February 2022), focusing on respondents aged 15–19 years. Gender and health status differences were calculated using the Kruskal-Wallis test. Of 218 adolescent respondents (77.1% female), a minority had security and privacy concerns. A notable proportion (41.3%) wished to control who could see their records, and those who reported better perceived health were more likely to want to manage access to their electronic health record (H = 13.569, p =.009). Most had not experienced unauthorized access to their records (75.2%) and had never shared health information on other online applications (85.8%). More than half (56.0%) perceived some information as sensitive, where mental health was the most common (76.0%). Most felt that reading their notes improved their trust for their healthcare professional (65.6%) and supported better communication with healthcare professionals (66.5%). In this national survey, adolescents generally reported few concerns about patient portals. Findings emphasize the need for security and privacy protection and to empower adolescents with greater control over access to their health information housed in electronic health record systems. [ABSTRACT FROM AUTHOR]

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future. [ABSTRACT FROM AUTHOR]

Background: Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. Study design and methods: A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. Results: The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). Interpretation: We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management. [ABSTRACT FROM AUTHOR]

Background: Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. Objectives: The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. Methods: We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. Results: A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. Conclusion: More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future. [ABSTRACT FROM AUTHOR]

Patient medical records and their accurate recording, storage, protection, and access are essential elements to high-quality healthcare. While many parts of the world have moved to traditional digital systems and electronic health records (EHRs), these systems require complex evaluation and large infrastructure investments, lack interoperability, and introduce the constantly-increasing challenges of cyber-attacks and digital security. The aim of this study is to address these challenges through a secure and accessible EHR management system, applied to allergy and family records, based on blockchain technology, the InterPlanetary File System (IPFS) protocol, and the health level 7 (HL7) fast healthcare interoperability resources standard. The proposal was carried out in four phases: (1) blockchain architecture design, (2) blockchain network design, (3) interoperability design, and (4) web application design. A performance evaluation of the system was conducted to determine the throughput and latency metrics. The results presented a maximum medical record reading and writing throughput of approximately eight transactions per second, with a write latency averaging 5,926 ms to 51,836 ms and a reading latency of 4,783 ms to 45,500 ms. With the addition of a survey of 21 patients and 10 healthcare professionals indicating that both groups strongly agree that the system meets the criteria of high-quality healthcare, all study results present a framework that could serve as a model for the adoption of standards-based, accessible, and secure EHR systems. [ABSTRACT FROM AUTHOR]

Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems. [ABSTRACT FROM AUTHOR]

Background: Lipoprotein(a) [Lp(a)] is a low-density lipoprotein variant with atherogenic, thrombogenic, and pro-inflammatory properties that may have numerous pathologic effects, including dyslipidemia. Screening for Lp(a) is clinically significant, due to its causal role in atherosclerotic cardiovascular disease (ASCVD). Among clinicians, however, there remains a general lack of both clinical awareness of Lp(a) and adequate tools to track Lp(a) testing in patients. Objective: To study factors affecting Lp(a) screening by: i) determining the effectiveness of messaging providers at a large community health system about Lp(a) screening and measuring the subsequent percentage of Lp(a) tests requested; and ii) by determining the percentage of patients who obtained Lp(a) testing after being advised by the provider. Methods: From December 2022 through March 2023, messages detailing the need for Lp(a) screening were sent via the Epic EHR™ to providers of patients meeting criteria for Lp(a) testing in advance of scheduled patient appointments. In this prospective study, providers were randomized into 2 groups: those receiving the pre-appointment message (Group 1) and those not receiving the pre-appointment message (Group 2). Results: Sending pre-appointment messages correlated with more Lp(a) orders (16.6 % v. 4.7 %, P < 0.001) and consequently with more tests performed (10.2 % v. 3.7 %, p < 0.001). Among provider types, nurse practitioners and physician assistants had the highest number of Lp(a) results per order (Z = 16.40, P < 0.001), achieving 30.8–39.1 % more test results, even if they did not receive the pre-appointment message. Distribution of Lp(a) values in patients was 59.7 % ≤ 29 mg/dL; 9.7 % > 29 and < 50mg/dL; and 30.6 % ≥ 50 mg/dL. Conclusion: Providers who received pre-appointment messages via an EHR were associated with requesting more tests and consequently receiving more Lp(a) results, compared with providers who did not receive messages.

AbstractObjectiveMethodsResultsConclusions\nHIGHLIGHTSSafety planning for suicide prevention is an important quality metric for Zero Suicide implementation. We describe the development, validation, and application of electronic health record (EHR) programs to measure uptake of safety planning practices across six integrated healthcare systems as part of a Zero Suicide evaluation study.Safety planning was documented in narrative notes and structured EHR templates using the Stanley Brown Safety Planning Intervention (SBSPI) in response to a high-risk cutoff score on the Columbia Suicide Severity Rating Scale (CSSRS). Natural Language Processing (NLP) metrics were developed and validated using chart review to characterize practices documented in narrative notes. We applied NLP to measure frequency of documentation in the narrative text and standard programming methods to examine structured SBSPI templates from 2010–2022.Chart reviews found three safety planning practices documented in narrative notes that were delivered to at least half of patients at risk: professional contacts, lethal means counseling for firearms, and lethal means counseling for medication access/storage. NLP methods were developed to identify these practices in clinical text with high levels of accuracy (Sensitivity, Specificity, & PPV ≥ 82%). Among visits with a high-risk CSSRS, 40% (Range 2–73% by health system) had an SBSPI template within 1 year of implementation.This is one of the first reports describing development of measures that leverage electronic health records to track use of suicide prevention safety plans. There are opportunities to use the methods developed here in future evaluations of safety planning.Measuring safety planning delivery in real-world systems to understand quality of suicide prevention care is challenging.Natural Language Processing (NLP) methods effectively identified some safety planning practices in electronic health records (EHR) from all notes ensuring a comprehensive measurement, but NLP will require updates/testing for local documentation practices.Structured safety planning templates in the EHR using the Stanley Brown Safety Planning Intervention improve ease and accuracy of measurement but may be less comprehensive than NLP for capturing all instances of safety planning documentation.Measuring safety planning delivery in real-world systems to understand quality of suicide prevention care is challenging.Natural Language Processing (NLP) methods effectively identified some safety planning practices in electronic health records (EHR) from all notes ensuring a comprehensive measurement, but NLP will require updates/testing for local documentation practices.Structured safety planning templates in the EHR using the Stanley Brown Safety Planning Intervention improve ease and accuracy of measurement but may be less comprehensive than NLP for capturing all instances of safety planning documentation. [ABSTRACT FROM AUTHOR]

Background: The burden of musculoskeletal conditions continues to grow in low‐ and middle‐income countries. Among thousands of surgical outreach trips each year, few organizations electronically track patient data to inform real‐time care decisions and assess trip impact. We report the implementation of an electronic health record (EHR) system utilized at point of care during an orthopedic surgical outreach trip. Methods: In March 2023, we implemented an EHR on an orthopedic outreach trip to guide real‐time care decisions. We utilized an effectiveness‐implementation hybrid type 3 design to evaluate implementation success. Success was measured using outcomes adopted by the World Health Organization, including acceptability, appropriateness, feasibility, adoption, fidelity, and sustainability. Clinical outcome measures included adherence to essential quality measures and follow‐up numerical rating system (NRS) pain scores. Results: During the 5‐day outreach trip, 76 patients were evaluated, 25 of which underwent surgery beforehand. The EHR implementation was successful as defined by: mean questionnaire ratings of acceptability (4.26), appropriateness (4.12), feasibility (4.19), and adoption (4.33) at least 4.00, WHO behaviorally anchored rating scale ratings of fidelity (6.8) at least 5.00, and sustainability (80%) at least 60% follow‐up at 6 months. All clinical quality measures were reported in greater than 80% of cases with all measures reported in 92% of cases. NRS pain scores improved by an average of 2.4 points. Conclusions: We demonstrate successful implementation of an EHR for real‐time clinical use on a surgical outreach trip. Benefits of EHR utilization on surgical outreach trips may include improved documentation, minimization of medical errors, and ultimately improved quality of care. [ABSTRACT FROM AUTHOR]

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians' workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in‐progress effort to overcome these challenges with cooperative, systematic data extraction and validation.A multi‐disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non‐White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de‐identified and converted to a publicly available resource. [ABSTRACT FROM AUTHOR]

This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people's attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1's findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2's results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling. [ABSTRACT FROM AUTHOR]

Objectives Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. Materials and Methods No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. Results The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P  <   2.2 × 10–16). Discussion Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available. [ABSTRACT FROM AUTHOR]

BackgroundChildhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.ObjectivesThe objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.MethodsWe utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.ResultsA total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.ConclusionMore research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Abstract Introduction Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high‐quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non‐small cell lung cancer (NSCLC) protocols in China, on the risk–benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. Methods To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross‐dimensional real‐world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit–risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. Discussion This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.

Introduction: The digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change. Methods: Qualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors. Results: The interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things. Discussion: The current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers. [ABSTRACT FROM AUTHOR]

Introduction: Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high‐quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non‐small cell lung cancer (NSCLC) protocols in China, on the risk–benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. Methods: To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross‐dimensional real‐world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit–risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. Discussion: This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies. [ABSTRACT FROM AUTHOR]

School nurses are important participants in School Medicaid (SM) programs nationally. Yet, the complexity of SM programs and required documentation are major barriers to implementing this program for nurses. School nurses are often required to participate in the SM program without having a clear understanding of the purpose and components of the overall program. With the expansion of SM programs due to the Centers for Medicare and Medicaid Services "free care" guidance change, nursing services and documentation are receiving more attention and scrutiny as more nursing services become eligible for reimbursement in states across the country. This article presents a clear overview of SM, its history, different components of state SM programs, school nursing documentation and challenges, and resources for school nurses who are interested in more information. [ABSTRACT FROM AUTHOR]

Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR. [ABSTRACT FROM AUTHOR]

Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules’ identification. Our approach is applied on a subcohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain. [ABSTRACT FROM AUTHOR]

Background/Aims: Traditional manual methods of extracting anesthetic and physiological data from the electronic health record rely upon visual transcription by a human analyst that can be labor‐intensive and prone to error. Technical complexity, relative inexperience in computer coding, and decreased access to data warehouses can deter investigators from obtaining valuable electronic health record data for research studies, especially in under‐resourced settings. We therefore aimed to develop, pilot, and demonstrate the effectiveness and utility of a pragmatic data extraction methodology. Methods: Expired sevoflurane concentration data from the electronic health record transcribed by eye was compared to an intermediate preprocessing method in which the entire anesthetic flowsheet narrative report was selected, copy‐pasted, and processed using only Microsoft Word and Excel software to generate a comma‐delimited (.csv) file. A step‐by‐step presentation of this method is presented. Concordance rates, Pearson correlation coefficients, and scatterplots with lines of best fit were used to compare the two methods of data extraction. Results: A total of 1132 datapoints across eight subjects were analyzed, accounting for 18.9 h of anesthesia time. There was a high concordance rate of data extracted using the two methods (median concordance rate 100% range [96%, 100%]). The median time required to complete manual data extraction was significantly longer compared to the time required using the intermediate method (240 IQR [199, 482.5] seconds vs 92.5 IQR [69, 99] seconds, p =.01) and was linearly associated with the number of datapoints (rmanual =.97, p <.0001), whereas time required to complete data extraction using the intermediate approach was independent of the number of datapoints (rintermediate = −.02, p =.99). Conclusions: We describe a pragmatic data extraction methodology that does not require additional software or coding skills intended to enhance the ease, speed, and accuracy of data collection that could assist in clinician investigator‐initiated research and quality/process improvement projects. [ABSTRACT FROM AUTHOR]

Workforce well-being and associated factors such as burnout, depression and documentation burden, have been identified as the highest concerns to be addressed. In academia, the new essentials of nursing practice including domain 8 for informatics and healthcare technology have become a focus for curricular revisions/enhancements. Our study focused on technology skills by using the technology of an academic EHR to measure baselines and progression of EHR use, sense of confidence, documentation competency, and post-graduation employer-based performance assessment. We provide results of an ongoing 1.5-year study and overarching strategy for university-wide deployment and financing. [ABSTRACT FROM AUTHOR]

In Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients' clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru. [ABSTRACT FROM AUTHOR]

The implementation of an Electronic Prescribing (EP) system offers numerous advantages in enhancing the efficiency of prescribing practices. To ensure successful implementation, a comprehensive understanding of the workflow in paper- based prescribing is crucial. In Iran, the Ministry of Health, and Medical Education (MOHME) has been actively involved in developing an EP system since 2011. The pilot results within MOHME have garnered significant support from all basic insurance organizations, primarily due to the importance of addressing financial considerations. As a result, these insurance organizations have taken the lead in the national development of the EP system, as responsibilities have shifted. The development of an Integrated Care Electronic Health Record (ICEHR or EHR) and the approach adopted by MOHME have paved the way for the creation of a standardized set of Application Programming Interfaces (APIs) based on openEHR and ISO13606 standards. These APIs facilitate the secure transfer of consolidated data from the EP systems, stored in the data warehouses of basic insurance organizations, to the Iranian EHR. This model follows an ICEHR architecture that emphasizes the transmission of this information to the Iranian EHR. This paper provides a detailed discussion of the various aspects and accomplishments related to these developments. [ABSTRACT FROM AUTHOR]

Importance This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. Objectives The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. Methods A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. Results To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. Conclusion The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science. [ABSTRACT FROM AUTHOR]

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelations of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets.

IntroductionThis study is part of the U.S. Food and Drug Administration (FDA)’s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA’s postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality.Materials and methodsThe study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR.ResultsThe volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met.DiscussionThis study describes the platform’s data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

Background: Most hospitals use electronic health records (EHR) to warn health care professionals of drug hypersensitivity (DH) and other allergies. Indiscriminate recording of patient self-reported allergies may bloat the alert system, leading to unjustified avoidances and increases in health costs. The aim of our study was to analyze hypersensitivities documented in EHR of patients at Lausanne University Hospital (CHUV). Methods: We conducted a retrospective study on patients admitted at least 24 h to CHUV between 2011 and 2021. After ethical clearance, we obtained anonymized data. Because culprit allergen could be either manually recorded or selected through a list, data was harmonized using a reference allergy database before undergoing statistical analysis. Results: Of 192,444 patients, 16% had at least one allergy referenced. DH constituted 60% of all allergy alerts, mainly beta-lactam antibiotics (BLA) (30%), NSAID (11%) and iodinated contrast media (ICM) (7%). Median age at first hospitalization and hospitalization length were higher in the allergy group. Female to male ratio was 2:1 in the allergic group. Reactions were limited to the skin in half of patients, and consistent with anaphylaxis in 6%. In those deemed allergic to BLA, culprit drug was specified in 19%, ‘allergy to penicillin’ otherwise. It was impossible to distinguish DH based on history alone or resulting from specialized work-up. Conclusions: Older age, longer hospital stays, and female sex increase the odds of in-patient allergy documentation. Regarding DH, BLA were referenced in 4% of inpatient records. Specific delabeling programs should be implemented to increase data reliability and patient safety.

Healthcare services and organizations rely on Electronic Health Records (EHRs) to manage, store, and transmit patient data records. Consequently, EHRs play a crucial role in providing high-quality services and maintaining the privacy and security of patients’ sensitive data. However, designing such complex systems with security and privacy concerns is anything but simple. This study aims to propose a Software Reference Architecture (SRA) tailored for Electronic Health Records (EHRs) with security and privacy considerations, intending to enhance the development of these systems. To achieve this goal, we analyze the classification of Reference Architectures (RAs), taking into account the primary security and privacy requirements of EHRs along with well-established architectural design methods. We propose a layered architecture for SRA with privacy and security considerations. Subsequently, we derive the following five architecture views for SRA: the feature diagram, the context diagram, the decomposition view, the layered view, and the deployment view. Each view showcases the SRA software architecture from a different perspective. Moreover, we conducted an evaluation of the proposed SRA through its application in a case study. Specifically, we applied the proposed SRA and derived the application architecture from a study focused on Brazilian EHRs. Our analysis highlights the potential issues arising from the absence of an SRA tailored for EHRs, particularly regarding privacy and security concerns surrounding patient data. Through this case study, we demonstrate the practical applicability of our proposed SRA in enhancing EHR systems.

IntroductionThe digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change.MethodsQualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors.ResultsThe interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things.DiscussionThe current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers.

With the continuous growth of cloud-based Electronic Health Record (EHR) systems and medical data, medical organizations are particularly concerned about storing patient data to provide fast services while adhering to privacy and security concerns. Existing EHR systems often face challenges in handling heterogeneous data and maintaining good performance with data growth. These systems mostly use relational databases or partially store data in a knowledge graph, making it challenging to handle big data and allowing flexible schema expansion. Hence, there is a need to address these problems. This paper provides a solution by proposing a novel graph-based EHR system integrating Attribute-Based Encryption and Semantic Web Technologies to ensure fine-grained EHR field-level security of patient records. Our approach leverages semantic context to query through a knowledge graph that stores encrypted medical data in the nodes, making it possible to handle heterogeneous data while ensuring optimal performance and preserving patient privacy.

Alzheimer's disease (AD) is the leading cause of dementia globally and one of the most serious future healthcare issue. AD is expected to rise from 27 million to 106 million cases in the next four decades impacting one in every 85 people on the planet. For the existing healthcare systems, the most frequent kind of dementia is a significant source of worry. AD usually refers to Untreated Schizophrenia, a degenerative neurological disorder defined by memory loss and disorientation. AD is the world's third greatest cause of mortality, after only heart disease and cancer. It has surpassed cancer as the most dreaded disease on the planet. AD is catastrophic in the long-term run since it slowly but gradually destroys the body's cells. A variety of efforts have been made to employ structural Magnetic Resonance Imaging (MRI) modalities to differentiate between people with AD and their healthy counterparts. These have also been examined as deep learning algorithms for the categorization of MRI data. It is difficult to find patients with modest cognitive decline who may acquire Alzheimer's. As a result, creating deep learning-based disease detection techniques to assist clinicians in detecting prospective Alzheimer's patients is crucial. The performance comparison of the Imaging, Electronic Health Record (EHR), and Single Nucleotide Polymorphisms (SNP) datasets are evaluated using the metrics Accuracy, Sensitivity, Specificity, and Multi Area. Different mistakes are added under the curves for gradient calculation. The research results are as follows: based on standard datasets the results show that the proposed feature selection algorithms discover a sub-optimal minimal level feature set from a larger input feature set for diagnosing Alzheimer's disease, with higher values for system performance in terms of Accuracy as well as losses against training and Accuracy and losses against validation. These results can demonstrate the model's suitability for the purpose. [ABSTRACT FROM AUTHOR]

The factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry. [ABSTRACT FROM AUTHOR]

Given the complexities of communication within health systems, we investigated how the implementation of secure messaging in addition to traditional paging would impact hospital communication. This study was implemented at Grady Health System (GHS), a large safety net academic hospital system in metro Atlanta that includes inpatient and ambulatory settings. GHS uses Epic Electronic Health Record (EHR), and secure messaging was performed using Epic Haiku Platform. To assess states of communication, we implemented pre- and post-surveys. The secure messaging data tracked from 2018 to 2022 demonstrated a rise in usage from 9,378 chats per month when it went live in August 2018 to greater than 200,000 monthly messages during the pandemic when social distancing measures were enacted. Monthly usage peaked in March 2022 with 378,932 messages. Pre-and-post survey questions using a Likert scale (1–4) showed increased agreement in the ability to reach all team members through secure chat amongst healthcare workers. Within our unit staff, communication improved by being more rapid and reliable, as the Likert scale means increased from 2.18 pre-survey to 2.63 post survey. Pre-and-post survey analysis indicates improved satisfaction across GHS stakeholders with the implementation of secure chat in addition to the existing direct-paging system. Next steps could include exchanging digital media through secure messaging to facilitate faster diagnosis and treatment of certain medical conditions. Secure messaging integrated within the EHR (including mobile devices) enhances communication between healthcare team members in a HIPAA-compliant way reducing the number of pages and phone calls. [ABSTRACT FROM AUTHOR]

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety. [ABSTRACT FROM AUTHOR]

The nursing practice is currently lagging in incorporating artificial intelligence technology. Nurses are also facing an abundance of issues that have only been emphasized after the recent COVID-19 pandemic. Staff shortages and dangerous nurse-patient ratios have led to increased documentation burden and putting patients' safety at risk. While nursing is unique in that it involves several nonroutine tasks making it difficult to implement certain technologies, there is plenty of opportunity to utilize various electronic health record algorithms, applications that aid in physical assessment, as well as certain robotic functions. These interventions will not only reduce the burden of documentation and simple repetitive tasks, but they can also help improve accuracy and give nurses more time at patients' bedsides. This paper discusses various AI algorithms specific to nursing practice that can serve as solutions to many issues as well as a potential implementation plan that could be used to integrate such technology into healthcare organizations. [ABSTRACT FROM AUTHOR]