Search

Ihuoma O Njoku,1 Eliza L Chin,2 Meredith CB Adams3 1Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA; 2American Medical Women’s Association, Schaumburg, IL, USA; 3Departments of Anesthesiology, Artificial Intelligence, Translational Neuroscience, and Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USACorrespondence: Ihuoma O Njoku, Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, 3535 Market Street, Philadelphia, PA, 19104, USA, Email Drihuomanjoku@gmail.com

Louise Underdahl,1,* Mary Ditri,2,* Lunthita M Duthely3,* 1College of Doctoral Studies, University of Phoenix, Phoenix, AZ, USA; 2Community Health, New Jersey Hospital Association, Princeton, NJ, USA; 3Obstetrics, Gynecology and Reproductive Sciences and the Department of Public Health Sciences, University of Miami Health System, Miami, FL, USA*These authors contributed equally to this workCorrespondence: Louise Underdahl, College of Doctoral Studies, University of Phoenix, 4025 S. Riverpoint Pkwy, Mail Stop CF-K601, Phoenix, AZ, 85040, USA, Email lunderdahl@email.phoenix.edu

Patient’s vital data will have an important role in future medicine. Best way to store this data is the patient’s electronic health record (EHR) in a structured form. In Germany, several laws, regulations and standards must be considered to store vital data in the health record, like SGB V, DIGAV and Medical Information Objects. In the near future, it will be possible to upload self-recorded vital data to the German EHR special smartphone apps. It will also be possible to automatically evaluate this data using machine learning.

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs.

Abstract Background Mobile apps facilitate patients’ access to portals and interaction with their healthcare providers. The COVID-19 pandemic accelerated this trend globally, but little evidence exists on patient portal usage in the Middle East, where internet access and digital literacy are limited. Our study aimed to explore how users utilize a patient portal through its related mobile app (MyChart by EPIC). Methods We conducted a cross-sectional survey of MyChart users, recruited from a tertiary care center in Lebanon. We collected MyChart usage patterns, perceived outcomes, and app quality, based on the Mobile Application Rating Scale (user version, uMARS), and sociodemographic factors. We examined associations between app usage, app quality, and sociodemographic factors using Pearson’s correlations, Chi-square, ANOVA, and t-tests. Results 428 users completed the survey; they were primarily female (63%), aged 41.3 ± 15.6 years, with a higher education level (87%) and a relatively high crowding index of 1.4 ± 0.6. Most of the sample was in good and very good health (78%) and had no chronic illnesses (67%), and accessed the portal through MyChart once a month or less (76%). The most frequently used features were accessing health records (98%), scheduling appointments (67%), and messaging physicians (56%). According to uMARS completers (n = 200), the objective quality score was 3.8 ± 0.5, and the subjective quality was 3.6 ± 0.7. No significant association was found between overall app usage and the mobile app quality measured via uMARS. Moreover, app use frequency was negatively associated with education, socioeconomic status, and perceived health status. On the other hand, app use was positively related to having chronic conditions, the number of physician visits and subjective app quality. Conclusion The patient portal usage was not associated with app quality but with some of the participants’ demographic factors. The app offers a user-friendly, good-quality interface to patient health records and physicians, appreciated chiefly by users with relatively low socioeconomic status and education. While this is encouraging, more research is needed to capture the usage patterns and perceptions of male patients and those with even lower education and socioeconomic status, to make patient portals more inclusive.

Abstract Background Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution. Methods A survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR. Results Race was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P

Abstract Background Electronic health records (EHRs) are widely accepted to enhance the health care quality, patient monitoring, and early prevention of various diseases, even when there is incomplete or missing information in them. Aim The present review sought to investigate the impact of EHR implementation on healthcare quality and medical decision in the context of epidemiological investigations, considering missing or incomplete data. Methods Google scholar, Medline (via PubMed) and Scopus databases were searched for studies investigating the impact of EHR implementation on healthcare quality and medical decision, as well as for studies investigating the way of dealing with missing data, and their impact on medical decision and the development process of prediction models. Electronic searches were carried out up to 2022. Results EHRs were shown that they constitute an increasingly important tool for both physicians, decision makers and patients, which can improve national healthcare systems both for the convenience of patients and doctors, while they improve the quality of health care as well as they can also be used in order to save money. As far as the missing data handling techniques is concerned, several investigators have already tried to propose the best possible methodology, yet there is no wide consensus and acceptance in the scientific community, while there are also crucial gaps which should be addressed. Conclusions Through the present thorough investigation, the importance of the EHRs’ implementation in clinical practice was established, while at the same time the gap of knowledge regarding the missing data handling techniques was also pointed out.

Abstract Background The adoption of Electronic Medical Records (EMR) by the healthcare sector can improve patient care and safety, facilitate structured research, and effectively plan, monitor, and assess disease. EMR adoptions in low-income countries like Ethiopia were delayed and failing more frequently, despite their critical necessity. The most popular way to solve the issue is to evaluate user preparedness prior to the adoption of EMR. However, little is known regarding the EMR readiness of healthcare professionals in this study setting. Therefore, the objective of this study was to assess the readiness and factors associated with health professional readiness toward EMR in Gamo Zone, Ethiopia. Methods An institution-based cross-sectional survey was conducted by using a pretested self-administered questionnaire on 416 study participants at public hospital hospitals in southern Ethiopia. STAT version 14 software was used to conduct the analysis after the data was entered using Epi-data version 3.2. A binary logistic regression model was fitted to identify factors associated with readiness. Finally, the results were interpreted using an adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value less than 0.05. Results A total of 400 participants enrolled in the study, with a response rate of 97.1%. A total of 65.25% (n = 261) [95% CI: 0.60, 0.69] participants had overall readiness, 68.75% (n = 275) [95% CI: 0.64, 0.73] had engagement readiness, and (69.75%) (n = 279) [95% CI: 0.65, 0.74] had core EMR readiness. Computer skills (AOR: 3.06; 95% CI: 1.49–6.29), EMR training (AOR: 2.00; 95% CI: 1.06–3.67), good EMR knowledge (AOR: 2.021; 95% CI: 1.19–3.39), and favorable attitude (AOR: 3.00; 95% CI: 1.76–4.97) were factors significantly associated with EMR readiness. Conclusion Although it was deemed insufficient, more than half of the respondents indicated a satisfactory level of overall readiness for the adoption of EMR. Moreover, having computer skills, having EMR training, good EMR knowledge, and favorable EMR attitude were all significantly and positively related to EMR readiness.

Introduction New legislation allows patients (with permitted exceptions) to read their clinical notes, leading to both benefits and ethical dilemmas. Medical students need a robust curriculum to learn documentation skills within this challenging context. We aimed to teach note-writing skills through a patient-centered lens with special consideration for the impact on patients and providers. We developed this session for first-year medical students within their foundational clinical skills course to place bias-free language at the forefront of how they learn to construct a medical note. Methods One hundred seventy-three first-year medical and dental students participated in this curriculum. They completed an asynchronous presession module first, followed by a 2-hour synchronous workshop including a didactic, student-led discussion and sample patient note exercise. Students were subsequently responsible throughout the year for constructing patient-centered notes, graded by faculty with a newly developed rubric and checklist of best practices. Results On postworkshop surveys, learners reported increased preparedness in their ability to document in a patient-centered manner (presession M = 2.2, midyear M = 3.9, p < .001), as rated on a 5-point Likert scale (1 = not prepared at all, 5 = very prepared), and also found this topic valuable to learn early in their training. Discussion This curriculum utilizes a multipart approach to prepare learners to employ clinical notes to communicate with patients and providers, with special attention to how patients and their care partners receive a note. Future directions include expanding the curriculum to higher levels of learning and validating the developed materials.

ObjectivePositive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals.MethodsUsing a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples.ResultsWe assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set.ConclusionWe developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

Abstract Introduction: More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records. Methods: This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models. Results: A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, p < 0.01) and gradient boosted machine (0.68, p = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category. Conclusions: Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.

Abstract Introduction: Recruitment of participants into research studies remains a major concern for investigators. Using clinical teams to identify potentially eligible patients can present a significant barrier. To overcome this, we implemented a process for using our patient portal, called MyChart, as a new institutional recruitment option utilizing our electronic health record’s existing functionality. Methods: To streamline the institutional approval process, we established a working group comprised of representatives from human subject protection, information technology, and privacy and vetted our process with many stakeholder groups. Our specific process for study approval is described and started with a consultation with our recruitment and retention function funded through our Clinical and Translational Science Award. Results: The time from consultation to the first message(s) sent ranged from 84 to 442 days and declined slightly over time. The overall patient response rate to MyChart messages about available research studies was 23% with one third of those saying they were interested in learning more. The response rate for Black and Hispanic patients was about 50% that of White patients. Conclusions: Many different types of studies from any medical specialty successfully identified interested patients using this option. Study teams needed support in defining appropriate inclusion/exclusion criteria to identify the relevant population in the electronic health records and they needed assistance writing study descriptions in plain language. Using MyChart for recruitment addressed a critical barrier and opened up the opportunity to provide a full recruitment consultation to identify additional recruitment channels the study teams would not have considered otherwise.

Electronic Health Records (EHRs) are electronically-stored patient medical histories shared among healthcare institutions. Recent studies show that EHRs experience healthcare data protection challenges, and the difficulty lies in providing access to the right individuals at the appropriate time and place. This study synthesizes and analyzes existing literature on access control solutions in EHRs through a systematic literature review. Using the 2020 PRISMA guidelines, a total of 20 qualified journal articles were examined and each proposed mechanism was grouped according to the four categories of access control: Identification, Authentication, Authorization, and Accountability (IAAA). Our findings reveal an interconnection between these categories, with the most popular authorization mechanism being Attribute-based Access Control (ABAC). Methodologies analyzed include a credential system (12 studies), authentication (10 studies), and accountability (2 studies); these most commonly used unique IDs, digital signatures and access control logs respectively. Prominent research gaps found in the sample literature are methodology implementation and standards compliance limitations, of which the former includes the lack of multi-factor authentication, emergency access, patient consent, and accountability. From these findings we infer that further research is needed to protect EHRs from these information security threats.

Abstract Objectives: Participation in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has numerous benefits, yet many eligible children remain unenrolled. This qualitative study sought to explore perceptions of a novel electronic health record (EHR) intervention to facilitate referrals to WIC and improve communication/coordination between WIC staff and healthcare professionals. Methods: WIC staff in three counties were provided EHR access and recruited to participate. An automated, EHR-embedded WIC participation screening and referral tool was implemented within 8 healthcare clinics; healthcare professionals within these clinics were eligible to participate. The interview guide was developed using the Consolidated Framework for Implementation Research to elicit perceptions of this novel EHR-based intervention. Semi-structured interviews were conducted via telephone. Interviews were recorded, transcribed, coded, and analyzed using thematic analysis. Results: Twenty semi-structured interviews were conducted with eight WIC staff, seven pediatricians, four medical assistants, and one registered nurse. Most participants self-identified as female (95%) and White (55%). We identified four primary themes: (1) healthcare professionals had a positive view of WIC but communication and coordination between WIC and healthcare professionals was limited prior to WIC having EHR access; (2) healthcare professionals favored WIC screening using the EHR but workflow challenges existed; (3) EHR connections between WIC and the healthcare system can streamline referrals to and enrollment in WIC; and (4) WIC staff and healthcare professionals recommended that WIC have EHR access. Conclusions: A novel EHR-based intervention has potential to facilitate healthcare referrals to WIC and improve communication/coordination between WIC and healthcare systems.

Electronic Health Record (EHR) systems are often configured to address challenges and improve patient safety for persons with Parkinson’s disease (PWP). For example, EHR systems can help identify Parkinson’s disease (PD) patients across the hospital by flagging a patient’s diagnosis in their chart, preventing errors in medication and dosing through the use of clinical decision support, and supplementing staff education through care plans that provide step-by-step road maps for disease-based care of a specific patient population. However, most EHR-based solutions are locally developed and, thus, difficult to scale widely or apply uniformly across hospital systems. In 2020, the Parkinson’s Foundation, a national and international leader in PD research, education, and advocacy, and Epic, a leading EHR vendor with more than 35% market share in the United States, launched a partnership to reduce risks to hospitalized PWP using standardized EHR-based solutions. This article discusses that project which included leadership from physician informaticists, movement disorders specialists, hospital quality officers, the Parkinson’s Foundation and members of the Parkinson’s community. We describe the best practice solutions developed through this project. We highlight those that are currently available as standard defaults or options within the Epic EHR, discuss the successes and limitations of these solutions, and consider opportunities for scalability in environments beyond a single EHR vendor. The Parkinson’s Foundation and Epic launched a partnership to develop best practice solutions in the Epic EHR system to improve safety for PWP in the hospital. The goal of the partnership was to create the EHR tools that will have the greatest impact on outcomes for hospitalized PWP.

Clinical weight management programs face low participation. The authors assessed whether using electronic health record (EHR) data can identify variation in referral, consent, and engagement in a pediatric overweight and obesity (OW/OB) intervention. Using Epic EHR data collected between August 2020 and April 2021, sociodemographic and clinical diagnostic data (ie, International Classification of Disease [ICD] codes from visit and problem list [PL]) were analyzed to determine their association with referral, consent, and engagement in an OW/OB intervention. Bivariate analyses and multivariable logistic regression modeling were performed, with Bayesian inclusion criterion score used for model selection. Compared with the 581 eligible patients, referred patients were more likely to be boys (60% vs. 54%, respectively; P = 0.04) and have a higher %BMIp95 (119% vs. 112%, respectively; P < 0.01); consented patients were more likely to have a higher %BMIp95 (120% vs. 112%, respectively; P < 0.01) and speak Spanish (71% vs. 59%, respectively; P = 0.02); and engaged patients were more likely to have a higher %BMIp95 (117% vs. 112%, respectively; P = 0.03) and speak Spanish (78% vs. 59%, respectively; P < 0.01). The regression model without either ICD codes or PL diagnoses was the best fit across all outcomes, which were associated with baseline %BMIp95 and health clinic location. Neither visit nor PL diagnoses helped to identify variation in referral, consent, and engagement in a pediatric OW/OB intervention, and their role in understanding participation in such interventions remains unclear. However, additional efforts are needed to refer and engage younger girls with less extreme cases of OW/OB, and to support non-Hispanic families to consent. [ABSTRACT FROM AUTHOR]

Introduction: Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning. Methods: We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone. Results: Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively. Conclusions: EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction. [ABSTRACT FROM AUTHOR]

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care. [ABSTRACT FROM AUTHOR]

Objective: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than prospective research. However, reviewing EHR manually is time intensive. We aimed to compare refractory status epilepticus (rSE) cases identified by human EHR review with a natural language processing (NLP)‐assisted rSE screen followed by a manual review. Methods: We used the NLP screening tool Document Review Tool (DrT) to generate regular expressions, trained a bag‐of‐words NLP classifier on EHRs from 2017 to 2019, and then tested our algorithm on data from February to December 2012. We compared results from manual review to NLP‐assisted search followed by manual review. Results: Our algorithm identified 1528 notes in the test set. After removing notes pertaining to the same event by DrT, the user reviewed a total number of 400 notes to find patients with rSE. Within these 400 notes, we identified 31 rSE cases, including 12 new cases not found in manual review, and 19 of the 20 previously identified cases. The NLP‐assisted model found 31 of 32 cases, with a sensitivity of 96.88% (95% CI = 82%–99.84%), whereas manual review identified 20 of 32 cases, with a sensitivity of 62.5% (95% CI = 43.75%–78.34%). Significance: DrT provided a highly sensitive model compared to human review and an increase in patient identification through EHRs. The use of DrT is a suitable application of NLP for identifying patients with a history of recent rSE, which ultimately contributes to the implementation of monitoring techniques and treatments in near real time. [ABSTRACT FROM AUTHOR]

Background: There is a need for scalable clinician education in rational medication prescribing and rational ordering of pathology and imaging to help improve patient safety and enable more efficient utilisation of healthcare resources. Our wider study evaluated the effectiveness of a multifaceted education intervention for general practitioners (GPs) in rational prescribing and ordering of pathology and imaging tests, in the context of Australia's online patient-controlled health record system, My Health Record (MHR), and found evidence for measurable behaviour change in pathology ordering among participants who completed the educational activities. This current study explored the mechanisms of behaviour change brought about by the intervention, with a view to informing the development of similar interventions in the future. Methods: This mixed methods investigation used self-reported questionnaires at baseline and post-education on MHR use and rational prescribing and test ordering. These were analysed using multi-level ordinal logistic regression models. Semi-structured interviews pre- and post-intervention were also conducted and were analysed thematically using the COM-B framework. Results: Of the 106 GPs recruited into the study, 60 completed baseline and 37 completed post-education questionnaires. Nineteen participants were interviewed at baseline and completion. Analysis of questionnaires demonstrated a significant increase in confidence using MHR and in self-reported frequency of MHR use, post-education compared with baseline. There were also similar improvements in confidence across the cohort pre-post education in deprescribing, frequency of review of pathology ordering regimens and evidence-based imaging. The qualitative findings showed an increase in GPs' perceived capability with, and the use of MHR, at post-education compared with baseline. Participants saw the education as an opportunity for learning, for reinforcing what they already knew, and for motivating change of behaviour in increasing their utilisation of MHR, and ordering fewer unnecessary tests and prescriptions. Conclusions: Our education intervention appeared to provide its effects through providing opportunity, increasing capability and enhancing motivation to increase MHR knowledge and usage, as well as rational prescribing and test ordering behaviour. There were overlapping effects of skills acquisition and confidence across intervention arms, which may have contributed to wider changes in behaviour than the specific topic area addressed in the education. Trial registration: Australian New Zealand Clinical Trials Registry (ACTRN12620000010998) (09/01/2020). [ABSTRACT FROM AUTHOR]

The subject of this article is the analysis of the Institute of Electronic Health Documentation, which has been discussed within the European Union for some time, but its current form in the legal systems of member states of the European Union does not correspond to the outlined vision. Electronic health documentation represents one of the pillars of e-Health, whose goal is to provide correct information at the right time, in the right place, and in the right form in all stages and processes of health care. The author deals with Slovak and European legislation related to electronic health documentation, presents the so-called electronic health book existing in the conditions of the Slovak Republic, and draws attention to the proposal for a new European regulation on the European Health Data Space, which aims to harmonize the rules for the creation and use of electronic health data, and thus strengthen the role of electronic health documentation for (including cross-border) provision of health care. The article aims to emphasize the most important challenges of the current and future legal regime of the transfer and exchange of electronic health data and other related ideas. [ABSTRACT FROM AUTHOR]

Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the embedding space can better align with existing medical knowledge. Yet, properly carrying out the integration is non-trivial. Medical concepts that are similar according to a medical ontology may not be necessarily close in the embedding space learned from the EHR data, as medical ontologies organize medical concepts for their own specific objectives. Any integration methodology without considering the underlying inconsistency will result in sub-optimal medical concept embedding and, in turn, degrade the performance of the downstream tasks. In this article, we propose a novel representation learning framework called ADORE (ADaptive Ontological REpresentations) that allows the medical ontologies to adapt their structures for more robust integrating with the EHR data. ADORE first learns multiple embeddings for each category in the ontology via an attention mechanism. At the same time, it supports an adaptive integration of categorical and multi-relational ontologies in the embedding space using a category-aware graph attention network. We evaluate the performance of ADORE on a number of predictive analytics tasks using two EHR datasets. Our experimental results show that the medical concept embeddings obtained by ADORE can outperform the state-of-the-art methods for all the tasks. More importantly, it can result in clinically meaningful sub-categorization of the existing ontological categories and yield attention values that can further enhance the model interpretability. [ABSTRACT FROM AUTHOR]

This study aims to: (1) Explore the benefits, dilemmas and challenges of applying blockchain technology to health data governance; (2) Explore the actual impacts of applying blockchain technology to health data governance. This study applies the perspectives of organizational governance as well as technology to achieve the research objectives. Case studies and in-depth interviews were conducted to grasp the possible changes and formulate strategies early. The results of the study indicate that different levels of organizations apply blockchain technology to health data governance, which generate different benefits, they will also face dilemmas and challenges accordingly. Furthermore, the implementation of blockchain technology can make breakthroughs in health data governance and technology management of the global medical industry, and drive the expansion of medical blockchain ecosystem. It also offers numerous opportunities for usage in the healthcare sector, e.g., in public health management, user-oriented medical research based on personal patient data as well as drug counterfeiting. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. [ABSTRACT FROM AUTHOR]

Background: Most chlamydia infections in Australia are diagnosed in general practice. The care cascade concept (testing, treatment and re-testing) can be utilised to explore the management of chlamydia infections. We explored the chlamydia care cascade among young people attending general practices in Australia. Methods: We analysed de-identified electronic medical record data for 16–29-year-old individuals attending 70 Australian general practices between January 2018 and December 2020. Five outcomes: (1) chlamydia testing, (2) positivity, (3) treatment, (4) re-testing and (5) re-infection were summarised as annual counts and proportions per calendar year. Logistic regression was used to investigate the association of age, gender and clinic location with each outcome. Results: During the study period, a total of 220 909 clinical episodes involving 137 358 16–29-year-olds were recorded. Of these episodes, 10.45% (n = 23 077, 95% CI 8.73–12.46) involved a chlamydia test. Of 1632 chlamydia cases, 88.79% (n = 1449, 95% CI 86.37–90.82) had appropriate antibiotics recorded as defined in Australian sexually transmitted infection management guidelines. Of 183 chlamydia cases that did not have appropriate antibiotics recorded, 46.45% (n = 85) had re-attended the clinic within 90 days of diagnosis. Among 1068 chlamydia cases that had appropriate antibiotic recorded in 2018 and 2019, 22.57% (n = 241, 95% CI 20.15–25.18) were re-tested within 6 weeks to 4 months of their diagnosis. One-third of episodes of chlamydia cases that did not have a re-test recorded (n = 281) had re-attended the clinics within 4 months of diagnosis. Conclusion: Our study provides insight into chlamydia management by analysing general practice medical records, indicating substantial gaps in testing and re-testing for 16–29-year-olds. These data can also be used to explore the impact of future interventions to optimise chlamydia management. Chlamydia infection in Australia is common in individuals under 30 years old. General practitioners play an important role in facilitating testing, treatment and re-testing of chlamydia infections. We identified substantial gaps in testing and re-testing for 16–29-year-olds attending general practitioner clinics, with only 10% of patients tested and 22% of positive cases re-tested within recommended timeframes. This study demonstrated the value of utilising routinely collected general practice data in a research setting and provided insight into chlamydia management in primary care. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Care partners are at the forefront of dementia care, yet little is known about patient portal use in the context of dementia diagnosis. METHODS: We conducted an observational cohort study of date/time‐stamped patient portal use for a 5‐year period (October 3, 2017–October 2, 2022) at an academic health system. The cohort consisted of 3170 patients ages 65+ with diagnosed dementia with 2+ visits within 24 months. Message authorship was determined by manual review of 970 threads involving 3065 messages for 279 patients. RESULTS: Most (71.20%) older adults with diagnosed dementia were registered portal users but far fewer (10.41%) had a registered care partner with shared access. Care partners authored most (612/970, 63.09%) message threads, overwhelmingly using patient identity credentials (271/279, 97.13%). DISCUSSION: The patient portal is used by persons with dementia and their care partners. Organizational efforts that facilitate shared access may benefit the support of persons with dementia and their care partners. Highlights: Patient portal registration and use has been increasing among persons with diagnosed dementia.Two thirds of secure messages from portal accounts of patients with diagnosed dementia were identified as being authored by care partners, primarily using patient login credentials.Care partners who accessed the patient portal using their own identity credentials through shared access demonstrate similar levels of activity to patients without dementia.Organizational initiatives should recognize and support the needs of persons with dementia and their care partners by encouraging awareness, registration, and use of proper identity credentials, including shared, or proxy, portal access. [ABSTRACT FROM AUTHOR]

BACKGROUND Hypertension is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance. METHODS We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017–18, which defined hypertension as BP ≥130/80 mm Hg or ≥1 antihypertensive medication. RESULTS The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80 mm Hg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%). CONCLUSIONS Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control. [ABSTRACT FROM AUTHOR]

The application of natural language processing and machine learning (ML) in electronic health records (EHRs) may help reduce dementia underdiagnosis, but models that are not designed to reflect minority populations may instead perpetuate underdiagnosis. To improve the identification of undiagnosed dementia, particularly in Black Americans (BAs), we developed support vector machine (SVM) ML models to assign dementia risk scores based on features identified in unstructured EHR data (via latent Dirichlet allocation and stable topic extraction in n = 1 M notes) and structured EHR data. We hypothesized that separate models would show differentiation between racial groups, so the models were fit separately for BAs (n = 5 K with dementia ICD codes, n = 5 K without) and White Americans (WAs; n = 5 K with codes, n = 5 K without). To validate our method, scores were generated for separate samples of BAs (n = 10 K) and WAs (n = 10 K) without dementia codes, and the EHRs of 1.2 K of these patients were reviewed by dementia experts. All subjects were age 65+ and drawn from the VA, which meant that the samples were disproportionately male. A strong positive relationship was observed between SVM-generated risk scores and undiagnosed dementia. BAs were more likely than WAs to have undiagnosed dementia per chart review, both overall (15.3% vs. 9.5%) and among Veterans with >90th percentile cutoff scores (25.6% vs. 15.3%). With chart reviews as the reference standard and varied cutoff scores, the BA model performed slightly better than the WA model (AUC = 0.86 with negative predictive value [NPV] = 0.98, positive predictive value [PPV] = 0.26, sensitivity = 0.61, specificity = 0.92 and accuracy = 0.91 at >90th percentile cutoff vs. AUC = 0.77 with NPV = 0.98, PPV = 0.15, sensitivity = 0.43, specificity = 0.91 and accuracy = 0.89 at >90th). Our findings suggest that race-specific ML models can help identify BAs who may have undiagnosed dementia. Future studies should examine model generalizability in settings with more females and test whether incorporating these models into clinical settings increases the referral of undiagnosed BAs to specialists. [ABSTRACT FROM AUTHOR]

Purpose: To prevent vision loss, it is important to monitor patients with age-related macular degeneration (AMD) for the development of choroidal neovascularization. The coronavirus disease 2019 (COVID-19) pandemic caused many patients to miss or delay visits. To offset those gaps in care, providers utilized telehealth (TH) to evaluate patients for symptoms of disease progression and provide health education on the importance of continuous monitoring. Methods: This study evaluates the impact of TH encounters on the rate of return for recommended in-person examinations for 1103 patients with non-neovascular (dry) AMD seen in an outpatient ophthalmology clinic in 2019 and due for return evaluation after the outbreak of COVID-19 in 2020. Logistic regression analysis was used to identify demographic, clinical, and sociomedical factors associated with TH utilization and in-person return. Results: 422 patients (38%) utilized TH during the study period. Patients who completed a TH encounter were more likely to return for an in-person examination as compared with those who did not receive TH (OR: 1.8, CI 95%: 1.4–2.3, P < 0.001). Completing a TH visit was associated with the detection of new wet AMD (OR: 3.3, 95% CI 1.04–10.6, P = 0.043), as well as with an earlier return for those patients who were found to have disease progression (62 ± 54 days vs. 100 ± 57 days, P = 0.049). Conclusion: Completing a TH visit increased the rate at which patients with dry AMD returned for recommended in-person eye examinations. In many cases, this permitted the earlier detection of wet AMD, which is linked with achieving better outcomes. [ABSTRACT FROM AUTHOR]

Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions. [ABSTRACT FROM AUTHOR]

Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function. We developed a deep learning model to interpret echocardiograms and examined the association of deep learning–derived echocardiographic measures with incident outcomes. We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes. Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R 2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures. Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. Methods: Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. Discussion: This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. Trial registration: ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www.clinicaltrials.gov/study/NCT05721326 [ABSTRACT FROM AUTHOR]

Introduction: Unhoused individuals have high rates of suicidal ideation (SI) and suicidal behaviors (SB), but few have studied the relative timing of homelessness and SI/SB. Our study examines the potential to use state‐wide electronic health record data from Rhode Island's health information exchange (HIE) to identify temporal relationships, service utilization, and associations of SI/SB among unhoused individuals. Methods: We use timestamped HIE data for 5368 unhoused patients to analyze service utilization and the relative timing of homelessness versus SI/SB onset. Multivariable models identified associations of SI/SB, hospitalization, and repeat acute care utilization within 30 days from clinical features representing 10,000+ diagnoses captured within the HIE. Results: The onset of SI typically precedes homelessness onset, while the onset of SB typically follows. Weekly rates of suicide‐related service utilization increased over 25 times the baseline rate during the week before and after homelessness onset. Over 50% of encounters involving SI/SB result in hospitalization. Of those engaging in acute care for suicide‐related reasons, we found high rates of repeat acute care encounters. Conclusion: HIEs are a particularly valuable resource for understudied populations. Our study demonstrates how longitudinal, multi‐institutional data from an HIE can be used to characterize temporal associations, service utilization, and clinical associations of SI and behaviors among a vulnerable population at scale. Increasing access to services that address co‐occurring SI/SB, mental health, and substance use is needed. [ABSTRACT FROM AUTHOR]

Due to the pervasive shortage of behavioral health (BH) specialists, collaborative partnerships between pediatric primary care practitioners (PPCPs) and BH specialists can enhance provision of BH services by PPCPs. We aimed to create a new model of collaborative care that was mostly virtual, affordable, and scalable. The pilot program was implemented in 18 practices (48 PPCPs serving approximately 150 000 patients) in 2 consecutive cohorts. Outcomes were assessed by administering pre-program and post-program surveys. Across the 18 practices, PPCPs reported significantly increased confidence in their BH knowledge and skills, and significantly increased their provision of target BH services. Barriers to BH service provision (resources, time, and staff) were unchanged. This compact, mostly virtual model of BH collaboration appears to be beneficial to PPCPs while also offering convenience to patients and affordability and scalability to the practice network. [ABSTRACT FROM AUTHOR]

Objective: To examine trends in diagnosis of headache and migraine in a large pediatric neurology cohort, and test whether an electronic health record (EHR)‐integrated headache questionnaire can increase specificity of diagnosis and likelihood of prescribing migraine treatment. Background: Under‐diagnosis of migraine contributes to the burden of disease. As we founded our Pediatric Headache Program in 2013, we recognized that the proportion of patients with headache who were given a diagnosis of migraine was much lower than expected. Methods: We developed a patient headache questionnaire, initially on paper (2013–2014), then in an electronic database (2014–2016), and finally integrated into our electronic health record (pilot: 2016, full: May 2017). We compared diagnoses and prescribed treatments for new patients who were given a headache diagnosis, looking at trends in the proportion of patients given specific diagnoses (migraine, etc.) versus the non‐specific diagnosis, "headache." Next, we conducted a prospective cohort study to test for association between provider use of the form and the presence of a specific diagnosis, then for an association between specific diagnosis and prescription of migraine treatment. Results: Between July 2011 and December 2022 the proportion of new headache patients who were given a diagnosis of migraine increased 9.7% and non‐specific headache diagnoses decreased 21.0%. In the EHR cohort (June 2017–December 2022, n = 15,122), use of the provider form increased the rate of specific diagnosis to 87.2% (1839/2109) compared to 75.5% (5708/7560) without a patient questionnaire, nearly doubling the odds of making a specific diagnosis (odds ratio [OR] 1.90, 95% confidence interval [CI]: 1.65–2.19). Compared to those given only a non‐specific headache diagnosis who were prescribed a migraine therapy 53.7% (1766/3286) of the time, 75.3% (8914/11836) of those given a specific diagnosis received a migraine therapy, more than doubling the odds of prescription (OR 2.39, 95% CI: 2.20–2.60). Conclusions: Interventions to improve specificity of diagnosis were effective and led to increased rates of prescription of migraine treatments. These results have been sustained over several years. This headache questionnaire was adapted into the Foundation system of EpicCare, so it is broadly available as a clinical and research tool for institutions that use this EHR software. [ABSTRACT FROM AUTHOR]

Background: Care delivery that is not person-centered has been called discordant care. There has been a shift to incorporate more of a person's narrative into their individual healthcare treatment plan to reduce discordant care. Aligning with this shift in healthcare delivery, we developed a person-centered narrative intervention (PCNI) to address existing gaps in delivery of person-centered care. Objectives: This study aimed to evaluate the feasibility of conducting a randomized study and describe the outcomes of PCNI to usual care on the following person (patient)-reported outcomes: perceptions of the quality of communication with their nurses and their psychosocial and existential well-being. Methods: This study's design was an Obesity-Related Behavioral Intervention Trials model Phase II proof-of-concept randomized study. The participants were people admitted to an acute care hospital diagnosed with heart failure and/or end-stage renal disease. Results: Despite COVID-19 challenges, the PCNI was feasible in an acute care setting; it showed a moderate positive difference between conditions in the person's perception of their quality of communication and a small positive difference in their perception of feeling heard and understood. For our secondary outcomes of anxiety, depression, and psychosocial illness effect, there were small or no effects in the acute care setting. Discussion: Using a person-centered narrative, such as the PCNI, can help inform delivery of care that incorporates a person's(patient's) beliefs, values, and preferences into their healthcare. This study used a pragmatic approach to evaluate the PCNI in real time in an acute care setting to assess patient-reported outcomes. These positive results in a small sample indicate the need for continued testing of the PCNI. These promising effects require further testing in a Phase III efficacy study within a larger randomized controlled clinical trial. [ABSTRACT FROM AUTHOR]

• From 2011 to 2022, 2698 patients were identified within the University of Rochester Medical Center who had a Lp(a) result. • Fourteen patients had Lp(a) measured in 2011, 598 patients in 2021, with 536 until August 2022. • More women than men were tested, with the majority Caucasian, never smokers, and about 11% with more than one Lp(a) measured. • The majority with it measured do not have a listed diagnosis of cerebral infarction, peripheral vascular disease, myocardial infarction, coronary artery disease, or aortic stenosis. Knowledge of lipoprotein(a) measurement in community practice is limited. The objective of this study is to evaluate the frequency of Lp(a) screening across the University of Rochester Medical Center (URMC). Descriptive data were collected for all URMC patients >= 18 years old who have had at least one Lp(a) measurement from January 2011 to August 2022 from the URMC electronic health record (EHR). Cardiovascular diagnoses were queried to define yearly frequency and demographic information. We identified 2,698 patients with at least one Lp(a) result. An increasing number of patients were tested per year. There were more women than men, and about 11% having more than one Lp(a) measured with the majority having a level <30 mg/dL (the normal-range in the UMRC lab). The majority do not have a listed diagnosis of cerebral infarction, peripheral vascular disease, myocardial infarction, coronary artery disease, or aortic stenosis. Across URMC, there has been a steady increase of Lp(a) measurements in the past several years. [ABSTRACT FROM AUTHOR]

The authors sought to determine the effectiveness of a self-administered computerized mental health screening tool in a general acute care emergency department (ED). Changes in patient care (diagnosis of a past-year psychiatric disorder, request for psychiatric consultation, psychiatric referral at discharge, or transfer to psychiatric facility) and patient ED return visits (3 months after discharge vs. 3 months before) were assessed among ED physicians (N=451) who received patients' computerized screening reports (N=207) and those who did not (N=244). All patients received copies of screening results. The computerized mental health screening tool identified previously undiagnosed psychiatric problems. However, no statistically significant differences were found in physician care or patient ED return visits. Computerized mental health screening did not result in further psychiatric diagnoses or treatment; it also did not significantly reduce patient ED return visits. Collaboration among EDs and mental health treatment agencies, organizations, and researchers is needed to facilitate appropriate treatment referrals and linkage. [ABSTRACT FROM AUTHOR]

Purpose: Improved hypereosinophilic syndrome (HES) ascertainment in electronic health record (EHR) databases may improve disease understanding and management. An algorithm to ascertain and characterize this rare condition was therefore developed and validated. Methods: Using the UK clinical practice research datalink (CPRD)‐Aurum database linked to the hospital episode statistics database (Admitted Patient Care data) from Jan 2012 to June 2019, this cross‐sectional study ascertained patients with a specific HES code (index). Patients with HES were matched (age, sex and index date) 1:29 with a non‐HES cohort. An algorithm was developed by identifying pre‐defined variables differing between cohorts; model‐fitting using Firth logistic regression and statistical determination of the top‐five performing models; and internal validation using Leave‐One‐Out Cross Validation. Final model sensitivity and specificity were determined at an 80% probability threshold. Results: The HES and non‐HES cohorts included 88 and 2552 patients, respectively; 270 models with four variables each (treatment used for HES, asthma code, white blood cell condition code, and blood eosinophil count [BEC] code) plus age and sex variables were tested. Of the top five models, the sensitivity model performed best (sensitivity, 69% [95% CI: 59%, 79%]; specificity, >99%). The strongest predictors of HES versus non‐HES cases (odds >1000 times greater) were an ICD‐10 code for white blood cell disorders and a BEC ≥1500 cells/μL in the 24 months pre‐index. Conclusions: Using a combination of medical codes, prescribed treatments data and laboratory results, the algorithm can help ascertain patients with HES from EHR databases; this approach may be useful for other rare diseases. [ABSTRACT FROM AUTHOR]

Background and Aims Patients hospitalized for acute heart failure (AHF) continue to be discharged on an inadequate number of guideline-directed medical therapies (GDMT) despite evidence that inpatient initiation is beneficial. This study aimed to examine whether a tailored electronic health record (EHR) alert increased rates of GDMT prescription at discharge in eligible patients hospitalized for AHF. Methods Pragmatic trial of messaging to providers about treatment of acute heart failure (PROMPT-AHF) was a pragmatic, multicenter, EHR-based, and randomized clinical trial. Patients were automatically enrolled 48 h after admission if they met pre-specified criteria for an AHF hospitalization. Providers of patients in the intervention arm received an alert during order entry with relevant patient characteristics along with individualized GDMT recommendations with links to an order set. The primary outcome was an increase in the number of GDMT prescriptions at discharge. Results Thousand and twelve patients were enrolled between May 2021 and November 2022. The median age was 74 years; 26% were female, and 24% were Black. At the time of the alert, 85% of patients were on β-blockers, 55% on angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor, 20% on mineralocorticoid receptor antagonist (MRA) and 17% on sodium-glucose cotransporter 2 inhibitor. The primary outcome occurred in 34% of both the alert and no alert groups [adjusted risk ratio (RR): 0.95 (0.81, 1.12), P =.99]. Patients randomized to the alert arm were more likely to have an increase in MRA [adjusted RR: 1.54 (1.10, 2.16), P =.01]. At the time of discharge, 11.2% of patients were on all four pillars of GDMT. Conclusions A real-time, targeted, and tailored EHR-based alert system for AHF did not lead to a higher number of overall GDMT prescriptions at discharge. Further refinement and improvement of such alerts and changes to clinician incentives are needed to overcome barriers to the implementation of GDMT during hospitalizations for AHF. GDMT remains suboptimal in this setting, with only one in nine patients being discharged on a comprehensive evidence-based regimen for heart failure. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods. The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance. Four hundred nineteen consecutive patients out of 1115 hospital admissions were enrolled in a single-center retrospective study. Patients were younger than 18 years and admitted from August 2018 to February 2020 in a pediatric cardiac intensive care unit (PCICU) undergoing cardiac surgery, invasive procedure (hemodynamic studies), and medical conditions with complete EHR records and discharged after 48 h or more. Results: Thirty-six variables were selected to build the algorithm according to commonly described cardiac surgery-associated AKI clinical predictors. We evaluated different models for different outcomes: binary AKI (no AKI vs. AKI), severe AKI (no-mild vs severe AKI), and multiclass classification (maximum AKI and the most frequent level of AKI, mode AKI). The algorithm performance was assessed with the area under the curve receiver operating characteristics (AUC ROC) for binary classification, with accuracy and K for multiclass classification. AUC ROC for binary AKI was 0.93 (95% CI 0.92–0.94), and for severe AKI was 0.99 (95% CI 0.98–1). Mode AKI accuracy was 0.95, and K was 0.80 (95% CI 0.94–0.96); maximum AKI accuracy was 0.92, and K was 0.71 (95% CI 0.91–0.93). The importance matrix plot demonstrated creatinine, basal creatinine, platelets count, adrenaline support, and lactate dehydrogenase for binary AKI with the addition of cardiopulmonary bypass duration for severe AKI as the most relevant variables of the model. Conclusions: We validated a ML model to detect AKI occurring after 48 h in a retrospective observational study that could help clinicians in individuating patients at risk of AKI, in which a preventive strategy can be determinant to improve the occurrence of renal dysfunction. [ABSTRACT FROM AUTHOR]

As blockchain technology evolves, it has become a crucial component in medical data sharing. However, current needs reveal that healthcare-focused blockchain schemes increasingly require the capabilities of modification and deletion. Moreover, traditional blockchain-based systems for medical data sharing often need help with a single point of failure, which undermines the system's robustness. To address these challenges, we propose Redact-Chain for Health, a scheme based on the redactable blockchain for managing shared healthcare data. This scheme allows users to encrypt data for privacy protection and decrypt data when sharing medical information. By substituting the SHA-256 with the chameleon hash, Redact-Chain for Health introduces a fine-grained data editing scheme, facilitating medical institutions in effectively editing and managing data on the blockchain. Moreover, Redact-Chain for Health integrates a distributed trapdoor management scheme. This scheme empowers medical institutions to manage the trapdoor of the chameleon hash effectively, thereby circumventing the issue of a single point of failure. Our scheme also incorporates a symmetric encryption-based authentication algorithm to deter potential cyberattacks. Lastly, the security analysis of our proposed system demonstrates its effectiveness in preserving patients' privacy, while performance analysis confirms Redact-Chain for Health's efficiency. [ABSTRACT FROM AUTHOR]

Evidence-based assessment (EBA) in mental health is a critical aspect of improving patient outcomes and addressing the gaps in mental health care. EBA involves the use of psychometric instruments to gather data that can inform clinical decision-making, inform policymakers, and serve as a basis for research and quality management. Despite its potential, EBA is often hindered by barriers such as workload and cost, leading to its underutilization. Regarding low- and middle-income countries (LMIC), the implementation of EBA is recognized as a key strategy to address and close the prevalent mental health treatment gap. To simplify the application of EBA including in LMIC, an international team of researchers and practitioners from Tanzania, Kosovo, Chile, and Switzerland developed the Mental Health Information Reporting Assistant (MHIRA). MHIRA is an open-source electronic health record that streamlines EBA by digitising psychometric instruments and organising patient data in a user-friendly manner. It provides immediate and convenient reports to inform clinical decision-making. The current article provides a comprehensive overview of the features and technical details of MHIRA, as well as insights from four implementation scenarios. The experience gained during the implementations as well as the user-feedback suggests that MHIRA has the potential to be successfully implemented in a variety of clinical contexts and simplify the use of EBA. However, further research is necessary to establish its potential to sustainably transform healthcare services and impact patient outcomes. In conclusion, MHIRA represents an important step in promoting the widespread adoption of EBA in mental health. It offers a promising solution to the barriers that have limited the use of EBA in the past and holds the potential to improve patient outcomes and support the ongoing efforts to address gaps in mental health care. [ABSTRACT FROM AUTHOR]

Background and Aim: The Hospital Information System is a complete one to provide highquality patient care and enhance community health, so it must be designed and produced accordingly. In this regard, the current research was carried out with the aim of providing the document of standards for producing Hospital Information System software for Iran. Materials and Methods: In this study, following extraction of the features and services of the Hospital Information System from the texts, they were matched with the generalities of the document of standards compiled by the Statistical Data Management and Information Technology Office of the Ministry of Health, Treatment, and Medical Education (SDMITO). Also, the Hospital Information System was reviewed observationally, all defects of document of standards were identified, and the document was amended throughout. After providing the proposed document of standards, it was consulted by a group of experts, which included ten health information management professors, ten medical informatics professors (with at least seven years of experience as members of the academic staff), and five heads of the information technology field of the Ministry of Health. An agreement coefficient of 85% was considered to accept and approve the document of standards. After obtaining the agreement coefficient, The Hospital Information System software production document of standards was provided. Results: The document of standards provided for the production of Hospital Information System software includes the Hospital Information System design meta model, Hospital Information System subtypes, standards for the structure and content of Electronic Health Record, information terminology standards, data classification standards, security data standards, data exchange standards, clinical services, and management services, which were placed in the four areas of “features”, “services”, “documentation requirements” and “rules and policies”. Conclusion: The application of this document of standards leads to the production of a higher quality, efficient, and standard Hospital Information System software, which is effective in improving the health level of society and provides the conditions for the implementation of Electronic Health Record. [ABSTRACT FROM AUTHOR]

Improving health equity in depression care and suicide screening requires that measures like the Patient Health Questionnaire 9 (PHQ-9) function similarly for diverse racial and ethnic groups. We evaluated PHQ-9 differential item functioning (DIF) between racial/ethnic groups in a retrospective cohort study of secondary electronic health record (EHR) data from eight healthcare systems. The population (n = 755,156) included patients aged 18–64 with mental health and/or substance use disorder (SUD) diagnoses who had a PHQ-9 with no missing item data in the EHR for primary care or mental health visits between 1/1/2009–9/30/2017. We drew two random samples of 1000 from the following racial/ethnic groups originally recorded in EHRs (n = 14,000): Hispanic, and non-Hispanic White, Black, Asian, American Indian/Alaska Native, Native Hawaiian/Other Pacific Islander, multiracial. We assessed DIF using iterative hybrid ordinal logistic regression and item response theory with p < 0.01 and 1000 Monte Carlo simulations, where change in model R2 > 0.01 represented non-negligible (e.g., clinically meaningful) DIF. All PHQ-9 items displayed statistically significant, but negligible (e.g., clinically unmeaningful) DIF between compared groups. The negligible DIF varied between random samples, although six items showed negligible DIF between the same comparison groups in both random samples. Our findings may not generalize to disaggregated racial/ethnic groups or persons without mental health and/or SUD diagnoses. We found the PHQ-9 had clinically unmeaningful cross-cultural DIF for adult patients with mental health and/or SUD diagnoses. Future research could disaggregate race/ethnicity to discern if within-group identification impacts PHQ-9 DIF. • We studied the cross-cultural differential item functioning (DIF) of the Patient Health Questionnaire 9 (PHQ-9) for adults with mental health and/or substance use disorders (SUD) seen in primary care or behavioral health at eight healthcare systems. • We found that all items in the PHQ-9 displayed statistically significant, but negligible (e.g., clinically unmeaningful) DIF based on broad racial and ethnic groups. • Six items displayed consistent negligible DIF for some racial and ethnic groups between two random samples. • While DIF levels were negligible, they may suggest within-group variations masked by grouping diverse racial and ethnic groups together into monolithic categories. • Future research should examine whether our findings generalize to disaggregated racial and ethnic groups of adults with mental health and/or SUD diagnoses. [ABSTRACT FROM AUTHOR]

Objective: The main purposes of this study were to validate the epilepsy diagnosis in incident epilepsy cases in the Danish National Patient Registry (DNPR), which contains information on nearly 9 000 000 individuals, and to identify persons in the validated cohort who fulfilled the International League Against Epilepsy (ILAE) criteria for drug‐resistant epilepsy (DRE). Methods: We reviewed a random sample of medical records from all individuals registered with a first diagnosis of epilepsy (International Classification of Diseases, 10th Revision [ICD‐10]: G40) or seizures (ICD‐10: G41, R56, or F445) in the Central Denmark Region from 2010 to 2019. In persons with a validated incident epilepsy diagnosis, we determined the proportion with DRE at the latest contact. We performed logistic regression analyses to identify clinical factors that correlated with risk of DRE. Results: Of 20 723 persons with a first diagnosis of epilepsy (n = 11 812) or seizures (n = 8911), we reviewed the medical records of n = 1067 with incident epilepsy and n = 610 with incident seizures. Among those with a register diagnosis of epilepsy, the diagnosis was confirmed in 838 cases (45% females, mean age at onset = 42.4 years), providing a positive predictive value (PPV) of 79% (95% confidence interval [CI] = 76%–81%). The PPV of focal epilepsy was 86% (95% CI = 82%–89%), and the PPV of generalized epilepsy was 71% (95% CI = 61%–80%). Of 740 patients with confirmed incident epilepsy and ≥1 year of follow‐up, 103 (14%) fulfilled the definition of DRE, 476 (64%) were drug responsive, and 161 (22%) had undefined responsiveness. In multivariable logistic regression analysis, early age at epilepsy onset, cognitive impairment, and a history of status epilepticus were associated with DRE. Significance: In the DNPR, we found a PPV of the epilepsy diagnosis of 79%. Among persons with confirmed epilepsy, 14% fulfilled ILAE criteria for DRE. Early age at epilepsy onset, cognitive impairment, and a history of status epilepticus were independently associated with drug resistance. [ABSTRACT FROM AUTHOR]