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This paper explains how value co-creation is affected by the introduction of tools to digitalise healthcare. In particular, this study examines how pregnant women in France collect, utilise, and share their health records after the launch of a new Electronic Health Record (EHR). The paper combines methods from the design field (cultural probes) and micro-phenomenological interviews to capture value co-creation and value co-destruction dynamics. Through analysing nine cases, we highlight the role of patients' expectations in potential value co-destruction with an EHR. We describe why it is important to know by whom and how co-creation is carried out to improve the launch of digital health applications. Finally, the paper emphasises that value co-creation is a long-term process that needs to be studied longitudinally. [ABSTRACT FROM AUTHOR]

Introduction: Child abuse is a leading cause of morbidity and mortality in children. The rate of missed child abuse in general emergency departments (ED), where 85% of children are evaluated, is higher than in pediatric EDs. We sought to evaluate the impact of an electronic health record (EHR)-embedded child-abuse clinical decision support system (CA-CDSS) in the identification and evaluation of child maltreatment in a network of EDs three years after implementation.Methods: We anonymously surveyed all 196 ED attending physicians and advanced practice practitioners (APP) in the University of Pittsburgh Medical Center network. The survey evaluated practitioner awareness of, attitudes toward, and changes in clinical practice prompted by the CA-CDSS. We also assessed practitioner recognition and evaluation of sentinel injuries.Results: Of the 71 practitioners (36%) who responded to the survey, 75% felt the tool raised child abuse awareness, and 72% had a face-to-face discussion with the child’s nurse after receiving a CA-CDSS alert. Among APPs, 72% consulted with the attending physician after receiving an alert. Many practitioners were unaware of at least one function of the CA-CDSS; 38% did not know who completed the child abuse screen (CAS); 54% were unaware that they could view the results of the CAS in the EHR, and 69% did not recognize the clinical decision support dashboard icon. Slightly over 20% of respondents felt that the CA-CDSS limited autonomy; and 4.5% disagreed with the recommendations in the physical abuse order set, which reflects American Academy of Pediatrics (AAP) guidelines. Greater than 90% of respondents correctly identified an intraoral injury and torso bruise in an infant as sentinel injuries requiring an evaluation for abuse.Conclusion: A child-abuse clinical decision support system embedded in the electronic health record was associated with communication among practitioners and was overall perceived as improving child abuse awareness in our system. Practitioners correctly recognized injuries concerning for abuse. Barriers to improving identification and evaluation of abuse include gaps in knowledge about the CA-CDSS and the presence of practitioners who disagree with the AAP recommendations for physical abuse evaluation and/or felt that clinical decision support in general limited their clinical autonomy.

ObjectivesHealth systems are increasingly accountable for patients and require accurate electronic health record (EHR) vital status. We recently demonstrated that 19% of seriously ill primary care patients in one system were not marked dead in the EHR and 80% of these decedents had an encounter or appointment outstanding after death. Herein we describe the mechanism of identifying decedents whose death is not captured at the level of the EHR, characterize these decedents, and describe medications refilled after death.Materials and methodsDescription of multistep process to identify deceased patients not marked dead in the EHR among a cohort of seriously ill primary care patients including public death file matching, utilization analysis, and chart abstraction. We compared decedents not marked dead in the EHR to known decedents and described pharmacy requests and refills.ResultsNearly 90% of encounters and appointments occurred because the health system EHR did not record the death although 11% of these encounters contained condolences or death notifications. Decedents not marked dead in the EHR were older and lived in more vulnerable areas than those marked dead. Of 146 refill requests after death, 88 medications were authorized.Discussion and conclusionMatching with a limited public death file is an inadequate solution to inaccurate vital status. Better workflows are needed to capture deaths about which clinicians and staff are aware, but will identify only a fraction of the decedents inaccurately listed as alive. Efforts are needed to connect EHRs with more specific sources of linkable decedent information.

AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. METHODS AND RESULTS: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VAs External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the HF ICD-code universe, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). CONCLUSIONS: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

OBJECTIVES: This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. MATERIALS AND METHODS: We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. RESULTS: About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. DISCUSSION: The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. CONCLUSION: A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs without special effort will be crucial to ensure future technology robustness and durability.

OBJECTIVE: We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool. MATERIALS AND METHODS: We used a mixed-methods approach with semi-structured interviews and surveys. The coded interviews were synthesized into physician-user journey maps (ie, visualization of a process to accomplish goals) and personas (ie, user types). Interviews were conducted via video. We developed physician journey maps and user personas informed by their goals, systems interactions, and experiences with pediatric asthma management. RESULTS: The physician end-user personas identified were: efficiency, relationship, and learning. Features of a potential asthma CDS tool sought varied by physician practice type and persona. It was important to the physician end-user that the asthma CDS tool demonstrate value by lowering workflow friction (ie, difficulty or obstacles), improving the environment surrounding physicians and patients, and using it as a teaching tool. Customizability versus standardization were important considerations for uptake. DISCUSSION: Different values and motivations of physicians influence their use and interaction with the EHR and CDS tools. These different perspectives can be captured by applying a UX design approach to the development process. For example, with the importance of customizability, one approach may be to build a core module with variations depending on end-user preference. CONCLUSION: A UX approach can drive design to help understand physician-users and meet their needs; ultimately with the goal of increased uptake.

BackgroundElectronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance.ObjectiveAs part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines.MethodsEight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements.ResultsDue to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites.ConclusionsThe process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes.

BACKGROUND: Patients seeking integrative health and medicine (IHM) modalities often present with multiple physical and psychological concerns. Research supports IHMs effectiveness for addressing symptoms over longer time periods. However, few studies have evaluated immediate outpatient effects. OBJECTIVE: This study describes pre-encounter patient-reported outcome (PRO) clusters and examines the immediate clinical effectiveness of IHM modalities on pain, stress, and anxiety among outpatients with moderate-to-severe symptoms. METHODS: A retrospective review was conducted of encounters among adults presenting to outpatient acupuncture, chiropractic, massage, integrative medicine consultation, or osteopathic manipulation treatment between January 2019 and July 2020. Encounters were included if patients reported pre-encounter pain, stress, or anxiety ≥4 on a numeric rating scale (NRS). Outcome analyses included random effects for patient and provider using a mixed model. RESULTS: Across 7335 clinical encounters among 2530 unique patients (mean age: 49.14 years; 81.0% female; 75.9% White; 15.8% Black/African American), the most common pre-encounter PRO clusters were pain, stress, and anxiety ≥4 (32.4%); pain ≥4 only (31.3%); and stress and anxiety ≥4 (15.6%). Clinically meaningful single-encounter mean [95% CI] changes were observed across all modalities in pain (-2.50 [-2.83, -2.17]), stress (-3.22 [-3.62, -2.82]), and anxiety (-3.05 [-3.37, -2.73]). CONCLUSION: Patients presenting to outpatient IHM with moderate-to-severe symptoms most often presented with pain, stress, and anxiety ≥4 on the NRS. Multiple IHM modalities yielded clinically meaningful (≥2 unit) immediate reductions in these symptoms. Future research measuring immediate and longitudinal effectiveness is needed to optimize the triage and coordination of IHM modalities to meet patients needs.

Background: Several clinical trials support the efficacy of music therapy (MT) for improving outcomes in hospitalized patients, but few studies have evaluated the real-world delivery and integration of MT across multiple medical centers. This article describes the rationale, design, and population characteristics of a retrospective study examining the delivery and integration of MT within a large health system. Methods: A retrospective electronic health record (EHR) review was conducted of hospitalized patients seen by and/or referred to MT between January 2017 and July 2020. MT was provided across ten medical centers, including an academic medical center, a freestanding cancer center, and eight community hospitals. Discrete demographic, clinical, and MT treatment and referral characteristics were extracted from the EHR, cleaned, and organized using regular expressions functions, and they were summarized using descriptive statistics. Results: The MT team (average 11.6 clinical fulltime equivalent staff/year) provided 14,261 sessions to 7378 patients across 9091 hospitalizations. Patients were predominantly female (63.7%), White (54.3%) or Black/African American (44.0%), 63.7 ± 18.5 years of age at admission, and insured under Medicare (51.1%), Medicaid (18.1%), or private insurance (14.2%). Patients hospitalizations (median length of stay: 5 days) were primarily for cardiovascular (11.8%), respiratory (9.9%), or musculoskeletal (8.9%) conditions. Overall, 39.4% of patients hospital admissions included a mental health diagnosis, and 15.4% were referred to palliative care. Patients were referred by physicians (34.7%), nurses (29.4%), or advanced practice providers (24.7%) for coping (32.0%), anxiety reduction (20.4%), or pain management (10.1%). Therapists provided sessions to patients discharged from medical/surgical (74.5%), oncology (18.4%), or intensive care (5.8%) units. Conclusions: This retrospective study indicates that MT can be integrated across a large health system for addressing the needs of socioeconomically diverse patients. However, future research is needed to assess MTs impact on health care utilization (i.e., length of stay and rates of readmission) and immediate patient-reported outcomes.

Lung nodules are frequently detected on low‐dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline‐discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening‐detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. Plain Language Summary: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening‐detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules).Although the vast majority of lung nodules are not lung cancer, it is important to have evidence‐based, standardized approaches to the evaluation and management of a lung nodule.The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms. Given the need to balance the benefits of early‐detection of lung cancer and the risks of diagnostic tests, it is imperative to develop evidence‐based strategies for lung nodule management. This report identifies six gaps in knowledge about lung nodule management and proposes six recommendations to improve the management approaches described in clinical practice guidelines. [ABSTRACT FROM AUTHOR]

Recently, the spotlight has been cast on smart healthcare by many researchers to provide better facilities to patients. Improved services, such as reducing health hazards, monitoring patient health, tracking disease trends, and enhancing service quality, can be offered by smart healthcare. Despite its numerous potential benefits, smart healthcare is associated with some security challenges. These challenges can be mitigated by utilizing blockchain technology, which is characterized by decentralization, cryptography, consensus mechanisms, transparency and accountability, smart contracts, ownership of data, immutability, and distributed ledger. Therefore, the latest blockchain technology is focused in this article to address the security challenges of smart healthcare. In this article, attention is given to smart healthcare, smart cities for smart healthcare, smart and secure healthcare, and cutting-edge technologies for smart cities and smart healthcare.Please provide author biography and photo. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Design Methods Results Conclusions Implications for the Profession Patient or Public Contribution Reporting Method Impact To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model.This methodological study employed a cross‐sectional secondary data analysis.This study used de‐identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest‐workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short‐term memory (LSTM) algorithms. The STROBE checklist for cross‐sectional studies was used for reporting.Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change.The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies.Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision‐making processes.We used de‐identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities.The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross‐sectional studies.Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services. [ABSTRACT FROM AUTHOR]

As delayed treatment of septic shock can lead to an irreversible health state, timely identification of septic shock holds immense value. While numerous approaches have been proposed to build early warning systems, these approaches primarily focus on predicting the future risk of septic shock, irrespective of its precise onset timing. Such early prediction systems without consideration of timeliness fall short in assisting clinicians in taking proactive measures. To address this limitation, we establish a timely warning system for septic shock with data-task engineering, a novel technique regarding the control of data samples and prediction targets. Leveraging machine learning techniques and the real-world electronic medical records from the MIMIC-IV (Medical Information Mart for Intensive Care) database, our system, TEW3S (Timely Early Warning System for Septic Shock), successfully predicted 94% of all shock events with one true alarm for every four false alarms and a maximum lead time of 8 hours. This approach emphasizes the often-overlooked importance of prediction timeliness and may provide a practical avenue to develop a timely warning system for acute deterioration in hospital settings, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Identification of persons experiencing homelessness (PEH) within healthcare systems is critical to facilitate patient and population-level interventions to address health inequities. Objective: We created an enhanced electronic health record (EHR) registry to improve identification of PEH within a safety net healthcare system. Design: We compared patients identified as experiencing homelessness in 2021, stratified by method of identification (i.e., through registration data sources versus through new EHR registry criteria). Main Measures: Sociodemographic and clinical characteristics, healthcare utilization, engagement with homeless service providers, and mortality. Key Results: In total, 10,896 patients met the registry definition of a PEH; 30% more than identified through standard registration processes; 78% were identified through only one data source. Compared with those identified only through registration data, PEH identified through new registry criteria were more likely to be female (42% vs. 25%, p < 0.001), Hispanic/Latinx or Black/African American (30% versus 25% and 25% vs. 18%, p < 0.0001), and Medicaid or Medicare beneficiaries (74% vs. 67% and 16% vs.10%, respectively, p < 0.0001). New data sources also identified a higher proportion of patients: at extremes of age (16% < 18 years and 9% ≥ 65 years vs. 2% and 5%, respectively, p < 0.0001), with increased clinical risk (31% with CRG 6–9 vs. 18%, p < 0.0001), and with a mental health diagnosis (56% vs. 42%, p < 0.0001), and a lower proportion of patients with a substance use diagnosis (39% vs. 54%, p < 0.0001) or criminal justice involvement (8% vs. 15%, p < 0.0001). Newly identified patients were more likely to be engaged in primary care (OR 2.03, 95% CI 1.83–2.26) but less likely to be engaged with homeless service providers (OR 0.70, 95% CI 0.63–0.77). Conclusions: Commonly utilized methods of identifying PEH within healthcare systems may underestimate the population and introduce reporting biases. Recognizing alternate identification methods may more comprehensively and inclusively identify PEH for intervention. [ABSTRACT FROM AUTHOR]

Despite increasing prevalence of hypertension in youth and high adult cardiovascular mortality rates, the long-term consequences of youth-onset hypertension remain unknown. This is due to limitations of prior research, such as small sample sizes, reliance on manual record review, and limited analytic methods, that did not address major biases. The Study of the Epidemiology of Pediatric Hypertension (SUPERHERO) is a multisite, retrospective registry of youth evaluated by subspecialists for hypertension disorders. Sites obtain harmonized electronic health record data using standardized biomedical informatics scripts validated with randomized manual record review. Inclusion criteria are index visit for International Classification of Diseases, 10th Revision (ICD-10) code–defined hypertension disorder on or after January 1, 2015, and age < 19 years. We exclude patients with ICD-10 code–defined pregnancy, kidney failure on dialysis, or kidney transplantation. Data include demographics, anthropomorphics, US Census Bureau tract, histories, blood pressure, ICD-10 codes, medications, laboratory and imaging results, and ambulatory blood pressure. SUPERHERO leverages expertise in epidemiology, statistics, clinical care, and biomedical informatics to create the largest and most diverse registry of youth with newly diagnosed hypertension disorders. SUPERHERO's goals are to reduce CVD burden across the life course and establish gold-standard biomedical informatics methods for youth with hypertension disorders. [ABSTRACT FROM AUTHOR]

Summary: Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR. Purpose: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. Methods: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. Results: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. Conclusion: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates. [ABSTRACT FROM AUTHOR]

Background: There is limited information about how on-line screening tools developed by integrated systems facilitated management of COVID-like illness patients. Methods: Using the Kaiser Permanente Northwest (KPNW) Electronic Health Record, we identified adult plan members who accessed online COVID-19 screening e-visits and enumerated their subsequent medical encounters, tests for SARS-CoV-2, and test outcomes. Results: Between May 2020 and December 2021, members completed 55,139 e-visits, with disproportionate representation among females (65% vs. 53% in the overall membership) and members aged <45 years (61% vs. 39%). Thirty percent of patients (16,953) were managed entirely through e-visits and 70% received subsequent in-person care. The percent of SARS-CoV-2 positive individuals was highest among the 1055 individuals triaged to inpatient care (17.9%), compared to 9.5% among those escalated to additional ambulatory care. Conclusions: The e-visit on-line screening tool helped KPNW assist thousands of patients with COVID-19 symptoms, avoid unnecessary in-person patient encounters, and preserved KPNW infection control and pandemic surge capacity. [ABSTRACT FROM AUTHOR]

This is a register-based study that examines the distribution of diagnoses made by general practitioners (GPs) in the public primary health care of the city of Vantaa, Finland. Data were gathered from the electronic health record (EHR) system and consisted of every record entered into the EHR system between 1 January 2016 and 31 December 2018. Both absolute numbers and relative proportions of the 10th edition of International Classification of Diseases (ICD-10) diagnosis recordings were reported and calculated. Among GP visits, the 88 most common diagnoses covered 75% of all diagnoses. The most common diagnoses were related to the musculoskeletal (3.8%, ICD code M54) and respiratory systems (6.0%, ICD-10 code J06). Primary health care GP services were mostly used by children (age <5 years) and older adults (>65 years). Health examinations – mostly children's and maternity clinics appointments/visits – covered 20% of the GP office visits. Women between the ages 15–79 years had relatively more GP visits compared to men. The 88 most commonly recorded diagnoses covered the majority of the GP visits. Health examinations for the healthy were an important part of GPs' work. In an urban Finnish city, GP services were predominantly used by children and older adults. [ABSTRACT FROM AUTHOR]

There is growing excitement about the clinical use of artificial intelligence and machine learning (ML) technologies. Advancements in computing and the accessibility of ML frameworks enable researchers to easily train predictive models using electronic health record data. However, several practical factors must be considered when employing ML on electronic health record data. We provide a primer on ML and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct ML models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. ML shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing the potential dangers of, and barriers to, implementation of ML models in the clinic. [ABSTRACT FROM AUTHOR]

Purpose Due to the low specificity of drug-drug interaction (DDI) warnings, hospitals and healthcare systems would benefit from the ability to customize alerts, thereby reducing the burden of alerts while simultaneously preventing harm. We developed a tool, called the Drug Interaction Customization Editor (DICE), as a prototype to identify features and functionality that could assist healthcare organizations in customizing DDI alerts. Methods A team of pharmacists, physicians, and DDI experts identified attributes expected to be useful for filtering DDI warnings. A survey was sent to pharmacists with informatics responsibilities and other medication safety committee members to obtain their opinions about the tool. The survey asked participants to evaluate the 4 sections of the DICE tool (General, Medication, Patient, and Visit) on a scale ranging from 0 (not useful) to 100 (very useful). The survey provided an opportunity for participants to express their opinions on the overall usefulness of the DICE tool and to provide other comments. Results The 50 survey respondents were mainly pharmacists (n = 47, 94%) with almost half (n = 23, 47%) having health information technology/informatics training. Most respondents (n = 33, 80%) were employed by organizations with over 350 beds. Respondents indicated the most useful features of the DICE tool were the ability to filter DDI warnings based on routes of administrations (mean [SD] rating scale score, 86.5 [21.6]), primary drug properties (85.7 [20.5]), patient attributes (85.6 [16.7]) and laboratory attributes (88.8 [18.0]). The overall impression of the DICE tool was rated at 82.8 (19.0), and when asked about the potential to reduce DDI alerts, respondents rated the tool at 83.7 (21.8). Conclusion The ability to customize DDI alerts using data elements currently within the electronic health records (EHRs) has the potential to decrease alert fatigue and override rates. This prototype DICE tool could be used by end users and vendors as a template for developing a more advanced DDI filtering tool within EHR systems. [ABSTRACT FROM AUTHOR]

Background: The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. Methods: We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. Results: We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time–motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. Discussion: Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010–2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. [ABSTRACT FROM AUTHOR]

Background: The prevalence of Post Traumatic Stress Disorder (PTSD) has been rising since the start of the COVID-19 pandemic, and affects females, sexual and gender minorities, and individuals with social risks at higher rates. Objective: This study examines if the prevalence of PTSD increased from 2019 to 2022 among patients who received care in a national network of safety-net, community-based health centers with integrated behavioral health programs that serve patients at high risk for PTSD. Methods: We analyzed electronic health record data from patients with 1 or more behavioral health visits during 2019 to 2022 using repeated cross-sectional data to visualize trends. Change in PTSD prevalence based on the diagnoses associated with each visit from 2019 to 2022 was assessed using a general linear mixed model adjusted for patient-level factors. Results: Over 4 years, 182 419 distinct patients received care for PTSD, with prevalence increasing from 2019 to 2022 (OR = 1.09, 95% CI = 1.08, 1.10). Females (OR = 1.81, 95% CI = 1.78, 1.84), LGBTQIA individuals (OR = 1.29, 95% CI = 1.26, 1.33), gender diverse individuals (OR = 1.36, 95% CI = 1.29, 1.42), and patients who reported 1 or more social determinants of health needs (OR = 1.52, 95% CI = 1.46, 1.57) were more likely to have a diagnosis of PTSD. Conclusions: In a worrying trend, PTSD prevalence has increased among people receiving integrated behavioral health care at community-based health centers, with disparities observed in subpopulations. Continued effort and investment are needed to meet this increasing need in safety-net settings, particularly for those subpopulations observed to have higher prevalence. [ABSTRACT FROM AUTHOR]

Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical–biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics. [ABSTRACT FROM AUTHOR]

Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations. [ABSTRACT FROM AUTHOR]

A metaphor conceptualizes one, typically abstract, experience in terms of another, more concrete, experience with the goal of making it easier to understand. Although combat metaphors have been well described in some health contexts, they have not been well characterized in the setting of critical illness. How do clinicians use combat metaphors when describing critically ill patients and families in the electronic health record? We included notes written about patients aged 18 years or older admitted to ICUs within a large hospital system from 2012 through 2020. We developed a lexicon of combat words and isolated note segments that contained any combat mentions. Combat mentions were defined systematically as a metaphor or not across two coders. Among combat metaphors, we used a grounded theory approach to construct a conceptual framework around their use. Across 6,404 combat-related mentions, 5,970 were defined as metaphors (Cohen κ, 0.84). The most common metaphors were "bout" (26.2% of isolated segments), "combat" (18.5%), "confront" (17.8%), and "struggle" (17.5%). We present a conceptual framework highlighting how combat metaphors can present as identity ("mom is a fighter") and process constructs ("struggling to breathe"). Identity constructs usually were framed around: (1) hope, (2) internal strength, (3) contextualization of current illness based on prior experiences, or (4) a combination thereof. Process constructs were used to describe: (1) "fighting for" (eg, working toward) a goal, (2) "fighting against" an unwanted force, or (3) experiencing internal turmoil. We provide a novel conceptual framework around the use of combat metaphors in the ICU. Further studies are needed to understand intentionality behind their use and how they impact clinician behaviors and patient and caregiver emotional responses. [ABSTRACT FROM AUTHOR]

Purpose: To describe the development and integration of an electronic health record‐driven, student dashboard that displays real‐time data relative to the students' patient management and clinic experiences at the University of Illinois Chicago, College of Dentistry. Materials and methods: Following development and implementation of the student dashboard, various objective metrics were evaluated to identify any improvements in the clinical patient management. A cross‐sectional retrospective chart review was completed of the electronic health record (axiUm, Exan, Coquitlam, BC, Canada) from January 2019 to April 2022 evaluating four performance metrics: student lockouts, note/code violations, overdue active patients, and overdue recall patients. Descriptive statistics were analyzed. The Kolmogorov–Smirnov test was applied to assess the normal distribution of data. Data were analyzed by the Kruskal–Wallis tests for potential differences between pre‐dashboard and post‐dashboard implementation years with the mean overdue active/recall patient to student ratio variables. Mann–Whitney U‐tests for between‐groups comparisons with Bonferroni correction for multiple comparisons were performed (α = 0.05). Descriptive statistics were performed to analyze the student utilization frequency of the dashboard. Results: Post‐implementation analysis indicated a slight decrease in the number of lockouts and note/code violation; and a statistically significant decrease in overdue active patients post‐dashboard (P < 0.001). On average, students accessed their dashboards 3.3 times a week. Conclusions: Implementation of a student dashboard through the electronic health record platform within an academic dental practice has the potential to assist students with patient management and is utilized regularly by the students. [ABSTRACT FROM AUTHOR]

The 21st Century Cures Act Final Rule mandates increased transparency and accessibility of the Electronic Health Record (EHR). This mandate has exacerbated tensions in adolescent healthcare between transparency and confidentiality. This study evaluates clinicians' perspectives on how well the EHR maintains confidentiality for adolescents in the post-Cures Act era. A web-based survey was distributed via the national listserv of the Society for Adolescent Health and Medicine in March–April 2022. English-speaking members practicing in the Unites States who used an EHR to document clinical visits with minors were eligible. The survey included questions about EHR training, features, and approach to information sharing with adolescent patients and proxies. Participants included 97 respondents from 32 states. Most participants were physicians (98%) and identified themselves as attending (89%). Several EHR vendors were represented, although the majority used Epic (76%). As has been seen in prior studies, there remains significant variability in approaches to patient portal access for adolescent patients and their adult proxies. Respondents report that training around adolescent-specific privacy issues remains infrequent and is perceived as inadequate. Adolescent providers, despite feeling confident in navigating the EHR generally, continue to report low rates of confidence in how well their EHR may protect adolescent privacy. Clinicians have persistent concerns about adolescent confidentiality postimplementation of the 21st Century Cures Act. Sharing sufficient information while protecting adolescent confidentiality might require standardization by EHR vendors to improve granularity of proxy information sharing. Healthcare institutions must also commit to training providers on management of adolescent confidentiality. [ABSTRACT FROM AUTHOR]

Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may outperform existing scores and can be integrated within the electronic health record (EHR) to provide real-time risk assessment without manual data entry. We present the first EHR-based machine learning model for GIB. The training cohort comprised 2546 patients and internal validation of 850 patients presenting with overt GIB (ie, hematemesis, melena, and hematochezia) to emergency departments of 2 hospitals from 2014 to 2019. External validation was performed on 926 patients presenting to a different hospital with the same EHR from 2014 to 2019. The primary outcome was a composite of red blood cell transfusion, hemostatic intervention (ie, endoscopic, interventional radiologic, or surgical), and 30-day all-cause mortality. We used structured data fields in the EHR, available within 4 hours of presentation, and compared the performance of machine learning models with current guideline-recommended risk scores, Glasgow-Blatchford Score, and Oakland Score. Primary analysis was area under the receiver operating characteristic curve. Secondary analysis was specificity at 99% sensitivity to assess the proportion of patients correctly identified as very low risk. The machine learning model outperformed the Glasgow-Blatchford Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P <.001) and Oakland Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P <.001). At the very-low-risk threshold of 99% sensitivity, the machine learning model identified more very-low-risk patients: 37.9% vs 18.5% for Glasgow-Blatchford Score and 11.7% for Oakland Score (P <.001 for both comparisons). An EHR-based machine learning model performs better than currently recommended clinical risk scores and identifies more very-low-risk patients eligible for discharge from the emergency department. [Display omitted] Electronic health record–based machine learning models can provide automated, interpretable risk stratification for acute gastrointestinal bleeding to identify very-low-risk patients who could benefit from outpatient management. [ABSTRACT FROM AUTHOR]

Background: The Institute for Safe Medication Practices and The Joint Commission recommend the implementation of bidirectional smart infusion pump interoperability with the electronic health record (EHR) to improve medication safety. However, there are barriers associated with implementation of this process. Purpose: The purpose of this process improvement project was to identify, evaluate, and implement improvements in pump integration with the EHR to decrease manual programming and increase the use of the smart pump functions (the dose error reduction system or infusion guardrails). Methods: A medication safety task force was formed, and end users were surveyed for barriers. The task force found and implemented solutions to improve the process. Results: Infusion pump integration rates for seven hospitals improved from 79.4% to 91.8% (p,.001), and the use of smart pump programming increased from 84.7% to 93.6%. Conclusions: Including all stakeholders in the project allowed barriers to be identified and solutions to be implemented. As more healthcare systems implement technology-related processes to improve patient safety, this work can guide medication safety teams on how to optimize technology use by removing barriers to the use of pump integration with the EHR. [ABSTRACT FROM AUTHOR]

Introduction: Competent navigation of an electronic health record (EHR) has become a critical skill for health professionals, and pharmacists need to be adept in EHR utilization given the extent of EHR involvement in routine medication management. Limited data exists related to current approaches to teaching EHR skills in pre‐Advanced Pharmacy Practice Experiences (pre‐APPE) pharmacy curricula. Objectives: The purpose of this study was to describe activities, utilization, and barriers to implementation of teaching EHRs (tEHRs) into pharmacy curricula in skills‐based courses. Methods: Faculty members of the Big Ten Academic Alliance Skills‐Based Education and Assessment Collaborative (BTAA‐SBEAC) were surveyed. The questionnaire included open‐ended questions related to tEHR type, curriculum placement, and current use of tEHRs, as well as barriers to incorporation of tEHRs in the curriculum. Inductive content analysis was used to evaluate qualitative responses. Results: A representative from all 10 colleges within the BTAA responded. Most programs focused on EHR teaching in the second and third years of the curriculum. All respondents reported having access to a tEHR for internal case building. The tEHR was used by all institutions to allow students to practice in gathering patient information as the first step in the Pharmacists' Patient Care Process. Other activities commonly performed using the tEHR included patient care documentation, medication order verification, prescription processing, and medication reconciliation. Faculty time and cost were barriers identified for successful tEHR implementation. Conclusion: Multiple skills are taught through the utilization of a tEHR among the colleges within the BTAA. These activities vary between institutions, and barriers exist that limit expansion and consistent integration across a skills‐based curriculum. Recognition of EHR utilization as an essential pharmacy skill can allow for development of formalized learning outcomes, leading to better support and integration throughout skills‐based courses. [ABSTRACT FROM AUTHOR]

Objectives Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses. Materials and Methods A mixed-methods design was used guided by Unified Theory of Acceptance and Use of Technology. Key EHR components included, Flowsheets , Medication Administration Records (MAR), Care Plan , Notes , and Navigators. We first identified 5 units with the highest documentation burden in 1 university hospital through EHR log file analyses. Four nurses per unit were recruited and engaged in interviews and surveys designed to examine their perceptions of ease of use and usefulness of the 5 EHR components. A combination of inductive/deductive coding was used for qualitative data analysis. Results Nurses acknowledged the importance of documentation for patient care, yet perceived the required documentation as burdensome with levels varying across the 5 components. Factors contributing to burden included non-EHR issues (patient-to-nurse staffing ratios; patient acuity; suboptimal time management) and EHR usability issues related to design/features. Flowsheets , Care Plan , and Navigators were found to be below acceptable usability and contributed to more burden compared to MAR and Notes. The most troublesome EHR usability issues were data redundancy, poor workflow navigation, and cumbersome data entry based on unit type. Discussion Overall, we used quantitative and qualitative data to highlight challenges with current nursing documentation features in the EHR that contribute to documentation burden. Differences in perceived usability across the EHR documentation components were driven by multiple factors, such as non-alignment with workflows and amount of duplication of prior data entries. Nurses offered several recommendations for improving the EHR, including minimizing redundant or excessive data entry requirements, providing visual cues (eg, clear error messages, highlighting areas where missing or incorrect information are), and integrating decision support. Conclusion Our study generated evidence for nurse EHR use and specific documentation usability issues contributing to burden. Findings can inform the development of solutions for enhancing multi-component EHR usability that accommodates the unique workflow of nurses. Documentation strategies designed to improve nurse working conditions should include non-EHR factors as they also contribute to documentation burden. [ABSTRACT FROM AUTHOR]

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare. Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, of the participants in Mayo Clinic Study of Aging (MCSA) from initial mild cognitive impairment (MCI) diagnosis to dementia onset through network analysis. Methods: We used the data from the MCSA, a prospective population-based cohort study of cognitive aging, and examined the changing association among variables (i.e., participants' health conditions) from the first visit of MCI diagnosis to the visit of dementia onset using network analysis. The number of participants for this study are 97 with the number of visits ranging from 2 visits (30 months) to 7 visits (105 months). We identified the network communities among variables from three-fold collection of instances: (i) the first MCI diagnosis, (ii) progression to dementia, and (iii) dementia diagnosis. We determine the variables that play a significant role in the dementia onset, aiming to identify and prioritize specific variables that prominently contribute towards developing dementia. In addition, we explore the sex-specific impact of variables in relation to dementia, aiming to investigate potential differences in the influence of certain variables on dementia onset between males and females. Results: We found correlation among certain variables, such as neuropsychiatric symptoms and chronic conditions, throughout the progression from MCI to dementia. Our findings, based on patterns and changing variables within specific communities, reveal notable insights about the time-lapse before dementia sets in, and the significance of progression of correlated variables contributing towards dementia onset. We also observed more changes due to certain variables, such as cognitive and functional scores, in the network communities for the people who progressed to dementia compared to those who does not. Most changes for sex-specific analysis are observed in clinical dementia rating and functional activities questionnaire during MCI onset are followed by chronic diseases, and then by NPI-Q scores. Conclusions: Network analysis has shown promising potential to capture significant longitudinal changes in health conditions, spanning from the MCI diagnosis to dementia progression. It can serve as a valuable analytic approach for monitoring the health status of individuals in cognitive impairment assessment. Furthermore, our findings indicate a notable sex difference in the impact of specific health conditions on the progression of dementia. [ABSTRACT FROM AUTHOR]

Background: This study investigates the impact of transitioning from paper and pencil (P&P) methods to electronic patient records (EPR) on workflow and usability in surgical ward rounds. Methods: Surgical ward rounds were audited by two independent observers to evaluate the effects of transitioning from P&P to EPR. Key observations included the number of medical personnel and five critical workflow aspects before and after EPR implementation. Additionally, usability was assessed using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ). Results: A total of 192 P&P and 160 EPR observations were analyzed. Physicians experienced increased administrative workload with EPR, while nurses adapted more easily. Ward teams typically consisted of two physicians and three or four nurses. Usability scores rated the system as "Not Acceptable" across all professional groups. Conclusions: The EPR system introduced usability challenges, particularly for physicians, despite potential benefits like improved data access. Usability flaws hindered system acceptance, highlighting the need for better workflow integration. Addressing these issues could improve efficiency and reduce administrative strain. As artificial intelligence becomes more integrated into clinical practice, healthcare professionals must critically assess AI-driven tools to ensure safe and effective patient care. [ABSTRACT FROM AUTHOR]

Background: In the last three decades, Electronic Health Records Systems (EHRSs) have become one of the top priorities of policymakers globally. Nowadays, EHRS reform is fast becoming a priority in developed countries. The introduction of EHRSs in Saudi healthcare organizations is considered one of the highest priorities of policymakers. Saudi Arabian e-health strategy pays reasonable attention to the EHRS implementation project. According to Saudi Vision 2030, the e-health transformation will be on top of IT projects in the country. However, an estimated 50% of EHRS implementations have failed. Project leadership and type of project management have been found to be crucial components of effective EHRS implementation. Aim of the study: To evaluate the role of centralized project management (CPM) in the implementation of large-scale EHRSs in Primary Healthcare Centers (PHCs) in Saudi Arabia (SA). Methods: A sequential explanatory mixed-methods approach involving a survey and semi-structured qualitative interview methods were utilized. Results: A total of 39 (75%) out of 53 policymakers at the Saudi Ministry of Health completed the survey, and 14 project team members were interviewed. Findings from both illustrated that adopting centralized project management approaches to implementation was beneficial in facilitating large-scale EHRS implementation and helped to overcome barriers that may have otherwise led to the failure of the project. EHRS interoperability and software selection were the factors that CPM influenced most positively. [ABSTRACT FROM AUTHOR]

Background: In Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system. Method: The proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee. Results: The MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction. Conclusion: The study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process. [ABSTRACT FROM AUTHOR]

Purpose: Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. Methods: We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs' performance. Results: The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. Conclusion: The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction: The care of critically ill patients involves communication and shared decision-making with families and determination of goals of care. Analyzing these aspects through electronic health records (EHRs) can support research in ICUs, associating them with outcomes. This review aims to explore studies that examine these topics. Methods: A scoping review was conducted through a systematic literature search of articles in PubMed, Web of Science, and Embase databases using MESH terms up to 2024, conducted in ICU settings, focusing on communication with families, shared decision-making, goals, and end-of-life care. Results: A total of 10 articles were included, divided into themes: Records and family, and records in quality improvement projects. Variables based on records with common characteristics were identified. Outcome analysis was performed through questionnaires to family members, healthcare professionals or by analyzing care processes. The studies revealed associations between family members' perceptions and mental health symptoms and documented elements such as communication, therapeutic limitations, social and spiritual support. Studies evaluating quality communication improvement projects did not show significant impact on documented care, except for those that assessed improvements based on palliative care. Conclusion: The analysis of documented care for critically ill patients can be conducted from various perspectives. Processes amenable to improvement, such as communication with family members, definition of goals of care, limitations, shared decision-making, evaluated through EHRs, are associated with mental health symptoms and perceptions of families of critically ill patients. Documentation-based studies can contribute to improvements in patient- and family-centered care in the ICU. [ABSTRACT FROM AUTHOR]

This study in Singapore analysed the incubation periods of the following SARS-CoV-2 variants: Wuhan-Hu-1, Delta, and Omicron. Three pandemic waves were examined: Wuhan-Hu-1 (January 2020–March 2021), Delta (May–October 2021), and Omicron (January–June 2022). Data from the SingHealth COVID-19 registry, covering patients from 23 January 2020 to 10 June 2022, were used to calculate incubation periods during the three time periods. The study found median incubation periods of 11 days for Wuhan-Hu-1, 3 days for Delta, and 3 days for Omicron (p-value: <0.001). This study highlighted the impact of different containment measures and the importance of robust EMR systems for tracking and managing infectious diseases. Key challenges included accurate contact tracing and IT infrastructure capabilities. The findings support the use of EMR data for future infectious disease preparedness in Singapore. [ABSTRACT FROM AUTHOR]

Background: Use of electronic health records (EHR) to provide real-world data for research is established, but using EHR to deliver randomised controlled trials (RCTs) more efficiently is less developed. The Allergy AntiBiotics And Microbial resistAnce (ALABAMA) RCT evaluated a penicillin allergy assessment pathway versus usual clinical care in a UK primary care setting. The aim of this paper is to describe how EHRs were used to facilitate efficient delivery of a large-scale randomised trial of a complex intervention embracing efficient participant identification, supporting minimising GP workload, providing accurate post-intervention EHR updates of allergy status, and facilitating participant follow up and outcome data collection. The generalisability of the EHR approach and health economic implications of EHR in clinical trials will be reported in the main ALABAMA trial cost-effectiveness analysis. Methods: A descriptive account of the adaptation of functionality within SystmOne used to deliver/facilitate multiple trial processes from participant identification to outcome data collection. Results: An ALABAMA organisation group within SystmOne was established which allowed sharing of trial functions/materials developed centrally by the research team. The 'ALABAMA unit' within SystmOne was also created and provided a secure efficient environment to access participants' EHR data. Processes of referring consented participants, allocating them to a trial arm, and assigning specific functions to the intervention arm were developed by adapting tools such as templates, reports, and protocols which were already available in SystmOne as well as pathways to facilitate allergy de-labelling processes and data retrieval for trial outcome analysis. Conclusions: ALABAMA is one of the first RCTs to utilise SystmOne EHR functionality and data across the RCT delivery, demonstrating feasibility and applicability to other primary care RCTs. Trial registration: ClinicalTrials.gov: NCT04108637, registered 05/03/2019. ISRCTN: ISRCTN20579216. [ABSTRACT FROM AUTHOR]

Purpose Real-time prescription benefits (RTPB) shows prescribers patient-, medication-, and pharmacy-specific information on medication pricing, prior authorization requirements, and lower-cost alternatives. RTPB is intended to improve patient satisfaction and prescription fill rates by decreasing out-of-pocket costs for prescriptions. Therefore, we evaluated how RTPB affects prescribing patterns by examining acceptance and subsequent fill rates for RTPB alternative suggestions. Methods RTPB was implemented in February 2022 using external vendor interfaces. Prescribing data from March 2022 to March 2023 were analyzed. RTPB displayed alerts for medications requiring prior authorization or when alternative medications would result in cost savings. Patients were included if their prescription received an RTPB response and they had a subsequent encounter with pharmacy fill data. Primary outcomes were alert acceptance rates and prescription fill rates across RTPB alert groups, with a secondary outcome of monthly copay savings for accepted alerts. Results RTPB requests received a response for 88% of prescriptions, with price estimates provided for 77.9% of them. Lower-cost alternatives accounted for 67.2% of alerts, while prior authorization requirements represented 15% of alerts. Prescribers selected a lower-cost alternative 32% of the time. For those with an RTPB alert, patients filled prescriptions 68% of the time when an alternative was chosen, compared to 59% of the time when the original prescription was retained (odds ratio, 1.5; 95% confidence interval, 1.5-1.6; P < 0.001). Patients saved an average of $27.77 per month on copay costs when alternatives were selected. Conclusion Implementation of RTPB was found to result in significant improvements in prescription fill rates and decrease patient copay costs, despite low alert acceptance rates. [ABSTRACT FROM AUTHOR]

The integration of genetic information (GI) into the electronic health record (EHR) seems inevitable as the mainstreaming of genomics continues. Such newly provided accessibility to GI could be beneficial for improving health care, as well as for supporting clinical decision‐making and health management. Notwithstanding these promising benefits, the automatic integration of GI into the EHR, allowing unrestricted access to one's GI through patient portals, carries various knowledge‐related risks for patients. This article is focused on the potential case of inadvertently revealing misattributed parentage through such practice. The article aims to identify key clinical and ethical implications of such revelation for adult patients. Clinical implications include, for example, altering the physician‐patient interaction and the need to enhance physician's genetic literacy to improve genetic‐information‐specific communication skills. Ethical implications yield arguments supporting disclosure of MP, such as autonomy, individuals' right to know medical information pertaining to them, and the right to know one's genetic origins. Arguments opposing disclosure of MP centre on the right not to know GI and concerns for post‐disclosure family relationships. Following the clinical and ethical analyses of these respective implications, we consider how such integration of GI into the EHR ought to be carried out, ethically. We therefore suggest a solution, featuring an autonomy‐based approach, built around EHR users' right not to know. Our solution of nuanced consent options (including a 'genetic ignorance option') is designed to enable patients' informed exposure to GI through the EHR, allowing them some control over their self‐ and familial narrative. [ABSTRACT FROM AUTHOR]

The use of narrative interventions in health care is an effective way to communicate connection between patients and clinicians. The electronic health record (EHR) is a primary mode of communicating patient information across clinical teams. Thus, incorporating a person-centered cocreated narrative with patients into the EHR is an opportunity to share a person's cultural values, beliefs, and preferences; provide connection; and foster positive patient-clinician interactions. This study of a person-centered narrative intervention was a randomized controlled trial to test intervention effects on the person's (patient) perceptions of the quality of communication with their nurses and their psychosocial and existential well-being. This article describes the clinical team's experiences and reach of the cocreated person-centered narrative integration into the patient's EHR. The data collected included (1) exit interviews (n = 14), (2) a usability survey (n = 8), and (3) data collected from the EHR for clinicians (n = 600) who accessed the uploaded narratives. Overall, the System Usability Scale and nurse participant's experiences provided confirmation that the person-centered narrative intervention was usable. There were also interprofessional groups of clinicians in the health care system who accessed the narratives. Future research should continue to identify core components and implementation strategies of EHR-integrated person-centered narratives in complex health care settings. [ABSTRACT FROM AUTHOR]

Objectives Physician burnout in the US has reached crisis levels, with one source identified as extensive after-hours documentation work in the electronic health record (EHR). Evidence has illustrated that physician preferences for after-hours work vary, such that after-hours work may not be universally burdensome. Our objectives were to analyze variation in preferences for after-hours documentation and assess if preferences mediate the relationship between after-hours documentation time and burnout. Materials and Methods We combined EHR active use data capturing physicians' hourly documentation work with survey data capturing documentation preferences and burnout. Our sample included 318 ambulatory physicians at MedStar Health. We conducted a mediation analysis to estimate if and how preferences mediated the relationship between after-hours documentation time and burnout. Our primary outcome was physician-reported burnout. We measured preferences for after-hours documentation work via a novel survey instrument (Burden Scenarios Assessment). We measured after-hours documentation time in the EHR as the total active time respondents spent documenting between 7 pm and 3 am. Results Physician preferences varied, with completing clinical documentation after clinic hours while at home the scenario rated most burdensome (52.8% of physicians), followed by dealing with prior authorization (49.5% of physicians). In mediation analyses, preferences partially mediated the relationship between after-hours documentation time and burnout. Discussion Physician preferences regarding EHR-based work play an important role in the relationship between after-hours documentation time and burnout. Conclusion Studies of EHR work and burnout should incorporate preferences, and operational leaders should assess preferences to better target interventions aimed at EHR-based contributors to burnout. [ABSTRACT FROM AUTHOR]

Objectives Outpatient rehabilitation (rehab) physical, occupational, and speech therapists use electronic health records (EHR), yet their documentation experiences, including any documentation burden, are not well researched. Therapists are a growing portion of the U.S. healthcare workforce, whose need is critical to the health of an aging population. We aimed to describe outpatient rehab therapists' documentation experiences and identify strategies for mitigating any documentation burden. Materials and Methods We used qualitative descriptive methodology to conduct 4 focus groups with outpatient rehab therapists at Hospital for Special Surgery, a multi-site orthopedic institution. Transcripts were inductively coded to identify themes and actionable strategies for improving the therapists' documentation experiences. Therapists provided feedback and prioritization of proposed strategies. Results A total of 13 therapists were interviewed. Five themes and 10 subthemes characterize the therapists' documentation experience by a feeling that documentation inhibits clinical care and work/life balance, a perceived lack of support and efficiencies, the desire to document to communicate clinical care, and a design vision for improving the EHR. Top prioritized strategies for improvement included use of timesaving templates, expanding dictation, decluttering the EHR interface, and support for free texting over discrete data capture. Discussion Outpatient rehab therapists experience documentation burden similar to that documented of physicians and nurses. Manual data entry imposes burden on therapists' time and clinical care. Conclusion A multi-faceted approach is needed for improving therapists' experiences including EHR redesign, technology supporting dictation and narrative to discrete data capture, and support from leadership and regulators. [ABSTRACT FROM AUTHOR]

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system’s reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient’s reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Abstract Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010–2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early.

Abstract Background With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare. Objective We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, of the participants in Mayo Clinic Study of Aging (MCSA) from initial mild cognitive impairment (MCI) diagnosis to dementia onset through network analysis. Methods We used the data from the MCSA, a prospective population-based cohort study of cognitive aging, and examined the changing association among variables (i.e., participants’ health conditions) from the first visit of MCI diagnosis to the visit of dementia onset using network analysis. The number of participants for this study are 97 with the number of visits ranging from 2 visits (30 months) to 7 visits (105 months). We identified the network communities among variables from three-fold collection of instances: (i) the first MCI diagnosis, (ii) progression to dementia, and (iii) dementia diagnosis. We determine the variables that play a significant role in the dementia onset, aiming to identify and prioritize specific variables that prominently contribute towards developing dementia. In addition, we explore the sex-specific impact of variables in relation to dementia, aiming to investigate potential differences in the influence of certain variables on dementia onset between males and females. Results We found correlation among certain variables, such as neuropsychiatric symptoms and chronic conditions, throughout the progression from MCI to dementia. Our findings, based on patterns and changing variables within specific communities, reveal notable insights about the time-lapse before dementia sets in, and the significance of progression of correlated variables contributing towards dementia onset. We also observed more changes due to certain variables, such as cognitive and functional scores, in the network communities for the people who progressed to dementia compared to those who does not. Most changes for sex-specific analysis are observed in clinical dementia rating and functional activities questionnaire during MCI onset are followed by chronic diseases, and then by NPI-Q scores. Conclusions Network analysis has shown promising potential to capture significant longitudinal changes in health conditions, spanning from the MCI diagnosis to dementia progression. It can serve as a valuable analytic approach for monitoring the health status of individuals in cognitive impairment assessment. Furthermore, our findings indicate a notable sex difference in the impact of specific health conditions on the progression of dementia.

Abstract Purpose Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. Methods We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs’ performance. Results The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. Conclusion The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes.

Abstract Background In Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system. Method The proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee. Results The MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction. Conclusion The study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process.