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INTRODUCTION: Niraparib first-line maintenance (1LM) therapy has demonstrated clinical benefit for patients with ovarian cancer (OC) in clinical trial and real-world settings, but data on factors associated with real-world patient outcomes remain limited. This analysis identified patient characteristics associated with time to next treatment (TTNT), a proxy for real-world progression-free survival, in patients with OC treated with 1LM niraparib monotherapy. METHODS: This retrospective observational study used a USA nationwide electronic health record-derived deidentified database and included adult patients diagnosed with OC who initiated 1LM niraparib monotherapy after first-line platinum-based chemotherapy. Patients were followed until the earliest occurrence of last clinical activity, death, or end of study period. TTNT was measured from 1LM niraparib initiation to the start of second-line treatment or death. Cox proportional hazards models assessed univariable and multivariable associations between baseline characteristics and TTNT. RESULTS: Of 7872 patients diagnosed with OC, 526 met the eligibility criteria and were included in this analysis. Median (IQR) duration of follow-up was 14.1 (7.4-23.6) months. In univariable analyses, age, BRCA/homologous recombination deficiency (HRD) status, socioeconomic status, stage at initial diagnosis, cytoreductive surgery type, and residual disease status were significantly associated with observed TTNT and were introduced into the multivariable model with other clinically relevant variables. In the multivariable analysis, BRCA/HRD status, cytoreductive surgery type, and residual disease status were significantly associated with observed TTNT after covariate adjustment. Conversely, age, Eastern Cooperative Oncology Group performance status, disease stage, niraparib starting dose status, and first-line bevacizumab use were not associated with observed TTNT. CONCLUSION: This real-world, retrospective, observational analysis offers valuable insights on prognostic factors associated with TTNT in patients with OC treated with 1LM niraparib monotherapy after first-line platinum-based chemotherapy. Future studies are needed to examine how additional patient characteristics associated with clinical outcomes may guide treatment decisions and improve outcomes.

BackgroundOne challenge for primary care providers caring for patients with nonalcoholic fatty liver disease is to identify those at the highest risk for clinically significant liver disease.AimTo derive a risk stratification tool using variables from structured electronic health record (EHR) data for use in populations which are disproportionately affected with obesity and diabetes.MethodsWe used data from 344 participants who underwent Fibroscan examination to measure liver fat and liver stiffness measurement [LSM]. Using two approaches, multivariable logistic regression and random forest classification, we assessed risk factors for any hepatic fibrosis (LSM > 7 kPa) and significant hepatic fibrosis (> 8 kPa). Possible predictors included data from the EHR for age, gender, diabetes, hypertension, FIB-4, body mass index (BMI), LDL, HDL, and triglycerides.ResultsOf 344 patients (56.4% women), 34 had any hepatic fibrosis, and 15 significant hepatic fibrosis. Three variables (BMI, FIB-4, diabetes) were identified from both approaches. When we used variable cut-offs defined by Youden's index, the final model predicting any hepatic fibrosis had an AUC of 0.75 (95% CI 0.67-0.84), NPV of 91.5% and PPV of 40.0%. The final model with variable categories based on standard clinical thresholds (i.e., BMI ≥ 30 kg/m2; FIB-4 ≥ 1.45) had lower discriminatory ability (AUC 0.65), but higher PPV (50.0%) and similar NPV (91.3%). We observed similar findings for predicting significant hepatic fibrosis.ConclusionsOur results demonstrate that standard thresholds for clinical risk factors/biomarkers may need to be modified for greater discriminatory ability among populations with high prevalence of obesity and diabetes.

OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the datasets imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

PurposeUse of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR.DesignLiterature review and quantitative analysis.SubjectsPublished manuscripts.MethodsFour graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions.Main outcome measuresNumber of studies included and numeric counts of billing codes used to define codified cohorts.ResultsIn total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts.ConclusionsSubstantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

OBJECTIVE: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. MATERIALS AND METHODS: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedmans test and critical difference diagrams. RESULTS: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. CONCLUSION: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases. RESEARCH QUESTION: Can we identify implicit bias in clinical notes, and are biases stable across time and geography? STUDY DESIGN AND METHODS: To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence). RESULTS: In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors. INTERPRETATION: Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

BACKGROUND: Physician communication failures during transfers of patients from the intensive care unit (ICU) to the general ward are common and can lead to adverse events. Efforts to improve written handoffs during these transfers are increasingly prominent, but no instruments have been developed to assess the quality of physician ICU-ward transfer notes. OBJECTIVE: To collect validity evidence for the modified nine-item Physician Documentation Quality Instrument (mPDQI-9) for assessing ICU-ward transfer note usefulness across several hospitals. METHODS: Twenty-four physician raters independently used the mPDQI-9 to grade 12 notes collected from three academic hospitals. A priori, we excluded the up-to-date and accurate domains, because these could not be assessed without giving the rater access to the complete patient chart. Assessments therefore used the domains thorough, useful, organized, comprehensible, succinct, synthesized, and consistent. Raters scored each domain on a Likert scale ranging from 1 (low) to 5 (high). The total mPDQI-9 was the sum of these domain scores. The primary outcome was the raters perceived clinical utility of the notes, and the primary measures of interest were criterion validity (Spearmans ρ) and interrater reliability (intraclass correlation [ICC]). RESULTS: Mean mPDQI-9 scores by note ranged from 19 (SD = 5.5) to 30 (SD = 4.2). Mean note ratings did not systematically differ by rater expertise (for interaction, P = 0.15). The proportion of raters perceiving each note as independently sufficient for patient care (the primary outcome) ranged from 33% to 100% across the set of notes. We found a moderately positive correlation between mPDQI-9 ratings and raters overall assessments of each notes clinical utility (ρ = 0.48, P

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

BackgroundIschemic cardiomyopathy is the leading cause of heart failure (HF). Most patients do not undergo coronary assessment after HF diagnosis. There are no randomized clinical trials of coronary assessment after HF diagnosis.MethodsUsing an electronic health record cohort of all individuals with HF within the San Francisco Health Network from 2001 to 2019, we identified factors associated with coronary assessment. Then, we studied the association of coronary assessment within 30 days of HF diagnosis with all-cause mortality and a composite of mortality and emergent angiography using a target trial emulation observational comparative-effectiveness approach. Target trial emulation is an approach to causal inference based on creating a hypothetical randomized clinical trial protocol and using observational data to emulate the protocol. We used propensity scores for covariate adjustment. We used national death records to improve the ascertainment of mortality and included falsification end points for the cause of death.ResultsAmong 14 829 individuals with HF (median, 62 years old; 5855 [40%] women), 3987 (26.9%) ever completed coronary assessment, with 2467/13 301 (18.5%) with unknown coronary artery disease status at HF diagnosis assessed. Women, older individuals, and people without stable housing were less likely to complete coronary assessment. Among 5972 eligible persons of whom 627 underwent early elective coronary assessment, coronary assessment was associated with lower mortality (hazard ratio, 0.84 [95% CI, 0.72-0.97]; P=0.025), reduced risk of the composite outcome (hazard ratio, 0.86 [95% CI, 0.73-1.00]), higher rates of revascularization (odds ratio, 7.6 [95% CI, 5.4-10.6]), and higher use of medical therapy (odds ratio, 2.5 [95% CI, 1.7-3.6]), but not the falsification end points.ConclusionsIn a safety-net population, disparities in coronary assessment after HF diagnosis are not fully explained by coronary artery disease risk factors. Early coronary assessment is associated with improved HF outcomes possibly related to higher rates of revascularization and guideline-directed medical therapy but with low certainty that this finding is not attributable to unmeasured confounding.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

BACKGROUND: Improving shared decision-making using a treat-to-target approach, including the use of clinical outcome measures, is important to providing high quality care for rheumatoid arthritis (RA). We developed an Electronic Health Record (EHR) integrated, patient-facing sidecar dashboard application that displays RA outcomes, medications, and lab results for use during clinical visits (RA PRO dashboard). The purpose of this study was to assess clinician perceptions and experiences using the dashboard in a university rheumatology clinic. METHODS: We conducted focus group (FG) discussions with clinicians who had access to the dashboard as part of a randomized, stepped-wedge pragmatic trial. FGs explored clinician perceptions towards the usability, acceptability, and usefulness of the dashboard. FG data were analyzed thematically using deductive and inductive techniques; generated themes were categorized into the domains of the Technology Acceptance Model (TAM). RESULTS: 3 FG discussions were conducted with a total of 13 clinicians. Overall, clinicians were enthusiastic about the dashboard and expressed the usefulness of visualizing RA outcome trajectories in a graphical format for motivating patients, enhancing patient understanding of their RA outcomes, and improving communication about medications. Major themes that emerged from the FG analysis as barriers to using the dashboard included inconsistent collection of RA outcomes leading to sparse data in the dashboard and concerns about explaining RA outcomes, especially to patients with fibromyalgia. Other challenges included time constraints and technical difficulties refreshing the dashboard to display real-time data. Methods for integrating the dashboard into the visit varied: some clinicians used the dashboard at the beginning of the visit as they documented RA outcomes; others used it at the end to justify changes to therapy; and a few shared it only with stable patients. CONCLUSIONS: The study provides valuable insights into clinicians perceptions and experiences with the RA PRO dashboard. The dashboard showed promise in enhancing patient-clinician communication, shared decision-making, and overall acceptance among clinicians. Addressing challenges related to data collection, education, and tailoring dashboard use to specific patient populations will be crucial for maximizing its potential impact on RA care. Further research and ongoing improvements in dashboard design and implementation are warranted to ensure its successful integration into routine clinical practice.

ObjectiveBlockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics.Materials and methodsThis systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized.ResultsFollowing screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility.DiscussionEthereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other.ConclusionWhile blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.

ObjectiveAccurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes.MethodsTraining and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed ('LN-Code' included LN ICD codes and 'LN-No Code' did not), calibrated and validated using standard performance metrics.ResultsA total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43-73%) but high specificity (92-97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups.ConclusionsThis study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability.

ImportanceIncreasing influenza vaccination rates is a public health priority. One method recommended by the US Centers for Disease Control and Prevention and others is for health systems to send reminders nudging patients to be vaccinated.ObjectiveTo evaluate and compare the effect of electronic health record (EHR)-based patient portal reminders vs text message reminders on influenza vaccination rates across a health system.Design, setting, and participantsThis 3-arm randomized clinical trial was conducted from September 7, 2022, to April 30, 2023, among primary care patients within the University of California, Los Angeles (UCLA) health system.InterventionsArm 1 received standard of care. The health system sent monthly reminder messages to patients due for an influenza vaccine by portal (arm 2) or text (arm 3). Arm 2 had a 2 × 2 nested design, with fixed vs responsive monthly reminders and preappointment vs no preappointment reminders. Arm 3 had 1 × 2 design, with preappointment vs no preappointment reminders. Preappointment reminders for eligible patients were sent 24 and 48 hours before scheduled primary care visits. Fixed reminders (in October, November, and December) involved identical messages via portal or text. Responsive portal reminders involved a September message asking patients about their plans for vaccination, with a follow-up reminder if the response was affirmative but the patient was not yet vaccinated.Main outcomes and measuresThe primary outcome was influenza vaccination by April 30, 2023, obtained from the UCLA EHR, including vaccination from pharmacies and other sources.ResultsA total of 262 085 patients (mean [SD] age, 45.1 [20.7] years; 237 404 [90.6%] adults; 24 681 [9.4%] children; 149 349 [57.0%] women) in 79 primary care practices were included (87 257 in arm 1, 87 478 in arm 2, and 87 350 in arm 3). At the entire primary care population level, none of the interventions improved influenza vaccination rates. All groups had rates of approximately 47%. There was no statistical or clinically significant improvement following portal vs text, preappointment reminders vs no preappointment reminders (portal and text reminders combined), or responsive vs fixed monthly portal reminders.Conclusions and relevanceAt the population level, neither portal nor text reminders for influenza vaccination were effective. Given that vaccine hesitancy may be a major reason for the lack of impact of portal or text reminders, more intensive interventions by health systems are needed to raise influenza vaccination coverage levels.Trial registrationClinicalTrials.gov Identifier: NCT05525494.

Three years after the start of the SARS-CoV-2 virus (COVID-19) pandemic, its effects continue to affect society and COVID-19 vaccination campaigns continue to be a topic of controversy and inconsistent practice. After experiencing spikes in COVID-19 cases, our University of California Davis Health Division of Hospital Medicine sought to understand the reasons underlying the low COVID-19 vaccination rates in our county and find approaches to improve the number of vaccinations among adults admitted to the inpatient setting. This quality improvement project aimed to increase COVID-19 primary and booster vaccine efforts through a multi-pronged approach of increased collaboration with specialised staff and optimisation of use of our electronic health record system.Our key interventions focused on developing a visual reminder of COVID-19 vaccine status using the functionality of our electronic medical record (EMR), standardising documentation of COVID-19 vaccine status and enhancing team-based vaccination discussions through team huddles and partnering with inpatient care coordinators. While our grassroots approach enhanced COVID-19 vaccination rates in the inpatient setting and had additional benefits such as increased collaboration among teams, system-level efforts often made a greater impact at our healthcare centre. For other institutions interested in increasing COVID-19 vaccination rates, our top three recommendations include integrating vaccination into pre-existing workflows, optimising EMR functionality and increasing vaccine accessibility in the inpatient setting.

BackgroundElectronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.MethodsA retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses.ResultsSome LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations.ConclusionFurther work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

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: Recent clinical guidelines for sepsis management emphasize immediate antibiotic initiation for suspected septic shock. Though hypotension is a high-risk marker of sepsis severity, prior studies have not considered the precise timing of hypotension in relation to antibiotic initiation and how clinical characteristics and outcomes may differ. Our objective was to evaluate antibiotic initiation in relation to hypotension to characterize differences in sepsis presentation and outcomes in patients with suspected septic shock. METHODS: Adults presenting to the emergency department (ED) June 2012-December 2018 diagnosed with sepsis (Sepsis-III electronic health record [EHR] criteria) and hypotension (non-resolving for ≥30 min, systolic blood pressure 60 min after (late) treatment. RESULTS: Among 2219 patients, 55% received early treatment, 13% immediate, and 32% late. The late subgroup often presented to the ED with hypotension (median 0 min) but received antibiotics a median of 191 min post-ED presentation. Clinical characteristics notable for this subgroup included higher prevalence of heart failure and liver disease (p

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

OBJECTIVES: Although guidelines recommend antiviral therapy for outpatients with COVID-19 who are at high risk of progressing to severe conditions, such as older adults, many patients do not receive appropriate treatment. Little is known, however, about the physician factors associated with the prescription of guideline-recommended antiviral therapy for patients with COVID-19. DESIGN: A cross-sectional study. SETTING: Data including outpatient visits in primary care clinics in Japan from April to August 2023. PARTICIPANTS: We analysed 30 953 outpatients aged ≥65 years treated with COVID-19 (mean (SD) age, 75.0 (7.6) years; 17 652 women (57.0%)) in 1394 primary care clinics. OUTCOME MEASURES: The primary outcome was the prescription of guideline-recommended antivirals (ie, nirmatrelvir-ritonavir or molnupiravir), adjusted for patient characteristics, months of visits and regions. RESULTS: Antiviral prescriptions were concentrated among a small proportion of physicians; for example, the top 10% of physicians that had the largest number of nirmatrelvir-ritonavir prescriptions accounted for 92.4% of all nirmatrelvir-ritonavir prescriptions. After adjusting for potential confounders, physicians with higher patient volumes were more likely to prescribe guideline-recommended antivirals to their patients (adjusted OR (aOR) for high vs low volume, 1.76; 95% CI 1.31 to 2.38; adjusted p

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Advance care planning (ACP) has been recognized as crucial by patients, families, and clinicians; however, different definitions and measurements have led to inconsistencies in practice and mixed evidence in the literature. This narrative review explores ACPs evolution, innovations, and outcomes using thematic analysis to synthesize data from randomized controlled trials, reviews, and editorials. Key findings include (1) ACP has evolved over the past several decades from a sole focus on code status and advance directive (AD) forms to a continuum of care planning over the life course focused on tailored preparation for patients and surrogate decision-makers and (2) ACP measurement has evolved from traditional outcome metrics, such as AD completion, to a comprehensive outcomes framework that includes behavior change theory, systems, implementation science, and a focus on surrogate outcomes. Since the recent development of an ACP consensus definition and outcomes framework, high-quality trials have reported mainly positive outcomes for interventions, especially for surrogates, which aligns with the patient desire to relieve decision-making burden for loved ones. Additionally, measurement of clinically meaningful ACP information, including documented goals of care discussions, is increasingly being integrated into electronic health records (EHR), and emerging, real-time assessments and natural language processing are enhancing ACP evaluation. To make things easier for patients, families, and care teams, clinicians and researchers can use and disseminate these evolved definitions; provide patients validated, easy-to-use tools that prime patients for conversations and decrease health disparities; use easy-to-access clinician training and simple scripts for interdisciplinary team members; and document patients values and preferences in the medical record to capture clinically meaningful ACP so this information is available at the point of care. Future efforts should focus on efficient implementation, expanded reimbursement options, and seamless integration of EHR documentation to ensure ACPs continued evolution to better serve patients and their care partners.

OBJECTIVE: Map multimorbidity-weighted index (MWI) conditions to International Classification of Diseases, 10th Revision (ICD-10), expand the conditions and codes to develop a new ICD-10-coded MWI (MWI-ICD10) and updated MWI-ICD9, and assess their consistency. DESIGN: Population-based retrospective cohort. SETTING: Large medical centre between 2013 and 2017. PARTICIPANTS: Adults ≥18 years old with encounters in each of 4 years (2013, 2014, 2016, 2017). MAIN OUTCOME MEASURES: MWI conditions mapped to ICD-10 codes, and additional conditions and codes added to produce a new MWI-ICD10 and updated MWI-ICD9. We compared the prevalence of ICD-coded MWI conditions within the ICD-9 era (2013-2014), within the ICD-10 era (2016-2017) and across the ICD-9-ICD-10 transition in 2015 (washout period) among adults present in both sets of comparison years. We computed the prevalence and change in prevalence of conditions when using MWI-ICD10 versus MWI-ICD9. RESULTS: 88 175 adults met inclusion criteria. Participants were 60.8% female, 50.5% white, with mean age 54.7±17.3 years and baseline MWI-ICD9 4.47±6.02 (range 0-64.33). Of 94 conditions, 65 had

BACKGROUND: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. METHODS: This was a retrospective cohort study of patients access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. RESULTS: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p

ObjectiveExisting research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.Materials and methodsWe retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.ResultsWord frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.DiscussionOur findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.ConclusionSocial work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

ImportanceProcedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety.ObjectiveTo examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation.Design, setting, and participantsThis quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023.ExposuresAutomated system for procedure data extraction and upload to a residency management software application.Main outcomes and measuresThe number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload.ResultsForty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%.Conclusions and relevanceIn this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface-based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.

ObjectivesThe number of surgeries performed in the United States has increased over the past two decades, with a shift to the ambulatory setting. Perioperative complications and mortality pose significant health care burdens. Inadequate preoperative assessment and documentation contribute to communication failure and poor patient outcomes. The aim of this quality improvement project was to design and implement a preoperative evaluation documentation template that not only improved communication during the perioperative pathway but also enhanced the overall user experience.MethodsWe implemented a revamped evidence-based documentation template in the electronic medical records of a health care organization across three internal medicine clinics on the downtown campus and seven satellite family medicine clinics. A pre- and postintervention design was used to assess the template utilization rate and clinician satisfaction.ResultsThe preoperative template utilization rate increased from 51.2% at baseline to 66.5% after the revamped template "went live" (p

BackgroundElectronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx.MethodsWe conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts.ResultsWorkshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists).ConclusionsThis is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.

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.

The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR datas power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting. [ABSTRACT FROM AUTHOR]

Intoduction: Common outcome measures for chronic neck pain are the Patient‐Specific Functional Scale (PSFS) and the neck disability index (NDI). The primary aim was to categorize the top‐rated, patient‐selected functional activity limitations of the PSFS to determine if there were consistent limited functional activities for individuals with chronic neck pain and how these compared to the constructs of activities on the NDI. The secondary aim was to determine the relationship between scores for individuals who completed both the NDI and PSFS. Design: A retrospective review of data extracted from the electronic medical record, EPIC, within two hospital‐based outpatient physical therapy clinics within a health care system. Methods: Retrospective analysis was performed on individual's characteristics, self‐selected functional activity limitations, and total scores of the PSFS and NDI. Most common categories of self‐selected functional activity limitations were developed by practicing physical therapists. These functional activity limitation categories of the PSFS were compared to the activities of the NDI. Mean PSFS total scores were correlated with the NDI total scores with Spearman's test. Results: Participants were individuals with chronic neck pain from January 2013–September 2018 (n = 2283). Movement‐based activities accounted for 60.8% of the functional activity limitations of the PSFS with the top functional activity limitations being cervical motion and exercise (32%). The PSFS total score moderately correlated with NDI (r = −0.50, p = <0.01) which may relate to the differences in constructs of the NDI and the top patient‐selected PSFS functional activity limitations found in this analysis. Conclusion: The results suggest that individuals with chronic neck pain present with similar categories of self‐selected functional activity limitations that differ from activities of the NDI. Additional research is needed to improve outcome measures to capture patient‐selected functional activity limitations and an individual's pain experience. [ABSTRACT FROM AUTHOR]

Background: Methemoglobinemia requires early identification and treatment, but limited knowledge exists regarding the current therapeutic approach taken by clinicians as well as the outcomes that occur in children. Objectives: To determine the current prevalence of this rare disease in the pediatric population, evaluate the impact of methemoglobin and functional hemoglobin levels, and assess how this disease is approached by clinicians. We hypothesize that methemoglobinemia prevalence is low and more methylene blue use would be observed in subjects with functional hemoglobin levels less than 7 g/dL. Design: This was a retrospective observational cohort study utilizing deidentified TriNetX® electronic health record (EHR) data. Methods: Using a multicenter EHR database, we evaluated subjective characteristics, diagnostic, laboratory results, medication, and procedural codes. Results: Ninety-eight children (mean age 5.3 ± 5.3 years) from 53 healthcare organizations were included. Methemoglobinemia prevalence was 0.0015% with an overall 30-day mortality of 6.1%. Subjects with methemoglobin percentages greater than 20% had a higher frequency of methylene blue administration (70.6% versus 24.7%, P =.0005). Critical care service requirements and methylene blue administration were similar in the subjects with functional hemoglobin less than 7 g/dL and more than 7 g/dL groups. Overall, 13 (13.2%) subjects underwent glucose-6-phosphate dehydrogenase deficiency (G6PD) testing. Conclusion: In our study, we found methemoglobinemia prevalence in children is low, there is a low frequency of G6PD testing despite methylene blue hemolysis risk, and subjects appeared to be treated similarly despite a low functional hemoglobin. These findings highlight the continued critical nature of this disease and may highlight opportunities for education aimed at improving care in children diagnosed with methemoglobinemia, particularly related to G6PD testing. [ABSTRACT FROM AUTHOR]

Background: Crohn's disease (CD) exhibits variability in colorectal cancer (CRC) incidence and prognostic factors due to diverse clinical and behavioral characteristics, presenting inconsistencies between Western and Eastern patients. Objectives: This study compared clinical characteristics between CD patients with CRC from the US and Korean tertiary referral centers and defined the prognostic factors related to mortality. Design: Retrospective study. Methods: We reviewed the electronic medical records of 236 adult CD patients with colorectal adenocarcinoma evaluated at Mayo Clinic Rochester, Florida, or Arizona (N = 200) and Asan Medical Center in Korea (N = 36) between January 1989 and August 2022. Results: Asan patients had a younger age, shorter CD duration, more colonic involvement (L2 plus L3), penetrating behavior, perianal fistula, and shorter biological treatment duration before CRC diagnosis than Mayo patients. Furthermore, despite significant differences in body mass index, smoking status, primary sclerosing cholangitis, immunomodulators, CRC diagnosis period, clinical presentation, CRC location, surgery, and some histopathological details between the two groups, overall survival was not statistically different (p value, 0.29, log-rank test). Advanced age (adjusted hazard ratio (aHR), 1.03 per year; 95% confidence interval (CI), 1.01–1.04; p value, <0.01), unresectable CRC (aHR, 5.02; 95% CI, 2.49–10.12; p value, <0.01), and advanced CRC stage (aHR, 1.45 per stage; 95% CI, 1.07–1.97; p value, 0.02) were significantly associated with increased risk of death. CD remission at CRC diagnosis (aHR, 0.26; 95% CI, 0.08–0.91; p value, 0.04), CRC diagnosis period of 2011–2022 (aHR relative to 1989–2000, 0.46; 95% CI, 0.25–0.87; p value, 0.02), and CRC diagnosis by surveillance (aHR, 0.56; 95% CI, 0.32–0.98; p value, 0.04) were significantly associated with decreased risk of death. Conclusion: Notably, some clinical features of CD with CRC differed between Asan and Mayo patients; however, overall survival was not different. CD remission, CRC surveillance, and more recent diagnosis of CRC were associated with a reduced risk of death. [ABSTRACT FROM AUTHOR]

Background: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. Methods: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. Results: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. Conclusions: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems. [ABSTRACT FROM AUTHOR]

Background: In recent years, eHealth has received much attention as an opportunity to increase efficiency within healthcare organizations. Adoption of eHealth might consequently help to solve perceived health workforce challenges, including labor shortages and increasing workloads among primary care professionals, who serve as the first point of contact for healthcare in many countries. The purpose of this systematic review was to investigate the impact of general eHealth use and specific eHealth services use on general practice workload in the pre-COVID-19 era. Methods: The databases of CINAHL, Cochrane, Embase, IEEE Xplore, Medline ALL, PsycINFO, Web of Science, and Google Scholar were searched, using combinations of keywords including 'eHealth', 'workload', and 'general practice'. Data extraction and quality assessment of the included studies were independently performed by at least two reviewers. Publications were included for the period 2010 – 2020, before the start of the COVID-19 pandemic. Results: In total, 208 studies describing the impact of eHealth services use on general practice workload were identified. We found that two eHealth services were mainly investigated within this context, namely electronic health records and digital communication services, and that the largest share of the included studies used a qualitative study design. Overall, a small majority of the studies found that eHealth led to an increase in general practice workload. However, results differed between the various types of eHealth services, as a large share of the studies also reported a reduction or no change in workload. Conclusions: The impact of eHealth services use on general practice workload is ambiguous. While a small majority of the effects indicated that eHealth increased workload in general practice, a large share of the effects also showed that eHealth use reduced workload or had no impact. These results do not imply a definitive conclusion, which underscores the need for further explanatory research. Various factors, including the study setting, system design, and the phase of implementation, may influence this impact and should be taken into account when general practices adopt new eHealth services. Study registration number: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020199897; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=199897. [ABSTRACT FROM AUTHOR]

Background: The Brazilian Ministry of Health has developed and provided the Citizen's Electronic Health Record (PEC e-SUS APS), a health information system freely available for utilization by all municipalities. Given the substantial financial investment being made to enhance the quality of health services in the country, it is crucial to understand how users evaluate this product. Consequently, this scoping review aims to map studies that have evaluated the PEC e-SUS APS. Methods: This scoping review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) framework, as well as by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist extension for scoping reviews (PRISMA-ScR). The research question was framed based on the "CoCoPop" mnemonic (Condition, Context, Population). The final question posed is, "How has the Citizen's Electronic Health Record (PEC e-SUS APS) been evaluated?" The search strategy will be executed across various databases (LILACS, PubMed/MEDLINE, Scopus, Web of Science, ACM Digital Library, and IEEE Digital Library), along with gray literature from ProQuest Dissertation and Theses Global and Google Scholar, with assistance from a professional healthcare librarian skilled in supporting systematic reviews. The database search will encompass the period from 2013 to 2024. Articles included will be selected by three independent reviewers in two stages, and the findings will undergo a descriptive analysis and synthesis following a "narrative review" approach. Independent reviewers will chart the data as outlined in the literature. Discussion: The implementation process for the PEC e-SUS APS can be influenced by the varying characteristics of the over 5500 Brazilian municipalities. These factors and other challenges encountered by health professionals and managers may prove pivotal for a municipality's adoption of the PEC e-SUS APS system. With the literature mapping to be obtained from this review, vital insights into how users have evaluated the PEC will be obtained. Systematic review registration: The protocol has been registered prospectively at the Open Science Framework platform under the number 10.17605/OSF.IO/NPKRU. [ABSTRACT FROM AUTHOR]

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance The incidence of late‐onset epilepsy (LOE) is rising, and these patients may use an excess of health care resources. This study aimed to measure pre‐/post‐diagnostic health care use (HCU) for patients with LOE compared to controls.This was an observational open cohort study covering years 1998–2019 using UK population‐based linked primary care (Clinical Practice Research Datalink [CPRD]) and hospital (HES) electronic health records. The participants included patients with incident LOE enrolled in CPRD and 1:10 age‐, sex‐, and general practice–matched controls. The exposure was incident LOE (diagnosed at age ≥65) using a 5‐year washout. The main outcome was all HCU (primary care [PC], accident and emergency [A&E], admitted patient and outpatient care) using inverse proportional weighting to PC use and HCU by setting. An interrupted time‐series analysis was used to examine pre‐/post‐diagnostic HCU between patients with LOE and controls over 4 years either side of diagnosis/matching date. An adjusted mixed‐effects negative binomial regression was used for post‐diagnosis HCU interactions.Of 2 569 874 people ≥65 years of age, 1048 (4%) developed incident LOE. Mean weighted total HCU increased by 32 visits per patient‐year (95% confidence interval [95% CI]: 13–50, p = .003) until LOE diagnosis, and then dropped by a mean of 60 visits per patient‐year (95% CI: −81 to −40). There was an acute rise and fall over the 1–2 years immediately pre‐/post‐diagnosis. Incident HCU remained higher for LOE compared to controls post‐diagnosis (adjusted incidence rate ratio: 1.72; 95% CI: 1.65–1.70; p < .001), including A&E, outpatient, and admitted care.Health care use demonstrates an acute on chronic rise over the 4 years before diagnosis of LOE. To what extent the partial reversal of the acute pre‐diagnosis rise, and the mediators of the accelerated increase compared to controls are attributed to epilepsy, comorbid and bidirectional disease states, or a combination of both warrants further exploration. [ABSTRACT FROM AUTHOR]

Objective: To analyze changes in renal function and associated risk factors in patients with bladder cancer undergoing robot-assisted radical cystectomy (RARC) with intracorporeal or extracorporeal urinary diversion (ICUD or ECUD). Methods: Clinical-pathological data was extracted from electronic medical records of 266 patients with bladder cancer who underwent RARC at our institution between August 2015 and August 2022. Postoperative renal function was assessed using the estimated glomerular filtration rate (eGFR). Result: Patients were classified into ECUD and ICUD groups based on the surgical approach. Significant differences in eGFR were observed between the two groups at 1, 2, and 3 years postoperatively. Moreover, 112 patients (42.1%) experienced long-term renal function injury. Independent risk factors for long-term renal function injury included the type of surgical approach, ureteroenteric anastomotic strictures, and pathological stage T3 or above. In terms of short-term renal function, 30 cases of acute kidney injury (AKI) were observed, with an incidence rate of 11.3%. No difference in AKI incidence was found between the groups. Conclusions: Postoperative AKI and chronic kidney injury are prevalent complications following RC. This study highlights that pathological stage, ureteroenteric anastomotic strictures, and ECUD significantly impact long-term renal function, but the type of urinary diversion (ileal conduit or orthotopic neobladder) had no effect on renal function, and ICUD was superior in terms of long-term renal injury rate. Therefore, precise preoperative assessment and the selection of appropriate surgical approach are crucial for preserving renal function in patients with bladder cancer. [ABSTRACT FROM AUTHOR]

Background: As the population ages, the occurrence of cognitive decline and dementia is continuously increasing. Frailty is a prevalent problem among older adults. Epidemiologic studies have shown a comorbidity between frailty and cognitive impairment. However, their relationship remains unclear. The frailty index is an important indicator for measuring frailty. This study aims to investigate the relationship between frailty index and cognitive dysfunction in older adults aged 60 years and older in the United States from the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Methods: Community-dwelling older adults aged 60 years or older from 2011 to 2014 were extracted from the NHANES database. The frailty index was calculated using the formula: frailty index = total number of deficits present/total number of deficits measured. The Animal Fluency (AF), the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's disease Delayed Recall (CERAD-DR), and Word Learning (CERADWL) were used to evaluate cognitive dysfunction. Firstly, weighted logistic regression analysis was used to explore the relationship between frailty index and cognitive dysfunction. Secondly, the influence of covariates on the frailty index was evaluated by subgroup analysis and interaction. Finally, the non-linear relationship is discussed by using the restricted cubic spline regression model. Results: Our study included a total of 2,574 patients, weighted logistic regression analysis, after adjusting for all covariates, showed that the frailty index was associated with every test score. The interaction showed that covariates had no significant effect on this association in AF. The association between the frailty index and AF in the restricted cubic spline regression model is non-linear. As the frailty index increased, the risk of AF reduction increased, suggesting a higher risk of cognitive dysfunction. Conclusion: In general, a high frailty index appears to be associated with an increased risk of cognitive dysfunction in the elderly. Consequently, protecting against cognitive decline necessitates making geriatric frailty prevention and treatment top priorities. [ABSTRACT FROM AUTHOR]

Background: Postoperative complications of pancreaticoduodenectomy (PD) are still a thorny problem. This study aims to verify the preventative impact of T-tube on them. Methods: The electronic medical records and follow-up data of patients who received pancreaticoduodenectomy in our center from July 2016 to June 2020 were reviewed. According to whether T tube was placed during the operation, the patients were divided into T-tube group and not-T-tube group. Propensity score matching analysis was performed to minimize selection bias. Results: A total of 330 patients underwent PD (Not-T-tube group =226, T-tube group=104). Propensity score matching resulted in 222 patients for further analysis (Not-T-tube group =134, T-tube group=88). Patients' demographics were comparable in the matched cohorts. Significantly higher incidences of clinically relevant postoperative pancreatic fistula (CR-POPF) ((14 (10.45%) VS 20 (22.73%)), P=0.013) were observed in the T-tube group. The total incidence of biliary anastomotic stricture (BAS) was 3.15%. The incidence was slightly lower in the T-tube group, but there was no statistically significant differentiation (6 (4.48%) VS 1 (1.14%), P=0.317). Conclusions: It is not feasible to prevent postoperative complications with the application of a T-tube in PD. [ABSTRACT FROM AUTHOR]

In certain cloud Electronic Medical Records (EMRs) applications, the data ownership may need to be transferred. In practice, not only the data but also the auditing ability should be transferred securely and efficiently. However, we investigate and find that most of the existing data ownership transfer protocols only work well between two individuals, and they become inefficient when dealing between two communities. The proposals for transferring tags between communities are problematic as well since, they require all members get involved or a fully trusted aggregator facilitates ownership transfer, which are unrealistic in certain scenarios. To alleviate these problems, in this paper we develop a secure auditing protocol with decentralized (t, n)-threshold ownership transfer for cloud EMRs. This protocol is designed to operate efficiently without requiring the mandatory participation of every user or the involvement of any trusted third-party. It is achieved by employing the threshold signature. Rigorous security analyses and comprehensive performance evaluations illustrate the security and practicality of our protocol. Specifically, according to the evaluations and comparisons, the communication and computational consumption is independent of the file size, i.e., it is constant in our protocol for both communities. [ABSTRACT FROM AUTHOR]

Background: The objectives of this study were to examine the association of psychiatric comorbidities and patient characteristics with treatment change and response as well as to assess the association between treatment change and healthcare resource utilization (HCRU) among adult patients with attention-deficit/hyperactivity disorder (ADHD) and psychiatric comorbidities. Methods: De-identified electronic health records from the NeuroBlu Database (2002–2021) were used to select patients ≥ 18 years with ADHD who were prescribed ADHD-specific medication. The index date was set as the first prescription of ADHD medication. The outcomes were treatment change (discontinuation, switch, add-on, or drop) and HCRU (inpatient, outpatient, composite) within 12 months of follow-up. Cox proportional-hazard model was used to assess the association between clinical and demographic patient characteristics and treatment change, while generalized linear model with negative binomial distribution and log link function was used to assess the association between key risk factors linked to treatment change and HCRU rates. Results: A total of 3,387 patients with ADHD were included (ADHD only: 1,261; ADHD + major depressive disorder (MDD): 755; ADHD + anxiety disorder: 467; ADHD + mood disorder: 164). Nearly half (44.8%) of the study cohort experienced a treatment change within the 12-month follow-up period. Treatment switch and add-on were more common in patients with ADHD and comorbid MDD and anxiety disorder (switch: 18.9%; add-on: 20.5%) compared to other cohorts (range for switch: 8.5–13.6%; range for add-on: 8.9–12.1%) Survival analysis demonstrated that the probability of treatment change within 12 months from treatment initiation in the study cohort was estimated to be 42.4%. Outpatient visit rates statistically significantly increased from baseline (mean [SD] 1.03 [1.84] visits/month) to 3 months post-index (mean [SD] 1.62 [1.91] visits/month; p < 0.001), followed by a gradual decline up to 12 months post-index. Being prescribed both a stimulant and a non-stimulant at index date was statistically significantly associated with increased risk of treatment change (adjusted hazard ratio: 1.64; 95% CI: 1.13, 2.38; p = 0.01). Conclusions: This real-world study found that treatment change was common among patients with ADHD and psychiatric comorbidities. These findings support the need for future studies to examine the unmet medical and treatment needs of this complex patient population. [ABSTRACT FROM AUTHOR]

Background: The high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs. Methods: We applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan. Results: With distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05). Conclusions: The immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis. [ABSTRACT FROM AUTHOR]

Background: Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals' perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction. Methods: In this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis. Results: Our findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes. Conclusion: The study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals' concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient's information need attention, to mitigate their frustrations and boost their confidence in the system. [ABSTRACT FROM AUTHOR]

Purpose: The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record's adoption process. Methods: Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type. Results: A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences. Conclusion: The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones. [ABSTRACT FROM AUTHOR]