Home environment factors associated with child BMI changes during COVID-19 pandemic.

BACKGROUND: The influence of home obesogenic environments, as assessed by the validated Family Nutrition and Physical Activity (FNPA) tool, and child obesity during the COVID pandemic were evaluated using electronic health records in this retrospective cohort study. METHODS: Historical data on BMI and the FNPA screening tool were obtained from annual well-child visits within the Geisinger Health System. The study examined youth ages 2-17 that had a BMI record and an FNPA assessment prior to the pandemic (BMI 3/1/19-2/29/20), 1 BMI record 3 months into the pandemic (6/1/20-12/31/20) and 1 BMI in the second year of the pandemic (1/1/21-12/31/21). Tertiles of obesity risk by FNPA score were examined. Mixed-effects linear regression was used to examine change in BMI slope (kg/m2 per month) pre-pandemic to pandemic using FNPA summary and subscales scores as predictors and adjusting for confounding factors. RESULTS: The analyses included 6,746 children (males: 51.7%, non-Hispanic white: 86.6%, overweight:14.8%, obesity:10.3%, severe obesity: 3.9%; mean(SD) age: 5.7(2.8) years). The rate of BMI change in BMI was greatest from early pandemic compared to pre-pandemic for children in lowest versus highest tertiles of FNPA summary score (0.079 vs. 0.044 kg/m2), FNPA-Eating (0.068 vs. 0.049 kg/m2), and FNPA-Activity (0.078 vs. 0.052 kg/m2). FNPA summary score was significantly associated with change in BMI from the pre-pandemic to early pandemic period (p = 0.014), but not associated with change in BMI during the later pandemic period. CONCLUSIONS: This study provides additional insight into the changes in the rate of BMI change observed among children and adolescents in the United States during the COVID-19 pandemic. The FNPA provides ample opportunity to continue our exploration of the negative impact of the COVID-19 pandemic on the longitudinal growth patterns among children and adolescents.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

BACKGROUND: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. OBJECTIVE: This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. METHODS: We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. RESULTS: The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. CONCLUSIONS: This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model.

BACKGROUND: The implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems. METHODS: A quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (ß), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05. RESULTS: The proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (ß = 0.215, p-value < 0.018), performance expectancy (ß = 0.245, p-value < 0.001), and attitude (ß = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention. CONCLUSION: The extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.

Development and integration of a clinical dashboard within a dental school setting.

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.

Impact of automated alerts on discharge opioid overprescribing after general surgery.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: The objectives of this study were to identify the most performed surgical procedures associated with the highest rates of discharge opioid overprescribing and to implement an electronic health record (EHR) alert to reduce discharge opioid overprescribing. METHODS: This quality improvement, before-and-after study included patients undergoing one of the identified target procedures-laparoscopic cholecystectomy, unilateral open inguinal hernia repair, and laparoscopic appendectomy-at an academic medical center. The alert notified providers when the prescribed opioid quantity exceeded guideline recommendations. The preimplementation cohort included surgical encounters from January 2020 to December 2021. The EHR alert was implemented in May 2022 following provider education via email and in-person presentations. The postimplementation cohort included surgical encounters from May to August 2022. The primary outcome was the proportion of patients with a discharge opioid supply exceeding guideline recommendations (overprescribing). RESULTS: A total of 1,478 patients were included in the preimplementation cohort, and 141 patients were included in the postimplementation cohort. The rate of discharge opioid overprescribing decreased from 48% in the preimplementation cohort to 3% in the postimplementation cohort, with an unadjusted absolute reduction of 45% (95% confidence interval, 41% to 49%; P < 0.001) and an adjusted odds ratio of 0.03 (95% confidence interval, 0.01 to 0.08; P < 0.001). Among patients who received opioids, the mean (SD) opioid supply at discharge decreased from 92 (43) oral morphine milligram equivalents (MME) (before implementation) to 57 (20) MME (after implementation) (P < 0.001). The proportion of patients who received additional opioid prescriptions within 1 to 14 days of hospital discharge did not change (P = 0.76). CONCLUSION: Implementation of an EHR alert along with provider education can reduce discharge opioid overprescribing following general surgery.

Impact of an End-User-Designed Education Bundle for Electronic Health Records System Education of House Officers.

Introduction The benefits of Electronic Health Records (EHR) use in clinical care are well documented. However, without proper education and training on EHR systems, clinicians may face challenges in utilizing these technological tools effectively. Suboptimal usage of EHR systems can affect productivity. This study assesses the effectiveness of an end-user-designed education bundle as a supplement to existing training in EHR training for house officers. Additionally, it evaluates the effectiveness of using non-conventional teaching modalities (i.e., short TikTok-style videos) to see how effective and accepted it was in comparison to traditional educational material. Methods A single-armed pre-post-study design consisting of 36 house officers was employed to evaluate the effectiveness of the intervention bundle. The bundle consists of a series of EHR tips and tricks as identified by experienced senior medical officers. The three components of the bundle are a handbook with consolidated tips and tricks, a long-form lecture video, and a series of TikTok-style videos. Distribution was done through healthcare collaborative platforms such as TigerConnect™ (Los Angeles, USA) and email. Results Participants found that the inclusion of our supplementary education bundle results in more effective training for EHR usage, with mean effectiveness with and without the educational bundle being 7.77 and 6.44, respectively (p < 0.001). There were also significant improvements in ease of finding information (7.67 vs 7.14, p = 0.016), performing general functions (7.50 vs 6.89, p = 0.0050), and overall efficiency (7.39 vs 6.92, p = 0.022). We also found TikTok-style videos were non-inferior to more traditional forms of education such as a handbook and traditional long-form lecture videos (p = 0.250). Conclusion An end-user-driven education bundle focusing on high-yield, advanced functions may be useful in enhancing the overall EHR system experience for junior doctors. Of note, TikTok-style videos may be no less effective than traditional methods of EHR teaching.

Improving Compliance With Evidence-Based Laboratory Testing Recommendations and Monitoring Associated Patient Outcomes.

A team comprising nursing, medical staff, and administrative leaders at an urban academic orthopedic hospital in the northeastern United States sought to revise a preoperative laboratory testing protocol based on evidence and practice guidelines. The goal was to decrease unnecessary tests by 20% without negatively affecting patient outcomes. After adding the revised protocol to the electronic health record, audits revealed that the target goal was not met and additional strategies were implemented, including educational webinars for surgeon office personnel who ordered tests, additional webinars for advanced practice professionals, and the creation of scorecards to track surgeons' progress. Overall, a downward trend in the ordering of unnecessary laboratory tests for patients without identified risks was observed, but a 20% reduction was not achieved. Surgical complications during the project were not associated with laboratory tests. Clinicians continue to use the revised preoperative laboratory testing protocol at the facility.

Measuring Documentation Burden in Healthcare.

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.

Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome.

Background: Previous epidemiological research has linked posttraumatic stress disorder (PTSD) with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records provide an opportunity to overcome clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex. Methods: PTSD was defined among biobank participants (N = 145,959) in 3 major healthcare systems using 2 ICD code-based definitions: broad (≥1 PTSD or acute stress codes vs. 0; n cases = 16,706) and narrow (≥2 PTSD codes vs. 0; n cases = 3325). Using a phenome-wide association study design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified phenome-wide association study analyses including a sex × diagnosis interaction term in each logistic regression. Results: A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While the strongest associations were shared between the 2 definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients. Conclusions: This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using electronic health records.

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that some people develop following a traumatic event. In addition to mental symptoms, PTSD can impact human health in ways other ways; for example, there are many known conditions that co-occur with PTSD, such as cardiovascular conditions. In this study, we set out to understand the breadth and degree to which PTSD co-occurs with medical outcomes in a sample of over 146,000 patients across 3 large medical systems. We found that both narrowly and broadly defined PTSD diagnosis co-occurred with hundreds of medical conditions, and the strongest associations were with other psychiatric disorders, respiratory conditions (asthma, GERD), sleep-related conditions, and pain. These results provide insights into future genetic studies of PTSD in large-scale biobanks and deepen our understanding of the complex needs of patients with PTSD.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS: Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS: The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION: Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Equivalence of Alcohol Use Disorder Symptom Assessments in Routine Clinical Care When Completed Remotely via Online Patient Portals Versus In Clinic via Paper Questionnaires: Psychometric Evaluation.

BACKGROUND: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in). OBJECTIVE: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care. METHODS: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test-Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist-an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities. RESULTS: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P=.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11). CONCLUSIONS: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown.

Ruthlessly Subtract to Build Oncology Programs and Add Value.

Building modern healthcare programs and systems caring for populations requires expert skills in strategy, finance, people operations, workflow, evaluation, and more. Build often connotes adding services and people, but it al.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. Materials and Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. Discussion: This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

INTRODUCTION: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS: Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.

Hybrid electronic record: An error reduction strategy for diverse medical prescription formats.

BACKGROUND:  This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. METHODS:  Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. RESULTS:  There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. CONCLUSION:  Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct.Contribution: Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

Missing Ethnicity and Language Information in Healthcare Records of Patients from the Asian Diaspora.

The electronic health record (EHR) should contain information to support culturally responsive care and research; however, the widely used default "Asian" demographic variable in most US social systems (including EHRs) lacks information to describe the diverse experience within the Asian diaspora (e.g., ethnicities, languages). This has a downstream effect on research, identifying disparities, and addressing health equity. We were particularly interested in EHRs of autistic patients from the Asian diaspora, since the presence of a developmental diagnosis might call for culturally responsive care around understanding causes, treatments, and services to support good outcomes. The aim of this study is to determine the degree to which information about Asian ethnicity, languages, and culture is documented and accessible in the EHR, and whether it is differentially available for patients with or without autism. Using electronic and manual medical chart review, all autistic and "Asian" children (group 1; n = 52) were compared to a randomly selected comparison sample of non-autistic and "Asian" children (group 2; n = 50). Across both groups, manual chart review identified more specific approximations of racial/ethnic backgrounds in 54.5% of patients, 56% for languages spoken, and that interpretation service use was underestimated by 13 percentage points. Our preliminary results highlight that culturally responsive information was inconsistent, missing, or located in progress notes rather than a central location where it could be accessed by providers. Recommendations about the inclusion of Asian ethnicity and language data are provided to potentially enhance cultural responsiveness and support better outcomes for families with an autistic child.

Patients and family attitudes about clinical and research sharing of electronic clinical data.

PURPOSE: To access the attitudes of service users about the sharing of health records for research and to foster collaboration between municipal health services and the specialist health services in Norway. METHODS: Members (n ≈ 2000) of the Norwegian mental health service users' organizations (SUO's), ADHD Norway, the Autism Association and the Tourette Association, representing Central Norway, participated in the study, (N = 108, 5.4% response rate). Descriptive statistics were used to evaluate distributions of responses to the questionnaire. RESULTS: Service users reported being aware that municipal health services collaborate with the specialist health service (62%), with mental health care in the specialist health service (57%), and child and adolescent psychiatric services (61%). A large proportion of individuals were aware of the benefits of sharing their health records (93%), have trust in the use of data by health authorities (81%), and were willing to share records to benefit fellow patients (84%). Personal experience (69%) and impressions from mainstream media (55%) had the most influential impact on users' views of the Health Platform, an electronic health communication system. A majority of users had a negative perception of the Health Platform, even though some expect it to become a valuable tool in the future (50%). CONCLUSIONS: Service users are aware of and positive about benefiting others by sharing health records. They trust the health authorities, however, have negative attitudes about the Health Platform, apparently based on personal experiences and media influence. However, service users can see the potential usefulness of the Health Platform in the future.

A randomized, embedded, pragmatic, Bayesian clinical trial examining clinical decision support for high flow nasal cannula management in children with bronchiolitis: design and statistical analysis plan.

BACKGROUND: High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS: The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION: Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.