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Background: Although the prescription of physical activity in clinical care has been advocated worldwide, in the Netherlands, "Exercise is Medicine" (E = M) is not yet routinely implemented in clinical care. Methods: A set of implementation strategies was pilot implemented to test its feasibility for use in routine care by clinicians in 2 departments of a university medical center. An extensive learning process evaluation was performed, using structured mixed methods methodology, in accordance with the Reach, Effect, Adoption, Implementation, and Maintenance framework. Results: From 5 implementation strategies employed (education, E = M tool embedded in the electronic medical records, lifestyle coach situated within the department, overviews of referral options, and project support), the presence of adequate project support was a strong facilitator of the implementation of E = M. Also, the presence of the lifestyle coach within the department seemed essential for referral rate. Although clinicians appreciated the E = M tool, barriers hampered its use in practice. Conclusions: Specific implementation strategies, tailored to the setting, are effective in facilitating the implementation of E = M with specific regard to education for clinicians on E = M, deployment of a lifestyle coach within a department, and project coordination. Care providers do see a future for lifestyle coaches who are structurally embedded in the hospital, to whom they can easily refer. [ABSTRACT FROM AUTHOR]

Background: Late-onset Pompe disease (LOPD) is an autosomal recessive lysosomal storage disorder that results in severe progressive proximal muscle weakness. Over time, reductions in muscle strength result in respiratory failure and a loss of ambulation. Delayed diagnosis of LOPD deprives patients of treatments that can enhance quality of life and potentially slow disease progression. The objective of this study is to determine if patients with a nonspecific diagnosis, such as muscle weakness, may be at risk for LOPD using retrospective chart review of patients seen at two centers: an academic center and a community health system. Results: Initial data pulls identified 80,070 patients with one of the ICD-10 codes of interest. Chart review found 551 of these patients also had at least one lab value commonly abnormal in individuals with LOPD and of these 110 scored as "at-risk". After removing phenocopies/other confirmed unrelated diagnoses, 46 individuals were contacted either directly or through their healthcare provider for genetic counseling. Three patients had pretest genetic counseling and were tested for decreased levels of acid-α-glucosidase. One patient was found to have deficient acid-α-glucosidase. Additionally, a physician educated through the program ordered LOPD testing for their patient and diagnosed them with LOPD. Conclusion: This study confirms that a symptom-based scoring tool and chart review combined with provider education can identify patients who are at increased likelihood to have a missed LOPD diagnosis. [ABSTRACT FROM AUTHOR]

Health maintenance organizations, doctors, hospitals, and various medical institutions provide large amounts of medical data to each of us. All these important data should be preserved to be viewed anytime and anywhere via Electronic Health Records (EHRs). Electronic Health Records mainly contain the patient's medical information, such as the patient's medical history, appointments, diagnosis, medicines, prescriptions, and current treatments. EHRs are often administered by a single vendor, which implies that all personal information is saved in data sets controlled by the vendor in charge of the archives. The requirement for a strategy and the vulnerability in security frameworks, EHR theft is rapidly becoming common. The novel framework built upon blockchain offers unparalleled security, transparency, and efficiency in handling sensitive medical information. By leveraging blockchain's decentralized architecture, patient records are securely stored across a distributed network, ensuring tamper-proof data integrity and protection against unauthorized access. The Healthcare Record Chain Management (HRCM) framework introduces a paradigm shift, enabling seamless interoperability among healthcare providers while maintaining patient privacy through cryptographic techniques. Moreover, smart contracts are embedded within the blockchain streamline administrative processes, automating tasks such as insurance claims and billing. As a result, the adoption of this innovative approach not only enhances data security and interoperability but also fosters trust among stakeholders, ultimately improving the quality and accessibility of healthcare services. The findings of the system were validated using real-life scenarios of various use cases and compared to the conventional health record system. [ABSTRACT FROM AUTHOR]

Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To identify barriers and facilitators to family‐level comfort with health‐related social needs (HRSN) data collection and documentation in the pediatric clinical setting.This qualitative study was nested within a pragmatic randomized controlled trial on social care integration in the pediatric clinical setting. We used a hybrid random‐purposive strategy to sample 60 caregivers of pediatric patients ages 0–25 presenting at two primary care clinics and one emergency department affiliated with a large pediatric healthcare system between September 2022 and 2023. We developed an interview guide and codebook to explore caregiver experiences with and perceptions of HRSN data collection and documentation.We conducted semi‐structured telephone interviews in English and Spanish with 60 caregivers. Interviews were conducted until thematic saturation was achieved and were transcribed verbatim. We used thematic analysis with constant comparison to code interviews and identify emerging themes.Our analysis yielded several barriers to caregiver comfort with HRSN data collection and documentation: (1) stigmatization by providers and medical staff and risk of child protective services involvement, (2) providers presuming connections between documented HRSN and medical complaints, (3) permanency of documented HRSN, (4) visibility of HRSN data by pediatric patients and caregiver proxies, and (5) fear that documented HRSN could negatively impact future insurance cost and coverage. We identified four facilitators to caregiver comfort: (1) clear communication regarding the purpose of HRSN data collection and use, (2) respect for caregiver autonomy, for example, by providing the option to decline participation, (3) training of data collection personnel to ensure privacy and compassionate care, and (4) consideration of timing within the medical visit, delaying assessment until medical concerns are addressed.Caregiver‐identified barriers and facilitators should be considered in clinically based HRSN data collection efforts to ensure that these programs are equitable and family‐centered. [ABSTRACT FROM AUTHOR]

Introduction and Objectives: Structured physical activity (PA) programs help to prevent and manage chronic diseases, yet systematic approaches to identify and enroll patients in these programs are lacking. Exercise is Medicine Greenville (EIMG) is a novel clinic-to-community model that identifies patients with chronic diseases in primary care settings and connects them to a structured, evidence-informed, community-based PA program. This study assessed influences on PA program enrollment using a mixed methods design. Methods: Data were collected from 12 clinics over 18 months. Electronic health record data were used to quantitively compare the characteristics of referred patients who did versus did not enroll. Semi-structured interviews were conducted with a subset of non-enrollees to elucidate barriers and facilitators to enrollment. Results: Of the 217 referred patients who were eligible, 84 (38.7%) enrolled in the PA program. A greater percentage of enrollees had a history of high cholesterol (73.8%) relative to non-enrollees (57.9%, χ2 (1, N = 217) = 5.66, P =.02). Twenty-six patients completed qualitative interviews. Three themes emerged from interviews: (1) positive referral experiences with opportunity for enhanced information sharing and improved flow; (2) strong patient motivation, perceived capability, and social support; and (3) external barriers, such as cost and time, that prevented enrollment. Conclusions: Findings can guide improvements to the EIMG model, thus increasing its positive impact on individual- and community-level health. Findings can also inform efforts to build similar clinic-to-community PA models at other health systems. [ABSTRACT FROM AUTHOR]

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

Background: Recent clinical trials and guideline updates have highlighted the efficacy and safety of sodium-glucose cotransporter-2 inhibitor (SGLT2i) use in patients with type 2 diabetes (T2D) and comorbidities including atherosclerotic cardiovascular disease (ASCVD), chronic kidney disease (CKD), or heart failure (HF). Objective: This study assesses the rates of guideline-based prescribing of SGLT2i in patients with T2D and one or more of the following comorbidities: ASCVD, CKD, or HF, prior to and after the 2022 American Diabetes Association (ADA) guideline publication within the Atrius Health clinical pharmacy, internal medicine, and specialty medicine departments. Methods: This is a retrospective chart review of data from the electronic medical record. Patients with the aforementioned criteria were included if they were managed by either the clinical pharmacy department, internal medicine, or specialty medicine departments. Patients were excluded if they did not have any of the comorbidities listed or a form of diabetes other than T2D. Results: Of the 10,631 patients enrolled, 354 (3.3%) were initiated on an SGLT2i during the study. The average number of SGLT2i initiations prior to the 2022 ADA guideline publication was five prescription starts per week. After the guideline publication initiation increased to seven prescription starts per week. Secondary outcomes showed the majority of SGLT2i prescriptions were started in the internal medicine department, followed by cardiology and nephrology. Conclusion: Overall utilization rates of SGLT2i are low but increased after the 2022 ADA guidelines were published. These results suggest opportunities to optimize the use of SGLT2i in this patient population. [ABSTRACT FROM AUTHOR]

Introduction/objectivesPrimary care organizations are increasingly collecting data on patients' social risks, bringing forth an unprecedented opportunity to present combined health and social data that clinical and social care providers could leverage to improve patient care and outcomes. Little is known, however, about how these data could be used and what combinations of specific data elements are most helpful. We explored how primary care staff who provide clinical or social care services view potential benefits of and use cases for combined patient-level clinical and social data.MethodsWe conducted qualitative interviews or focus groups with 39 social and clinical care providers representing 6 healthcare organizations in San Diego County, California. Interviews were transcribed and analyzed using a deductive thematic analysis approach.ResultsOverall, both clinical and social care providers noted the value of access to both types of data. Participants highlighted 3 benefits from integrating social and clinical data. The data could: (1) offer providers a more holistic view of patients' circumstances; (2) strengthen their ability to tailor care to patients' medical and social conditions concurrently; and (3) enhance coordination across care team members. Interviewees cited specific examples of ways social and clinical data could be paired to improve care.ConclusionsSocial and clinical care providers alike envisioned multiple uses and benefits of accessing combined individual-level clinical and social data, highlighting the potential for practice and policy innovations to facilitate access and uptake of combined data. Future research should focus on ways to increase accessibility of cross-sector data and evaluate the impact of care informed by combined data on patient social and health outcomes.

OBJECTIVES: Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). MATERIALS AND METHODS: This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. RESULTS: MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. DISCUSSION: To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. CONCLUSION: MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Treatment based on syndrome differentiation is the core diagnostic and therapeutic thinking of traditional Chinese medicine (TCM), which is the key to determine clinical efficacy. Nowadays, research based on clinical data is the main method to explore the law of TCM syndrome and treatment, but the internal relationship of the key factors of "disease-syndrome-formula-medicine-effect" has not been truly and comprehensively analyzed, resulting in low clinical value of research results. Therefore, the author systematically sorted out the core problems of poor matching between electronic medical record and clinical research, the effect of data governance on data accuracy, difficulties to discover the law of TCM syndrome and treatment by data analysis methods. In addition, in the context of data driven, the big data platform of TCM clinical research should be established, and the data governance and analysis technology with artificial intelligence as the core should be developed, so as to realize the integration of clinical practice and research, providing new ideas and methods for the research of the law of TCM syndrome and treatment and promoting the development of TCM.

Named Entity Recognition (NER) in Chinese Electronic Medical Records (EMRs) is crucial for enhancing healthcare quality and efficiency. However, the unique complexity of the Chinese language and the unstructured format of medical texts create significant challenges. To address these issues, we propose MSCNER, a unified Multi-Scale Embedding Network designed specifically for NER in Chinese EMRs. MSCNER navigates linguistic and contextual challenges by employing a character relation classification scheme. The model first extracts detailed contextual information through an information extraction module and a context modeler. It then incorporates multi-scale feature extraction to gather comprehensive features across characters, words, and positions. Additionally, a weight allocation module based on an attention mechanism optimizes the recognition of complex and discontinuous entities. Experimental results on three benchmark Chinese EMR datasets demonstrate that MSCNER achieves state-of-the-art performance. It significantly surpasses existing models in terms of accuracy and reliability. These findings underscore the potential of MSCNER to improve NER in medical applications, paving the way for more effective and scalable healthcare data systems and broader applications in other language processing tasks.

Abstract Background This study aimed to increase the index of suspicion for transthyretin amyloidosis (ATTR) among cardiologists leading to increased screening for amyloidosis. Methods A retrospective algorithm was created to identify patients at risk for ATTR. A list of these patients and instructions on how to order amyloidosis testing were given to cardiologists, who then determined if further evaluation was warranted. The ordering trends of Technetium 99 m-Pyrophosphate (PYP) scans and the number of ordering physicians before and after this intervention were recorded across the entire practice. Results The algorithm identified 349 potential high-risk patients of which only 23 eventually had PYP scans performed resulting in 2 equivocal and 1 positive results. Across the practice, over the 28 months before initiating this protocol, PYP scans were ordered for 22 patients of which 6 were equivocal or positive. Over the 23-month course of this project, 142 PYP scans were ordered of which 18 were equivocal or positive. The number of ordering providers increased from 7 prior to the protocol’s implementation to 22 by the end of this project within 23 months. On change point analysis, PYP scan ordering increased after protocol initiation (regression coefficient 1.27 vs. 6.31, p

Zhen Ruan,1,&ast; Xianwen Chen,1,&ast; Menghuan Song,1 Ruxu Jia,2 Hang Luo,3 Carolina Oi Lam Ung,1,4,5 Hao Hu1,4,5 1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, Macau SAR, People’s Republic of China; 2Global Business School for Health, University College London, London, UK; 3Shanghai Palan DataRx Co., Ltd, Shanghai, People’s Republic of China; 4Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, Macau SAR, People’s Republic of China; 5Centre for Pharmaceutical Regulatory Sciences, University of Macau, Macau, Macau SAR, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hao Hu, University of Macau, Room 1050, E12 Research Building, Macau, Macau SAR, People’s Republic of China, Email haohu@um.edu.moAim: This study aimed to conduct a retrospective observational study in China to investigate the real-world utilization of glucagon-like peptide-1 receptor (GLP-1RA) in China.Methods: Type 2 diabetes mellitus (T2DM) patients were retrieved from the electronic medical records of 18 hospitals from 2016 to 2020. A descriptive analysis detailed patient characteristics and clinical outcomes. Multivariate logistic regression analysed the factors associated with daily and weekly GLP-1RA.Results: Fifteen thousand one hundred and seventy-six individuals were included. At the 6-month follow-up, the overall estimated mean change from baseline in HbA1c was − 1.26± 1.91% (p < 0.001), the “Weekly GLP-1RA” group was − 1.58± 2.03% (p < 0.001), and the “Daily GLP-1RA” group was − 1.25± 1.90% (p < 0.001). At the 12-month follow-up, the overall estimated mean change from baseline in HbA1c was − 0.95± 1.80% (p < 0.001), the “Weekly GLP-1RA” group was − 1.05± 1.93% (p < 0.001), and the “Daily GLP-1RA” group was − 0.95± 1.80% (p < 0.001). At 6 months following GLP-1RA initiation, there were statistically significant improvements in the mean TC, LDL-C, and TG at 6 months or 12 months separately following GLP-1RA initiation. Statistically significant improvements were observed in the mean HDL-C after 6 months. Compared with the baseline (11.92%), the proportion of patients who had an incidence of all hypoglycemia was lower at the 6-month follow-up (9.73%). Patients with dyslipidemia were more likely to use weekly GLP-1RA (OR =1.61, 95% CI: 1.27– 2.06, p < 0.001).Conclusion: In China, weekly GLP-1RA demonstrated better effectiveness compared to the daily GLP-1RA. The results confirmed the efficacy of GLP-1RA in clinical trials.Keywords: type 2 diabetes mellitus, glucagon-like peptide-1 receptor agonist, GLP-1RA, retrospective, China, electronic medical record, real-world

Abstract 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.

Named Entity Recognition (NER) in Chinese Electronic Medical Records (EMRs) is crucial for enhancing healthcare quality and efficiency. However, the unique complexity of the Chinese language and the unstructured format of medical texts create significant challenges. To address these issues, we propose MSCNER, a unified Multi-Scale Embedding Network designed specifically for NER in Chinese EMRs. MSCNER navigates linguistic and contextual challenges by employing a character relation classification scheme. The model first extracts detailed contextual information through an information extraction module and a context modeler. It then incorporates multi-scale feature extraction to gather comprehensive features across characters, words, and positions. Additionally, a weight allocation module based on an attention mechanism optimizes the recognition of complex and discontinuous entities. Experimental results on three benchmark Chinese EMR datasets demonstrate that MSCNER achieves state-of-the-art performance. It significantly surpasses existing models in terms of accuracy and reliability. These findings underscore the potential of MSCNER to improve NER in medical applications, paving the way for more effective and scalable healthcare data systems and broader applications in other language processing tasks. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research. [ABSTRACT FROM AUTHOR]

Multistate transition models (MSTMs) are valuable tools depicting disease progression. However, due to the complexity of MSTMs, larger sample size and longer follow-up time in real-world data, the computation of statistical estimation and inference for MSTMs becomes challenging. A bounded Taylor series in Newton–Raphson procedure is proposed which leverages the uniformization technique to derive maximum likelihood estimates and corresponding covariance matrix. The proposed method, namely uniformization Taylor-bounded Newton–Raphson, is validated in three simulation studies, which demonstrate the accuracy in parameter estimation, the efficiency in computation time and robustness in terms of different situations. This method is also illustrated using a large electronic medical record data related to statin-induced side effects and discontinuation. [ABSTRACT FROM AUTHOR]

Background: Goals of care conversations are essential to delivery of goal concordant care. Infrequent and inconsistent goals of care documentation potentially limit delivery of goal concordant care. Methods: At Kaiser Permanente San Francisco Cancer Center, a standardized documentation template was designed and implemented to increase goals of care documentation by oncologists. The centralized, prompt-based template included value clarification of the goals and values of advanced cancer patients beyond treatment preferences. Documented conversations using the template during the initial pilot period were reviewed to characterization the clinical context in which conversations were recorded. Common goals and motivators were also identified. Results: A total of 178 advanced cancer patients had at least 1 documented conversation by a medical oncologist using the goals of care template. Oncologists consistently documented within the template goals of therapy and motivating factors in decision making. The most frequently documented goals of care were "Avoiding Pain and Suffering," "Physical Independence," and "Living as Long as Possible." The least recorded goal was "Comfort Focused Treatment Only." Conclusions: Review of oncologist documented goals of care conversations using a prompt-based template allowed for characterization of the clinical context, therapy goals and motivators of advanced cancer patients. Communication of goals of care conversations by oncologists using a standardized prompt-based template within a centralized location has the potential to improve delivery of goal concordant care. [ABSTRACT FROM AUTHOR]

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean‐shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real‐world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis. [ABSTRACT FROM AUTHOR]

Introduction Methods Findings Conclusions The complexity of managing patients with end‐stage kidney disease on hemodialysis underscores the importance of implementing quality improvement (QI) initiatives to enhance patient safety and prioritize patient‐centered care. To address this, we established a QI committee at our tertiary academic center focusing on evidence‐based practices, patient‐centered approaches, and cost efficiency. To facilitate the seamless implementation of QI initiatives, we leveraged the capabilities of our electronic medical record (EMR) system.This review details effective strategies for optimizing use of an EMR system to successfully implement QI efforts. Drawing from our experience, we provide detailed descriptions and practical insights that can be applied to other EMRs.The creation of a secure and accessible dashboard, offering real‐time data on quality metrics, stands out as the most notable feature. This dashboard operates through an algorithm that merges data from both our dialysis and hospital EMR systems. Its primary objectives are to streamline the identification of high‐priority patients, enhance team communication, and facilitate tracking of quality indicators. Additionally, we integrated clinical pathways, checklists, and standardized protocols into the renal EMR to ensure smooth implementation of QI interventions. Notable examples of these interventions include an incremental hemodialysis protocol, a new hemodialysis start checklist, vaccination care plans, and personalized kidney transplant workups. Programmed electronic automatic reminders have proven invaluable in ensuring timely follow‐ups of assigned tasks. The EMR has also contributed to medication optimization and deprescribing by generating patient lists based on specific medication classes. Finally, the EMR's capability to swiftly generate lists of patients with specific features has significantly facilitated targeted QI interventions.Leveraging the capabilities of an EMR system can be crucial for enhancing care of hemodialysis patients and implementing effective QI initiatives. [ABSTRACT FROM AUTHOR]

Introduction: Medication nonadherence is common in systemic lupus erythematosus (SLE) and associated with morbidity and mortality. We explored the reliability of pharmacy data within the electronic medical record (EMR) to examine factors associated with nonadherence to SLE medications. Methods: We included patients with SLE who were prescribed ≥1 SLE medication for ≥90 days. We compared two datasets of pharmacy fill data, one within the EMR and another from the vendor who obtained this information from pharmacies and prescription benefit managers. Adherence was defined by medication possession ratio (MPR) ≥80%. In addition to MPR for each SLE medication, we evaluated the weighted-average MPR and the proportion of patients adherent to ≥1 SLE medication and to all SLE medications. We used logistic regression to examine factors associated with adherence. Results: Among 181 patients (median age 36, 96% female, 58% Black), 98% were prescribed hydroxychloroquine, 34% azathioprine, 33% mycophenolate, 18% methotrexate, and 7% belimumab. Among 1276 pharmacy records, 74% overlapped between linked EMR-pharmacy data and data obtained directly from the vendor. Only 9% were available from the vendor but not through linked EMR-pharmacy data. The weighted-average MPR was 57%; 45% were adherent to hydroxychloroquine, 46% to ≥1 SLE medication, and 32% to all SLE medications. Older age was associated with adherence in univariable and multivariable analyses. Discussion: Our study showed that obtaining linked EMR-pharmacy data is feasible with minimal missing data and can be leveraged in future adherence research. Younger patients were more likely to be nonadherent and may benefit from targeted intervention. [ABSTRACT FROM AUTHOR]

Objectives: Smoking is an important modifiable risk factor in all vascular diseases and verbal advice from providers has been shown to increase rates of tobacco cessation. We sought to identify factors that will improve tobacco cessation and recall of receiving verbal cessation advice in vascular surgery patients at a single institution. Methods: The study is a retrospective cohort study. Patients seen in outpatient vascular surgery clinic who triggered a tobacco Best Practice Advisory (BPA) during their office visits over a 10-month period were contacted post-clinic and administered surveys detailing smoking status, cessation advice recall, and validated scales for nicotine dependence and willingness to quit smoking. This BPA is a "hard stop" that requires providers to document actions taken. Charts were reviewed for tobacco cessation documentation. Nine-digit zip-codes identified the area deprivation index, a measure of socioeconomic status. Univariate analysis was used to identify factors associated with cessation and advice recall. Results: One hundred out of 318 (31.4%) patients responded to the survey. Epic Slicer Dicer found 97 BPA responses. To dismiss the BPA, 89 providers (91.8%) selected "advised tobacco cessation" and "Unable to Advise" otherwise. Of the 318 patients, 115 (36.1%) had cessation intervention documented in their provider notes and 151 (47.5%) received written tobacco cessation advice. Of survey respondents, 70 recalled receiving verbal advice, 27 recalled receiving written advice, 28 reported receiving offers of medication/therapy for cessation. 55 patients reported having tobacco cessation plans, and among those 17 reported having quit tobacco. Recall of receiving written advice (P <.001) and recall of receiving medication/therapy (P =.008) were associated with recall of receiving verbal cessation advice. Conclusions: Providing patients with tobacco cessation medication/therapy and written tobacco cessation education during office visits is associated with increased patients' recall of tobacco cessation advice. Vascular surgeons should continue to provide directed tobacco cessation advice. [ABSTRACT FROM AUTHOR]

Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life. [ABSTRACT FROM AUTHOR]

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks. [ABSTRACT FROM AUTHOR]

The application of digital technology in health services has significantly improved efficiency and service quality. Electronic Medical Record (E.M.R.) is a technology implementation that records, stores, and manages patient medical data electronically. This study aims to analyze the impact of using E.M.R. on the productivity and efficiency of health services at the Sriamur Bekasi Community Health Center. This research uses the Wellbeing method with a qualitative approach. Primary data was collected through in-depth interviews and questionnaires distributed to the medical and administrative staff of the Sriamur Community Health Center. Secondary data on the number of patients before and after E.M.R. implementation was also analyzed. The leading indicators measured include service time, quality, error rate, and human resource utilization. The research results show a significant increase in productivity and efficiency after E.M.R. implementation. The number of patients served per day increases, and the time required for recording and retrieving patient data is reduced. Respondents indicated high satisfaction with using E.M.R., with the majority assessing that the system helps speed up administrative processes and improve the accuracy of medical data. Implementing E.M.R. at the Sriamur Bekasi Community Health Center has increased the medical staff's operational efficiency and productivity. This technology makes it easier to access and manage patient data and reduces the administrative burden so that medical personnel can focus more on health services. To maximize the benefits of E.M.R., ongoing training and regular evaluation of existing systems and procedures are required. [ABSTRACT FROM AUTHOR]

Background and Objectives: Modernization and population aging have increased the prevalence of systemic diseases, such as diabetes and hypertension, which are often accompanied by various dental diseases. Our aim was to investigate associations between common dental conditions and major systemic diseases in an elderly Korean population. Materials and Methods: Utilizing electronic medical record data from 43,525 elderly patients, we examined the prevalence of systemic diseases (diabetes, hypertension, rheumatoid arthritis, osteoporosis, dementia) and dental conditions (caries, periodontal disease, pulp necrosis, tooth loss). The analysis focused on the correlations between these diseases. Results: Significant associations were found between systemic diseases and an increased prevalence of dental conditions. Patients with systemic diseases, especially those with multiple conditions, had higher incidences of periodontal disease and tooth loss. The correlation was particularly strong in patients with diabetes and rheumatoid arthritis. Interestingly, temporomandibular joint disorder was less frequent in this cohort. Conclusions: The findings highlight the importance of integrated dental care in managing systemic diseases in elderly populations. Enhanced dental monitoring and proactive treatment are essential due to the strong association between systemic diseases and dental conditions. Collaboration between dental and medical professionals is crucial for comprehensive care that improves health outcomes and quality of life for elderly patients. [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]

Background: Electronic medical records (EMRs) have been widely implemented in Australian hospitals. Their usability and design to support clinicians to effectively deliver and document care is essential, as is their impact on clinical workflow, safety and quality, communication, and collaboration across health systems. Perceptions of, and data about, usability of EMRs implemented in Australian hospitals are key to successful adoption. Objective: To explore perspectives of medical and nursing clinicians on EMR usability utilising free-text data collected in a survey. Method: Qualitative analysis of one free-text optional question included in a web-based survey. Respondents included medical and nursing/midwifery professionals in Australian hospitals (85 doctors and 27 nurses), who commented on the usability of the main EMR used. Results: Themes identified related to the status of EMR implementation, system design, human factors, safety and risk, system response time, and stability, alerts, and supporting the collaboration between healthcare sectors. Positive factors included ability to view information from any location; ease of medication documentation; and capacity to access diagnostic test results. Usability concerns included lack of intuitiveness; complexity; difficulties communicating with primary and other care sectors; and time taken to perform clinical tasks. Conclusion: If the benefits of EMRs are to be realised, there are good reasons to address the usability challenges identified by clinicians. Easy solutions that could improve the usability experience of hospital-based clinicians include resolving sign-on issues, use of templates, and more intelligent alerts and warnings to avoid errors. Implications: These essential improvements to the usability of the EMR, which are the foundation of the digital health system, will enable hospital clinicians to deliver safer and more effective health care. [ABSTRACT FROM AUTHOR]

Objectives: Upper extremity deep vein thrombosis (UEDVT) is associated with pulmonary embolism and other complications, but there are no recommendations for UEDVT prophylaxis. The purpose of this study was to establish incidence and risk factors for UEDVT and to determine efficacy of pharmacologic prophylaxis for UEDVT prevention. Methods: For this retrospective cohort study, we identified medical patients aged 18 years and older admitted to 13 Cleveland Clinic hospitals from January 2011 to December 2019. Patients with venous thromboembolism (VTE) on admission, length of stay <1 day, and who received therapeutic anticoagulation were excluded. The potential risk factors included demographics, comorbidities, and medical procedures. Comorbidities were identified via International Classification of Diseases codes, (ICD9 and ICD10), procedures from flowsheets, and prophylaxis from medications administered in the electronic medical record. DVT events were identified by a combination of International Classification of Diseases codes and confirmed by chart review. We performed multivariable logistic regression to identify independent risk factors and the association between VTE prophylaxis and UEDVT. The model's C statistic was obtained using 1000 bootstrap runs. Results: Of 194,809 patients, 496 (0.25% of cohort, 36.8% of all VTE) developed UEDVT by 14 days. In the logistic regression model (bias-corrected C statistic 0.87), 11 risk factors predicted UEDVT, the strongest being peripherally inserted central catheter (odds ratio [OR] 4.62, 95% confidence interval [CI] 3.81-5.60) and central venous catheter (OR 3.57, 95% CI 2.91-4.37). The predicted risk among individuals ranged from 0.02% to 23.4%. Prophylaxiswas negatively associated with the development of UEDVT (OR 0.72, 95% CI 0.60-0.87). Conclusions: UEDVT is rare but some patients are high risk. Therefore, UEDVTrisk factors should be added toVTE risk assessmentmodels, and patients at high risk for UEDVT should receive chemoprophylaxis. [ABSTRACT FROM AUTHOR]

Rationale: Cardiac monitoring has often been identified as an area of overutilization and remains a limited resource in many hospitals. With the aim of reducing telemetry overuse, we added clinical decision support to our health system's telemetry order with guidance on appropriate indications for monitoring. The new order requires selection of an appropriate clinical indication. Aims and Objectives: In this study, we aimed to understand provider engagement with this tool by assessing concordance between selected indications within the order and the clinical presence of those conditions as documented within the patient chart. Methods: We randomly selected 100 telemetry orders from July to October 2022 across four different hospitals at NYU Langone Health. Two independent, blinded reviewers used a structured protocol to identify documentation of actual indications for telemetry in each selected chart. We calculated the rate of concordance between indications selected in the order and indications that were determined to be clinically present on chart review. Results: There were 30,839 telemetry orders placed during the study timeframe. Overall concordance between the selection within the order and the actual indication was 48% (95% confidence interval [CI], 38.21%−57.79%). We observed especially low concordance rates for vague indications, such as 'Other', and for 'Confirmed Stroke', which was the only indication allowing for indefinite telemetry. Conclusion: The overall low concordance suggests a disconnect between the support tool and clinical practice. Providers are more likely to select an indication that reduces downstream work regardless of a patient's true clinical indication. [ABSTRACT FROM AUTHOR]

Introduction: Dementia is a common co-morbidity in older people who require urgent or emergency ambulance attendance and influences clinical decisions and care pathways. Following an initial audit of dementia data and consultation with staff, a specific section (tab) to record dementia was introduced on an ambulance service electronic patient record (ePR). This includes a dementia diagnosis button and a free-text section. We aimed to assess whether and how this improved recording. Methods: To re-audit the proportion of ambulance ePRs where dementia is recorded for patients aged ≥65 years, and to describe the frequency of recording in patients aged <65; to analyse discrepancies in the place of recording dementia on the ePR by comparing data from the new dementia tab and other sections of the ePR. Results: We included 112,193 ePRs of patients aged ≥65 with ambulance attendance from a six-month period. The proportion with dementia recorded in patients aged ≥65 was 16.5%, increasing to 19.9% in patients aged ≥75, as compared to 13.5% (≥65) and 16.5% (≥75) in our previous audit. In this audit, of the 16.5% (n = 18,515) of records with dementia recorded, 69.9% (n = 12,939) used the dementia button and 25.4% (n = 4704) recorded text in the dementia tab. Dementia was recorded in ePR free-text fields (but not the dementia tab) in 29.7% of records. Eighteen other free-text fields were used in addition to, or instead of, the dementia tab, including the patient's social history, previous medical history and mental health. Dementia was present on the ePR of 0.4% (n = 461) of patients aged <65. Conclusions: An ePR dementia tab enabled ambulance clinicians to standardise the location of recording dementia and may have facilitated increased recording. We would recommend other ambulance trusts capture this information in a specific section to improve information sharing and to inform care planning for this patient group. [ABSTRACT FROM AUTHOR]

Background: The accuracy of documentation of body weight and fluid balance in hospitalized patients is frequently questioned. Methods: We conducted a survey to understand provider perceptions of the accuracy of intake, output, and weight charting in the electronic medical record. We sent a six-item questionnaire to nurses and physicians who provide inpatient service in a communitybased teaching hospital of the Northeastern United States. We compared the response difference between nurses and physicians by Fisher exact test. Results: One hundred eight nurses and 39 physicians participated in the survey. Both nurses and physicians responded that the accuracy of documentation is crucial. However, only 25.7% of participating physicians and 38.3% of participating nurses considered that documentation in the electronic medical record is reliable. Both physicians and nurses assumed that the nurses are too busy to collect and document the data, and the variability of non-patient weight and variations in body weight measurement under different conditions account for inaccuracies in the documented body weight. Conclusions: Assessing the accuracy of documenting intake, output, and body weight in the electronic medical record is warranted. Providers believe that educating patients about fluid balance and volume assessment help to improve the accuracy in charting intake, output, and body weight in the electronic medical record. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of this study was to understand the influence of clinician encouragement and sociodemographic factors on whether patients access online electronic medical records (EMR). MATERIALS AND METHODS: We analyzed 3279 responses from the Health Information National Trends Survey 5 cycle 4 survey, a cross-sectional, nationally representative survey administered by the National Cancer Institute. Frequencies and weighted proportions were calculated to compare clinical encouragement and access to their online EMR. Using multivariate logistic regression, we identified factors associated with online EMR use and clinician encouragement. RESULTS: In 2020, an estimated 42% of US adults accessed their online EMR and 51% were encouraged by clinicians to access their online EMR. In multivariate regression, respondents who accessed EMR were more likely to have received clinician encouragement (odds ratio [OR], 10.3; 95% confidence interval [CI], 7.7-14.0), college education or higher (OR, 1.9; 95% CI, 1.4-2.7), history of cancer (OR, 1.5; 95% CI, 1.0-2.3), and history of chronic disease (OR, 2.3; 95% CI, 1.7-3.2). Male and Hispanic respondents were less likely to have accessed EMR than female and non-Hispanic White respondents (OR, 0.6; 95% CI, 0.5-0.8, and OR, 0.5; 95% CI, 0.3-0.8, respectively). Respondents receiving encouragement from clinicians were more likely to be female (OR, 1.7; 95% CI, 1.3-2.3), have college education (OR, 1.5; 95% CI, 1.1-2.0), history of cancer (OR, 1.8; 95% CI, 1.3-2.5), and greater income levels (OR, 1.8-3.6). DISCUSSION: Clinician encouragement of patient EMR use is strongly associated with patients accessing EMR, and there are disparities in who receives clinician encouragement related to education, income, sex, and ethnicity. CONCLUSIONS: Clinicians have an important role to ensure that all patients benefit from online EMR use.

ABSTRACT Predicting cisplatin‐induced acute kidney injury (Cis‐AKI) before its onset is important. We aimed to develop a predictive model for Cis‐AKI using patient clinical information based on an interpretable machine learning algorithm. This single‐center retrospective study included hospitalized patients aged ≥ 18 years who received the first course of cisplatin chemotherapy from January 1, 2011, to December 31, 2020, at Nagoya City University Hospital. Cis‐AKI‐positive patients were defined using the serum creatinine criteria of the Kidney Disease Improving Global Outcomes guideline within 14 days of the last day of cisplatin administration in the first course. Patients who received cisplatin but did not develop AKI were considered negative. The CatBoost classification model was constructed with 29 explanatory variables, including laboratory values, concomitant medications, medical history, and cisplatin administration information. In total, 1253 patients were included, of whom 119 developed Cis‐AKI (9.5%). The median time of AKI onset was 7 days, and the interquartile range was 5–8 days. The mean ± standard deviation of the total cisplatin dose in the initial treatment was 77.9 ± 27.1 mg/m2 in Cis‐AKI‐positive patients and 69.3 ± 22.6 mg/m2 in Cis‐AKI‐negative patients. The predictive performance was an ROC‐AUC of 0.78. Model interpretation using SHapley Additive exPlanations showed that concomitant use of intravenous magnesium preparations was negatively correlated with Cis‐AKI, whereas loop diuretics were positively correlated. This suggests the need for magnesium preparations to prevent AKI, although the effects of diuretics may be small. Our model can predict Cis‐AKI early and may be helpful for its avoidance.

BackgroundHeadaches are common complaints in the emergency department (ED) and have raised concern about acute medication overuse. Chinese medicine is a major complementary and alternative medicine in China and effective for headaches. This study aims to summarize characteristics of headache patients at EDs and the utilization of Chinese medicine for headache managements in EDs.MethodsThe study conducted a retrospective analysis based on existing electronic medical records at EDs from four branches of Guangdong Provincial Hospital of Chinese Medicine. Only complete medical records with a first diagnosis of headache within the specified timeframe were included. Data was extracted, screened and standardized using a structured approach. Descriptive analyses and Apriori algorithm-based association rules were employed for the study.ResultsA total of 3,355 medical records were analyzed, with over 86% of headaches classified as non-urgent. Approximately 97% of the patients received a general diagnosis of headaches without further classification. Hypertension was the most prevalent concomitant diagnosis, affecting 27.42% of the patients. Western medicine was prescribed to 66% of the patients for headaches and co-existing conditions, while each type of acute medication was prescribed to fewer than 10% of the patients. Conversely, over one-third of the patients utilized headache-specific patented Chinese herbal medicine products. Additionally, oral and topical Chinese herbal medicine treatments were also administered for headaches in the emergency departments.ConclusionThe majority of headaches consulting in the EDs were non-urgent and treated with various forms of Chinese medicine, alone or in conjunction of western medicine. Chinese herbal medicine may be promoted as alternatives to Western acute medications for treating benign headaches.

Electronic Medical Record Systems (EMRs) are integral to the work of nursing, medical and allied health professionals in Australia and other countries. Successful adoption of EMR systems is reliant upon their usability and effective use. Usability issues impact safety and quality, workflow, communication, and collaboration. The objective of the study was to measure clinician (nurse, medical and allied health professionals) experience of EMR usability in Australia. We conducted an observational study using a validated, cross-sectional survey, the National Usability-focused Health Information System Scale (NuHISS). Thirteen usability statements collect clinician impressions of EMRs related to ease of use, benefits and collaboration and technical quality. This paper presents responses of Australian clinicians using EMRs in primary care, hospitals and public and private sectors. In 2023, 534 health professionals from Australia submitted valid survey responses. The largest respondent group comprised nurses and midwives, working in publicly funded hospitals and having over three years of experience with the EMR mainly used. A majority (69%) agreed that the EMR system is stable and does not crash and 62% felt that the system responds quickly to inputs. Regarding ease of use of the EMR, 50% disagreed that the arrangement of fields and functions is logical, while 58% found the terminology clear and understandable. Sixty-two percent (62%) disagreed that routine tasks can be performed without extra steps, and 65% felt that significant training to learn the EMR is required. Although 63% agreed it is easy to obtain necessary patient information, 45% disagreed that entering and documenting data is quick and smooth. There were mixed responses regarding the EMR system's role in preventing medication errors, with 50% agreeing that it helps prevent errors and 27% disagreeing. There was agreement (74%) that the EMR system supports collaboration and information sharing within the same health service. Respectively, 51% and 47% disagreed regarding support of their EMR for collaboration between different health services and between clinicians and patients. We highlight the importance of understanding clinicians’ experiences with EMR usability. Our findings suggest areas where EMR usability can be strengthened to enhance user experience and support clinicians in delivering high quality, safe care. The study’s findings provide valuable insights for EMR system developers, vendors, and healthcare organisations, emphasising the need to improve usability to realise the full benefits of EMRs and support a digitally enabled healthcare system. Addressing these issues through targeted interventions is essential to enhance clinician satisfaction with the EMRs used, reduce burnout and improve patient care.

Background: Physician wellbeing and burnout are significant threats to the healthcare workforce. Mobile electronic medical record access and smartphones allow for efficient communication in healthcare but may lead to workplace telepressure (WPT). Methods: An IRB-approved survey related to five domains of burnout [WPT, smartphone usage, boundary control, and psychologic detachment] was circulated. Internal medicine and general surgery faculty and residents were surveyed between 3/2021 and 6/2021. Survey results were analyzed for internal consistency with a Cronbach alpha coefficient and validation against a known physician burnout scale. Results: The domains were internally valid with a Cronbach alpha of 0.888. Validation against the physician burnout scale was significantly correlated with WPT domains but was overall positively correlated across domains. Surgical trainees reported the highest burnout rate related to every domain. Conclusion: Survey-based WPT burnout scales provide insight into the daily pressures on physicians. Targeted interventions to limit WPT are needed to improve physician wellbeing.

Abstract Background ICU nurses provide critical care and meticulously document electronic medical records (EMRs), tracking vital signs, interventions, and medication hourly. Despite China’s ICUs effectively integrating real-time monitor and ventilator data into EMRs, challenges persist. Patient movements can introduce inaccuracies, and the demands of critical care may lead nurses to miss assessments like pain and nutrition. Traditional manual EMR verification is inefficient and error-prone, highlighting the urgent need for standardized, technology-aided EMR practices in ICU nursing. Objective This study aimed to describe the development and evaluation of an electronic medical records quality control system implemented in a Chinese tertiary care ICU setting, where current practices impact the accuracy of electronic medical records. Methods A prospective controlled trial was conducted with 600 ICU patients in Zhejiang Province from January to December 2023. An automated EMR quality control system was implemented in July 2023, facilitating real-time data collection and quality control for vital signs, medication management, and nursing evaluations. Results After implementing the ICU nursing electronic medical record quality control system, the prevalence of false data on vital signs decreased from 9 to 1.33%. Additionally, the incidence of incomplete medication administration dropped from 3.33 to 1.67%, and the rate of missing evaluations of assessment items in EMRs was reduced from 8 to 1.33%. Besides, the average time spent on quality control of the electronic medical records was 62 (48,76) seconds per record, which was significantly lower than the 264 (195.5,337.5) seconds using the traditional method. The nurses’ satisfaction with the nursing electronic medical record quality control was (105.73 ± 9.31). Conclusions The ICU nursing electronic medical record quality control system has led to substantial improvements in the quality and reliability of EMRs. The reduction in false data on vital signs, instances of incomplete medication administration, and missing evaluations of assessment items demonstrates the system’s positive impact on nursing documentation practices. These improvements not only enhance the accuracy of patient records but also contribute to better patient care and safety within the ICU setting.

Abstract Background Electronic medical record (EMR) systems provide timely access to clinical information and have been shown to improve medication safety. However, EMRs can also create opportunities for error, including system-related errors or errors that were unlikely or not possible with the use of paper medication charts. This study aimed to determine the detection and mitigation strategies adopted by a health district in Australia to target system-related errors and to explore stakeholder views on strategies needed to curb future system-related errors from emerging. Methods A qualitative descriptive study design was used comprising semi-structured interviews. Data were collected from three hospitals within a health district in Sydney, Australia, between September 2020 and May 2021. Interviews were conducted with EMR users and other key stakeholders (e.g. clinical informatics team members). Participants were asked to reflect on how system-related errors changed over time, and to describe approaches taken by their organisation to detect and mitigate these errors. Thematic analysis was conducted iteratively using a general inductive approach, where codes were assigned as themes emerged from the data. Results Interviews were conducted with 25 stakeholders. Participants reported that most system-related errors were detected by front-line clinicians. Following error detection, clinicians either reported system-related errors directly to the clinical informatics team or submitted reports to the incident information management system. System-related errors were also reported to be detected via reports run within the EMR, or during organisational processes such as incident investigations or system enhancement projects. EMR redesign was the main approach described by participants for mitigating system-related errors, however other strategies, like regular user education and minimising the use of hybrid systems, were also reported. Conclusions Initial detection of system-related errors relies heavily on front-line clinicians, however other organisational strategies that are proactive and layered can improve the systemic detection, investigation, and management of errors. Together with EMR design changes, complementary error mitigation strategies, including targeted staff education, can support safe EMR use and development.

Jaclyn Lewis Albin,1 Alexandra J Mignucci,2 Milette Siler,3 David Dungan,4 Cary Neff,5 Basma Faris,6 Caleb Scott McCardell,7 Timothy S Harlan8 1Departments of Internal Medicine and Pediatrics, University of Texas at Southwestern, Dallas, Texas, USA; 2Department of Family Medicine, University of California San Diego, San Diego, CA, USA; 3Moncrief Cancer Institute and the University of Texas Southwestern Medical Center, Fort Worth and Dallas, TX, USA; 4Departments of Internal Medicine and Pediatrics, Duly Health and Care, Lombard and Downers Grove, IL, USA; 5Conscious Food Solutions, Inc, Oro Valley, AZ, USA; 6Raquel and Jaime Gilinski Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 7Department of Neurology, Rutgers University, New Brunswick, NJ, USA; 8Department of Medicine, George Washington University School of Medicine, Washington, DC, USACorrespondence: Timothy S Harlan, C/O GWU Culinary Medicine Program, Seva Teaching Kitchen, 1810 K Street, Washington, DC, 20006, USA, Email drdrmrmd@mac.comAbstract: Diet plays a pivotal role in health outcomes, influencing various metabolic pathways and accounting for over 20% of risk-attributable disability adjusted life years (DALYs). However, the limited time during primary care visits often hinders comprehensive guidance on dietary and lifestyle modifications. This paper explores the integration of electronic consultations (eConsults) in Culinary Medicine (CM) as a solution to bridge this gap. CM specialists, with expertise in the intricate connections between food, metabolism, and health outcomes, offer tailored dietary recommendations through asynchronous communication within the electronic health record (EHR) system. The use of CM eConsults enhances physician-patient communication and fosters continuous medical education for requesting clinicians. The benefits extend directly to patients, providing access to evidence-based nutritional information to address comorbidities and improve overall health through patient empowerment. We present a comprehensive guide for CM specialist physicians to incorporate CM eConsults into their practices, covering the historical context of eConsults, their adaptation for CM, billing methods, and insights from the implementation at UT Southwestern Medical Center. This initiative delivers expanded access to patient education on dietary risks and promotes interprofessional collaboration to empower improved health.Plain Language Summary: What you eat significantly impacts your health, affecting various aspects including weight, blood sugar, and inflammation. This paper highlights how health-related issues are linked to diet and presents one solution to help doctors guide patients more effectively. Often, the limited time during medical visits makes it challenging for doctors to provide detailed advice on lifestyle changes. Additional common barriers are that many doctors lack nutrition expertise, and access to nutrition experts such as registered dietitian nutritionists can be limited geographically and financially. This paper introduces the concept of electronic consultations (eConsults) in Culinary Medicine (CM) to help overcome this challenge. CM specialists are licensed healthcare professionals who understand how food influences the body and can use eConsults to offer personalized dietary recommendations. EConsults occur via a secure electronic medical record system that connects doctors and specialists, ensuring efficient communication. Patients benefit by gaining access to reliable nutritional information tailored to their specific health needs. This innovative approach also enhances communication between doctors and patients and helps doctors stay updated on the new research about how nutrition and food impact health. The paper provides a practical guide for doctors to integrate CM eConsults into their practices, making it easier to give valuable advice on dietary risks and promote healthier lifestyles. Overall, this initiative represents a significant step in improving patient nutrition education and fostering positive changes in health through the power of informed dietary choices.Keywords: nutrition, dietary, communication, electronic medical record

Pei-Cheng Yeh,1,2 Kuen-Wei Yeh,3– 5 Jiun-Lang Huang6,7 1Graduate Institute of Clinical Dentistry, School of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan, Republic of China; 2Division of Endodontics, Department of Stomatology, Taichung Veterans General Hospital, Taichung, Taiwan, Republic of China; 3Investigation Bureau, Ministry of Justice, New Taipei City, Taiwan, Republic of China; 4Department of Electrical Engineering, Chinese Culture University, Taipei, Taiwan, Republic of China; 5Department of Information, Chinese Culture University, Taipei, Taiwan, Republic of China; 6Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China; 7Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan, Republic of ChinaCorrespondence: Kuen-Wei Yeh, Email m49009@mjib.gov.twBackground: Growing cyberattacks have made it more challenging to maintain healthcare information system (HIS) security in medical institutes, especially for hospitals that provide patient portals to access patient information, such as electronic health record (EHR).Objective: This work aims to evaluate the patient portal security risk of Taiwan’s EEC (EMR Exchange Center) member hospitals and analyze the association between patient portal security, hospital location, contract category and hospital type.Methods: We first collected the basic information of EEC member hospitals, including hospital location, contract category and hospital type. Then, the patient portal security of individual hospitals was evaluated by a well-known vulnerability scanner, UPGUARD, to assess website if vulnerable to high-level attacks such as denial of service attacks or ransomware attacks. Based on their UPSCAN scores, hospitals were classified into four security ratings: absolute low risk, low to medium risk, medium to high risk and high risk. Finally, the associations between security rating, contract category and hospital type were analyzed using chi-square tests.Results: We surveyed a total of 373 EEC member hospitals. Among them, 20 hospital patient portals were rated as “absolute low risk”, 104 hospital patient portals as “low to medium risk”, 99 hospital patient portals as “medium to high risk” and 150 hospital patient portals as “high risk”. Further investigation revealed that the patient portal security of EEC member hospitals was significantly associated with the contract category and hospital type (P< 0.001).Conclusion: The analysis results showed that large-scale hospitals generally had higher security levels, implying that the security of low-tier and small-scale hospitals may warrant reinforcement or strengthening. We suggest that hospitals should pay attention to the security risk assessment of their patient portals to preserve patient information privacy.Keywords: security risk assessment, healthcare information system, electronic health record, electronic medical record, EMR Exchange Center, vulnerability scanner

Abstract The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

Abstract Objective This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). Materials and methods The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. Results The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. Discussion The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system’s high reliability and suitability has been affirmed through empirical validation. Conclusion The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.