Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis.

Background: Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor. Objective: This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status. Methods: Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs). Results: Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79). Conclusions: Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.

Background: Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person's complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications. Aim: The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages. Objective: The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system. Methods: To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection. Results: The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females. Conclusion: As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.

Dual Channel Endoscopic Mucosal Resection.

Endoscopic mucosal resection (EMR) is the recommended technique for colon polypectomy for nonpedunculated lesions that are >20 mm in size not requiring excision. Dual-channel EMR (DC-EMR) uses an endoscope with two working channels to facilitate easier submucosal injection, snare resection, and clip closure of polypectomy defects. There is also promising early literature indicating that this endoscopic modality can reduce the overall learning curve present for single-channel colonoscopy EMR. This chapter will describe the steps and techniques required to perform DC-EMR, potential complications, recommended postprocedure surveillance, and future directions.

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.

INTRODUCTION: There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients. AIM: We intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that monitors mortality based on heavy metal exposure among hypertension patients. METHODS: Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2018). We developed 5 ML models for mortality prediction among hypertension patients by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, we chose the optimally performing model after parameter adjustment by genetic algorithm (GA) for prediction. Finally, in order to visualize the model's ability to make decisions, we used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 2347 participants in total. RESULTS: A best-performing eXtreme Gradient Boosting (XGB) with GA for mortality prediction among hypertension patients by 13 heavy metals was selected (AUC 0.959; 95% CI 0.953-0.965; accuracy 96.8%). According to sum of SHAP values, cadmium (0.094), cobalt (2.048), lead (1.12), tungsten (0.129) in urine, and lead (2.026), mercury (1.703) in blood positively influenced the model, while barium (- 0.001), molybdenum (- 2.066), antimony (- 0.398), tin (- 0.498), thallium (- 2.297) in urine, and selenium (- 0.842), manganese (- 1.193) in blood negatively influenced the model. CONCLUSIONS: Hypertension patients' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.

Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data.

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

Clinical audit of endoscopic sub-mucosal dissection performed for complex lateral spreading colorectal tumors from a region non-endemic for colorectal cancer.

BACKGROUND: Endoscopic resection is currently the treatment of choice for laterally spreading tumors (LSTs). Endoscopic sub-mucosal dissection (ESD) can achieve higher enbloc resection and R0 resection, albeit at a slightly higher risk of complications. Given scarce data on ESD from India, we performed a retrospective analysis of our experience with colorectal ESD (CR-ESD) to know its clinical efficacy and complications as well as to assess the learning curve of CR-ESD in non-endemic-areas. METHODS: Retrospective analysis of prospectively maintained datasheet performed. All patients with large (>2cm), complex or recurrent colorectal LST who underwent ESD at our center between 2012 and 2021 were included in the study. Various baseline lesion-related parameters, procedure-related parameters, enbloc resection (ER) rates, R0 margins and adverse event rates were retrieved. CUSUM analysis was performed to calculate the minimum required procedures to achieve competency in CR-ESD. RESULTS: Total 149 patients were included in the study; mean patient age was 61.36±18.21 years. Most patients had lesions in rectum (n=102; 68.5%) followed by sigmoid colon (n=25; 16.8%). The mean lesion size was 46.62 ± 25.46 mm and the mean procedure duration for ESD was 219.30 ± 150.05 min. ER was achieved in 94.6% of lesions. R0 resection was achieved in 132 patients (88.6%). Overall, six (4%) adverse events were noted, of which one required surgical intervention. As many as 105 patients (70.5%) had adenomatous lesions on histology. Seventy-four patients underwent follow-up colonoscopy, of which three had a recurrence of adenomatous lesions and five had post-resection stricture requiring endoscopic dilation. CUSUM curve analysis calculated the learning curve for ESD was 47 resections for ER and 55 for the occurrence of AEs, with a composite CUSUM at 47 procedures. CONCLUSION: CR-ESD even in non-endemic area is associated with high en bloc resection rates, R0 resection rates and acceptable complication profile. Approximately 50 cases of CR-ESD are required to achieve competency.

Treatment outcomes of endoscopic resection for nonampullary duodenal neuroendocrine tumors.

Background: The incidence rate of duodenal neuroendocrine tumors has been increasing in recent years. Endoscopic resection [ER; endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD)] is recommended for nonampullary duodenal neuroendocrine tumors (NAD-NETs) ≤10 mm in diameter that are confined to the submucosal layer and without lymph node or distant metastasis. However, the efficacy and safety of and indications for EMR/ESD remain unclear. Methods: Between November 2011 and April 2021, 12 NAD-NETs in 12 patients who underwent either EMR or ESD were analyzed retrospectively. The rates of en bloc resection, complete resection, pathologic complete resection, margin involvement, lymphovascular invasion, perineural invasion, complications and prognosis were determined during follow-up (median observation period 53.0 months). Results: EMR was performed for two tumors, and ESD was performed for ten tumors. En bloc resection was performed for both tumors (100%) in the EMR group, and complete resection was achieved in one case (50%). Pathological complete resection was achieved in one case (50%), while in the ESD group, these three rates were 90% (9/10), 80% (8/10), and 80% (8/10), respectively. Intraoperative perforation occurred in one patient (10%) during ESD treatment, with no intraoperative or delayed bleeding in either group. Recurrence and distant metastasis were not observed during the mean follow-up period of 53.0 months (range, 18-131 months). Conclusions: For NAD-NETs that measure ≤10 mm in size, are confined to the submucosal layer and have neither suspicious lymph nodes nor distant metastasis, ER (EMR and ESD) may be a safe, effective, and feasible endoscopic technique for removing them.

Barriers and facilitators to effective electronic health record-based sepsis screening in the pediatric intensive care unit.

Introduction: The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children's Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis. Materials and Methods: We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience. Results: In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions. Discussion: Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety. Conclusion: While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

Determination of 16 ultraviolet-absorbing compounds in marine invertebrates by using LC-USI-MS/MS coupled with QuEChERS.

In this study, we examined multiple endocrine-disrupting ultraviolet-absorbing compounds (UVACs) in marine invertebrates used in personal care products and packaging. Modified QuEChERS and liquid chromatography UniSpray ionization tandem mass spectrometry were used to identify 16 UVACs in marine invertebrates. Matrix-matched calibration curves revealed high linearity (r ≥ 0.9929), with limits of detection and quantification of 0.006-1.000 and 0.020-3.000 ng/g w.w., respectively. In oysters, intraday and interday analyses revealed acceptable accuracy (93%-120%) and precision (≤18%), except for benzophenone (BP) and ethylhexyl 4-(dimethylamino) benzoate. Analysis of 100 marine invertebrate samples revealed detection frequencies of 100%, 98%, 89%, 64%, and 100% for BP, 4-hydroxybenzophenone, 4-methylbenzophenone, 4-methylbenzylidene camphor, and benzophenone-3 (BP-3), respectively. BP and BP-3 were detected at concentrations of 4.40-27.39 and < 0.020-0.560 ng/g w.w., respectively, indicating their widespread presence. Overall, our proposed method successfully detected UVACs in marine invertebrates, raising concerns regarding their potential environmental and health effects.

Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

Distal Cap-assisted Endoscopic Mucosal Resection allows the safe and effective resection of adherent, dysplastic lesions in the setting of inflammatory bowel disease: a multicenter, retrospective study with video.

BACKGROUND AND AIMS: Endoscopic mucosal resection (EMR) is established as the primary approach for large/complex dysplastic lesions. However, submucosal fibrosis caused by previous attempts at removal, biopsy, inflammation, or tattoo can cause a benign "negative lift sign" and create difficulty for EMR. Here, we present the use of distal-cap assisted EMR (EMR-DC) specifically for the use of resecting dysplastic colon lesions when submucosal fibrosis is present in IBD patients. METHODS: 16 IBD patients were retrospectively evaluated from two high volume centers. The patient demographics, lesion pathology and classification, outcomes including time and success of resection, SAEs within 30 days of the procedure, and efficacy were measured. RESULTS: 75% of patients treated with EMR-DC achieved complete resection with 0 serious adverse events within 30 days of the procedure. CONCLUSION: EMR-DC represents an attractive option for the resection of adherent dysplastic lesions in chronic IBD which is effective, safe, and inexpensive.

Endoscopic Resections for Barrett's Neoplasia: A Long-Term, Single-Center Follow-Up Study.

Background and Objectives: Endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD) are both well-established and effective treatments for dysplasia and early cancer in Barrett's esophagus (BE). This study aims to compare the short- and long-term outcomes associated with these procedures in treating Barrett's neoplasia. Materials and Methods: This single-center retrospective cohort study included 95 patients, either EMR (n = 67) or ESD (n = 28), treated for Barrett's neoplasia at Sahlgrenska University Hospital between 2004 and 2019. The primary outcome was the complete (en-bloc) R0 resection rate. Secondary outcomes included the curative resection rate, additional endoscopic resections, adverse events, and overall survival. Results: The complete R0 resection rate was 62.5% for ESD compared to 16% for EMR (p < 0.001). The curative resection rate for ESD was 54% versus 16% for EMR (p < 0.001). During the follow-up, 22 out of 50 patients in the EMR group required additional endoscopic resections (AERs) compared to 3 out of 21 patients in the ESD group (p = 0.028). There were few adverse events associated with both EMR and ESD. In both the stratified Kaplan-Meier survival analysis (Log-rank test, Chi-square = 2.190, df = 1, p = 0.139) and the multivariate Cox proportional hazards model (hazard ratio of 0.988; 95% CI: 0.459 to 2.127; p = 0.975), the treatment group (EMR vs. ESD) did not significantly impact the survival outcomes. Conclusions: Both EMR and ESD are effective and safe treatments for BE neoplasia with few adverse events. ESD resulted in higher curative resection rates with fewer AERs, indicating its potential as a primary treatment modality. However, the survival analysis showed no difference between the methods, highlighting their comparable long-term outcomes.

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

BACKGROUND: Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. OBJECTIVE: The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. METHODS: To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. RESULTS: As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. CONCLUSIONS: Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54365.

Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study.

Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

Removal of GIT lesions and the role of impedance of the injection solution-an innovative approach to known methods.

In this work, for the first time, the specific impedances of various injection solutions as well as the surface and tissue impedance after injection of these solutions were analyzed and compared regarding the radio-frequency surgical cutting process. The impedances of 0.9% NaCl, 4% gelatine, 6% hydroxyethyl starch, 10% glycerol/5% fructose, 10% glucose, 5% and 20% albumin, blood, and blood plasma as well as aqua destillata have been tested in vitro. Even if EMR and ESD are routinely used in clinical practice, there is so far no easy, fast, and safe method to remove larger lesions en bloc. We show that the impedance of the injected solution shows to be a crucial factor for safe removal, especially of larger lesions (Ø > 20 mm) and more importantly in accordance with the requirements of oncology and pathology. KEY MESSAGES: Impedance is playing a crucial factor in the radio-frequency (RF)-surgery. With a higher Impedance there will be less current necessary to reach the aimed voltage. Injection solution Aqua destillata and 10% Glucose, show significantly higher Impedances. Higher impedances lead to less surgical related complications. Minor changes in existing method to improve patent safety.

Security Risk Assessment for Patient Portals of Hospitals: A Case Study of Taiwan.

Background: 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.

Successes and challenges of best practice alerts to identify and engage individuals living with hepatitis C virus.

Introduction: Many individuals living with hepatitis C virus (HCV) are unaware of their diagnosis and/or have not been linked to programs providing HCV care. The use of electronic medical record (EMR) systems may assist with HCV infection identification and linkage to care. Methods: In October 2021, we implemented HCV serology-focused best practice alerts (BPAs) at The Ottawa Hospital (TOH) via our EMR (EPIC). Our BPAs were programmed to identify previously tested HCV seropositive individuals. Physicians were prompted to conduct HCV RNA testing and submit consultation requests to the TOH Viral Hepatitis Program. We evaluated data post-BPA implementation to assess the design and related outcomes. Results: From 1 September 2022 to 15 December 2022, a total of 2,029 BPAs were triggered for 139 individuals. As a consequence of the BPA prompts, nine HCV seropositive and nine HCV RNA-positive individuals were linked to care. The proportion of total consultations coming from TOH physicians increased post-BPA implementation. The BPA alerts were frequently declined, and physician engagement with our BPAs varied across specialty groups. Programming issues led to unnecessary BPA prompts (e.g., no hard stop to the prompts even though the individual was treated and cured and individuals linked to care without first undergoing HCV RNA testing). A fixed 6-month lookback period for test results limited our ability to identify many individuals. Conclusion: An EMR-based BPA can assist with the identification and engagement of HCV-infected individuals in care. However, challenges including issues with programming, time commitment toward BPA configuration, productive communication between healthcare providers and the programming team, and physician responsiveness to the BPAs require attention to optimize the impact of BPAs.

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis.

BACKGROUND: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians' records, it has yet to be widely applied to pharmaceutical care records. OBJECTIVE: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients' diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians' records. METHODS: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. RESULTS: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data. CONCLUSIONS: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.