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.

Patient Input into the Electronic Health Record: Co-Designing Solutions with Patients and Healthcare Professionals.

Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR.

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.

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.

Discharge Perfection: Assessing Documentation Quality at Mardan Medical Complex, Pakistan (2024 Audit Debut).

Background Patient discharge summaries not only play a vital role in ensuring continuity of care and patient safety but also serve as a communication tool between the primary and tertiary care settings. However, despite their paramount importance, most discharge summaries are either inaccurate or miss essential clinical information, posing considerable danger to patients. This clinical audit assesses the quality of discharge summaries at Mardan Medical Complex, Mardan, Pakistan, to identify areas for improvement. Aim The aim of this study is to assess the discharge summaries of patients at Mardan Medical Complex in Mardan, Pakistan, with a focus on their completeness, accuracy, and timeliness. Methods A cross-sectional, observational, and retrospective study was carried out in the Medical A ward of Mardan Medical Complex, Mardan, Pakistan, from September 2023 to October 2023. Out of the 897 discharge slips, a sample size of 105 participants was determined using Epi Info software. A systematic random sampling technique was used. Data was extracted from the hospital management information system and evaluated using a clinical audit tool based on standard guidelines from the Royal College of Physicians, Islamabad Healthcare Regulatory Authority, and Khyber Pakhtunkhwa Health Care Commission. To analyze the data, descriptive statistics were applied. Results The clinical audit revealed significant deficiencies in discharge summaries. Important patient demographics, such as contact details and safety alerts, were completely absent in 100% of the cases, and 48% of the summaries lacked the father's name. Admission details were similarly inadequate, with nearly all summaries missing critical information like admission time and reasons for admission. Clinical summaries and procedural details were absent in 73% and 87% of the cases, respectively. Discharge planning also showed major gaps, as special instructions according to the primary diagnosis and discharge destination were frequently neglected. Follow-up visits were recommended in only 71% of cases. Additionally, there were significant errors in in-home medication prescriptions, with 61% missing medication doses, 28% missing the route of administration, and 20% lacking the duration of treatment. Conclusions This clinical audit identified critical areas for improvement by revealing significant errors in the quality of discharge summaries at Mardan Medical Complex. It is recommended that standardized discharge slip templates be implemented, healthcare workers receive proper training, and thorough monitoring be conducted before patients are discharged. These measures aim to enhance the standard of documentation. Additionally, regular future clinical audits are essential for tracking the impact of these interventions and ensuring patient safety and continuity of care.

Addressing Statistical Power and Increasing Diversity in Hospice Research: Electronic Medical Record Participant Identification Compared to Nurse Referral Approaches to Recruitment.

CONTEXT: Recruitment of targeted samples into hospice clinical trials is often challenging. While electronic medical records (EMR) are commonly used in hospital-based research, it is uncommon in hospice research. The community setting and the variability in hospices and their medical record creates unique challenges. OBJECTIVES: This paper compares recruitment in two hospice randomized controlled trials, each of which had a group recruited by using the EMR identification and a group recruited by nurse referral. We sought to answer three questions: 1) What is the impact of using the EMR to identify hospice participants for clinical research? 2) How do the referral count and consent rate (referrals that ultimately result in verbal informed consent to participate in research) differ between hospice agencies using an EMR participant identification approach compared to those using a nurse referral approach? and 3) What are the challenges associated with using the EMR to identify potential research participants? METHOD: Recruitment data from two hospice clinical trials was combined into a new database. Data from hospice nurse referral agencies was compared with data from those agencies who participated in EMR-identified referrals. RESULTS: The EMR identification process was feasible and efficient, resulting in more referrals and more consented participants than the nurse referral method. Of particular interest is that 8% more black caregivers were recruited using the EMR identification process than the nurse referral. CONCLUSIONS: The EMR-identified recruitment process is the recommended method in hospice research.

Oncology nursing in the Eastern Mediterranean Region: listening to the workforce.

BACKGROUND: Over half the countries in the World Health Organization (WHO) Eastern Mediterranean Region (EMR) are experiencing conflict or are socially fragile, compromising cancer care. Nonetheless, throughout the EMR, competent nurses are major players in the cancer care team. The aim of this paper is to portray the challenges and opportunities for oncology nursing in the EMR. METHODS: This paper draws upon the relevant literature on oncology nursing across EMR with a focus on Afghanistan, Lebanon, Somaliland, and Iran. To enhance the scant nursing literature and obtain real-life experiences, short interviews were undertaken with nine nurses and two doctors, personal contacts of the authors, working in cancer care in those countries. RESULTS: Against the general background of vast economic constraints in health services, the lack of recognition of oncology nursing as a speciality and high rates of nurse migration, many oncology nurses in EMR are fighting for professional recognition and some are working under unsafe conditions. Undeterred by these circumstances, nurses are making every effort to care compassionately for people with cancer. CONCLUSIONS: The perspectives of the cancer workforce in EMR both foster an appreciation of cultural diversity and provide the evidence and motivation for oncology nurses worldwide to further collaborate via global nursing organisations to strive for country-specific recognition and change in nursing practice.

Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study.

Background: The National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. These activities are challenging because there are no standards for data collection. The World Health Organization (WHO) introduced a standard for health data collection during disasters for emergency medical teams (EMTs) in the form of a minimum dataset (MDS). Meanwhile, the Ministry of Health of Indonesia launched the SATUSEHAT platform to integrate all electronic medical records in Indonesia based on Fast Healthcare Interoperability Resources (FHIR). Objective: This study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR. Methods: We extracted variables from 2 EMT MDS medical records-the WHO and Association of Southeast Asian Nations (ASEAN) versions-and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server. Results: The total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap. Conclusions: We successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters.

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.

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.

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.

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.

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.

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries.

The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

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.