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Electronic Health Records offer an opportunity to improve patient care (in terms of quality and/or safety) by making available patient health information stored in a single Clinical Data Repository. We aimed to estimate the frequency of hypoglycemic recurrences in hospitalized adult patients in non-critical areas. We designed a cross sectional study with hospitalizations between 2017 and 2018, which included patients with at least one hypoglycemia health record (defined as a value less then 70 mg/dL, either by capillary glycemic monitoring or serum measurement). Recurrence was defined as those who presented a second event, with at least 2 hours of difference. We included 1884 patients, and 573 presented recurrences, yielding a global prevalence of 30.41% (95%CI 28.34-32.54). Due to the high frequency, it is important to identify vulnerable populations, to implement preventive measures to assist clinicians for decision-making tasks, as a clinical decision support system.Due to the COVID-19 pandemic, changes and improvements regarding the organization have been made to adapt quickly at the Emergency Department (ED) of the Hospital Italiano de Buenos Aires, Argentina. This article describes the design, implementation, and use of an electronic dashboard which provided monitoring of patients discharged home, during follow-up with telehealth. It was useful to access essential information to organize and coordinate professional work and patients' surveillance, providing highly relevant data in real-time as proxy variables for quality and safety during home isolation. The implemented tool innovated in the integration of technologies within a real context. The information management was crucial to optimize services and decision-making, as well to guarantee safety for healthcare workers and patients.The fourth industrial revolution is based on cyber-physical systems and the connectivity of devices. It is currently unclear what the consequences are for patient safety as existing digital health technologies become ubiquitous with increasing pace and interact in unforeseen ways. In this paper, we describe the output from a workshop focused on identifying the patient safety challenges associated with emerging digital health technologies. We discuss six challenges identified in the workshop and present recommendations to address the patient safety concerns posed by them. A key implication of considering the challenges and opportunities for Patient Safety Informatics is the interdisciplinary contribution required to study digital health technologies within their embedded context. The principles underlying our recommendations are those of proactive and systems approaches that relate the social, technical and regulatory facets underpinning patient safety informatics theory and practice.Non-routine events (NREs) are any aspect of care perceived by clinicians as a deviation from optimal care. The reporting of NREs to peers (or care teams) may help healthcare organizations improve patient safety in high-risk work environments (e.g., surgery). While various factors, including care structure and organizational factors may influence a clinician's NRE reporting behavior, their role has not been systematically studied. We conducted a retrospective study relying on NREs and electronic health records to determine if perioperative interaction structures among clinicians are associated with the frequency of NRE reporting in a large academic medical center. The data covers November 1, 2016, to January 31, 2019 and includes 295 perioperative clinicians, 225 neonatal surgical cases, and 543 NREs. Using network analysis, we measured a clinician's status in interaction structures according to the sociometric factors of degree, betweenness, and eigenvector centrality. We applied a proportional odds model to measure the relationship between each sociometric factor and NRE reporting frequency. Our findings indicate that the centrality of clinicians is directly associated with the quantity of NREs per surgical case.At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.Patient Centered Outcomes Research (PCOR) and health care delivery system transformation require investments in development of tools and techniques for rapid dissemination of clinical and operational best practices. This paper explores the current technology landscape for patient-centered clinical decision support (PC CDS) and what is needed to make it more shareable, standards-based, and publicly available with the goal of improving patient care and clinical outcomes. The landscape assessment used three sources of information (1) a 22-member technical expert panel; (2) a literature review of peer-reviewed and grey literature; and (3) key informant interviews with PC CDS stakeholders. We identified ten salient technical considerations that span all phases of PC CDS development; our findings suggest there has been significant progress in the development and implementation of PC CDS but challenges remain.Operating rooms are a major cost factor in a hospital's budget. Therefore, there is a need for process optimization related to the operating rooms (OR). However, the collection of key figures for process optimization is often done manually by medical staff. This can be erroneous, inaccurate, time consuming, and incomplete. Automated, data-driven approaches are intended to address these problems and help to get the most precise picture possible of what is happening within the OR. At Heidelberg University Hospital (UKHD), a distributed AI based streaming analytics architecture was set up and integrated into the Medical Data Integration Center (MeDIC). This architecture can process, store, and visualize heterogeneous data from different sources. Data from medical devices and the video stream of the wall mounted cameras of four integrated operating rooms are ingested into our system. Aggregated and analyzed in real-time computed key figures including OR state and utilization numbers are visualized in a dashboard for monitoring and decision support. Because of high data protection hurdles the proposed system, especially the video analytics, was trained and tested with statists and did not run during real procedures. Studies to evaluate and test the system during live surgeries are planned.Breast cancer represents 23% of all cancers diagnosed among women each year. BRCA1 and BRCA2 are tumor suppressor genes related to the most frequent form of hereditary breast and ovarian cancer, as well as other types of cancer. The aim of this work is to describe the development of Clinical Decision Support Systems (CDSS) for referral to genetic counseling in patients at increased risk of pathogenic variants in BRCA1 and BRCA2, and to describe results during the pilot study implementation (from January 5, 2021 to March 5, 2021). To achieve integration and system interoperability, we used FHIR and CDS-Hooks within the CDSS development. A total of 142 alerts were triggered by the system for 72 physicians in 98 patients. Results showed an acceptance rate for the recommendation of 2.1%, which could improve using intrusive alerts in all of the hooks.Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.Clinical decision support systems have been widely used in healthcare, yet few studies have concurrently measured the clinical effectiveness of CDSSs, and the appropriateness of alerts with physicians' response to alerts. We conducted a retrospective analysis of prescriptions caused disease-medication related alerts. Medication orders for outpatients' prescriptions, all aged group were included in this study. All the prescriptions were reviewed, and medication orders compared with a widely used medication reference (UpToDate) and other standard guidelines. We reviewed 1,409 CDS alerts (2.67% alert rate) on 52,654 prescriptions ordered during the study period. 545 (38.70%) of alerts were overridden. Override appropriateness was 2.20% overall. YK-4-279 in vitro However, the rate of alert acceptance was higher, ranging from 11.11 to 92.86%. The MedGuard system had a lower overridden rate than other systems reported in previous studies. The acceptance rate of alerts by physicians was high. Moreover, false-positive rate was low. The MedGuard system has the potential to reduce alert fatigue and to minimize the risk of patient harm.