OBJECTIVE: Intravenous medication errors continue to significantly impact patient safety and outcomes. This study sought to clarify the complexity and risks of the intravenous administration process.
METHODS: A qualitative focus group interview study.
METHODS: Focused interviews were conducted using process mapping with frontline nurses responsible for medication administration in September 2020.
METHODS: Front line experiened nurses from a Japanese tertiary teaching hospital.
METHODS: The primary outcome measure was to identify the mental models frontline nurses used during intravenous medication administration, which influence their interactions with patients, and secondarily, to examine the medication process gaps between the mental models nurses perceive and the actual defined medication administration process.
RESULTS: We found gaps between the perceived clinical administration process and the real process challenges with an emphasis on the importance of verifying to see if the drug was ordered for the patient immediately before its administration.
CONCLUSIONS: This novel and applied improvement approach can help nurses and managers better understand the process vulnerability of the infusion process and develop a deeper understanding of the administration steps useful for reliably improving the safety of intravenous medications.

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BACKGROUND: Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report.
OBJECTIVE: This systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes.
METHODS: This systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate.
RESULTS: The project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024.
CONCLUSIONS: Evaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance.
BACKGROUND: PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747.
UNASSIGNED: PRR1-10.2196/51614.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team\'s technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation.

This Viewpoint describes the potential benefits and harms of using artificial intelligence (AI) in health care decision-making processes.

UNASSIGNED: In 2008, the Department of Health (DOH) issued Administrative Order 2008-0023 that called for an \"effective and efficient monitoring system that will link all patient safety initiatives\". However, there are still no explicit and harmonized targets to measure effectiveness and to provide benchmarks that assess whether previous efforts were helpful.
UNASSIGNED: The study aimed to describe the status of patient safety performance measures and indicators on the international patient safety goals (IPSGs) in select hospitals in the Philippines.
UNASSIGNED: Descriptive, cross-sectional design was used to investigate currently used performance measures and indicators. Data collection included administration of a Hospital Patient Safety Indicators Questionnaire (HPSIQ) that summarized the currently used patient safety measures and indicators in the sampled Level 2 and level 3 hospitals and triangulation by review of documents such as hospital databases, protocols on reporting, and manuals for information gathering regarding patient safety. Performance measures were categorized using the Donabedian framework. Core indicators were identified through review of standards that cut across the six IPSGs and evaluation of overarching processes and concepts in patient safety.
UNASSIGNED: Forty-one level 2 and 3 hospitals participated in the study. Most performance indicators were process measures (52%), while structure (31%) and outcome measures (17%) accounted for the rest. There is an obvious lack of structural requirements for patient safety in the hospitals included in this study. Less than half the hospitals surveyed implement risk assessment and management consistently. Reporting of events, near-misses, and patient safety data are widely varied among hospitals. Data utilization for quality improvement is not fully established in many of the hospitals. Patient engagement is not integrated in service delivery and performance measurement but is crucial in promoting patient safety.
UNASSIGNED: Mechanisms to improve hospitals\' capacity to monitor, anticipate, and reduce risk of patient harm during the provision of healthcare should be provided. Having a unified set of definitions and protocols for measurement will facilitate reliable monitoring and improvement. Leadership and governance, both internal (e.g., hospital administrators) and external (e.g., DOH) that recognize a data-driven approach to policymaking and improvement of service delivery are crucial in promoting patient safety.

UNASSIGNED: When adverse events (AE) occur, there are different consequences for healthcare professionals. The environment in which professionals work can influence the experience. This study aims to explore the experiences of second victims (SV) among health professionals in Argentina.
UNASSIGNED: A phenomenological study was used with in-depth interviews with healthcare professionals. Audio recordings and verbatim transcriptions were analyzed independently for themes, subthemes, and codes.
UNASSIGNED: Three main themes emerged from the analysis: navigating the experience, the environment, and the turning point. Subthemes were identified for navigating the experience to describe the process: receiving the impact, transition, and taking action.
UNASSIGNED: SVs undergo a process after an AE. The environment is part of this experience. It is a turning point in SVs\' professional and personal lives. Improving the psychological safety (PS) environment is essential for ensuring the safety of SVs.

UNASSIGNED: Acknowledging peer support as the cornerstone in mitigating the psychosocial burden arising from the second victim phenomenon, this study assesses the economic benefits of a Peer Support Program (PSP), compared to data of the Resilience In Stressful Events (RISE) program in the US, within the acute inpatient care sector in Germany.
UNASSIGNED: Employing a Markov model, this economic evaluation analyzes the cost benefits, including sick day and dropout costs, over a 1-year period, comparing scenarios with and without the Peer Support Program from a hospital perspective. The costs were calculated as an example based on a hospital with 1,000 employees. The estimations are considered conservative.
UNASSIGNED: The anticipated outcomes demonstrate an average cost saving of €6,672 per healthcare worker participating in the Peer Support Program, leading to an annual budgetary impact of approximately €6,67 Mio. for the studied hospital.
UNASSIGNED: The integration of a PSP proves economically advantageous for German hospitals, not only preserving financial resources but also reducing absenteeism, and mitigating turnover, thereby enhancing overall patient care.

Calibration of an analytical measurement procedure is an important basis for the reliability of patient results. Many publications and as well as procedures on how to estimate quality control and interpret those results have been become available over the years. In this publication we are focusing on the critical part of the calibration as there are no clear communication or guidelines on how to perform it. Usually only the recommendation of the reagent or instrument manufacturer is available. We would like to point out this gap to invite for a discussion and improvement of the current situation.