Perioperative Management of Noncardiac Implanted Medical Devices.

Proactive safety management in health care : towards a broader view of risk analysis, error recovery, and safety culture

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. CLINICALTRIAL PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/51614

BackgroundArtificial 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.ObjectiveThis 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.MethodsThis 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.ResultsThe 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.ConclusionsEvaluations 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.Trial RegistrationPROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747International Registered Report Identifier (IRRID)PRR1-10.2196/51614

본 연구의 목적은 간호대학생의 환자안전간호와 관련된 요인을 융·복합적 측면에서 체계적으로 고찰하고, 메 타분석을 통해 관련 요인의 효과크기를 파악하는 것이다. 연구방법은 문헌검색은 Medline, Embases, CINAHL, DBpia, Research Information Service System(Riss), Korean Studies Information Service System(Kiss) 을 이용하였으며, 국외 데이터베이스는 MeSH용어와 Emtree를 활용하여 검색하였다. 검색식은 [(patient safety or patient harm or safety management) and (students, nursing)] or [(patient safety or patient harm or safety management) and (education, nursing, graduate)] 이었다. 문헌선정은 PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)를 이용하였다. 연구결과 관련 요인으로는 간호수행, 지식, 태도, 자신감, 인식 및 인지 등이 확인되었고, 그 중 환자안전간호 수행과 관련성이 높은 요인에는 자신감, 태도, 인식, 지식 순으로 파악되었다.

Cybersecurity vulnerabilities of cardiac implantable electronic devices: Communication strategies for clinicians—Proceedings of the Heart Rhythm Society's Leadership Summit

Risk and safety management in infertility and assisted reproductive technology (ART): from the doctor's office to the ART procedure

Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions

Safe and Secure? Healthcare in The Cyberworld

DuPont: Safety Management in a Re-Engineered Corporate Culture

Medical devices significantly enhance healthcare by integrating advanced technology to improve patient outcomes. Ensuring their safety and reliability requires a delicate balance between innovation and rigorous oversight, managed through the collaborative efforts of standards development organizations, standards accrediting organizations, and regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This article explores the historical evolution of medical device regulation, the role of standards organizations, and the impact of regulatory practices on device safety. Highlighting the critical need for stringent regulations, informed by instances where medical devices caused patient harm, we discuss the processes and collaborations between various international standards and regulatory frameworks that ensure device safety and effectiveness. This comprehensive review addresses the complexities of regulatory compliance and standardization, aiming to bridge the knowledge gap among healthcare providers and enhance the implementation of safety standards in medical technology.

Practice Advisory for the Perioperative Management of Patients with Cardiac Rhythm Management Devices: Pacemakers and Implantable Cardioverter–Defibrillators

Health information technology safety: the perfect storms.

Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.

PurposeMedical devices are used to monitor, replace, or modify anatomy or physiological processes. They are important health care innovations that enable effective treatment using less invasive techniques, and they improve health care delivery and patient outcomes. Devices can also introduce risk of harm to patients. Our objective was to propose a surveillance system framework to improve the safety associated with the use of medical devices in a hospital.Materials and methodsThe proposed medical device surveillance system incorporates multiple components to accurately document and assess the appropriate actions to reduce the risk of incidents, adverse events, and patient harm. The assumptions on which the framework is based are highlighted. The surveillance system was designed from the perspective of a tertiary teaching hospital that includes dedicated hospital staff whose mandate is to provide safe patient care to inpatients and outpatients and biomedical engineering services.ResultsThe main components of the surveillance system would include an adverse medical device events database, a medical device/equipment library, education and training, and an open communication and feedback strategy. Close linkages among these components and with external medical device/equipment networks to the hospital must be established and maintained. A feedback mechanism on medical device-related incidents, as well as implementation and evaluation strategies for the surveillance system are described to ensure a seamless transition and a high satisfactory level among the hospital staff. The direct cost items of the proposed surveillance system for consideration, and its potential benefits are outlined.ConclusionThe effectiveness of the proposed medical device surveillance system framework can be measured after it has been implemented in a Canadian hospital facility.

ObjectiveThis study aimed to extract incident and accident reports associated with high-flow nasal cannula (HFNC) therapy from the publicly available database maintained by the Japan Council for Quality Health Care (JCQHC) and to identify contributing factors using the P-mSHEL model (Patient-management-Software-Hardware-Environment-Liveware), with the goal of providing insights for improving HFNC safety.MethodsAmong 94,069 incident reports (defined as cases without patient harm) and 56,783 accident reports (cases with patient harm) submitted between 2010 and 2023, a total of 170 HFNC-related cases (131 incidents and 39 accidents) were identified. Quantitative variables included the time of occurrence, patient demographics, and involvement of healthcare professionals. Qualitative data were classified into six categories (P, m, S, H, E, L) using the P-mSHEL model. For accident cases, the presence or absence of sequelae was analyzed using logistic regression.ResultsIn accident cases, the Patient (P) factor (odds ratio = 2.65, p = 0.006) and the Management (m) factor (odds ratio = 2.46, p = 0.033) were significantly associated with the occurrence of sequelae. In incident cases, the Liveware (L) factor (i.e., human error) was involved in 80.2% of reports, and the Hardware (H) factor (i.e., medical devices) in 52.7%, highlighting the critical roles of human and device-related factors.ConclusionTo ensure the safe use of HFNC, it is essential to implement risk mitigation strategies targeting the Patient and Management factors, such as comprehensive patient screening and strengthened organizational systems. Furthermore, given the high involvement of the Liveware and Hardware factors, structured educational programs and practical interventions for medical device operation are warranted.