This project represents the first systematic assessment of the US Food and Drug Administration's postmarket surveillance of legally marketed artificial intelligence and machine learning based medical devices. We focus on the Manufacturer and User Facility Device Experience database-the FDA's central tool for tracking the safety of marketed AI/ML devices. In particular, we evaluate the data pertaining to adverse events associated with approximately 950 medical devices incorporating AI/ML functions for devices approved between 2010 through 2023, and we find that the existing system is insufficient for properly assessing the safety and effectiveness of AI/ML devices. In particular, we make three contributions: (1) characterize the adverse event reports for such devices, (2) examine the ways in which the existing FDA adverse reporting system for medical devices falls short, and (3) suggest changes FDA might consider in its approach to adverse event reporting for devices incorporating AI/ML functions.

To examine health disparities in the management and clinical outcomes of singleton pregnancies diagnosed with acute-onset severe hypertension (AOSH) during hospitalization.

Retrospective cohort study (2016-2021) of pregnant patients with AOSH, defined as systolic blood pressure (SBP) ≥ 160 mmHg or diastolic blood pressure (DBP) ≥ 110 mmHg ≥ 15 min apart.

The primary outcome was timely therapy of AOSH, defined as correctly dosed first-line antihypertensive medication administered <60 min. Secondary outcomes were adverse maternal and perinatal composite outcomes. Multivariate logistic regression models were built to assess the association between patient factors, type of OBGYN practice, institutional factors, and the primary and secondary outcomes.

Of the 732 patients, 298 (40.7 %) were non-Hispanic white, 297 (40.6 %) were non-Hispanic black, and 137 (18.7 %) were Hispanic. The primary outcome was not associated with race/ethnicity or other patient factors. The odds of timely emergent therapy of AOSH were 50 % higher in the 15:00 h-22:59 h nursing shift versus other shifts (aOR = 1.48; 95 % CI: 1.02, 2.18; P = 0.04). Furthermore, patients managed by private OBGYN physicians had significantly lower odds of adverse perinatal composite outcomes than those managed by academic OBGYN physicians (aOR = 0.39, 95 % CI: 0.21, 0.73; P = 0.003). Maternal adverse outcomes were not associated with any of the study factors.

Timely anti-hypertensive therapy for AOSH was not influenced by maternal race/ethnicity, language, or insurance status. However, timely therapy was more likely during the evening shift, and perinatal outcomes were better with private OBGYN practice.

Turnaround time (TAT) has evolved into a complex metric in the current era of diagnostic radiology. Initially monitoring a radiologist's ability to efficiently generate a report, there is now a hierarchy of stakeholders who are affected by its modern-day interpretation and use. This article explores "more than the metric" and reviews the multi-faceted use and implementation of TAT through the lens of a large academic hospital network. The authors highlight "The Good" of turnaround time as a useful quality metric that guides patient outcomes, organizational performance, and revenue generation, and they discuss how it has been implemented at their institution to positively influence workflow and guide departmental staffing. The "bad" implications of TAT are discussed, which can strain radiology departmental workflows and propagate issues such as delays in finalizing reports for inpatient and outpatient worklists in the setting of rapidly growing imaging volumes. Broader systemic issues of TAT are discussed within "The Ugly" section of this review, which highlights TAT's negative impact on resident education, physician burnout, and its creation of "New School" and "Old School" radiologist practices. The article provides further discussion of existing strategies and future directions that can mitigate the negative impacts of TAT, including various staffing models, approaches to optimize workflow systems, and methods for re-prioritizing STAT cases using artificial intelligence. The authors conclude with a call for balanced, continued implementation of TAT as a metric but not at the expense of sacrificing the quality of work performed by radiologists and trainees.

 This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS).

 We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between November 25, 2023, and January 15, 2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use.

 A total of 47/143 providers completed the survey. Thirty-eight out of 47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). Thirty-eight out of 47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights into patient care, but almost three-quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47).

 Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.

The integration of AI into healthcare is widely anticipated to revolutionize medical diagnostics, enabling earlier, more accurate disease detection and personalized care.

In this study, we developed and validated an AI-assisted diagnostic support tool using only routinely ordered and broadly available blood tests to predict the presence of major chronic and acute diseases as well as rare disorders.

Our model was tested on both retrospective and prospective datasets comprising over one million patients. We evaluated the diagnostic performance by (1) implementing ensemble learning (mean ROC-AUC.9293 and mean DOR 63.96); (2) assessing the model's sensitivity via risk scores to simulate its screening effectiveness; (3) analyzing the potential for early disease detection (30-270 days before clinical diagnosis) through creating historical patient timelines and (4) conducting validation on real-world clinical data in collaboration with Synlab Hungary, to assess the tool's performance in clinical setting.

Uniquely, our model not only considers stable blood values but also tracks changes from baseline across 15 years of patient history. Our AI-driven automated diagnostic tool can significantly enhance clinical practice by recognizing patterns in common and rare diseases, including malignancies. The models' ability to detect diseases 1-9 months earlier than traditional clinical diagnosis could contribute to reduced healthcare costs and improved patient outcomes. The automated evaluation also reduces evaluation time of healthcare providers, which accelerates diagnostic processes. By utilizing only routine blood tests and ensemble methods, the tool demonstrates high efficacy across independent laboratories and hospitals, making it an exceptionally valuable screening resource for primary care physicians.

In this editorial, we discuss a recently published manuscript by Blüthner et al in the World Journal of Gastroenterology, with a specific focus on the delayed diagnosis of inflammatory bowel disease (IBD). IBD, which includes Crohn's disease and ulcerative colitis, is a chronic intestinal disorder. A time lag may exist between the onset of inflammation and the appearance of signs and symptoms, potentially leading to an incorrect or delayed diagnosis, a situation referred to as the delayed diagnosis of IBD. Early diagnosis is crucial for effective patient treatment and prognosis, yet delayed diagnosis remains common. The reasons for delayed diagnosis of IBD are numerous and not yet fully understood. One key factor is the nonspecific nature of IBD symptoms, which can easily be mistaken for other conditions. Additionally, the lack of specific diagnostic methods for IBD contributes to these delays. Delayed diagnosis of IBD can result in numerous adverse consequences, including increased intestinal damage, fibrosis, a higher risk of colorectal cancer, and a decrease in the quality of life of the patient. Therefore, it is essential to diagnose IBD promptly by raising physician awareness, enhancing patient education, and developing new diagnostic methods.

Few studies have described the insights of frontline health care providers and patients on how the diagnostic process can be improved in the emergency department (ED), a setting at high risk for diagnostic errors. The authors aimed to identify the perspectives of providers and patients on the diagnostic process and identify potential interventions to improve diagnostic safety.

Semistructured interviews were conducted with 10 ED physicians, 15 ED nurses, and 9 patients/caregivers at two separate health systems. Interview questions were guided by the ED-Adapted National Academies of Sciences, Engineering, and Medicine Diagnostic Process Framework and explored participant perspectives on the ED diagnostic process, identified vulnerabilities, and solicited interventions to improve diagnostic safety. The authors performed qualitative thematic analysis on transcribed interviews.

The research team categorized vulnerabilities in the diagnostic process and intervention opportunities based on the ED-Adapted Framework into five domains: (1) team dynamics and communication (for example, suboptimal communication between referring physicians and the ED team); (2) information gathering related to patient presentation (for example, obtaining the history from the patients or their caregivers; (3) ED organization, system, and processes (for example, staff schedules and handoffs); (4) patient education and self-management (for example, patient education at discharge from the ED); and (5) electronic health record and patient portal use (for example, automatic release of test results into the patient portal). The authors identified 33 potential interventions, of which 17 were provider focused and 16 were patient focused.

Frontline providers and patients identified several vulnerabilities and potential interventions to improve ED diagnostic safety. Refining, implementing, and evaluating the efficacy of these interventions are required.

Diagnostic errors are a source of morbidity and mortality in intensive care unit (ICU) patients. However, contextual factors influencing clinicians' diagnostic performance have not been studied in authentic ICU settings. We sought to determine the accuracy of ICU clinicians' diagnostic impressions and to characterize how various contextual factors, including self-reported stress levels and perceptions about the patient's prognosis and complexity, impact diagnostic accuracy. We also explored diagnostic calibration, i.e. the balance of accuracy and confidence, among ICU clinicians.

We conducted an observational cohort study in an academic medical ICU. Between June and August 2019, we interviewed ICU clinicians during routine care about their patients' diagnoses, their confidence, and other contextual factors. Subsequently, using adjudicated final diagnoses as the reference standard, two investigators independently rated clinicians' diagnostic accuracy and on each patient on a given day ("patient-day") using 5-point Likert scales. We conducted analyses using both restrictive and conservative definitions of clinicians' accuracy based on the two reviewers' ratings of accuracy.

We reviewed clinicians' responses for 464 unique patient-days, which included 255 total patients. Attending physicians had the greatest diagnostic accuracy (77-90 %, rated as three or higher on 5-point Likert scale) followed by the team's primary fellow (73-88 %). Attending physician and fellows were also least affected by contextual factors. Diagnostic calibration was greatest among ICU fellows.

Additional studies are needed to better understand how contextual factors influence different clinicians' diagnostic reasoning in the ICU.

Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms.

We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach.

Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting.Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12-1.24) to 4.5 (1.8-11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive.Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction.The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies.

This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations.

The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178885.

To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard.

We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software.

We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU.

This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.

To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR).

Single center randomized crossover study.

Quaternary care academic hospital.

Attending and in-training critical care physicians, and advanced practice providers.

AMP.

We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078).

When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting.

Inappropriate laboratory test ordering is a significant and persistent problem. Many causes have been identified and studied. Medical laboratory professionals (MLPs) are technical staff within clinical laboratories who are uniquely positioned to comment on why inappropriate ordering occurs. We aimed to characterize existing MLP perceptions in this domain to reveal new or underemphasized interventional targets.

We developed and disseminated a self-administered survey to MLPs in Canada, including open-ended responses to questions about the causes of inappropriate laboratory test ordering.

Four primary themes were identified from qualitative analysis: ordering-provider factors, communication factors, existing test-ordering processes, and patient factors. Although these factors can largely be found in previous literature, some are under-studied.

MLP insights into nonphysician triage ordering and poor result communication provide targets for further investigation. A heavy focus on individual clinician factors suggests that current understandings and interprofessional skills in the MLP population can be improved.

Diagnostic error or delay (DEOD) is common in the acute care setting and results in poor patient outcomes. Many factors contribute to DEOD, but little is known about how contributors may differ across acute care areas and professional roles. As part of a sequential exploratory mixed methods research study, we surveyed acute care clinical stakeholders about the frequency with which different factors contribute to DEOD. Survey respondents could also propose solutions in open text fields. N = 220 clinical stakeholders completed the survey. Care Team Interactions, Systems and Process, Patient, Provider, and Cognitive factors were perceived to contribute to DEOD with similar frequency. Organization and Infrastructure factors were perceived to contribute to DEOD significantly less often. Responses did not vary across acute care setting. Physicians perceived Cognitive factors to contribute to DEOD more frequently compared to those in other roles. Commonly proposed solutions included: technological solutions, organization level fixes, ensuring staff know and are encouraged to work to the full scope of their role, and cultivating a culture of collaboration and respect. Multiple factors contribute to DEOD with similar frequency across acute care areas, suggesting the need for a multi-pronged approach that can be applied across acute care areas.

Researchers have identified lengthy diagnosis delays in patients with hypermobile Ehlers-Danlos syndrome and hypermobility spectrum disorders (hEDS/HSD), but the reason for these delays is unclear.

This review seeks to synthesize the existing qualitative research about hEDS/HSD to understand the reasons for diagnosis delay.

We searched PubMed, Scopus, CINAHL, Google Scholar, and Dissertations and Theses databases for all qualitative studies about hEDS/HSD that mentioned the diagnosis process. A total of 283 studies were retrieved, from which we identified 13 studies to include in this synthesis.

The reviewers identified and organized diagnosis delay themes under four overarching categories: disease, patient, provider, and system. Disease factors included the nature of the symptoms and lack of a confirmatory test. Patient factors included psychological and emotional responses, seeing multiple providers, and receiving multiple diagnoses. Provider factors related to limited knowledge and attitudes. System factors included silo-based health care systems and bureaucratic barriers.

Diagnosis delays result from complex, overlapping, and interacting factors. Nurse practitioners have a critical role in improving care and reducing diagnosis delays in patients with hEDS/HSD. Further research is needed to understand the causes and consequences of diagnosis delays in hEDS/HSD.