Perspectives on the role of industry in informatics research and authorship.

OBJECTIVES: Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship. PROCESS: We convened an expert panel at the American Medical Informatics Association 2022 Annual Symposium to explore the role of industry in informatics research and authorship with community input. The panel summarized session themes and prepared recommendations. CONCLUSIONS: Authorship for informatics research, regardless of affiliation, should be determined by International Committee of Medical Journal Editors uniform requirements for authorship. All authors meeting criteria should be included, and categorical rejection based on author affiliation is unethical. Informatics research should be evaluated based on its scientific rigor; all sources of bias and conflicts of interest should be addressed through disclosure and, when possible, methodological mitigation.

Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States.

OBJECTIVE: The COVID-19 pandemic presented a challenge to inpatient safety. It is unknown whether there were spillover effects due to COVID-19 into non-COVID-19 care and safety. We sought to evaluate the changes in inpatient Agency for Healthcare Research and Quality patient safety indicators (PSIs) in the United States before and during the first surge of the pandemic among patients admitted without COVID-19. METHODS: We analyzed trends in PSIs from January 2019 to June 2020 in patients without COVID-19 using data from IBM MarketScan Commercial Database. We included members of employer-sponsored or Medicare supplemental health plans with inpatient, non-COVID-19 admissions. The primary outcomes were risk-adjusted composite and individual PSIs. RESULTS: We analyzed 1,869,430 patients admitted without COVID-19. Among patients without COVID-19, the composite PSI score was not significantly different when comparing the first surge (Q2 2020) to the prepandemic period (e.g., Q2 2020 score of 2.46 [95% confidence interval {CI}, 2.34-2.58] versus Q1 2020 score of 2.37 [95% CI, 2.27-2.46]; P = 0.22). Individual PSIs for these patients during Q2 2020 were also not significantly different, except in-hospital fall with hip fracture (e.g., Q2 2020 was 3.42 [95% CI, 3.34-3.49] versus Q4 2019 was 2.45 [95% CI, 2.40-2.50]; P = 0.01). CONCLUSIONS: The first surge of COVID-19 was not associated with worse inpatient safety for patients without COVID-19, highlighting the ability of the healthcare system to respond to the initial surge of the pandemic.

National learning curves among robotic thoracic surgeons in the United States: Quantifying the impact of procedural experience on efficiency and productivity gains.

OBJECTIVE: This study aims to characterize the aggregate learning curves of US surgeons for robotic thoracic procedures and to quantify the impact on productivity. METHODS: National average console times relative to cumulative case number were extracted from the My Intuitive application (Version 1.7.0). Intuitive da Vinci robotic system data for 56,668 lung resections performed by 870 individual surgeons between 2021 and 2022 were reviewed. Console time and hourly productivity (work relative value units/hour) were analyzed using linear regression models. RESULTS: Average console times improved for all robotic procedures with cumulative case experience (P = .003). Segmentectomy and thymectomy had the steepest initial learning curves with a 33% and 34% reduction of the average console time for proficient (51-100 cases) relative to novice surgeons (1-10 cases), respectively. The hourly productivity increase for proficient surgeons ranged from 11.4 work relative value units/hour (+26%) for lobectomy to 17.0 work relative value units/hour (+50%) for segmentectomy. At the expert level (101+ cases), average console times continued to decrease significantly for esophagectomy (-18%) and lobectomy (-23%), but only minimally for wedge resections (-1%) (P = .003). The work relative value units/hour increase at the expert level reached 50% for lobectomy and 40% for esophagectomy. Surgeon experience level, dual console use, system model, and robotic stapler use were factors independently associated with console time for robotic lobectomy. CONCLUSIONS: The aggregate learning curve for robotic thoracic surgeons in the United States varies significantly by procedure type and demonstrate continued improvements in efficiency beyond 100 cases for lobectomy and esophagectomy. Improvements in efficiency with growing experiences translate to substantial productivity gains.

Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

[This corrects the article DOI: 10.2196/43960.].

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

BACKGROUND: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. OBJECTIVE: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. METHODS: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. RESULTS: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). CONCLUSIONS: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

OBJECTIVE: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting. BACKGROUND: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown. METHODS: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy. CONCLUSIONS: Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals.

PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. RESULTS: Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. CONCLUSIONS: The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Parent Activation in the Neonatal Intensive Care Unit.

OBJECTIVE: Patient activation is the knowledge, skills, and confidence to manage one's health; parent activation is a comparable concept related to a parent's ability to manage a child's health. Activation in adults is a modifiable risk factor and associated with clinical outcomes and health care utilization. We examined activation in parents of hospitalized newborns observing temporal trends and associations with sociodemographic characteristics, neonate characteristics, and outcomes. STUDY DESIGN: Participants included adult parents of neonates admitted to a level-IV neonatal intensive care unit in an academic medical center. Activation was measured with the 10-item Parent version of the Patient Activation Measure (P-PAM) at admission, discharge, and 30 days after discharge. Associations with sociodemographic variables, health literacy, clinical variables, and health care utilization were evaluated. RESULTS: A total of 96 adults of 64 neonates were enrolled. The overall mean P-PAM score on admission was 81.8 (standard deviation [SD] = 18), 88.8 (SD = 13) at discharge, and 86.8 (SD = 16) at 30-day follow-up. Using linear mixed regression model, P-PAM score was significantly associated with timing of measurement. Higher P-PAM scores were associated with higher health literacy (p = 0.002) and higher in mothers compared to fathers (p = 0.040). There were no significant associations of admission P-PAM scores with sociodemographic characteristics. Parents of neonates who had a surgical diagnosis had a statistically significant (p = 0.003) lower score than those who did not. There were no associations between discharge P-PAM scores and neonates' lengths of stay or other indicators of illness severity. CONCLUSION: Parental activation in the NICU setting was higher than reported in the adult and limited pediatric literature; scores increased from admission to discharge and 30-day postdischarge. Activation was higher in mothers and parents with higher health literacy. Additional larger scale studies are needed to determine whether parental activation is associated with long-term health care outcomes as seen in adults. KEY POINTS: · Little is known about activation in parents of neonates.. · Activation plays a role in health outcomes in adults.. · Larger studies are needed to explore parent activation..

Mutational profiles of head and neck squamous cell carcinomas based upon human papillomavirus status in the Veterans Affairs National Precision Oncology Program.

BACKGROUND: Patients with advanced head and neck squamous cell carcinoma (HNSCC) associated with human papillomavirus (HPV) demonstrate favorable clinical outcomes compared to patients bearing HPV-negative HNSCC. We sought to characterize the association between HPV status and mutational profiles among patients served by the Veterans Health Administration (VHA). METHODS: We performed a retrospective analysis of all Veterans with primary HNSCC tumors who underwent next-generation sequencing (NGS) through the VHA's National Precision Oncology Program between July 2016 and February 2019. HPV status was determined by clinical pathology reports of p16 immunohistochemical staining; gene variant pathogenicity was classified using OncoKB, an online precision oncology knowledge database, and mutation frequencies were compared using Fisher's exact test. RESULTS: A total of 79 patients met inclusion criteria, of which 48 (60.8%) had p16-positive tumors. Patients with p16-negative HNSCC were more likely to have mutations in TP53 (p < 0.0001), and a trend towards increased mutation frequency was observed within NOTCH1 (p = 0.032) and within the composite CDK/Rb pathway (p = 0.065). Mutations in KRAS, NRAS, HRAS, and FBXW7 were exclusively identified within p16-positive tumors, and a trend towards increased frequency was observed within the PI3K pathway (p = 0.051). No difference in overall mutational burden was observed between the two groups. CONCLUSIONS: In accordance with the previous studies, no clear molecular basis for improved prognosis among patients harboring HPV-positive disease has been elucidated. Though no targeted therapies are approved based upon HPV-status, current efforts to trial PI3K inhibitors and mTOR inhibitors across patients with HPV-positive disease bear genomic rationale based upon the current findings.

Understanding a Care Management System's Role in Influencing a Transitional-Aged Youth Program's Practice: Mixed Methods Study.

BACKGROUND: Extended foster care programs help prepare transitional-aged youth (TAY) to step into adulthood and live independent lives. Aspiranet, one of California's largest social service organizations, used a social care management solution (SCMS) to meet TAY's needs. OBJECTIVE: We aimed to investigate the impact of an SCMS, IBM Watson Care Manager (WCM), in transforming foster program service delivery and improving TAY outcomes. METHODS: We used a mixed methods study design by collecting primary data from stakeholders through semistructured interviews in 2021 and by pulling secondary data from annual reports, system use logs, and data repositories from 2014 to 2021. Thematic analysis based on grounded theory was used to analyze qualitative data using NVivo software. Descriptive analysis of aggregated outcome metrics in the quantitative data was performed and compared across 2 periods: pre-SCMS implementation (before October 31, 2016) and post-SCMS implementation (November 1, 2016, and March 31, 2021). RESULTS: In total, 6 Aspiranet employees (4 leaders and 2 life coaches) were interviewed, with a median time of 56 (IQR 53-67) minutes. The majority (5/6, 83%) were female, over 30 years of age (median 37, IQR 32-39) with a median of 6 (IQR 5-10) years of experience at Aspiranet and overall field experience of 10 (IQR 7-14) years. Most (4/6, 67%) participants rated their technological skills as expert. Thematic analysis of participants' interview transcripts yielded 24 subthemes that were grouped into 6 superordinate themes: study context, the impact of the new tool, key strengths, commonly used features, expectations with WCM, and limitations and recommendations. The tool met users' initial expectations of streamlining tasks and adopting essential functionalities. Median satisfaction scores around pre- and post-WCM workflow processes remained constant between 2 life coaches (3.25, IQR 2.5-4); however, among leaders, post-WCM scores (median 4, IQR 4-5) were higher than pre-WCM scores (median 3, IQR 3-3). Across the 2 study phases, Aspiranet served 1641 TAY having consistent population demographics (median age of 18, IQR 18-19 years; female: 903/1641, 55.03%; race and ethnicity: Hispanic or Latino: 621/1641, 37.84%; Black: 470/1641, 28.64%; White: 397/1641, 24.19%; Other: 153/1641, 9.32%). Between the pre- and post-WCM period, there was an increase in full-time school enrollment (359/531, 67.6% to 833/1110, 75.04%) and a reduction in part-time school enrollment (61/531, 11.5% to 91/1110, 8.2%). The median number of days spent in the foster care program remained the same (247, IQR 125-468 years); however, the number of incidents reported monthly per hundred youth showed a steady decline, even with an exponentially increasing number of enrolled youth and incidents. CONCLUSIONS: The SCMS for coordinating care and delivering tailored services to TAY streamlined Aspiranet's workflows and processes and positively impacted youth outcomes. Further enhancements are needed to better align with user and youth needs.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

The Association of the First Surge of the COVID-19 Pandemic with the High- and Low-Value Outpatient Care Delivered to Adults in the USA.

BACKGROUND: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown. OBJECTIVE: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed. DESIGN: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior. PARTICIPANTS: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database. MAIN MEASURES: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value). KEY RESULTS: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71). CONCLUSIONS: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use.

Electronic Patient Portal Access, Retention in Care, and Viral Suppression Among People Living With HIV in Southeastern United States: Observational Study.

BACKGROUND: Approximately 1.1 million people living with HIV live in the United States, and the incidence is highest in Southeastern United States. Electronic patient portal prevalence is increasing and can improve engagement in primary medical care. Retention in care and viral suppression-measures of engagement in HIV care-are associated with decreased HIV transmission, morbidity, and mortality. OBJECTIVE: We aimed to determine if patient portal access among people living with HIV was associated with retention and viral suppression. METHODS: We conducted an observational cohort study among people living with HIV in care at the Vanderbilt Comprehensive Care Clinic (Nashville, Tennessee) from 2011-2016. Individual access was defined as patient portal account registration at any point in the year prior. Retention was defined as ≥2 kept appointments or HIV lab measurements ≥3 months apart within a 12-month period. Viral suppression was defined as the last viral load in the calendar year <200 copies/mL. We calculated adjusted prevalence ratios (aPRs) and 95% CIs using modified Poisson regression with generalized estimating equations to estimate the association of portal access with retention and viral suppression. RESULTS: We included 4237 people living with HIV contributing 16,951 person-years of follow-up (median 5, IQR 3-5 person-years). The median age was 43 (IQR 33-50) years. Of the 4237 people living with HIV, 78.1% (n=4237) were male, 40.8% (n=1727) were Black non-Hispanic, and 56.5% (n=2395) had access. Access was independently associated with retention (aPR 1.13, 95% CI 1.10-1.17) and viral suppression (aPR 1.18, 95% CI 1.14-1.22). CONCLUSIONS: In this population, patient portal access was associated with retention and viral suppression. Future prospective studies should assess the impact of increasing portal access among people living with HIV on these HIV outcomes.

Assessing and addressing COVID-19 information needs via a weather application.

We describe implementation and usage of a coronavirus disease 2019 (COVID-19) digital information hub delivered through the widely adopted The Weather Company (TWC) application and explore COVID-19 knowledge, behaviors, and information needs of users. TWC deployed the tool, which displayed local case counts and trends, in March 2020. Unique users, visits, and interactions with tool features were measured. In August 2020, a cross-sectional survey assessed respondent characteristics, COVID-19 knowledge, behaviors, and preferences. TWC COVID-19 hub averaged 1.97 million unique users with over 2.6 million visits daily and an average interaction time of 1.63 min. Respondents reported being knowledgeable about COVID-19 (92.3%) and knowing relevant safety precautions (90.9%). However, an average of 35.3% of respondents reported not increasing preventive practices across behaviors surveyed due to information about COVID-19. In conclusion, we find a free weather application delivered COVID-19 data to millions of Americans. Despite confidence in knowledge and best practices for prevention, over one-third of survey respondents did not increase practice of preventive behaviors due to information about COVID-19.

Physicians' Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle-Income Countries.

As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians' perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle-income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle-income countries for cancer care.

A tribute to Karen Greenwood and her contributions to the American Medical Informatics Association.

After 25 years of service to the American Medical Informatics Association (AMIA), Ms Karen Greenwood, the Executive Vice President and Chief Operating Officer, is leaving the organization. In this perspective, we reflect on her accomplishments and her effect on the organization and the field of informatics nationally and globally. We also express our appreciation and gratitude for Ms Greenwood's role at AMIA.

SARS-CoV-2 Testing of Aerosols Emitted During Pediatric Minimally Invasive Surgery: A Prospective, Case-Controlled Study.

BACKGROUND: The COVID-19 pandemic has presented significant safety concerns for healthcare providers, especially those performing aerosol-generating procedures. Several surgical societies issued early warnings that aerosols generated during minimally invasive surgery (MIS) could harbor infectious quantities of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This study tested the hypothesis that MIS-aerosols contain SARS-CoV-2. METHODS: To evaluate SARS-CoV-2 presence in aerosols emitted during intracavitary MIS, children <18 years who required emergent MIS and were discovered to be SARS-CoV-2-positive were enrolled. Swabs were obtained from the port in-line with a filtered smoke evacuation system, the tubing adjacent to this port, the fluid collection chamber and filter, and the distal endotracheal tube (ETT). All swabs were analyzed for SARS-CoV-2 using quantitative reverse-transcription polymerase chain reaction. To evaluate viral distribution in tissues, fluorescence in situ hybridization for SARS-CoV-2 was performed on resected specimens. Outcomes were recorded, and participating healthcare workers were tracked for SARS-CoV-2 conversion. RESULTS: From July 1, 2020, to June 30, 2021, 11 children requiring emergent MIS were discovered preoperatively to be SARS-CoV-2 positive (median age: 14 years [5-17]). SARS-CoV-2 was detected only in ETT swabs and not in surgical aerosols or specimens. Median operative time was 56.5 minutes (IQR: 46-66), and postoperative stay was 21.2 hours (IQR: 1.97-57.57). No complications or viral eruption were recorded, and none of 63 healthcare workers tested positive for SARS-CoV-2 within 6 weeks. DISCUSSION: SARS-CoV-2 was detected only in ETT secretions and not in surgical aerosols or specimens among a pediatric cohort of asymptomatic patients having emergent MIS.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.