Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach.

OBJECTIVES: Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU. DESIGN: Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework. SETTING: International survey study. PATIENT/SUBJECTS: Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support. INTERVENTIONS: An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions. MEASUREMENTS AND MAIN RESULTS: A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews. CONCLUSIONS: In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.

Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review.

Mental illness leading to suicide attempts is prevalent in a large portion of the population especially in low and middle-income nations. There remains a significant social stigma associated with mental illness that can lead to stigmatization of patients. Hence, patients are reluctant to communicate their problems to health care providers. Physicians have difficulty in timely identification of patients at risk for suicide. Novel and rigorously designed strategies are needed to determine the population at risk for suicide. This would be the first step in overcoming the multitude of barriers in the management of mental illness. Clinical tools and the use of electronic medical records (EMR) are time intensive. Recently, several artificial intelligence (AI)-based predictive technologies have gained momentum. The aim of this review is to summarize the recent advances in this landscape.

Influence of medical trainee sleep pattern (chronotype) on burn-out and satisfaction with work schedules: a multicentre observational study.

BACKGROUND: Medical trainees' work schedule is designed to cover duties without consideration of differences in circadian rhythms during a 24-hour period (chronotype). OBJECTIVE: To explore chronotype variation among medical trainees and understand its association with burn-out and schedule satisfaction. METHODS: In a multicentre observational study, we conducted two surveys between 1 October 2018 and 1 April 2019. Trainees from nine centres across the USA participated. We measured burn-out using Maslach Burnout Inventory (MBI), and trainee chronotype using the Morningness-Eveningness Questionnaire (MEQ). RESULTS: 324 (32%) out of 1012 responded to our survey. Participants were 51% female and had a mean age of 30.8 years. Most participants had an intermediate MEQ type (65%). A large proportion of participants had burn-out on at least one of three tested MBI scales (62%); 5% of participants had burn-out on all three MBI scales. More participants with evening MEQ type had burn-out (66%) compared with morning MEQ type (55%), however, the results were not statically significant (p=0.294). Overall satisfaction with work shifts was 6.5 (95% CI 6.3 to 6.7), with higher satisfaction with day shift 7.7 (95% CI 7.5 to 7.9) and lowest satisfaction with overnight 24-hour call 3.5 (95% CI 3.2 to 3.9). Satisfaction was lower in trainees with burn-out 6.0 (95% CI 5.7 to 6.4), (p<0.001). In the follow-up survey, burn-out was present in at least one scale in 64% compared with 60% of respondents in the initial survey. CONCLUSION: Burn-out is prevalent among medical trainees. Improving alignment between trainee preferences may improve performance, reduce human errors and burn-out.

Duplicate inferior vena cava filters: more is not always better.

Duplication of the inferior vena cava (IVC) has been reported in literature. This achieves clinical significance in the setting of lower extremity venous thromboembolism with a contraindication for anticoagulation. We describe a case of lower extremity deep vein thrombosis with duplicate IVC. Anticoagulation was contraindicated in this case leading to successful treatment with double IVC filters. We conducted a PubMed search for all current English language published literature, where filters were placed in the presence of duplicate IVC. We suggest that patients with deep vein thrombosis should have an accurate assessment of venous anatomy before IVC filter placement. Duplication of IVC, although rare, should be considered as this has management implications.