Graduate medical education success coaching for residents and fellows: a single-institution real-world experience.

While coaching has been employed as a success strategy in many areas such as athletics and business for decades, its use is relatively new in the medical field despite evidence of its benefits. Implementation and engagement regarding coaching in graduate medical education (GME) for residents and fellows is particularly scarce. We report our three-year experience of a GME success coaching program that aims to help trainees reach their full potential by addressing various areas of medical knowledge, clinical skills, efficiency, interpersonal skills and communication, professionalism, and mental health and well-being. The majority of participants (87%) were identified by themselves, their program director, and/or the GME coaches to have more than one area of need. The majority (79%) of referrals were identified by the coaches to have additional needs to the reasons for referral. We provide a framework for implementation of a GME coaching program and propose that coaching in GME may provide an additional safe environment for learners to reveal areas of concerns or difficulty that otherwise would not be disclosed and/or addressed.

The Autonomy Toolbox: A Multicenter Collaborative to Promote Resident Autonomy.

OBJECTIVES: Autonomy is necessary for resident professional development and well-being. A recent focus on patient safety has increased supervision and decreased trainee autonomy. Few validated interventions exist to improve resident autonomy. We aimed to use quality improvement methods to increase our autonomy metric, the Resident Autonomy Score (RAS), by 25% within 1 year and sustain for 6 months. METHODS: We developed a bundled-intervention approach to improve senior resident (SR) perception of autonomy on Pediatric Hospital Medicine (PHM) services at 5 academic children's hospitals. We surveyed SR and PHM faculty perceptions of autonomy and targeted interventions toward areas with the highest discordance. Interventions included SR and faculty development, expectation-setting huddles, and SR independent rounding. We developed a Resident Autonomy Score (RAS) index to track SR perceptions over time. RESULTS: Forty-six percent of SRs and 59% of PHM faculty completed the needs assessment survey querying how often SRs were afforded opportunities to provide autonomous medical care. Faculty and SR ratings were discordant in these domains: SR input in medical decisions, SR autonomous decision-making in straightforward cases, follow-through on SR plans, faculty feedback, SR as team leader, and level of attending oversight. The RAS increased by 19% (3.67 to 4.36) 1 month after SR and faculty professional development and before expectation-setting and independent rounding. This increase was sustained throughout the 18-month study period. CONCLUSIONS: SRs and faculty perceive discordant levels of SR autonomy. We created an adaptable autonomy toolbox that led to sustained improvement in perception of SR autonomy.

Machine learning-based clinical decision support for infection risk prediction.

Background: Healthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment. Methods: This study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics. Results: Our best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings. Conclusion: The predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.

Gig Economy Riders on Social Media in Thailand: Contested Identities and Emergent Civil Society Organisations.

The emergence of the gig economy has generated a new class of workers who are categorised as independent "partners" instead of employees with rights to labour protection. Triggered by observations of a protest movement by platform-based delivery riders in Thailand, we engaged in seven months of digital ethnographic research of riders' interactions online to understand the emergence of informal groups facilitating mutual aid and collective action. Civil society research has neglected to analyse such groups within the gig economy. The study finds that social media is a site for the development and contestation of identity narratives. We observed a "Hero" narrative that glorifies delivery riders' independent status and a "Worker" narrative that challenges riders' conditions. We argue that these collective identity narratives crucially facilitate or inhibit the emergence of labour-oriented civil society organisations, thus contributing to third sector research that examines civil society in the Global South.

Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19.

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Faster clinical response to the onset of adverse events: A wearable metacognitive attention aid for nurse triage of clinical alarms.

OBJECTIVE: This study evaluates the potential for improving patient safety by introducing a metacognitive attention aid that enables clinicians to more easily access and use existing alarm/alert information. It is hypothesized that this introduction will enable clinicians to easily triage alarm/alert events and quickly recognize emergent opportunities to adapt care delivery. The resulting faster response to clinically important alarms/alerts has the potential to prevent adverse events and reduce healthcare costs. MATERIALS AND METHODS: A randomized within-subjects single-factor clinical experiment was conducted in a high-fidelity 20-bed simulated acute care hospital unit. Sixteen registered nurses, four at a time, cared for five simulated patients each. A two-part highly realistic clinical scenario was used that included representative: tasking; information; and alarms/alerts. The treatment condition introduced an integrated wearable attention aid that leveraged metacognition methods from proven military systems. The primary metric was time for nurses to respond to important alarms/alerts. RESULTS: Use of the wearable attention aid resulted in a median relative within-subject improvement for individual nurses of 118% (W = 183, p = 0.006). The top quarter of relative improvement was 3,303% faster (mean; 17.76 minutes reduced to 1.33). For all unit sessions, there was an overall 148% median faster response time to important alarms (8.12 minutes reduced to 3.27; U = 2.401, p = 0.016), with 153% median improvement in consistency across nurses (F = 11.670, p = 0.001). DISCUSSION AND CONCLUSION: Existing device-centric alarm/alert notification solutions can require too much time and effort for nurses to access and understand. As a result, nurses may ignore alarms/alerts as they focus on other important work. There has been extensive research on reducing alarm frequency in healthcare. However, alarm safety remains a top problem. Empirical observations reported here highlight the potential of improving patient safety by supporting the meta-work of checking alarms.