Rationale: Prone positioning reduces mortality in patients with severe acute respiratory distress syndrome (ARDS), a feature of severe COVID-19. Despite this, most patients with ARDS do not receive this life-saving therapy. Objectives: To identify determinants of prone positioning utilization, to develop specific implementation strategies, and to incorporate strategies into an overarching response to the COVID-19 crisis. Methods: We used an implementation mapping approach guided by implementation science frameworks. We conducted semi-structured interviews with 30 ICU clinicians who staffed 12 ICUs within the Penn Medicine health system and the University of Michigan Medical Center. We performed thematic analysis utilizing the Consolidated Framework for Implementation Research (CFIR). We then conducted three focus groups with a task force of ICU leaders to develop an implementation menu, using the Expert Recommendations for Implementing Change (ERIC) framework. The implementation strategies were adapted as part of the Penn Medicine COVID-19 pandemic response. Results: We identified five broad themes of determinants of prone positioning: knowledge, resources, alternative therapies, team culture, and patient factors, which collectively spanned all five CFIR domains. The task force developed five specific implementation strategies: educational outreach, learning collaborative, clinical protocol, prone positioning team, and automated alerting, elements of which were rapidly implemented at Penn Medicine.

Critical care clinicians experienced high rates of burnout and depression early in the coronavirus disease 2019 (COVID-19) pandemic. [1][2][3] We sought to longitudinally evaluate burnout, depression, and professional fulfillment -as measures of overall clinician wellnessamong critical care healthcare professionals at seven hospitals within our hospital network. We hypothesized that well-being and depression would initially worsen over time but would improve with the arrival of the vaccine, and that burnout rates would be higher among nonphysicians with less professional time dedicated to non-clinical activities such as education and research, which may allow time for renewal. MethodsWe administered a questionnaire quarterly to attending physicians, advanced practice providers (APPs; including nurse practitioners and physician assistants), respiratory therapists (RTs), and clinical pharmacists who staffed ICUs of seven hospitals at Penn Medicine. Nurses did not participate due to concurrent research studies and leadership concern about survey fatigue. As detailed elsewhere, our integrated, academic medical center leveraged the health system Critical Care Alliance to create the COVID-19 Task Force, which served to develop and disseminate standardized clinical protocols, educate critical care clinicians, and monitor and optimize outcomes. 4,5 We invited participants by email in July/

Objective Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. Materials and Methods We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV). Results We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49–0.54) followed by random forests (SBS 0.49, 95% CI 0.47–0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37–0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%–56.6%) at a sensitivity of 80%. Discussion Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models. Conclusions NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.

Background Behavioral economic insights have yielded strategies to overcome implementation barriers. For example, default strategies and accountable justification strategies have improved adherence to best practices in clinical settings. Embedding such strategies in the electronic health record (EHR) holds promise for simple and scalable approaches to facilitating implementation. A proven-effective but under-utilized treatment for patients who undergo mechanical ventilation involves prescribing low tidal volumes, which protects the lungs from injury. We will evaluate EHR-based implementation strategies grounded in behavioral economic theory to improve evidence-based management of mechanical ventilation. Methods The Implementing Nudges to Promote Utilization of low Tidal volume ventilation (INPUT) study is a pragmatic, stepped-wedge, hybrid type III effectiveness implementation trial of three strategies to improve adherence to low tidal volume ventilation. The strategies target clinicians who enter electronic orders and respiratory therapists who manage the mechanical ventilator, two key stakeholder groups. INPUT has five study arms: usual care, a default strategy within the mechanical ventilation order, an accountable justification strategy within the mechanical ventilation order, and each of the order strategies combined with an accountable justification strategy within flowsheet documentation. We will create six matched pairs of twelve intensive care units (ICUs) in five hospitals in one large health system to balance patient volume and baseline adherence to low tidal volume ventilation. We will randomly assign ICUs within each matched pair to one of the order panels, and each pair to one of six wedges, which will determine date of adoption of the order panel strategy. All ICUs will adopt the flowsheet documentation strategy 6 months afterwards. The primary outcome will be fidelity to low tidal volume ventilation. The secondary effectiveness outcomes will include in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay, and occurrence of potential adverse events. Discussion This stepped-wedge, hybrid type III trial will provide evidence regarding the role of EHR-based behavioral economic strategies to improve adherence to evidence-based practices among patients who undergo mechanical ventilation in ICUs, thereby advancing the field of implementation science, as well as testing the effectiveness of low tidal volume ventilation among broad patient populations. Trial registration ClinicalTrials.gov, NCT04663802. Registered 11 December 2020.

This survey study examined perceptions of patients, caregivers and health care professionals on the number of hospital-free days required for detection of a minimum clinically important difference or noninferiority margin of new interventions.

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