Jonathan Bae

BACKGROUND: Healthcare is approaching a tipping point as burnout and dissatisfaction with work-life integration (WLI) in healthcare workers continue to increase. A scale evaluating common behaviours as actionable examples of WLI was introduced to measure work-life balance. OBJECTIVES: (1) Explore differences in WLI behaviours by role, specialty and other respondent demographics in a large healthcare system. (2) Evaluate the psychometric properties of the work-life climate scale, and the extent to which it acts like a climate, or group-level norm when used at the work setting level. (3) Explore associations between work-life climate and other healthcare climates including teamwork, safety and burnout. METHODS: Cross-sectional survey study completed in 2016 of US healthcare workers within a large academic healthcare system. RESULTS: 10 627 of 13 040 eligible healthcare workers across 440 work settings within seven entities of a large healthcare system (81% response rate) completed the routine safety culture survey. The overall work-life climate scale internal consistency was α=0.830. WLI varied significantly among healthcare worker role, length of time in specialty and work setting. Random effects analyses of variance for the work-life climate scale revealed significant between-work setting and within-work setting variance and intraclass correlations reflected clustering at the work setting level. T-tests of top versus bottom WLI quartile work settings revealed that positive work-life climate was associated with better teamwork and safety climates, as well as lower personal burnout and burnout climate (p<0.001). CONCLUSION: Problems with WLI are common in healthcare workers and differ significantly based on position and time in specialty. Although typically thought of as an individual difference variable, WLI appears to operate as a climate, and is consistently associated with better safety culture norms.

Importance: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated. Objectives: To prospectively and externally validate a machine learning model that predicts in-hospital mortality for all adult patients at the time of hospital admission and to design the model using commonly available electronic health record data and accessible computational methods. Design, Setting, and Participants: In this prognostic study, electronic health record data from a total of 43 180 hospitalizations representing 31 003 unique adult patients admitted to a quaternary academic hospital (hospital A) from October 1, 2014, to December 31, 2015, formed a training and validation cohort. The model was further validated in additional cohorts spanning from March 1, 2018, to August 31, 2018, using 16 122 hospitalizations representing 13 094 unique adult patients admitted to hospital A, 6586 hospitalizations representing 5613 unique adult patients admitted to hospital B, and 4086 hospitalizations representing 3428 unique adult patients admitted to hospital C. The model was integrated into the production electronic health record system and prospectively validated on a cohort of 5273 hospitalizations representing 4525 unique adult patients admitted to hospital A between February 14, 2019, and April 15, 2019. Main Outcomes and Measures: The main outcome was in-hospital mortality. Model performance was quantified using the area under the receiver operating characteristic curve and area under the precision recall curve. Results: A total of 75 247 hospital admissions (median [interquartile range] patient age, 59.5 [29.0] years; 45.9% involving male patients) were included in the study. The in-hospital mortality rates for the training validation; retrospective validations at hospitals A, B, and C; and prospective validation cohorts were 3.0%, 2.7%, 1.8%, 2.1%, and 1.6%, respectively. The area under the receiver operating characteristic curves were 0.87 (95% CI, 0.83-0.89), 0.85 (95% CI, 0.83-0.87), 0.89 (95% CI, 0.86-0.92), 0.84 (95% CI, 0.80-0.89), and 0.86 (95% CI, 0.83-0.90), respectively. The area under the precision recall curves were 0.29 (95% CI, 0.25-0.37), 0.17 (95% CI, 0.13-0.22), 0.22 (95% CI, 0.14-0.31), 0.13 (95% CI, 0.08-0.21), and 0.14 (95% CI, 0.09-0.21), respectively. Conclusions and Relevance: Prospective and multisite retrospective evaluations of a machine learning model demonstrated good discrimination of in-hospital mortality for adult patients at the time of admission. The data elements, methods, and patient selection make the model implementable at a system level.

BACKGROUND: Improving the resiliency of healthcare workers is a national imperative, driven in part by healthcare workers having minimal exposure to the skills and culture to achieve work-life balance (WLB). Regardless of current policies, healthcare workers feel compelled to work more and take less time to recover from work. Satisfaction with WLB has been measured, as has work-life conflict, but how frequently healthcare workers engage in specific WLB behaviours is rarely assessed. Measurement of behaviours may have advantages over measurement of perceptions; behaviours more accurately reflect WLB and can be targeted by leaders for improvement. OBJECTIVES: 1. To describe a novel survey scale for evaluating work-life climate based on specific behavioural frequencies in healthcare workers.2. To evaluate the scale's psychometric properties and provide benchmarking data from a large healthcare system.3. To investigate associations between work-life climate, teamwork climate and safety climate. METHODS: Cross-sectional survey study of US healthcare workers within a large healthcare system. RESULTS: 7923 of 9199 eligible healthcare workers across 325 work settings within 16 hospitals completed the survey in 2009 (86% response rate). The overall work-life climate scale internal consistency was Cronbach α=0.790. t-Tests of top versus bottom quartile work settings revealed that positive work-life climate was associated with better teamwork climate, safety climate and increased participation in safety leadership WalkRounds with feedback (p<0.001). Univariate analysis of variance demonstrated differences that varied significantly in WLB between healthcare worker role, hospitals and work setting. CONCLUSIONS: The work-life climate scale exhibits strong psychometric properties, elicits results that vary widely by work setting, discriminates between positive and negative workplace norms, and aligns well with other culture constructs that have been found to correlate with clinical outcomes.

Importance: Electronic health records (EHRs) are considered a potentially significant contributor to clinician burnout. Objective: To describe the association of EHR usage, sex, and work culture with burnout for 3 types of clinicians at an academic medical institution. Design, Setting, and Participants: This cross-sectional study of 1310 clinicians at a large tertiary care academic medical center analyzed EHR usage metrics for the month of April 2019 with results from a well-being survey from May 2019. Participants included attending physicians, advanced practice providers (APPs), and house staff from various specialties. Data were analyzed between March 2020 and February 2021. Exposures: Clinician demographic characteristics, EHR metadata, and an institution-wide survey. Main Outcomes and Measures: Study metrics included clinician demographic data, burnout score, well-being measures, and EHR usage metadata. Results: Of the 1310 clinicians analyzed, 542 (41.4%) were men (mean [SD] age, 47.3 [11.6] years; 448 [82.7%] White clinicians, 52 [9.6%] Asian clinicians, and 21 [3.9%] Black clinicians) and 768 (58.6%) were women (mean [SD] age, 42.6 [10.3] years; 573 [74.6%] White clinicians, 105 [13.7%] Asian clinicians, and 50 [6.5%] Black clinicians). Women reported more burnout (survey score ≥50: women, 423 [52.0%] vs men, 258 [47.6%]; P = .008) overall. No significant differences in EHR usage were found by sex for multiple metrics of time in the EHR, metrics of volume of clinical encounters, or differences in products of clinical care. Multivariate analysis of burnout revealed that work culture domains were significantly associated with self-reported results for commitment (odds ratio [OR], 0.542; 95% CI, 0.427-0.688; P < .001) and work-life balance (OR, 0.643; 95% CI, 0.559-0.739; P < .001). Clinician sex significantly contributed to burnout, with women having a greater likelihood of burnout compared with men (OR, 1.33; 95% CI, 1.01-1.75; P = .04). An increased number of days spent using the EHR system was associated with less likelihood of burnout (OR, 0.966; 95% CI, 0.937-0.996; P = .03). Overall, EHR metrics accounted for 1.3% of model variance (P = .001) compared with work culture accounting for 17.6% of variance (P < .001). Conclusions and Relevance: In this cross-sectional study, sex-based differences in EHR usage and burnout were found in clinicians. These results also suggest that local work culture factors may contribute more to burnout than metrics of EHR usage.

BACKGROUND: Interventions to decrease burnout and increase well-being in health care workers (HCWs) and improve organizational safety culture are urgently needed. This study was conducted to determine the association between Positive Leadership WalkRounds (PosWR), an organizational practice in which leaders conduct rounds and ask staff about what is going well, and HCW well-being and organizational safety culture. METHODS: This study was conducted in a large academic health care system in which senior leaders were encouraged to conduct PosWR. The researchers used data from a routine cross-sectional survey of clinical and nonclinical HCWs, which included a question about recall of exposure of HCWs to PosWR: "Do senior leaders ask for information about what is going well in this work setting (e.g., people who deserve special recognition for going above and beyond, celebration of successes, etc.)?"-along with measures of well-being and safety culture. T-tests compared work settings in the first and fourth quartiles for PosWR exposure across SCORE (Safety, Communication, Operational Reliability, and Engagement) domains of safety culture and workforce well-being. RESULTS: Electronic surveys were returned by 10,627 out of 13,040 possible respondents (response rate 81.5%) from 396 work settings. Exposure to PosWR was reported by 63.1% of respondents overall, with a mean of 63.4% (standard deviation = 20.0) across work settings. Exposure to PosWR was most commonly reported by HCWs in leadership roles (83.8%). Compared to work settings in the fourth (< 50%) quartile for PosWR exposure, those in the first (> 88%) quartile revealed a higher percentage of respondents reporting good patient safety norms (49.6% vs. 69.6%, p < 0.001); good readiness to engage in quality improvement activities (60.6% vs. 76.6%, p < 0.001); good leadership accessibility and feedback behavior (51.9% vs. 67.2%, p < 0.001); good teamwork norms (36.8% vs. 52.7%, p < 0.001); and good work-life balance norms (61.9% vs. 68.9%, p = 0.003). Compared to the fourth quartile, the first quartile had a lower percentage of respondents reporting emotional exhaustion in themselves (45.9% vs. 32.4%, p < 0.001), and in their colleagues (60.5% vs. 47.7%, p < 0.001). CONCLUSION: Exposure to PosWR was associated with better HCW well-being and safety culture.