A Perioperative Mental Health Intervention for Depressed and Anxious Older Surgical Patients: Results From a Feasibility Study.

OBJECTIVES: The perioperative period is challenging and stressful for older adults. Those with depression and/or anxiety have an increased risk of adverse surgical outcomes. We assessed the feasibility of a perioperative mental health intervention composed of medication optimization and a wellness program following principles of behavioral activation and care coordination for older surgical patients. METHODS: We included orthopedic, oncologic, and cardiac surgical patients aged 60 and older. Feasibility outcomes included study reach, the number of patients who agreed to participate out of the total eligible; and intervention reach, the number of patients who completed the intervention out of patients who agreed to participate. Intervention efficacy was assessed using the Patient Health Questionnaire for Anxiety and Depression (PHQ-ADS). Implementation potential and experiences were collected using patient surveys and qualitative interviews. Complementary caregiver feedback was also collected. RESULTS: Twenty-three out of 28 eligible older adults participated in this study (mean age 68.0 years, 65% women), achieving study reach of 82% and intervention reach of 83%. In qualitative interviews, patients (n = 15) and caregivers (complementary data, n = 5) described overwhelmingly positive experiences with both the intervention components and the interventionist, and reported improvement in managing depression and/or anxiety. Preliminary efficacy analysis indicated improvement in PHQ-ADS scores (F = 12.13, p <0.001). CONCLUSIONS: The study procedures were reported by participants as feasible and the perioperative mental health intervention to reduce anxiety and depression in older surgical patients showed strong implementation potential. Preliminary data suggest its efficacy for improving depression and/or anxiety symptoms. A randomized controlled trial assessing the intervention and implementation effectiveness is currently ongoing.

Measuring the cognitive effort associated with task switching in routine EHR-based tasks.

OBJECTIVE: Clinical work involves performing overlapping, time-sensitive tasks that frequently require clinicians to switch their attention between multiple tasks. We developed a methodological approach using EHR-based audit logs to determine switch costs-the cognitive burden associated with task switching-and assessed its magnitude during routine EHR-based clinical tasks. METHOD: Physician trainees (N = 75) participated in a longitudinal study where they provided access to their EHR-based audit logs. Physicians' audit log actions were used to create a taxonomy of EHR tasks. These tasks were transformed into task sequences and the time spent on each task in a sequence was computed. Within these task sequences, instances of task switching (i.e., switching from one task to the next) and non-switching were identified. The primary outcome of interest was the time spent on a post-switch task. Using a mixed-effects regression model, we compared the durations of post-switch and non-switch tasks. RESULTS: 2,781,679 audit log events over 117,822 sessions from 75 physicians were analyzed. Physicians spent most time on chart review (Median (IQR) = 5,439 (2,492-8,336) seconds), note review (1,936 (827-3,321) seconds), and navigating the EHR interface (1,048 (365.5-2,006) seconds) daily. Post task switch activity times were greater for documentation (Median increase = 5 s), order entry (Median increase = 3 s) and results review (Median increase = 3 s). Mixed-effects regression showed that time spent on tasks were longer following a task switch (ß = 0.03; 95% CIlower = 0.027, CIupper = 0.034), with greater post-swtich task times for imaging, order entry, note review, handoff, note entry, chart review and best practice advisory tasks. DISCUSSION: Increased task switching time-an indicator of the cognitive burden associated with switching between tasks-is prevalent in routine EHR-based tasks. We discuss the cumulative impact of incremental switch costs have on overall EHR workload, wellness, and error rates. Relying on theoretical cognitive foundations, we suggest pragmatic design considerations for mitigating the effects of cognitive burden associated with task switching.

Characterizing the Patterns of Electronic Health Record-Integrated Secure Messaging Use: Cross-Sectional Study.

BACKGROUND: Communication among health care professionals is essential for the delivery of safe clinical care. Secure messaging has rapidly emerged as a new mode of asynchronous communication. Despite its popularity, relatively little is known about how secure messaging is used and how such use contributes to communication burden. OBJECTIVE: This study aims to characterize the use of an electronic health record-integrated secure messaging platform across 14 hospitals and 263 outpatient clinics within a large health care system. METHODS: We collected metadata on the use of the Epic Systems Secure Chat platform for 6 months (July 2022 to January 2023). Information was retrieved on message volume, response times, message characteristics, messages sent and received by users, user roles, and work settings (inpatient vs outpatient). RESULTS: A total of 32,881 users sent 9,639,149 messages during the study. Median daily message volume was 53,951 during the first 2 weeks of the study and 69,526 during the last 2 weeks, resulting in an overall increase of 29% (P=.03). Nurses were the most frequent users of secure messaging (3,884,270/9,639,149, 40% messages), followed by physicians (2,387,634/9,639,149, 25% messages), and medical assistants (1,135,577/9,639,149, 12% messages). Daily message frequency varied across users; inpatient advanced practice providers and social workers interacted with the highest number of messages per day (median 19). Conversations were predominantly between 2 users (1,258,036/1,547,879, 81% conversations), with a median of 2 conversational turns and a median response time of 2.4 minutes. The largest proportion of inpatient messages was from nurses to physicians (972,243/4,749,186, 20% messages) and physicians to nurses (606,576/4,749,186, 13% messages), while the largest proportion of outpatient messages was from physicians to nurses (344,048/2,192,488, 16% messages) and medical assistants to other medical assistants (236,694/2,192,488, 11% messages). CONCLUSIONS: Secure messaging was widely used by a diverse range of health care professionals, with ongoing growth throughout the study and many users interacting with more than 20 messages per day. The short message response times and high messaging volume observed highlight the interruptive nature of secure messaging, raising questions about its potentially harmful effects on clinician workflow, cognition, and errors.

Effects of Persistent Exposure to COVID-19 on Mental Health Outcomes Among Trainees: a Longitudinal Survey Study.

BACKGROUND: The rapid spread of the coronavirus disease 2019 (COVID-19) has created considerable strain on the physical and mental health of healthcare workers around the world. The effects have been acute for physician trainees-a unique group functioning simultaneously as learners and care providers with limited autonomy. OBJECTIVE: To investigate the longitudinal effects of physician trainee exposure to patients being tested for COVID-19 on stress, anxiety, depression, and burnout using three surveys conducted during the early phase of the pandemic. DESIGN: Longitudinal survey study. PARTICIPANTS: All physician trainees (N = 1375) at an academic medical center. MAIN MEASURE: Assess the relationship between repeated exposure to patients being tested for COVID-19 and stress, anxiety, depression, and burnout. KEY RESULTS: Three hundred eighty-nine trainees completed the baseline survey (28.3%). Of these, 191 and 136 completed the ensuing surveys. Mean stress, anxiety, and burnout decreased by 21% (95% confidence interval (CI): - 28 to - 12%; P < 0.001), 25% (95% CI: - 36 to - 11%; P < 0.001), and 13% (95% CI: - 18 to - 7%; P < 0.001), respectively, per survey. However, for each survey time point, there was mean increase in stress, anxiety, and burnout per additional exposure: stress [24% (95% CI: + 12 to + 38%; P < 0.001)], anxiety [22% (95% CI: + 2 to + 46%; P = 0.026)], and burnout [18% (95% CI: + 10 to + 28%; P < 0.001)]. For depression, the association between exposure was strongest for the third survey, where mean depression scores increased by 33% per additional exposure (95% CI: + 18 to + 50%; P < 0.001). CONCLUSIONS: Training programs should adapt to address the detrimental effects of the "pileup" of distress associated with persistent exposure through adaptive programs that allow flexibility for time off and recovery.

Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study.

BACKGROUND: The temporal progression and workload-related causal contributors to physician burnout are not well-understood. OBJECTIVE: To characterize burnout's time course and evaluate the effect of time-varying workload on burnout and medical errors. DESIGN: Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks. PARTICIPANTS: Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center. MAIN MEASURES: Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events. KEY RESULTS: Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7-1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1-0.6). In multivariable analysis, increased total EHR time (ß=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016-0.226), increased patient load (ß=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053-0.207), and increased chart review time (ß=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015-0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76-1.89). CONCLUSIONS: Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities.

Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.

BACKGROUND: Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders. METHODS: The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020. RESULTS: Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit. CONCLUSIONS: A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach.

Predicting physician burnout using clinical activity logs: Model performance and lessons learned.

BACKGROUND: Burnout is a significant public health concern affecting more than half of the healthcare workforce; however, passive screening tools to detect burnout are lacking. We investigated the ability of machine learning (ML) techniques to identify burnout using passively collected electronic health record (EHR)-based audit log data. METHOD: Physician trainees participated in a longitudinal study where they completed monthly burnout surveys and provided access to their EHR-based audit logs. Using the monthly burnout scores as the target outcome, we trained ML models using combinations of features derived from audit log data-aggregate measures of clinical workload, time series-based temporal measures of EHR use, and the baseline burnout score. Five ML models were constructed to predict burnout as a continuous score: penalized linear regression, support vector machine, neural network, random forest, and gradient boosting machine. RESULTS: 88 trainee physicians participated and completed 416 surveys; greater than10 million audit log actions were collected (Mean [Standard Deviation] = 25,691 [14,331] actions per month, per physician). The workload feature set predicted burnout score with a mean absolute error (MAE) of 0.602 (95% Confidence Interval (CI), 0.412-0.826), and was able to predict burnout status with an average AUROC of 0.595 (95% CI 0.355-0.808) and average accuracy 0.567 (95% CI 0.393-0.742). The temporal feature set had a similar performance, with MAE 0.596 (95% CI 0.391-0.826), and AUROC 0.581 (95% CI 0.343-0.790). The addition of the baseline burnout score to the workload features improved the model performance to a mean AUROC of 0.829 (95% CI 0.607-0.996) and mean accuracy of 0.781 (95% CI 0.587-0.936); however, this performance was not meaningfully different than using the baseline burnout score alone. CONCLUSIONS: Current findings illustrate the complexities of predicting burnout exclusively based on clinical work activities as captured in the EHR, highlighting its multi-factorial and individualized nature. Future prediction studies of burnout should account for individual factors (e.g., resilience, physiological measurements such as sleep) and associated system-level factors (e.g., leadership).

Continuous real-time prediction of surgical case duration using a modular artificial neural network.

BACKGROUND: Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration. METHODS: Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs. RESULTS: The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%). CONCLUSIONS: A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.

Natural trajectory of recovery of COVID-19 associated olfactory loss.

IMPORTANCE: Prevalence of post-viral olfactory loss has increased dramatically due to the frequency and severity of olfactory dysfunction associated with infection by the SARS-CoV-2 virus. OBJECTIVE: To determine the trajectory of COVID-19 olfactory loss over a six-month period. A key secondary objective is to assess predictive factors associated with the recovery of olfaction. DESIGN: Longitudinal repeated-measures study that enrolled from May 5, 2020 to February 2, 2021, with the last date of data collection on June 15, 2021. SETTING: Barnes-Jewish HealthCare/Washington University School of Medicine facilities (Saint Louis, Missouri, USA). PARTICIPANTS: Individuals who tested positive for SARS-CoV-2 by real-time polymerase chain reaction on nasopharyngeal swab and indicated olfactory loss on COVID-19 screening questionnaire. Individuals were excluded if they had previously diagnosed history of olfactory loss, neurodegenerative disorders, <18 years of age, admitted to hospital service, unable to read, write, and understand English, or lacked computer or internet access. INTERVENTIONS/EXPOSURES: Watch and wait for spontaneous recovery. MAIN OUTCOME(S) AND MEASURE(S): Participants completed olfactory assessments every 30 days for six months. Each assessment consisted of the University of Pennsylvania Smell Identification Test (UPSIT), an objective "scratch-and-sniff" test, and Clinical Global Impressions (CGI), a subjective Likert rating scale. RESULTS: The mean age was 41 years old (SD = 16). 39 (80 %) were female and 42 (86 %) white. At baseline assessment of objective olfaction, 18 (36 %) participants had anosmia or severe hyposmia. Subjective, complete recovery at six months was 81 % (95 % CI 74 % to 88 %). Likelihood of recovery was associated with age <50 years (aHR = 8.1 (95 % CI 1.1 to 64.1)) and mild olfactory loss at baseline (UPSIT = 30-33 for males and 31-34 for females) (aHR 6.2 (95 % CI 1.2 to 33.0)). CONCLUSIONS AND RELEVANCE: The trajectory of olfactory recovery among adults with COVID-19 olfactory loss illustrated rapid recovery within 2-3 weeks of infection, and by six months 81 % had recovered based on self-report. Age <50 years old and mild severity of olfactory loss at baseline were associated with increased likelihood of recovery of olfaction. These findings can be used to inform shared decision-making with patients.

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders.

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.

Surgical Complications in Older Adults Predict Decline in Self-Perceived Cognitive Function in the Ensuing Year: A Cohort Study.

BACKGROUND: Surgical complications are common among older adults and are potential indicators of poorer long-term outcomes. The authors examined the effects of in-hospital complications on changes in older adults' self-perceived cognitive function in the year after surgery. METHOD: The authors conducted a prospective longitudinal study with 2,155 older adults (age ≥ 65) undergoing surgery, investigating the association between self-reported, in-hospital complications after surgery and Patient-Reported Outcomes Measurement Information System Applied Cognition-Abilities survey (4 items, cognitive function) at 30 days and 1 year after surgery. Surveys were scored on a continuous scale of 0-100, with higher scores representing better self-perceived cognitive functioning. Patient characteristics including demographics, type of complications, surgery type, pain, and activities of daily living were also collected. RESULTS: Having one in-hospital complication was associated with a decrease of 1.79 points (95% confidence interval (CI): -2.78, -0.80), indicating lower self-perceived cognitive functioning at 1 year after surgery; having two or more in-hospital complications was associated with 2.82 point (95% CI: -4.50, -1.15) decrease at 1 year after surgery. Models specific to complication type indicated that respiratory [-3.04, (95% CI: -5.50, -0.57)], neural [-2.11, (95% CI: -3.97, -0.25)], and general complications [-2.39, (95% CI: -3.51, -1.28)] were associated with statistically significant decreases in cognitive function. DISCUSSION: Older surgical patients who suffer in-hospital complications show greater decline in self-perceived cognitive function during the ensuing year. Geriatric specialists may be able to intervene in the immediate perioperative period to reduce complications and possibly mitigate cognitive decline among older adults.

Systematic Review of Intraoperative Anesthesia Handoffs and Handoff Tools.

Intraoperative handoffs between anesthesia clinicians are critical for care continuity. However, such handoffs pose a significant threat to patient safety. This systematic review synthesizes the empirical evidence on the (a) effect of intraoperative handoffs on outcomes and (b) effect of intraoperative handoff tools on outcomes. All studies on intraoperative handoffs and handoff tools published until September 2019, in any study setting and population, and with no prespecified criteria on the type of comparison and outcome were included. Data extracted from the included studies were aggregated to identify common patterns related to the type of surgery, clinician(s) involved, patient population, handoff tool, the tool design approach (where relevant), tool implementation strategies, and finally, all reported clinical and process outcomes. Quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). Fourteen studies met the inclusion criteria. All included studies used adult patients. Eight studies were retrospective cohort studies that used administrative or electronic health record (EHR)-based databases to investigate the effects of intraoperative handoffs on morbidity and mortality. These studies included a total of 680,855 surgeries, with 139,426 of these surgeries having at least 1 handoff (20.47%). Seven of the studies found a positive association between intraoperative handoffs and considered outcomes. However, a pooled meta-analysis across these studies was not feasible across the retrospective studies due to differing surgical populations and varying definitions of the considered outcomes. Six studies used a nonrandomized prospective design to evaluate the effects of handoff tools on process-based outcomes such as clinician satisfaction, information transfer, handoff duration, and adherence. Five of the 6 handoff tools were checklist based. All prospective tool-based studies relied on small samples and reported a significant improvement on the considered process-based outcomes. The median quality score among retrospective (median [interquartile range {IQR}] = 9 [1]) was significantly higher than that of prospective (median [IQR] = 5 [1.5]) studies (U = 21, P = .0017). This systematic review provides a unique appraisal of the current state of intraoperative handoff research. To improve the quality and outcomes of handoffs, future efforts should focus on design and implementation of standardized handoff tools integrated within EHR systems, consider the use of similar metrics for evaluating handoff process and clinical outcomes, and improve the execution and reporting of studies using standard protocols and guidelines.

Correction: Work-Related and Personal Factors Associated With Mental Well-Being During the COVID-19 Response: Survey of Health Care and Other Workers.

[This corrects the article DOI: 10.2196/21366.].

Risk factors associated with physician trainee concern over missed educational opportunities during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic resulted in a transformation of clinical care practices to protect both patients and providers. These changes led to a decrease in patient volume, impacting physician trainee education due to lost clinical and didactic opportunities. We measured the prevalence of trainee concern over missed educational opportunities and investigated the risk factors leading to such concerns. METHODS: All residents and fellows at a large academic medical center were invited to participate in a web-based survey in May of 2020. Participants responded to questions regarding demographic characteristics, specialty, primary assigned responsibility during the previous 2 weeks (clinical, education, or research), perceived concern over missed educational opportunities, and burnout. Multivariable logistic regression was used to assess the relationship between missed educational opportunities and the measured variables. RESULTS: 22% (301 of 1375) of the trainees completed the survey. 47% of the participants were concerned about missed educational opportunities. Trainees assigned to education at home had 2.85 [95%CI 1.33-6.45] greater odds of being concerned over missed educational opportunities as compared with trainees performing clinical work. Trainees performing research were not similarly affected [aOR = 0.96, 95%CI (0.47-1.93)]. Trainees in pathology or radiology had 2.51 [95%CI 1.16-5.68] greater odds of concern for missed educational opportunities as compared with medicine. Trainees with greater concern over missed opportunities were more likely to be experiencing burnout (p = 0.038). CONCLUSIONS: Trainees in radiology or pathology and those assigned to education at home were more likely to be concerned about their missed educational opportunities. Residency programs should consider providing trainees with research or at home clinical opportunities as an alternative to self-study should future need for reduced clinical hours arise.

A Qualitative Study of Perioperative Depression and Anxiety in Older Adults.

OBJECTIVE: We had three aims 1) understand barriers to perioperative management of anxiety and depression in older surgical patients; 2) identify preferences and requirements for interventions to manage their anxiety and depression; and 3) explore the feasibility of implementing such interventions in perioperative care. DESIGN: A qualitative study using semistructured interviews was conducted. SETTING: Participants were recruited at a large academic medical center. PARTICIPANTS: We interviewed older surgical patients and clinicians to characterize their perspectives on management of anxiety and depression symptoms, with emphasis on patient needs, barriers, and potential interventions to address these needs. MEASUREMENTS: We used the Consolidated Framework for Intervention Research to guide the development of interview questions related to intervention implementation feasibility. Thematic analysis was used to analyze interview responses. RESULTS: Forty semistructured interviews were conducted. Key barriers for perioperative management of depression and anxiety included fear of surgery, acute pain, postoperative neurocognitive disorders, limited understanding of what to expect regarding surgery and recovery, and overwhelmingly complex medication management. Patients and clinicians suggested that a bundled mental health management intervention targeted for older surgical patient population comprised of behavioral and pharmacologic strategies can help mitigate anxiety and depression symptoms during the perioperative period. Clinicians emphasized the need for a collaborative engagement strategy that includes multiple stakeholders in the design, planning, and implementation of such an intevention. CONCLUSION: New care models need to be developed to integrate mental health care into the current perioperative care practice.

Work-Related and Personal Factors Associated With Mental Well-Being During the COVID-19 Response: Survey of Health Care and Other Workers.

BACKGROUND: The response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created an unprecedented disruption in work conditions. This study describes the mental health and well-being of workers both with and without clinical exposure to patients with coronavirus disease (COVID-19). OBJECTIVE: The aim of this study is to measure the prevalence of stress, anxiety, depression, work exhaustion, burnout, and decreased well-being among faculty and staff at a university and academic medical center during the SARS-CoV-2 pandemic and describe work-related and personal factors associated with their mental health and well-being. METHODS: All faculty, staff, and postdoctoral fellows of a university, including its medical school, were invited in April 2020 to complete an online questionnaire measuring stress, anxiety, depression, work exhaustion, burnout, and decreased well-being. We examined associations between these outcomes and factors including work in high-risk clinical settings and family/home stressors. RESULTS: There were 5550 respondents (overall response rate of 34.3%). Overall, 34% of faculty and 14% of staff (n=915) were providing clinical care, while 61% of faculty and 77% of staff were working from home. Among all workers, anxiety (prevalence ratio 1.37, 95% CI 1.09-1.73), depression (prevalence ratio 1.28, 95% CI 1.03-1.59), and high work exhaustion (prevalence ratio 1.24, 95% CI 1.13-1.36) were independently associated with community or clinical exposure to COVID-19. Poor family-supportive behaviors by supervisors were also associated with these outcomes (prevalence ratio 1.40, 95% CI 1.21-1.62; prevalence ratio 1.69, 95% CI 1.48-1.92; and prevalence ratio 1.54, 95% CI 1.44-1.64, respectively). Age <40 years and a greater number of family/home stressors were also associated with these poorer outcomes. Among the subset of clinicians, caring for patients with COVID-19 and working in high-risk clinical settings were additional risk factors. CONCLUSIONS: Our findings suggest that the pandemic has had negative effects on the mental health and well-being of both clinical and nonclinical employees. Mitigating exposure to COVID-19 and increasing supervisor support are modifiable risk factors that may protect mental health and well-being for all workers.

Digital Translucence: Adapting Telemedicine Delivery Post-COVID-19.

In nearly 1 month, with a rapidly expanding corona virus disease 2019 (COVID-19), telemedicine has been transformed into an essential service for delivering routine clinical care. This transformation occurred as a crisis management response-driven by the need to provide care for patients with physical distancing measures in place. However, the current rapid adoption of telemedicine presents a transitional state between one that existed before the pandemic and one that could potentially be better aligned with the delivery of a personalized model of care. Using the conceptual framework of digital translucence-situating virtual encounters with more nuanced information regarding patients-we describe the role of integrated remote monitoring and virtual care tools aligned with the patient's electronic health record for adapting telemedicine delivery post-COVID-19.

A graph-based approach for characterizing resident and nurse handoff conversations.

Prior research has used a variety of qualitative and quantitative approaches for studying handoff communication. Due to the dynamic and interactive nature of handoffs, characterizing the structure and content of these conversations is challenging. In this paper, we use a graph-based approach to characterize handoff communication as a conversation network. Conversation networks were used to compare the structural properties of resident-resident and nurse-nurse handoff communication. Resident (n = 149) and nurse (n = 126) handoff conversations from general medicine units were coded using a previously validated clinical content framework. The coded conversations were then translated into separate resident and nurse conversation networks, and were compared using 11 network measures. Transition probabilities were used to identify commonly repeating sub-networks within resident and nurse conversations. There were significant differences between resident and nurse conversation networks in 10 of the 11 network measures. There were also significant differences in the structure of conversations: compared to resident conversations, nurse conversations were focused on fewer clinical content categories and had more branching and switching between clinical content categories; however, there were clinically-relevant organic relationships in the order of presentation of clinical content among both resident and nurse handoff conversations. We discuss the potential for using graph-based approach as an alternative method for characterizing interactive conversations and also suggest future directions for using network-based approaches for analyzing handoff conversations.