Comparative assessment of content overlap between written documentation and verbal communication: An observational study of resident sign-outs

OBJECTIVE: Effective sign-outs involve verbal communication supported by written or electronic documentation. We investigated the clinical content overlap between sign-out documentation and face-to-face verbal sign-out communication. METHODS: We audio-recorded resident verbal sign-out communication and collected electronically completed ( written ) sign-out documentation on 44 sign-outs in a General Medicine service. A content analysis framework with nine sign-out elements was used to qualitatively code both written and verbal sign-out content. A content overlap framework based on the comparative analysis between written and verbal sign-out content characterized how much written content was verbally communicated. Using this framework, we computed the full, partial, and no overlap between written and verbal content. RESULTS: We found high a high degree of full overlap on patient identifying information [name (present in 100% of sign-outs), age (96%), and gender (87%)], past medical history [hematology (100%), renal (100%), cardiology (79%), and GI (67%)], and tasks to-do (97%); lesser degree of overlap for active problems (46%), anticipatory guidance (46%), medications/treatments (15%), pending labs/studies/procedures (7%); and no overlap for code status (\u3c1%), allergies (0%) and medical record number (0%). DISCUSSION AND CONCLUSION: Three core functions of sign-outs are transfer of information, responsibility, and accountability. The overlap-highlighting what written content was communicated-characterizes how these functions manifest during sign-outs. Transfer of information varied with patient identifying information being explicitly communicated and remaining content being inconsistently communicated. Transfer of responsibility was explicit, with all pending and future tasks being communicated. Transfer of accountability was limited, with limited discussion of written contingency plans

Facilitating exploratory search by model-based navigational cues

ABSTRACT We present an extension of a computational cognitive model of social tagging and exploratory search called the semantic imitation model. The model assumes a probabilistic representation of semantics for both internal and external knowledge, and utilizes social tags as navigational cues during exploratory search. We used the model to generate a measure of information scent that controls exploratory search behavior, and simulated the effects of multiple presentations of navigational cues on both simple information retrieval and exploratory search performance based on a previous model called SNIF-ACT. We found that search performance can be significantly improved by these model-based presentations of navigational cues for both experts and novices. The result suggested that exploratory search performance depends critically on the match between internal knowledge (domain expertise) and external knowledge structures (folksonomies). Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge

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

Exposure to COVID-19 patients increases physician trainee stress and burnout

The coronavirus disease 2019 (COVID-19) pandemic has put considerable physical and emotional strain on frontline healthcare workers. Among frontline healthcare workers, physician trainees represent a unique group-functioning simultaneously as both learners and caregivers and experiencing considerable challenges during the pandemic. However, we have a limited understanding regarding the emotional effects and vulnerability experienced by trainees during the pandemic. We investigated the effects of trainee exposure to patients being tested for COVID-19 on their depression, anxiety, stress, burnout and professional fulfillment. All physician trainees at an academic medical center (n = 1375) were invited to participate in an online survey. We compared the measures of depression, anxiety, stress, burnout and professional fulfillment among trainees who were exposed to patients being tested for COVID-19 and those that were not, using univariable and multivariable models. We also evaluated perceived life stressors such as childcare, home schooling, personal finances and work-family balance among both groups. 393 trainees completed the survey (29% response rate). Compared to the non-exposed group, the exposed group had a higher prevalence of stress (29.4% vs. 18.9%), and burnout (46.3% vs. 33.7%). The exposed group also experienced moderate to extremely high perceived stress regarding childcare and had a lower work-family balance. Multivariable models indicated that trainees who were exposed to COVID-19 patients reported significantly higher stress (10.96 [95% CI, 9.65 to 12.46] vs 8.44 [95% CI, 7.3 to 9.76]; P = 0.043) and were more likely to be burned out (1.31 [95% CI, 1.21 to1.41] vs 1.07 [95% CI, 0.96 to 1.19]; P = 0.002]. We also found that female trainees were more likely to be stressed (P = 0.043); while unmarried trainees were more likely to be depressed (P = 0.009), and marginally more likely to have anxiety (P = 0.051). To address these challenges, wellness programs should focus on sustaining current programs, develop new and targeted mental health resources that are widely accessible and devise strategies for creating awareness regarding these resources

Transmission dynamics: Data sharing in the COVID-19 era

Problem: The current coronavirus disease 2019 (COVID-19) pandemic underscores the need for building and sustaining public health data infrastructure to support a rapid local, regional, national, and international response. Despite a historical context of public health crises, data sharing agreements and transactional standards do not uniformly exist between institutions which hamper a foundational infrastructure to meet data sharing and integration needs for the advancement of public health. Approach: There is a growing need to apply population health knowledge with technological solutions to data transfer, integration, and reasoning, to improve health in a broader learning health system ecosystem. To achieve this, data must be combined from healthcare provider organizations, public health departments, and other settings. Public health entities are in a unique position to consume these data, however, most do not yet have the infrastructure required to integrate data sources and apply computable knowledge to combat this pandemic. Outcomes: Herein, we describe lessons learned and a framework to address these needs, which focus on: (a) identifying and filling technology gaps ; (b) pursuing collaborative design of data sharing requirements and transmission mechanisms; (c) facilitating cross-domain discussions involving legal and research compliance; and (d) establishing or participating in multi-institutional convening or coordinating activities. Next steps: While by no means a comprehensive evaluation of such issues, we envision that many of our experiences are universal. We hope those elucidated can serve as the catalyst for a robust community-wide dialogue on what steps can and should be taken to ensure that our regional and national health care systems can truly learn, in a rapid manner, so as to respond to this and future emergent public health crises

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 \u3c40 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

Identifying and addressing barriers to implementing core electronic health record use metrics for ambulatory care: Virtual consensus conference proceedings

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS:  We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION:  Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop\u27s outputs offers promise for overcoming barriers to implementing EHR use metrics

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

BACKGROUND: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. METHODS: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. FINDINGS: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. INTERPRETATION: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. FUNDING: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS)

Effect of acute postsurgical pain trajectories on 30-day and 1-year pain

Untreated pain after surgery leads to poor patient satisfaction, longer hospital length of stay, lower health-related quality of life, and non-compliance with rehabilitation regimens. The aim of this study is to characterize the structure of acute pain trajectories during the postsurgical hospitalization period and quantify their association with pain at 30-days and 1-year after surgery. This cohort study included 2106 adult (≥18 years) surgical patients who consented to participate in the SATISFY-SOS registry (February 1, 2015 to September 30, 2017). Patients were excluded if they did not undergo invasive surgeries, were classified as outpatients, failed to complete follow up assessments at 30-days and 1-year following surgery, had greater than 4-days of inpatient stay, and/or recorded fewer than four pain scores during their acute hospitalization period. The primary exposure was the acute postsurgical pain trajectories identified by a machine learning-based latent class approach using patient-reported pain scores. Clinically meaningful pain (≥3 on a 0-10 scale) at 30-days and 1-year after surgery were the primary and secondary outcomes, respectively. Of the study participants (N = 2106), 59% were female, 91% were non-Hispanic White, and the mean (SD) age was 62 (13) years; 41% of patients underwent orthopedic surgery and 88% received general anesthesia. Four acute pain trajectory clusters were identified. Pain trajectories were significantly associated with clinically meaningful pain at 30-days (p = 0.007), but not at 1-year (p = 0.79) after surgery using covariate-adjusted logistic regression models. Compared to Cluster 1, the other clusters had lower statistically significant odds of having pain at 30-days after surgery (Cluster 2: [OR = 0.67, 95%CI (0.51-0.89)]; Cluster 3:[OR = 0.74, 95%CI (0.56-0.99)]; Cluster 4:[OR = 0.46, 95%CI (0.26-0.82)], all p\u3c0.05). Patients in Cluster 1 had the highest cumulative likelihood of pain and pain intensity during the latter half of their acute hospitalization period (48-96 hours), potentially contributing to the higher odds of pain during the 30-day postsurgical period. Early identification and management of high-risk pain trajectories can help in ascertaining appropriate pain management interventions. Such interventions can mitigate the occurrence of long-term disabilities associated with pain