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ObjectiveStudies describe COVID-19 patient characteristics and outcomes across populations, but reports of variation across healthcare facilities are lacking. The objectives were to examine differences in COVID-19 patient volume and mortality across facilities, and understand whether facility variation in mortality was due primarily to differences in patient versus facility characteristics.DesignObservational cohort study with multilevel mixed effects logistic regression modelling.SettingThe Veterans Health Administration (VA) is the largest healthcare system in the USA.ParticipantsPatients with COVID-19.Main outcomeAll-cause mortality within 45 days after COVID-19 testing (March-May, follow-up through 16 July 2020).ResultsAmong 13 510 patients with COVID-19, 3942 (29.2%) were admitted (2266/3942 (57.5%) ward; 1676/3942 (42.5%) intensive care unit (ICU)) and 679/3942 (17.2%) received mechanical ventilation. Marked heterogeneity was observed across facilities in median age (range: 34.3-83.9 years; facility mean: 64.7, SD 7.2 years); patient volume (range: 1-737 at 160 facilities; facility median: 48.5, IQR 14-105.5); hospital admissions (range: 1-286 at 133 facilities; facility median: 11, IQR 1-26.5); ICU caseload (range: 1-85 at 115 facilities; facility median: 4, IQR 0-12); and mechanical ventilation (range: 1-53 at 90 facilities; facility median: 1, IQR 0-5). Heterogeneity was also observed in facility mortality for all patients with COVID-19 (range: 0%-29.7%; facility median: 8.9%, IQR 2.4%-13.7%); inpatients (range: 0%-100%; facility median: 18.0%, IQR 5.6%-28.6%); ICU patients (range: 0%-100%; facility median: 28.6%, IQR 14.3%-50.0%); and mechanical ventilator patients (range: 0%-100%; facility median: 52.7%, IQR 33.3%-80.6%). The majority of variation in facility mortality was attributable to differences in patient characteristics (eg, age).ConclusionsMarked heterogeneity in COVID-19 patient volume, characteristics and mortality were observed across VA facilities nationwide. Differences in patient characteristics accounted for the majority of explained variation in mortality across sites. Variation in unadjusted COVID-19 mortality across facilities or nations should be considered with caution.

Introduction: Effective communication between clinicians and patients has been shown to improve patient outcomes, reduce malpractice liability, and is now being tied to reimbursement. Use of a communication strategy known as “scripting” has been suggested to improve patient satisfaction in multiple hospital settings, but the frequency with which medical students use this strategy and whether this affects patient perception of medical student care is unknown. Our objective was to measure the use of targeted communication skills after an educational intervention as well as to further clarify the relationship between communication element usage and patient satisfaction. Methods: Medical students were block randomized into the control or intervention group. Those in the intervention group received refresher training in scripted communication. Those in the control group received no instruction or other intervention related to communication. Use of six explicit communication behaviors were recorded by trained study observers: 1) acknowledging the patient by name, 2) introducing themselves as medical students, 3) explaining their role in the patient’s care, 4) explaining the care plan, 5) providing an estimated duration of time to be spent in the emergency department (ED), and 6) notifying the patient that another provider would also be seeing them. Patients then completed a survey regarding their satisfaction with the medical student encounter.  Results: We observed 474 medical student-patient encounters in the ED (231 in the control group and 243 in the intervention group). We were unable to detect a statistically significant difference in communication element use between the intervention and control groups. One of the communication elements, explaining steps in the care plan, was positively associated with patient perception of the medical student’s overall communication skills. Otherwise, there was no statistically significant association between element use and patient satisfaction. Conclusion: We were unable to demonstrate any improvement in student use of communication elements or in patient satisfaction after refresher training in scripted communication. Furthermore, there was little variation in patient satisfaction based on the use of scripted communication elements.  Effective communication with patients in the ED is complicated and requires further investigation on how to provide this skill set.

Patient-family member dyads experience transitions through illness as an interdependent team. This study measures the association of depression, anxiety, and health-related quality of life (HRQOL) of older adult primary care patient-family member dyads. Baseline data from 1,808 patient-family member dyads enrolled in a trial testing early detection of Alzheimer's disease and related dementias in primary care. Actor-Partner Independence Model was used to analyze dyadic relationships between patients' and family members' depression (PHQ-9), anxiety (GAD-7), and HRQOL (SF-36 Physical Component Summary score and Mental Component Summary score). Family member mean (SD) age is 64.2 (13) years; 32.2% male; 84.6% White; and 64.8% being the patient's spouse/partner. Patient mean (SD) age is 73.7 (5.7) years; 47% male; and 85.1% White. For HRQOL, there were significant actor effects for patient and family member depression alone and depression and anxiety together on their own HRQOL (p < 0.001). There were significant partner effects where family member depression combined with anxiety was associated with the patient's physical component summary score of the SF-36 (p = 0.010), and where the family member's anxiety alone was associated with the patient's mental component summary score of the SF-36 (p = 0.031). Results from this study reveal that many dyads experience covarying health status (e.g. depression, anxiety) even prior to entering a caregiving situation. [ABSTRACT FROM AUTHOR]

INTRODUCTION: As the number of older intensive care unit (ICU) survivors grows, there is an urgent need to identify modifiable risk factors for post‐ICU dementia. METHODS: We performed a secondary data analysis of 3144 ICU patients ≥ 50 years of age without a history of dementia or severe mental illness who were screened as part of the Pharmacological Management of Delirium (PMD) study. Delirium was assessed using the Confusion Assessment Method for the ICU. Dementia was identified using International Classification of Diseases Ninth and Tenth revision codes for dementia or prescription of anti‐dementia medication. RESULTS: Average age (standard deviation) was 65.2 ± 9.5 years; 50.4% were female; and 37.3% were Black. Analyses identified stroke (adjusted hazard ratio [HR] 2.49; 95% confidence interval [CI: 1.52, 4.07], P < 0.001), and depression (adjusted HR 3.03; 95% CI [1.80, 5.10], P < 0.001) as post‐ICU risk factors for dementia. DISCUSSION: Future studies will need to examine whether interventions targeting post‐ICU stroke and depression can lower dementia incidence in ICU survivors. Highlights: Risk factors for post–intensive care unit (ICU) dementia were distinct from those of Alzheimer's disease.Cardiovascular risk factors were not associated with dementia in older ICU survivors.Post‐ICU stroke was associated with a higher risk of dementia in older ICU survivors.Post‐ICU depression was associated with a higher risk of dementia in older ICU survivors. [ABSTRACT FROM AUTHOR]