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BACKGROUND US healthcare underperforms on quality and safety metrics. Inpatient care constitutes an immense opportunity to intervene to improve care. OBJECTIVE Describe a model of inpatient care and measure its impact. DESIGN A quantitative assessment of the implementation of a new model of care. The graded implementation of the model allowed us to follow outcomes and measure their association with the dose of the implementation. SETTING AND PATIENTS Inpatient medical and surgical units in a large academic health center. INTERVENTION Eight interventions rooted in improving interprofessional collaboration (IPC), enabling data‐driven decisions, and providing leadership were implemented. MEASUREMENTS Outcome data from August 2012 to December 2013 were analyzed using generalized linear mixed models for associations with the implementation of the model. Length of stay (LOS) index, case‐mix index–adjusted variable direct costs (CMI‐adjusted VDC), 30‐day readmission rates, overall patient satisfaction scores, and provider satisfaction with the model were measured. RESULTS The implementation of the model was associated with decreases in LOS index (P < 0.0001) and CMI‐adjusted VDC (P = 0.0006). We did not detect improvements in readmission rates or patient satisfaction scores. Most providers (95.8%, n = 92) agreed that the model had improved the quality and safety of the care delivered. CONCLUSIONS Creating an environment and framework in which IPC is fostered, performance data are transparently available, and leadership is provided may improve value on both medical and surgical units. These interventions appear to be well accepted by front‐line staff. Readmission rates and patient satisfaction remain challenging. Journal of Hospital Medicine 2015;10:773–779. © 2015 Society of Hospital Medicine

Background: COVID-19 is a novel disease caused by SARS-CoV-2. Methods: We conducted a retrospective evaluation of patients admitted with COVID-19 to one site in March 2020. Patients were stratified into 3 groups: survivors who did not receive mechanical ventilation (MV), survivors who received MV, and those who received MV and died during hospitalization. Results: There were 140 hospitalizations; 22 deaths (mortality rate 15.7%), 83 (59%) survived and did not receive MV, 35 (25%) received MV and survived; 18 (12.9%) received MV and died. Thee mean age of each group was 57.8, 55.8 and 72.7 years, respectively (P = .0001). Of those who received MV and died, 61% were male (P = .01). More than half the patients (n = 90, 64%) were African American. First measured d-dimer >575.5 ng/mL, procalcitonin > 0.24 ng/mL, lactate dehydrogenase >445.6 units/L, and brain natriuretic peptide (BNP) >104.75 pg/mL had odds ratios of 10.5, 5, 4.5 and 2.9, respectively for MV (P < .05 for all). Peak BNP >167.5 pg/mL had an odds ratio of 6.7 for inpatient mortality when mechanically ventilated (P = .02). Conclusions: Age and gender may impact outcomes in COVID-19. D-dimer, procalcitonin, lactate dehydrogenase and BNP may serve as early indicators of disease trajectory.