Background:The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.Objective: To estimate the timing of surges in clinical demand and the best-and worst-case scenarios of local COVID-19induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.Setting: 3 hospitals in an academic health system. Patients:All people living in the greater Philadelphia region. Measurements:The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best-and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. Conclusion:Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

IMPORTANCE Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. OBJECTIVE To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. DESIGN, SETTING, AND PARTICIPANTS This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N = 14 607) who had an outpatient oncology encounter during the study period. INTERVENTIONS (1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (Ն10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance. MAIN OUTCOMES AND MEASURES Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group. RESULTS The sample consisted of 78 clinicians and 14 607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001). CONCLUSIONS AND RELEVANCE In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life.

Critical care interventions are expensive and have a narrow safety margin. It is essential to develop structured and validated approaches to study the delivery of this resource. In this study, the critical care service model performed favorably both in terms of quality and cost.

Generic drugs are low-cost, therapeutically equivalent versions of brand-name drugs. Use of generic drugs increases patient adherence and improves health outcomes. 1 However, a 2009 survey of physicians showed that 23% disagreed that generic dr ugs were as effec tive as brand-name drugs and 50% reported quality concerns, leading more than one-quarter not to recommend generic drugs as first-line therapy. 2 Because generic drugs now make up more than 85% of prescriptions, 3 we reassessed physicians' perceptions and determined how professional or demographic characteristics predict physicians' support of generic drug prescribing. c This question asked whether physicians believe that generics cause more adverse effects than brand-name drugs. Responses inverted for consistency of interpretation with the other questions in this Table.

are considering increasing price transparency at the time of order entry. However, evidence of its impact on clinician ordering behavior is inconsistent and limited to single-site evaluations of shorter duration.OBJECTIVE To test the effect of displaying Medicare allowable fees for inpatient laboratory tests on clinician ordering behavior over 1 year. DESIGN, SETTING, AND PARTICIPANTSThe Pragmatic Randomized Introduction of Cost data through the electronic health record (PRICE) trial was a randomized clinical trial comparing a 1-year intervention to a 1-year preintervention period, and adjusting for time trends and patient characteristics. The trial took place at 3 hospitals in Philadelphia between April 2014 and April 2016 and included 98 529 patients comprising 142 921 hospital admissions.INTERVENTIONS Inpatient laboratory test groups were randomly assigned to display Medicare allowable fees (30 in intervention) or not (30 in control) in the electronic health record. MAIN OUTCOMES AND MEASURESPrimary outcome was the number of tests ordered per patient-day. Secondary outcomes were tests performed per patient-day and Medicare associated fees. RESULTSThe sample included 142 921 hospital admissions representing patients who were 51.9% white (74 165), 38.9% black (55 526), and 56.9% female (81 291) with a mean (SD) age of 54.7 (19.0) years. Preintervention trends of order rates among the intervention and control groups were similar. In adjusted analyses of the intervention group compared with the control group over time, there were no significant changes in overall test ordering behavior (0.