Eliminating Monitor Overuse (EMO) Type III Effectiveness-Deimplementation Cluster-Randomized Trial: Statistical Analysis Plan

Background: Deimplementing overused health interventions is essential to maximizing quality and value while minimizing harm, waste, and inefficiencies. Three national guidelines discourage continuous pulse oximetry (SpO2) monitoring in children who are not receiving supplemental oxygen, but the guideline-discordant practice remains prevalent, making it a prime target for deimplementation. This paper details the statistical analysis plan for the Eliminating Monitor Overuse (EMO) SpO2 trial, which compares the effect of two competing deimplementation strategies (unlearning only vs. unlearning plus substitution) on the sustainment of deimplementation of SpO2 monitoring in children with bronchiolitis who are in room air. Methods: The EMO Trial is a hybrid type 3 effectiveness-deimplementation trial with a longitudinal cluster-randomized design, conducted in Pediatric Research in Inpatient Settings Network hospitals. The primary outcome is deimplementation sustainment, analyzed as a longitudinal difference-in-differences comparison between study arms. This analysis will use generalized hierarchical mixed-effects models for longitudinal clustering outcomes. Secondary outcomes include the length of hospital stay and oxygen supplementation duration, modeled using linear mixed-effects regressions. Using the well-established counterfactual approach, we will also perform a mediation analysis of hospital-level mechanistic measures on the association between the deimplementation strategy and the sustainment outcome. Discussion: We anticipate that the EMO Trial will advance the science of deimplementation by providing new insights into the processes, mechanisms, and likelihood of sustained practice change using rigorously designed deimplementation strategies. This pre-specified statistical analysis plan will mitigate reporting bias and support data-driven approaches

US public opinion regarding proposed limits on resident physician work hours

<p>Abstract</p> <p>Background</p> <p>In both Europe and the US, resident physician work hour reduction has been a source of controversy within academic medicine. In 2008, the Institute of Medicine (IOM) recommended a reduction in resident physician work hours. We sought to assess the American public perspective on this issue.</p> <p>Methods</p> <p>We conducted a national survey of 1,200 representative members of the public via random digit telephone dialing in order to describe US public opinion on resident physician work hour regulation, particularly with reference to the IOM recommendations.</p> <p>Results</p> <p>Respondents estimated that resident physicians currently work 12.9-h shifts (95% CI 12.5 to 13.3 h) and 58.3-h work weeks (95% CI 57.3 to 59.3 h). They believed the maximum shift duration should be 10.9 h (95% CI 10.6 to 11.3 h) and the maximum work week should be 50 h (95% CI 49.4 to 50.8 h), with 1% approving of shifts lasting >24 h (95% CI 0.6% to 2%). A total of 81% (95% CI 79% to 84%) believed reducing resident physician work hours would be very or somewhat effective in reducing medical errors, and 68% (95% CI 65% to 71%) favored the IOM proposal that resident physicians not work more than 16 h over an alternative IOM proposal permitting 30-h shifts with ≥5 h protected sleep time. In all, 81% believed patients should be informed if a treating resident physician had been working for >24 h and 80% (95% CI 78% to 83%) would then want a different doctor.</p> <p>Conclusions</p> <p>The American public overwhelmingly favors discontinuation of the 30-h shifts without protected sleep routinely worked by US resident physicians and strongly supports implementation of restrictions on resident physician work hours that are as strict, or stricter, than those proposed by the IOM. Strong support exists to restrict resident physicians' work to 16 or fewer consecutive hours, similar to current limits in New Zealand, the UK and the rest of Europe.</p

Applying mathematical models to predict resident physician performance and alertness on traditional and novel work schedules

Background: In 2011 the U.S. Accreditation Council for Graduate Medical Education began limiting first year resident physicians (interns) to shifts of ≤16 consecutive hours. Controversy persists regarding the effectiveness of this policy for reducing errors and accidents while promoting education and patient care. Using a mathematical model of the effects of circadian rhythms and length of time awake on objective performance and subjective alertness, we quantitatively compared predictions for traditional intern schedules to those that limit work to ≤ 16 consecutive hours. Methods: We simulated two traditional schedules and three novel schedules using the mathematical model. The traditional schedules had extended duration work shifts (≥24 h) with overnight work shifts every second shift (including every third night, Q3) or every third shift (including every fourth night, Q4) night; the novel schedules had two different cross-cover (XC) night team schedules (XC-V1 and XC-V2) and a Rapid Cycle Rotation (RCR) schedule. Predicted objective performance and subjective alertness for each work shift were computed for each individual’s schedule within a team and then combined for the team as a whole. Our primary outcome was the amount of time within a work shift during which a team’s model-predicted objective performance and subjective alertness were lower than that expected after 16 or 24 h of continuous wake in an otherwise rested individual. Results: The model predicted fewer hours with poor performance and alertness, especially during night-time work hours, for all three novel schedules than for either the traditional Q3 or Q4 schedules. Conclusions: Three proposed schedules that eliminate extended shifts may improve performance and alertness compared with traditional Q3 or Q4 schedules. Predicted times of worse performance and alertness were at night, which is also a time when supervision of trainees is lower. Mathematical modeling provides a quantitative comparison approach with potential to aid residency programs in schedule analysis and redesign

Decreasing handoff-related care failures in children\u27s hospitals

Bigham, M. T., Logsdon, T. R., Manicone, P. E., Landrigan, C. P., Hayes, L. W., Randall, K. H., . . . Sharek, P. J. (2014). Decreasing handoff-related care failures in children\u27s hospitals. Pediatrics, 134(2), e572-e579