BACKGROUND Previous studies attempting to distinguish preventable from nonpreventable readmissions reported challenges in completing reviews efficiently and consistently. OBJECTIVES (1) Examine the efficiency and reliability of a Web‐based fault tree tool designed to guide physicians through chart reviews to a determination about preventability. (2) Investigate root causes of general pediatrics readmissions and identify the percent that are preventable. DESIGN/SETTING/PATIENTS General pediatricians from The Children's Hospital of Philadelphia used a Web‐based fault tree tool to classify root causes of all general pediatrics 15‐day readmissions in 2014. INTERVENTION/MEASUREMENTS The tool guided reviewers through a logical progression of questions, which resulted in 1 of 18 root causes of readmission, 8 of which were considered potentially preventable. Twenty percent of cases were cross‐checked to measure inter‐rater reliability. RESULTS Of the 7252 discharges, 248 were readmitted, for an all‐cause general pediatrics 15‐day readmission rate of 3.4%. Of those readmissions, 15 (6.0%) were deemed potentially preventable, corresponding to 0.2% of total discharges. The most common cause of potentially preventable readmissions was premature discharge. For the 50 cross‐checked cases, both reviews resulted in the same root cause for 44 (86%) of files (κ = 0.79; 95% confidence interval: 0.60‐0.98). Completing 1 review using the tool took approximately 20 minutes. CONCLUSION The Web‐based fault tree tool helped physicians to identify root causes of hospital readmissions and classify them as either preventable or not preventable in an efficient and consistent way. It also confirmed that only a small percentage of general pediatrics 15‐day readmissions are potentially preventable. Journal of Hospital Medicine 2016;11:329–335. © 2016 Society of Hospital Medicine

The Innovation Unit model successfully engaged frontline staff in improvement work and established a sustainable system and framework for managing rigorous improvement portfolios at the unit level. Other hospitals and health care delivery settings may find our quality improvement approach helpful, especially because it is rooted in the microsystem of care delivery.

Without access to needed health and employment support services, working individuals with serious mental illness risk developing long term dependence on federal disability programs. Minnesota's DMIE intervention aimed to prevent or delay the disability progression by providing working persons with mental illness a comprehensive set of health, behavioral health, and employment support services, coordinated through a navigator. Potentially eligible study participants were identified through analyses of the Minnesota MMIS using an algorithm targeting mental health service and pharmacy utilization. The eligible sample was stratified and randomly assigned to the intervention (n = 1,257) or control (n = 300) group. Data sources included MMIS, other administrative data, and navigator encounter data to capture utilization of health, mental health, employment support services, and other public services, as well as annual earnings. Participants also completed an annual survey. Multivariate analyses found that individuals in the intervention group had greater access to health and mental health services, greater improvements in functioning, and were significantly less likely to report applying for SSDI at the end of 12 months of enrollment. Multivariate analyses focusing only on individuals in the intervention group demonstrated that participants who engaged with their navigator had better mental health status and were significantly less inclined to apply for SSDI after 12 months of the intervention. Study findings are important because SSDI beneficiaries with psychiatric disabilities are the fastest-growing, largest, and most costly disability group in the SSDI program.

In health care policy circles, "bending the cost curve down" may qualify as the phrase of the decade. More than 1 million web search references are testament to its widespread use, intended to express a focus on reducing health care costs. Yet it should not surprise anyone familiar with technocratic vocabulary that there is no clear agreement on what the phrase means. 1 Nonetheless, with health care spending nearing one-fi fth of the nation's gross domestic product, 2 the nationwide clamor to "bend the cost curve down" is understandable. In this commentary, however, we discuss the shortfalls of focusing on costs alone and instead propose a national commitment to "bending the value curve up."Although fi nding ways to control health care spending is certainly important, focusing on cost alone is neither suffi cient nor appropriate. Calling out costs in isolation may be disconcerting to many stakeholders, including those whose health care services or livelihoods rely on this important sector of the economy. For example, patients might associate cutting costs with decreased access or hastily provided care, and clinicians may fear lower salaries or undue administrative pressures. Moreover, slicing away at high-quality and lifesaving programs for the poor, the young, the disabled, or the elderly or increasing the fi nancial burden for disadvantaged populations is shortsighted and unfair. 3,4 Beyond this, focusing on cost alone ignores the benefi ts to life and well-being that result from high-quality health care spending, even if it is associated with a high price tag. Rather than simply asking whether we are spending too much, we should also ask whether we are achieving the kind of care experiences and outcomes we would expect for the price we are paying.Value is the quality of an output divided by the cost to achieve it. The numerator of the value ratio can be defi ned by using the 6 Institute of Medicine domains of quality: safety, effectiveness, effi ciency, timeliness, patient-centeredness, and equity. 5 The denominator would be the resources consumed in terms of money, time, and labor. Applying the value ratio includes determining which outcomes should be targeted for quality and cost measures. For example, Michael Porter's value framework organizes outcomes for any given medical condition into 3 successive tiers: health status achieved or retained, process of recovery, and sustainability of health. 6 Although relevant metrics and outcomes will vary immensely across medical conditions and patient populations, it seems far more sensible to place these value-based measures at the center of the discussion about improving health care rather than a blunt attempt to cut costs regardless of outcomes.Despite $2.8 trillion spent on health care, health outcomes in the United States are not consistently good. Research has demonstrated that evidence-based care AUTHORS

BACKGROUND: Though regional variation in healthcare spending has received national attention, it has not been widely studied in pediatrics. OBJECTIVES: (1) To evaluate regional variation in costs of care for 3 inpatient pediatric conditions, (2) assess potential drivers of variation, and (3) estimate cost savings from reducing variation. DESIGN/SETTING/PATIENTS: Retrospective cohort study of hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 children's hospitals from October 2014 to September 2015. INTERVENTION/MEASUREMENTS: Variation in trimmed standardized costs were assessed within and across regions. Linear mixed effects models were adjusted for patient-and encounter-level variables to assess drivers of variation.