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PURPOSE: For individuals with diabetes, diabetes health status may not align with A1C targets. Patients may use nonclinical targets when assessing their diabetes management success. Identifying these targets is important in developing patient-centered management plans. The purpose of this study was to identify patient markers of successful diabetes management among patients in an urban academic health system. METHODS: A secondary analysis of semistructured interviews was completed with 89 adults with type 1 or type 2 diabetes. Participants had a recent diabetes-related emergency department (ED) visits or hospitalization or were primary care patients with an A1C >7.5%. Interviews were conducted to saturation. Demographic data were collected via self-report and electronic medical records. Interviews were analyzed using conventional content analysis. This analysis focused on patient perceptions of successful management coded to "measuring management success." RESULTS: Although most participants cited A1C or blood glucose as a marker of successful diabetes management, they had varied understanding of these metrics. Most used a combination of targets from the following categories: 1) A1C, blood glucose, and numbers; 2) engagement in medical care; 3) taking medication and medication types; 4) symptoms; 5) diet, exercise, and weight; and 6) stress management and social support. CONCLUSION: Individuals not meeting glycemic goals and/or with recent diabetes-related ED visits or hospitalizations had varied understanding of A1C and blood glucose targets. They use multiple additional markers of successful management and had a desire for management discussions that incorporate these markers. These measures should be incorporated into their care plans along with clinical targets.

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OBJECTIVES: We determined the impact of including race, ethnicity, and poverty in risk adjustment models for emergency care-sensitive conditions mortality that could be used for hospital pay-for-performance initiatives. We hypothesized that adjusting for race, ethnicity, and poverty would bolster rankings for hospitals that cared for a disproportionate share of nonwhite, Hispanic, or poor patients. METHODS: We performed a cross-sectional analysis of patients admitted from the emergency department to 157 hospitals in Pennsylvania with trauma, sepsis, stroke, cardiac arrest, and ST-elevation myocardial infarction. We used multivariable logistic regression models to predict in-hospital mortality. We determined the predictive accuracy of adding patient race and ethnicity (dichotomized as non-Hispanic white vs. all other Hispanic or nonwhite patients) and poverty (uninsured, on Medicaid, or lowest income quartile zip code vs. all others) to other patient-level covariates. We then ranked each hospital on observed-to-expected mortality, with and without race, ethnicity, and poverty in the model, and examined characteristics of hospitals with large changes between models. RESULTS: The overall mortality rate among 170,750 inpatients was 6.9%. Mortality was significantly higher for nonwhite and Hispanic patients (adjusted odds ratio [aOR] = 1.27, 95% confidence interval [CI] = 1.19-1.36) and poor patients (aOR = 1.21, 95% CI = 1.12-1.31). Adding race, ethnicity, and poverty to the risk adjustment model resulted in a small increase in C-statistic (0.8260 to 0.8265, p = 0.002). No hospitals moved into or out of the highest-performing decile when adjustment for race, ethnicity, and poverty was added, but the three hospitals that moved out of the lowest-performing decile, relative to other hospitals, had significantly more nonwhite and Hispanic patients (68% vs. 11%, p < 0.001) and poor patients (56% vs. 10%, p < 0.001). CONCLUSIONS: Sociodemographic risk adjustment of emergency care-sensitive mortality improves apparent performance of some hospitals treating a large number of nonwhite, Hispanic, or poor patients. This may help these hospitals avoid financial penalties in pay-for-performance programs.

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OBJECTIVES: To determine how frequently patients revisit the emergency department after an initial encounter, and to describe revisit capture rates for the same hospital, health system, and geographic region. DATA SOURCES/STUDY SETTING: Florida state data from January 1, 2010, to June 30, 2011, from the Healthcare Cost and Utilization Project. STUDY DESIGN: This is a retrospective cohort study of emergency department return visits among Florida adults over an 18-month period. We evaluated pairs of index and 30-day return emergency department visits and compared capture rates for hospital, health system, and geographic units. DATA COLLECTION/EXTRACTION METHODS: Data were obtained from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey Database. PRINCIPAL FINDINGS: Among 9,416,212 emergency department visits, 22.6 percent (2,124,441) were associated with a 30-day return. Seventy percent (1,477,772) of 30-day returns occurred to the same hospital. The 30-day return capture rates were highest within the same geographic area: county-level capture at 92 percent (IQR=86-96 percent) versus health system capture at 75 percent (IQR = 68-81 percent). CONCLUSIONS: Acute care utilization patterns are often independent of health system boundaries. Current population-based health care models that attribute patients to a single provider or health system may be strengthened by considering geographic patterns of acute care utilization.

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The Patient-Centered Outcomes Research Institute (PCORI) was established by Congress in 2010 to promote the conduct of research that could better inform patients in making decisions that reflect their desired health outcomes. PCORI has established five national priorities for research around which specific funding opportunities are issued: 1) assessment of prevention, diagnosis, and treatment options; 2) improving healthcare systems; 3) communication and dissemination research; 4) addressing disparities; and 5) improving methods for conducting patient-centered outcomes research. To date, implementation of patient-centered research in the emergency care setting has been limited, in part because of perceived challenges in meeting PCORI priorities such as the need to focus on a specific disease state or to have planned follow up. We suggest that these same factors that have been seen as challenges to performing patient-centered research within the emergency setting are also potential strengths to be leveraged to conduct PCORI research. This paper explores factors unique to patient-centered emergency care research and highlights specific areas of potential alignment within each PCORI priority.

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INTRODUCTION: The failure to rescue (FTR) rate is the probability of death after a major complication and was defined in elective surgical cohorts. In elective surgery, the precedence rate (proportion of deaths preceded by major complications) approaches 100%, but recent studies in trauma report rates of only 20-25%. We hypothesised that use of high quality data would result precedence rates in higher than those derived from national datasets, and we further sought to characterise the nature of those deaths not preceded by major complications. METHODS: Prospectively collected data from 2006 to 2010 from a single level I trauma centre were used. Patients age >16 years with AIS ≥2 who survived beyond the trauma bay were included. Complications, mortality, FTR, and precedence rates were calculated. Chart abstraction was performed for registry deaths without recorded complications to verify the absence of complications and determine the cause of death, after which outcomes were re-calculated. RESULTS: A total of 8004 patients were included (median age 41 (IQR 25-75), 71% male, 82% blunt, median ISS 10 (IQR 5-18)). Using registry data the precedence rate was 55%, with 132/293 (45%) deaths occurring without antecedent major complications. On chart abstraction, 11/132 (8%) patients recorded in the registry as having no complication prior to death were found to have major complications. Complication and FTR rates after chart abstraction were statistically significantly different than those derived from registry data alone (complications 16.5% vs. 16.3, FTR 12.3 vs.13, p=0.001), but this difference was unlikely to be clinically meaningful. Patients dying without complications predominantly (87%) had neurologic causes of demise. CONCLUSIONS: Use of data with near-complete ascertainment of complications results in precedence rates much higher than those from national datasets. Patients dying without precedent complications at our centre largely succumbed to progression of neurologic injury. Attempts to use FTR to compare quality between centres should be limited to high quality data. LEVEL OF EVIDENCE: Level III. RETROSPECTIVE COHORT STUDY: Outcomes.

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OBJECTIVES: This study explored what smartphone health applications (apps) are used by patients, how they learn about health apps, and how information about health apps is shared. METHODS: Patients seeking care in an academic ED were surveyed about the following regarding their health apps: use, knowledge, sharing, and desired app features. Demographics and health information were characterized by summary statistics. RESULTS: Of 300 participants, 212 (71%) owned smartphones, 201 (95%) had apps, and 94 (44%) had health apps. The most frequently downloaded health apps categories were exercise 46 (49%), brain teasers 30 (32%), and diet 23 (24%). The frequency of use of apps varied as six (6%) of health apps were downloaded but never used, 37 (39%) apps were used only a few times, and 40 (43%) health apps were used once per month. Only five apps (2%) were suggested to participants by health care providers, and many participants used health apps intermittently (55% of apps ≤ once a month). Participants indicated sharing information from 64 (59%) health apps, mostly within social networks (27 apps, 29%) and less often with health care providers (16 apps, 17%). CONCLUSIONS: While mobile health has experienced tremendous growth over the past few years, use of health apps among our sample was low. The most commonly used apps were those that had broad functionality, while the most frequently used health apps encompassed the topics of exercise, diet, and brain teasers. While participants most often shared information about health apps within their social networks, information was less frequently shared with providers, and physician recommendation played a small role in influencing patient use of health apps.

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OBJECTIVES: Although 72-hour emergency department (ED) revisits are increasingly used as a hospital metric, there is no known empirical basis for this 72-hour threshold. The objective of this study was to determine the timing of ED revisits for adult patients within 30 days of ED discharge. METHODS: This was a retrospective cohort study of all nonfederal ED discharges in Florida and Nebraska from April 1, 2010, to March 31, 2011, using data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). ED discharges were followed forward to identify ED revisits occurring at any hospital within the same state within 30 days. The cumulative hazard of an ED revisit was plotted. Parametric and nonparametric modeling was performed to characterize the rate of ED revisits. RESULTS: There were 4,782,045 ED discharges, with 7.5% (95% confidence interval [CI] = 7.4% to 7.5%) associated with 3-day revisits, and 22.4% (95% CI = 22.3% to 22.4%) associated with 30-day revisits, inclusive of the 3-day revisits. A double-exponential model fit the data best (p < 0.0001), and a single hinge point at 9 days (multivariate adaptive regression splines [MARS] model) yielded the best linear fit to the data, suggesting 9 days as the most reasonable cutoff for identification of acute ED revisits. Multiple stratified and subgroup analyses produced similar results. Future work should focus on identifying primary reasons for potentially avoidable return ED visits instead of on the revisit occurrence itself, thus more directly measuring potential lapses in delivery of high-quality care. CONCLUSIONS: Almost one-quarter of ED discharges are linked to 30-day ED revisits, and the current 72-hour ED metric misses close to 70% of these patients. Our findings support 9 days as a more inclusive cutoff for studies of ED revisits.

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STUDY OBJECTIVE: Collective knowledge and coordination of vital interventions for time-sensitive conditions (ST-segment elevation myocardial infarction [STEMI], stroke, cardiac arrest, and septic shock) could contribute to a comprehensive statewide emergency care system, but little is known about population access to the resources required. We seek to describe existing clinical management strategies for time-sensitive conditions in Pennsylvania hospitals. METHODS: All Pennsylvania emergency departments (EDs) open in 2009 were surveyed about resource availability and practice patterns for time-sensitive conditions. The frequency with which EDs provided essential clinical bundles for each condition was assessed. Penalized maximum likelihood regressions were used to evaluate associations between ED characteristics and the presence of the 4 clinical bundles of care. We used geographic information science to calculate 60-minute ambulance access to the nearest facility with these clinical bundles. RESULTS: The percentage of EDs providing each of the 4 clinical bundles in 2009 ranged from 20% to 57% (stroke 20%, STEMI 32%, cardiac arrest 34%, sepsis 57%). For STEMI and stroke, presence of a board-certified/board-eligible emergency physician was significantly associated with presence of a clinical bundle. Only 8% of hospitals provided all 4 care bundles. However, 53% of the population was able to reach this minority of hospitals within 60 minutes. CONCLUSION: Reliably matching patient needs to ED resources in time-dependent illness is a critical component of a coordinated emergency care system. Population access to critical interventions for the time-dependent diseases discussed here is limited. A population-based planning approach and improved coordination of care could improve access to interventions for patients with time-sensitive conditions.

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OBJECTIVES: The Institute of Medicine (IOM)'s "Future of Emergency Care" report recommended the categorization and regionalization of emergency care, but no uniform system to categorize hospital emergency care capabilities has been developed. The absence of such a system limits the ability to benchmark outcomes, to develop regional systems of care, and of patients to make informed decisions when seeking emergency care. The authors sought to pilot the deployment of an emergency care categorization system in two states. METHODS: A five-tiered emergency department (ED) categorization system was designed, and a survey of all Pennsylvania and Wisconsin EDs was conducted. This 46-item survey described hospital staffing, characteristics, resources, and practice patterns. Based on responses, EDs were categorized as limited, basic, advanced, comprehensive, and pediatric critical care capable. Prehospital transport times were then used to determine population access to each level of care. RESULTS: A total of 247 surveys were received from the two states (247 of 297, 83%). Of the facilities surveyed, roughly one-quarter of hospitals provided advanced care, 10.5% provided comprehensive care, and 1.6% provided pediatric critical care. Overall, 75.1% of the general population could reach an advanced or comprehensive ED within 60 minutes by ground transportation. Among the pediatric population (age 14 years and younger), 56.2% could reach a pediatric critical care or comprehensive ED, with another 19.5% being able to access an advanced ED within 60 minutes. CONCLUSIONS: Using this categorization system, fewer than half of all EDs provide advanced or comprehensive emergency care. While the majority of the population has access to advanced or comprehensive care within an hour, a significant portion (25%) does not. This article describes how an ED categorization scheme could be developed and deployed across the United States. There are implications for prehospital planning, patient decision-making, outcomes measurement, interfacility transfer coordination, and development of regional emergency care systems.

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