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PurposeTo examine the difference between rural and urban hospitals as to their overall level of readiness for stage 2 meaningful use of electronic health records (EHRs) and to identify other key factors that affect their readiness for stage 2 meaningful use.MethodsA conceptual framework based on the theory of organizational readiness for change was used in a cross-sectional multivariate analysis using 2,083 samples drawn from the HIMSS Analytics survey conducted with US hospitals in 2013.FindingsRural hospitals were less likely to be ready for stage 2 meaningful use compared to urban hospitals in the United States (OR = 0.49) in our final model. Hospitals' past experience with an information exchange initiative, staff size in the information system department, and the Chief Information Officer (CIO)'s responsibility for health information management were identified as the most critical organizational contextual factors that were associated with hospitals' readiness for stage 2. Rural hospitals lag behind urban hospitals in EHR adoption, which will hinder the interoperability of EHRs among providers across the nation. The identification of critical factors that relate to the adoption of EHR systems provides insights into possible organizational change efforts that can help hospitals to succeed in attaining meaningful use requirements.ConclusionRural hospitals have increasingly limited resources, which have resulted in a struggle for these facilities to attain meaningful use. Given increasing closures among rural hospitals, it is all the more important that EHR development focus on advancing rural hospital quality of care and linkages with patients and other organizations supporting the care of their patients.

BackgroundAn estimated 4% of hospital admissions acquired healthcare-associated infections (HAIs) and accounted for $9.8 (USD) billion in direct cost during 2011. In 2010, nearly 140 000 of the 3.5 million potentially preventable hospitalizations (PPHs) may have acquired an HAI. There is a knowledge gap regarding the co-occurrence of these events.AimsTo estimate the period occurrences and likelihood of acquiring an HAI for the PPH population.MethodsRetrospective, cross-sectional study using logistic regression analysis of 2011 Texas Inpatient Discharge Public Use Data File including 2.6 million admissions from 576 acute care hospitals. Agency for Healthcare Research and Quality Prevention Quality Indicator software identified PPH, and existing administrative data identification methodologies were refined for Clostridium difficile infection, central line-associated bloodstream infection, catheter-associated urinary tract infection, and ventilator-associated pneumonia. Odds of acquiring HAIs when admitted with PPH were adjusted for demographic, health status, hospital, and community characteristics.FindingsWe identified 272 923 PPH, 14 219 HAI, and 986 admissions with PPH and HAI. Odds of acquiring an HAI for diabetic patients admitted for lower extremity amputation demonstrated significantly increased odds ratio of 2.9 (95% confidence interval: 2.16-3.91) for Clostridium difficile infection. Other PPH patients had lower odds of acquiring HAI compared to non-PPH patients, and results were frequently significant.ConclusionsClinical implications include increased risk of HAI among diabetic patients admitted for lower extremity amputation. Methodological implications include identification of rare events for inpatient subpopulations and the need for improved codification of HAIs to improve cost and policy analyses regarding allocation of resources toward clinical improvements.

Objective: Electronic cigarette use represents an important college health concern. This investigation assessed demographic and behavioral correlates associated with actual and perceived e-cigarette use among a national sample of American college students. Methods: Respondents (n = 19,861) comprised college students from over 40 distinct American higher education institutions. Multivariable logistic regression assessed whether (1) alcohol, tobacco or marijuana use were associated with e-cigarette use; (2) perceived peer use of alcohol, tobacco or marijuana were associated with perceived e-cigarette use. Results: Approximately 5% of survey respondents self-reported using e-cigarettes within the past month. More than 7 out of every 10 respondents, however, thought the 'typical student' was an e-cigarette user. As perceptions of typical student substance use increased, respondents were far more likely to contend the typical student used e-cigarettes. Discussion: In addition to adopting smoke-free campus policies, university officials should disseminate accurate information regarding e-cigarette behaviors of students. [ABSTRACT FROM AUTHOR]

Volume of certain surgical procedures has been linked to patient outcomes. The Leapfrog Group and others have recommended evidence-based referral using specific volume thresholds for nonemergent cases. The literature is limited on the effect of such referral on hospitals, especially in rural areas. To examine the impact of evidence-based referral by volume standard for 5 hospital procedures (abdominal aortic artery repair, coronary angioplasty, coronary artery bypass graft, esophageal cancer surgery, and pancreatic resection) in a largely rural state. Healthcare Cost and Utilization Project Iowa State Inpatient Dataset was analyzed to identify hospitals meeting the volume standard versus those not meeting the standard. Relatively few hospitals perform these procedures in Iowa. Hospitals performing the procedures at a volume above the threshold standard set by the Leapfrog Group tend to be larger, receive more transfers from other hospitals for these procedures, and perform fewer of these procedures on an emergency basis. In Iowa, hospitals that met the volume standard did not differ from hospitals that did not meet the volume standard in risk-adjusted mortality rates. The impact of evidence-based referral would be substantial in terms of travel time for some procedures (ie, coronary artery bypass graft, pancreatic resection, and esophageal cancer surgery) and produce considerable lost revenue for some hospitals. Evidence-based referral would be associated with substantial burden for some patients and hospitals in Iowa. This negative impact does not appear to be offset by improvement in in-hospital mortality rates. These initial findings suggest that there are a number of issues that need to be considered, especially in a rural state, before evidence-based referral is embraced as a means to enhance patient outcomes.

BackgroundHealth risk assessments are becoming more popular as a tool to conveniently and effectively reach community-dwelling adults who may be at risk for serious chronic conditions such as coronary heart disease (CHD). The use of such instruments to improve adults’ risk factor awareness and concordance with clinically measured risk factor values could be an opportunity to advance public health knowledge and build effective interventions. ObjectiveThe objective of this study was to determine if an Internet-based health risk assessment can highlight important aspects of agreement between respondents’ self-reported and clinically measured CHD risk factors for community-dwelling adults who may be at risk for CHD. MethodsData from an Internet-based cardiovascular health risk assessment (Heart Aware) administered to community-dwelling adults at 127 clinical sites were analyzed. Respondents were recruited through individual hospital marketing campaigns, such as media advertising and print media, found throughout inpatient and outpatient facilities. CHD risk factors from the Framingham Heart Study were examined. Weighted kappa statistics were calculated to measure interrater agreement between respondents’ self-reported and clinically measured CHD risk factors. Weighted kappa statistics were then calculated for each sample by strata of overall 10-year CHD risk. Three samples were drawn based on strategies for treating missing data: a listwise deleted sample, a pairwise deleted sample, and a multiple imputation (MI) sample. ResultsThe MI sample (n=16,879) was most appropriate for addressing missing data. No CHD risk factor had better than marginal interrater agreement (κ>.60). High-density lipoprotein cholesterol (HDL-C) exhibited suboptimal interrater agreement that deteriorated (eg, κ