Purpose

To 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.

Methods

A 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.

Findings

Rural 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.

Conclusion

Rural 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.

Background

An 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.

Aims

To estimate the period occurrences and likelihood of acquiring an HAI for the PPH population.

Methods

Retrospective, 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.

Findings

We 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.

Conclusions

Clinical 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.

In community-based wellness programs, Social Security Numbers (SSNs) are rarely collected to encourage participation and protect participant privacy. One measure of program effectiveness includes changes in health care utilization. For the 65 and over population, health care utilization is captured in Medicare administrative claims data. Therefore, methods as described in this article for linking participant information to administrative data are useful for program evaluations where unique identifiers such as SSN are not available. Following fuzzy matching methodologies, participant information from the National Study of the Chronic Disease Self-Management Program was linked to Medicare administrative data. Linking variables included participant name, date of birth, gender, address, and ZIP code. Seventy-eight percent of participants were linked to their Medicare claims data. Linking program participant information to Medicare administrative data where unique identifiers are not available provides researchers with the ability to leverage claims data to better understand program effects.

OBJECTIVES:To evaluate the concordance between self-reported data and variables obtained from Medicare administrative data in terms of chronic conditions and health care utilization. DESIGN:Retrospective observational study. PARTICIPANTS:We analyzed data from a sample of Medicare beneficiaries who were part of the National Study of Chronic Disease Self-Management Program (CDSMP) and were eligible for the Centers for Medicare and Medicaid Services (CMS) pilot evaluation of CDSMP (n = 119). METHODS:Self-reported and Medicare claims-based chronic conditions and health care utilization were examined. Percent of consistent numbers, kappa statistic (κ), and Pearson's correlation coefficient were used to evaluate concordance. RESULTS:The two data sources had substantial agreement for diabetes and chronic obstructive pulmonary disease (COPD) (κ = 0.75 and κ = 0.60, respectively), moderate agreement for cancer and heart disease (κ = 0.50 and κ = 0.47, respectively), and fair agreement for depression (κ = 0.26). With respect to health care utilization, the two data sources had almost perfect or substantial concordance for number of hospitalizations (κ = 0.69-0.79), moderate concordance for ED care utilization (κ = 0.45-0.61), and generally low agreement for number of physician visits (κ ≤ 0.31). CONCLUSION:Either self-reports or claim-based administrative data for diabetes, COPD, and hospitalizations can be used to analyze Medicare beneficiaries in the US. Yet, caution must be taken when only one data source is available for other types of chronic conditions and health care utilization.