Variation in clinical outcome following shock wave lithotripsy

PURPOSE: We measure and compare operator specific success rates of extracorporeal shock wave lithotripsy (ESWL) performed by 12 urologists in 1 unit to determine interoperator variation. MATERIALS AND METHODS: From January 1, 1994 to September 1, 1997 a total of 5,769 renal and ureteral stones received 9,607 ESWL treatments by 15 urologists with a Dornier MFL 5000 lithotriptor. The 3-month followup data are available for 4,409 stones. Outcome measures consisted of patient demographics, stone characteristics, technical details of lithotripsy, and stone-free and success rates by treating urologists. RESULTS: Treatment results were analyzed for 12 urologists (surgeons A to L) who treated more than 100 stones each, totaling 4,244 with followup information available. Mean stone-free and success rates were 50.6% and 72.3%, respectively. Surgeon A had significantly higher stone-free and success rates of 56.2% and 76.7%, respectively (p<0.05), with treatment results from 877 stones, which was a significantly higher number than others (p<0.05). Significant differences existed in mean number of shocks delivered among urologists (p = 0.0001), with surgeons A and J delivering the highest mean numbers (2,317 and 2,801, respectively). There was no difference in treatment duration (p = 0.75) but variation existed among urologists in terms of mean maximum treatment voltage (p = 0.0001). Mean fluoroscopy time at 4.1 minutes was higher for surgeon A than others (p<0.05). Mean complication rate following ESWL was 4.9% with no difference among urologists (p = 0.175). Re-treatment was required in 21.7% of cases and surgeon A had the lowest rate (15.9%, p<0.05). CONCLUSIONS: We demonstrated clinically and statistically significant intra-institutional differences in success rates following ESWL. The best results were obtained by the urologist who treated the greatest number of patients, used a high number of shocks and had the longest fluoroscopy time. Accurate targeting is crucial when using a lithotriptor, such as the Dornier MFL 5000, with a narrow focal zone of 6.5 mm. in diameter. Other centers should be encouraged to develop similar programs of outcome analysis in an attempt to improve performance.     

Antibiotic use in US hospitals: quantification,quality measures and stewardship

A majority of patients hospitalized in the US hospitals receive an antibiotic during their hospitalization. Furthermore, up to half of antibiotics prescribed in hospitals are inappropriate. In the setting of continued emergence of antibiotic-resistant pathogens and a limited pipeline of new antimicrobials, attention to optimizing antibiotic use in healthcare settings is essential. We review the measures of antibiotic consumption in the USA, the evolving metrics for comparing antibiotic use (known as benchmarking), trends in antibiotic use, the structure and outcome measures of Antimicrobial Stewardship Programs and interventions to optimize antimicrobial use.     

Evidence‐Based Practice in a Global Context

This commentary examines how the adoption of benchmarking can promote the internationalization of evidence‐based practice. Given the increasing evidence documenting the role of culture and context in the shaping of experiences and expressions of distress, as well as influencing attitudes and conceptions of mental health services, benchmarking may not represent the best approach to addressing global public health agendas. A brief overview of the sequential approach to developing, evaluating, and disseminating treatments in novel cultural contexts is presented, in which benchmarking can play an important, albeit secondary role. It is argued that in a global context, benchmarking's role may better function to promote dissemination within particular contexts, rather than between them.     

The strength of primary care in Europe: an international comparative study

Background

A suitable definition of primary care to capture the variety of prevailing international organisation and service-delivery models is lacking.

Aim

Evaluation of strength of primary care in Europe.

Design and setting

International comparative cross-sectional study performed in 2009–2010, involving 27 EU member states, plus Iceland, Norway, Switzerland, and Turkey.

Method

Outcome measures covered three dimensions of primary care structure: primary care governance, economic conditions of primary care, and primary care workforce development; and four dimensions of primary care service-delivery process: accessibility, comprehensiveness, continuity, and coordination of primary care. The primary care dimensions were operationalised by a total of 77 indicators for which data were collected in 31 countries. Data sources included national and international literature, governmental publications, statistical databases, and experts’ consultations.

Results

Countries with relatively strong primary care are Belgium, Denmark, Estonia, Finland, Lithuania, the Netherlands, Portugal, Slovenia, Spain, and the UK. Countries either have many primary care policies and regulations in place, combined with good financial coverage and resources, and adequate primary care workforce conditions, or have consistently only few of these primary care structures in place. There is no correlation between the access, continuity, coordination, and comprehensiveness of primary care of countries.

Conclusion

Variation is shown in the strength of primary care across Europe, indicating a discrepancy in the responsibility given to primary care in national and international policy initiatives and the needed investments in primary care to solve, for example, future shortages of workforce. Countries are consistent in their primary care focus on all important structure dimensions. Countries need to improve their primary care information infrastructure to facilitate primary care performance management.     

Design and Implementation of a Web-Based Reporting and Benchmarking Center for Inpatient Glucometrics

Background:Insulin is a top source of adverse drug events in the hospital, and glycemic control is a focus of improvement efforts across the country. Yet, the majority of hospitals have no data to gauge their performance on glycemic control, hypoglycemia rates, or hypoglycemic management. Current tools to outsource glucometrics reports are limited in availability or function.Methods:Society of Hospital Medicine (SHM) faculty designed and implemented a web-based data and reporting center that calculates glucometrics on blood glucose data files securely uploaded by users. Unit labels, care type (critical care, non–critical care), and unit type (eg, medical, surgical, mixed, pediatrics) are defined on upload allowing for robust, flexible reporting. Reports for any date range, care type, unit type, or any combination of units are available on demand for review or downloading into a variety of file formats. Four reports with supporting graphics depict glycemic control, hypoglycemia, and hypoglycemia management by patient day or patient stay. Benchmarking and performance ranking reports are generated periodically for all hospitals in the database.Results:In all, 76 hospitals have uploaded at least 12 months of data for non–critical care areas and 67 sites have uploaded critical care data. Critical care benchmarking reveals wide variability in performance. Some hospitals achieve top quartile performance in both glycemic control and hypoglycemia parameters.Conclusions:This new web-based glucometrics data and reporting tool allows hospitals to track their performance with a flexible reporting system, and provides them with external benchmarking. Tools like this help to establish standardized glucometrics and performance standards.     

Summarizing benchmarks in the national database of nursing quality indicators using bootstrap confidence intervals

When summarizing the benchmarks for nursing quality indicators with confidence intervals around the means, bounds too high or too low are sometimes found due to small sample size or violation of the normality assumption. Transforming the data or truncating the confidence intervals at realistic values can solve the problem of out of range values. However, truncation does not improve upon the non-normality of the data, and transformations are not always successful in normalizing the data. The percentile bootstrap has the advantage of providing realistic bounds while not relying upon the assumption of normality and may provide a convenient way of obtaining appropriate confidence intervals around the mean for nursing quality indicators.