Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic ( or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction ( AMI) ( n = 3427), or stroke ( n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals ( long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups ( same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided. Conclusions: Diagnosis-outcome indicators based on administrative data require validation as markers of significant risk adjusted SV. Validated indicators allow quantification of realisable outcome benefits if all hospitals achieved best performer levels. The overall level of quality of care within single institutions cannot be inferred from the results of one or a few indicators.

Differences in sharing knowledge interpersonally and via databases: The role of evaluation apprehension and perceived benefits

Knowledge sharing is an essential component of effective knowledge management. However, evaluation apprehension, or the fear that your work may be critiqued, can inhibit knowledge sharing. Using the general framework of social exchange theory, we examined the effects of evaluation apprehension and perceived benefit of knowledge sharing ( such as enhanced reputation) on employees' knowledge sharing intentions in two contexts: interpersonal (i.e., by direct contact between two employees) and database (i.e., via repositories). Evaluation apprehension was negatively associated with knowledge sharing intentions in both contexts while perceived bene. it was only positively associated with knowledge sharing intentions in the database context. Moreover, compared to the interpersonal context, evaluation apprehension was higher and knowledge sharing lower in the database context. Finally, the negative effects of evaluation apprehension upon knowledge sharing intentions were worse when perceived benefits were low compared to when perceived benefits were high.

Public reporting of hospital outcomes based on administrative data: risks and opportunities

In the wake of findings from the Bundaberg Hospital and Forster inquiries in Queensland, periodic public release of hospital performance reports has been recommended. A process for developing and releasing such reports is being established by Queensland Health, overseen by an independent expert panel. This recommendation presupposes that public reports based on routinely collected administrative data are accurate; that the public can access, correctly interpret and act upon report contents; that reports motivate hospital clinicians and managers to improve quality of care; and that there are no unintended adverse effects of public reporting. Available research suggests that primary data sources are often inaccurate and incomplete, that reports have low predictive value in detecting outlier hospitals, and that users experience difficulty in accessing and interpreting reports and tend to distrust their findings.

A systematic review of successes and failures in home telehealth: preliminary results

We conducted a systematic review of the literature to identify studies in home telehealth that compared a home telehealth intervention with a non-telehealth standard/usual care alternative in terms of administrative changes, patient management decisions, patient outcomes, caregiver outcomes, economic impact or social impact on patients. A search of various databases produced 6643 references. Of these 769 papers were selected for more detailed investigation. These papers, combined with hand searching of relevant telehealth journals and cross-referencing of citations in identified publications, resulted in 138 papers referring to 130 projects for review. In this preliminary analysis we used a quality appraisal approach that took into account the study design. An additional analysis of patient numbers was then used to calculate a net evidence score. A large proportion of studies (80%) were randomised controlled trials. Only 22 projects (17%) reported economic data deemed to be sufficient for appraisal. Evidence exists for the clinical effectiveness of home telehealth in diabetes, the general area of mental health, high risk pregnancy monitoring, heart failure and cardiac disease.

Multiresolution spatial databases: Making web-based spatial advances faster

Spatial data has now been used extensively in the Web environment, providing online customized maps and supporting map-based applications. The full potential of Web-based spatial applications, however, has yet to be achieved due to performance issues related to the large sizes and high complexity of spatial data. In this paper, we introduce a multiresolution approach to spatial data management and query processing such that the database server can choose spatial data at the right resolution level for different Web applications. One highly desirable property of the proposed approach is that the server-side processing cost and network traffic can be reduced when the level of resolution required by applications are low. Another advantage is that our approach pushes complex multiresolution structures and algorithms into the spatial database engine. That is, the developer of spatial Web applications needs not to be concerned with such complexity. This paper explains the basic idea, technical feasibility and applications of multiresolution spatial databases.

Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.