Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description--including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations--should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

Most QI strategies produced small to modest improvements in glycemic control. Team changes and case management showed more robust improvements, especially for interventions in which case managers could adjust medications without awaiting physician approval. Estimates of the effectiveness of other specific QI strategies may have been limited by difficulty in classifying complex interventions, insufficient numbers of studies, and publication bias.

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ContextSubstantial discrepanies exist between clinical diagnoses and findings at autopsy. Autopsy may be used as a tool for quality management to analyze diagnostic discrepanies.ObjectiveTo determine the rate at which autopsies detect important, clinically missed diagnoses, and the extent to which this rate has changed over time.Data SourcesA systematic literature search for English-language articles available on MEDLINE from 1966 to April 2002, using the search terms autopsy, postmortem changes, post-mortem, postmortem, necropsy, and posthumous, identified 45 studies reporting 53 distinct autopsy series meeting prospectively defined criteria. Reference lists were reviewed to identify additional studies, and the final bibliography was distributed to experts in the field to identify missing or unpublished studies.Study SelectionIncluded studies reported clinically missed diagnoses involving a primary cause of death (major errors), with the most serious being those likely to have affected patient outcome (class I errors).Data ExtractionLogistic regression was performed using data from 53 distinct autopsy series over a 40-year period and adjusting for the effects of changes in autopsy rates, country, case mix (general autopsies; adult medical; adult intensive care; adult or pediatric surgery; general pediatrics or pediatric inpatients; neonatal or pediatric intensive care; and other autopsy), and important methodological features of the primary studies.Data SynthesisOf 53 autopsy series identified, 42 reported major errors and 37 reported class I errors. Twenty-six autopsy series reported both major and class I error rates. The median error rate was 23.5% (range, 4.1%-49.8%) for major errors and 9.0% (range, 0%-20.7%) for class I errors. Analyses of diagnostic error rates adjusting for the effects of case mix, country, and autopsy rate yielded relative decreases per decade of 19.4% (95% confidence interval [CI], 1.8%-33.8%) for major errors and 33.4% (95% [CI], 8.4%-51.6%) for class I errors. Despite these decreases, we estimated that a contemporary US institution (based on autopsy rates ranging from 100% [the extrapolated extreme at which clinical selection is eliminated] to 5% [roughly the national average]), could observe a major error rate from 8.4% to 24.4% and a class I error rate from 4.1% to 6.7%.ConclusionThe possibility that a given autopsy will reveal important unsuspected diagnoses has decreased over time, but remains sufficiently high that encouraging ongoing use of the autopsy appears warranted.

QI strategies are associated with improved hypertension control. A focus on hypertension by someone in addition to the patient's physician was associated with substantial improvement. Future research should examine the contributions of individual QI strategies and their relative costs.

The Ontario AMI mortality prediction rules predict quite accurately 30-day and one-year mortality after an AMI in linked hospital discharge databases of AMI patients from Ontario, Manitoba and California. These models may also be useful to outcomes and quality measurement researchers in other jurisdictions.

Despite advances in supportive care, fulminant-phase inhalational anthrax is usually fatal. Initiation of antibiotic or anthrax antiserum therapy during the prodromal phase is associated with markedly improved survival, although other aspects of care, differences in clinical circumstances, or unreported factors may contribute to this observed reduction in mortality. Efforts to improve early diagnosis and timely initiation of appropriate antibiotics are critical to reducing mortality.

RCTs suggest that elective induction of labor at 41 weeks of gestation and beyond is associated with a decreased risk for cesarean delivery and meconium-stained amniotic fluid. There are concerns about the translation of these findings into actual practice; thus, future studies should examine elective induction of labor in settings where most obstetric care is provided.