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Background: Increases in adverse clinical outcomes have been documented when hospital nurse staffing is inadequate. Since most hospitals limit nurse staffing to levels for average rather than peak patient census, substantial census increases create serious potential stresses for both patients and nurses. By reducing unnecessary variability, hospitals can reduce many of these stresses and thereby improve patient safety and quality of care., The Source and Nature of Variability in Demand: The variability in the daily patient census is a combination of the natural (uncontrollable) variability contributed by the emergency department and the artificial (potentially controllable) peaks and valleys of patient flow into the hospital fromelective admissions. Once artificial variability in demand is significantly reduced, a substantial portion of the peaks and valleys in census disappears; the remaining censsus variability is largely patient and disease driven. When artificial variability has been minimized, a hospital must have sufficient resources for the remaining patient-driven peaks in demand, over which it has no control, if it is to deliver an optimal level of care., Discussion: Study of operational issues in health care delivery, and acting on what is learned, is critical. Al forms of artificial variation in the demand and supply of health care services should be identified, and pilot programs to test operational changes should be conducted.

The financial implications of implementing quality improvements are often poorly understood. Simply put, does improving quality yield a return on investment? We examine four cases--management of high-cost pharmaceuticals, diabetes management, smoking cessation, and wellness programs in the workplace--to understand the financial and clinical implications of improving care. We explore costs and benefits, in both the short and the long term, to four stakeholders with different and sometimes conflicting interests: providers, purchasers and employers, individual patients, and society. Finally, we recommend policy changes to better align financial incentives for superior quality of care.

Context: The quality of a process can only be tested against its agreed objectives. Editorial peer-review is widely used, yet there appears to be little agreement about how to measure its effects or processes., Methods: To identify outcome measures used to assess editorial peer review as performed by biomedical journals, we analyzed studies identified from 2 systematic reviews that measured the effects of editorial peer review on the quality of the output (ie, published articles) or of the process itself (eg, reviewers' comments)., Results: Ten studies used a variety of instruments to assess the quality of articles that had undergone peer review. Only 1, nonrandomized study compared the quality of articles published in peer-reviewed and non-peer-reviewed journals. The others measured the effects of variations in the peer-review process or used a before-and-after design to measure the effects of standard peer review on accepted articles. Eighteen studies measured the quality of reviewers' reports under different conditions such as blinding or after training. One study compared the time and cost of different review processes., Conclusions: Until we have properly defined the objectives of peer-review, it will remain almost impossible to assess or improve its effectiveness. The research needed to understand the broader effects of peer review poses many methodologic problems and would require the cooperation of many parts of the scientific community.

Context: Editorial peer review is widely used to select submissions to journals for publication and is presumed to improve their usefulness. Sufficient research on peer review has been published to consider a synthesis of its effects., Methods: To examine the evidence of the effects of editorial peer-review processes in biomedical journals, we conducted electronic and full-text searches of private and public databases to June 2000 and corresponded with the World Association of Medical Editors, European Association of Science Editors, Council of Science Editors, and researchers in the field to locate comparative studies assessing the effects of any stage of the peer-review process that made some attempt to control for confounding. Nineteen of 135 identified studies fulfilled our criteria. Because of the diversity of study questions, methods, and outcomes, we did not pool results., Results: Nine studies considered the effects of concealing reviewer/author identity. Four studies suggested that concealing reviewer or author identity affected review quality (mostly positively); however, methodological limitations make their findings ambiguous, and other studies' results were either negative or inconclusive. One study suggested that a statistical checklist can improve report quality, but another failed to find an effect of publishing another checklist. One study found no evidence that training referees improves performance and another showed increased interrater reliability; both used open designs, making interpretation difficult. Two studies of how journals communicate with reviewers did not demonstrate any effect on review quality. One study failed to show reviewer bias, but the findings may not be generalizable. One nonrandomized study compared the quality of articles published in peer-reviewed vs other journals. Two studies showed that editorial processes make articles more readable and improve the quality of reporting, but the findings may have limited generalizability to other journals., Conclusions: Editorial peer review, although widely used, is largely untested and its effects are uncertain.

Overwhelming evidence now indicates that the quality of reporting of randomized, controlled trials (RCTs) is less than optimal. Recent methodologic analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which boast the elimination of systematic error as their primary hallmark. Systematic error in RCTs reflects poor science, and poor science threatens proper ethical standards. A group of scientists and editors developed the CONSORT (Con solidated S tandards o f R eporting T rials) statement to improve the quality of reporting of RCTs. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have adopted the CONSORT statement. The CONSORT statement facilitates critical appraisal and interpretation of RCTs by providing guidance to authors about how to improve the reporting of their trials. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the CONSORT statement. The meaning and rationale for each checklist item are presented. For most items, at least one published example of good reporting and, where possible, references to relevant empirical studies are provided. Several examples of flow diagrams are included. The CONSORT statement, this explanatory and elaboration document, and the associated Web site ( http://www.consort-statement.org ) should be helpful resources to improve reporting of randomized trials. Throughout the text, terms marked with an asterisk are defined at end of text.

For a very long time, evidence from research has contributed to clinical decision making. Over the past 50 years, however, the nature of clinical research evidence has drastically changed compared with previous eras: its standards are higher, the tools for assembling and analyzing it are more powerful, and the context in which it is used is less authoritarian. The consequence has been a shift in both the concept and the practice of clinical decision making known as evidence-based medicine. Evidence-based decisions, by definition, use the strongest available evidence, are often more quantitatively informed than decisions made in the traditional fashion; and sometimes run counter to expert opinion. The techniques of evidence-based medicine are also helpful in resolving conflicting opinions. Evidence-based medicine did not simply appear in vacuo; its roots extend back at least as far as the great French Encyclopedia of the 18th century, and the subsequent work of Pierre Louis in Paris in the early 19th century. The power of the evidence-based approach has been enhanced in recent years by the development of the techniques of systematic review and meta-analysis. While this approach has its critics, we would all want the best available evidence used in making decisions about our care if we got sick. It is only fair that the patients under our care receive nothing less.

Predicting the likelihood that large databases will become an important instrument for medical quality improvement is at least as difficult as most prognostication. This attempt at prediction starts by trying to ask the right questions: Can data serve as the agent for meaningful improvement? Is meaningful improvement possible without data? What elements are necessary and sufficient for improving the quality of medical care? It then looks for answers, starting with an unconventional excursion into the history of database use. This is followed by a recognition that the use of large databases for medical quality improvement is a true innovation, the future of which will probably be determined as much by the social and emotional forces that govern the diffusion of all innovations as by the technical strength of databases themselves. Finally, it examines some of the limitations and pitfalls that are likely to be associated with the increasing use of large databases in medicine.

Objective: To compare patients' and physicians' opinions on the importance of discrete elements of health care as determinants of the quality of outpatient care., Design: Analysis of results of a mailed survey., Setting: Community-based internal medicine practices., Participants: 74 general internists and 814 patients randomly selected from the practices of these internists., Measures: 125 elements of care that covered nine domains were identified: physician clinical skill, physician interpersonal skill, support staff, office environment, provision of information, patient involvement, nonfinancial access, finances, and coordination of care. Participants rated each element on its importance to high-quality care on a 4-point scale: 1 = not important; 2 = of medium importance; 3 = of high importance; and 4 = essential. Patients' and physicians' ratings were compared for individual elements of care and for elements aggregated into domains., Results: Survey response rates were 93% for physicians and 60% for patients. In an element-by-element comparison of ratings, ratings by the two groups differed substantially for 58% of the attributes. The most striking difference was seen in the domain of provision of information (median ratings, 3.56 for patients and 2.85 for physicians; P < 0.001). Ratings by the two groups also differed in the domains of clinical skill (3.75 for patients and 3.35 for physicians; P < 0.001), nonfinancial access (3.00 for patients and 2.87 for physicians; P < 0.001), and finances (3.00 for patients and 2.80 for physicians; P = 0.006). When relative rankings of the domains were compared, both groups agreed that clinical skill is most important; however, patients ranked provision of information second in importance whereas physicians ranked it sixth., Conclusions: Patients and physicians agreed that the most crucial element of outpatient care is clinical skill, but they disagreed about the relative importance of other aspects of care, particularly effective communication of health-related information. These differences in perception may influence the quality of interactions between physicians and patients.

A probability model expresses the relation between the presence of clinical findings (input or independent variables) and the probability that a clinical state will occur (the dependent variable); for example, it expresses the probability that a disease is present or will develop or the probability that an outcome state will be reached. Probability models are developed by using selected study groups. Although these models are most often used to make predictions for groups of patients, they can also predict clinical states for individual patients. The following seven criteria provide a basis for the critical appraisal of probability models. In particular, physicians can use these criteria to decide when a specific probability model should be used to make a prediction in an individual patient. Five of the criteria are concerned with the applicability of a model to a particular patient: 1) the comparability of the patient and the study group used to develop the model; 2) the congruence between the clinical state of interest to patient and physician and the model's outcome; 3) the availability of all input variables where and when the prediction is to be made; 4) the usefulness of a quantitative estimate of the predicted clinical state; and 5) the degree of uncertainty in the probability estimate. The other two criteria are concerned with how well the probability model "works": 6) the fit of probabilities calculated from the model to the outcomes actually observed and 7) the model's ability to discriminate between outcome states relative to chance and to other, more traditional, prediction methods. We illustrate the use of these criteria by applying them, in the form of questions, to a convenient, tabular version of a model that estimates a patient's chances of surviving for 10 years after having definitive surgical therapy for primary cutaneous melanoma.

American medicine is in the midst of a professional evolution driven by a refocusing of medicine's regard for the patient's viewpoint. Historically, medicine has been largely physician centered, but physicians have begun to incorporate patients' perspectives in ways that increasingly matter. Some call this shift "patient-centered" care. In support of the view that this refocusing reflects a broad professional shift, we describe the evolution to patient-centered care in many areas of medicine: patient care, health-related law, medical education, research, and quality assessment.

Dissatisfaction with the current internal medicine residency experience has led to proposals for various new curricula. Unfortunately, the more basic question of "What is a curriculum?" or more accurately, "What curriculum model is most appropriate for clinical training?" has remained largely unexamined. The dominant curriculum model, although valuable, may not be adequate for the task. In its place we propose an experimental model that is more congruent with the realities of graduate medical education.