Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.

Objectives-We sought to identify interest in different modes of self-management support among diabetes patients cared for in public hospitals, and to assess whether demographic or disease-specific factors were associated with patient preferences. We explored the possible role of a perceived communication need in influencing interest in self-management support.Methods-Telephone survey of a random sample of 796 English and Spanish-speaking diabetes patients (esponse rate 47%) recruited from 4 urban US public hospital systems. In multivariate models, we measured the association of race/ ethnicity, primary language, self-reported health literacy, self-efficacy, and diabetes-related factors on patients' interest in three self-management support strategies (telephone support, group medical visits, and internet -based support). We explored the extent to which patients believed that better communication with providers would improve their diabetes control, and whether this perception altered the relationship between patient factors and selfmanagement support acceptance. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. CI 0.33-0.93). African-Americans were more interested than Whites in all 3 self-management support strategies. Patients with limited self-reported health literacy were more likely to be interested in telephone support than those not reporting literacy deficits. Forty percent reported that their diabetes would be better controlled if they communicated better with their health care provider. This perceived communication benefit was independently associated with interest in self-management support (p<0.001), but its inclusion in models did not alter the strengths of the main associations between patient characteristics and self-management support preferences. NIH Public AccessConclusion-Many diabetes patients in safety-net settings report an interest in receiving selfmanagement support, but preferences for modes of delivery of self-management support vary by race/ethnicity, language proficiency, and self-reported health literacy.Practice Implications-Public health systems should consider offering a range of selfmanagement support services to meet the needs of their diverse patient populations. More broad dissemination and implementation of self-management support may help address the unmet need for better provider communication among diabetes patients in these settings.

No abstract

No abstract

No abstract