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This review is published as a Cochrane Review in the Cochrane Database of Systematic Reviews 2017, Issue 4. Cochrane Reviews are regularly updated as new evidence emerges and in response to comments and criticisms, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Review.

, D. (2009). Decision aids for people facing health treatment or screening decisions (Review). Cochrane Database of Systematic Reviews, (3), [CD001431].

This review is published as a Cochrane Review in the Cochrane Database of Systematic Reviews 2017, Issue 4. Cochrane Reviews are regularly updated as new evidence emerges and in response to comments and criticisms, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Review.

In most instances, unplanned readmissions to hospital indicate bad health outcomes for patients. Sometimes they are due to a medical error or the provision of suboptimal patient care. Other times, they are unavoidable because they are due to the development of new conditions or the deterioration of refractory, severe chronic conditions.Hospital readmissions are frequently used to gauge patient care. Many organizations use them as a metric for institutional or regional quality of care.1 The widespread public reporting of hospital readmissions and their use in considerations for funding implicitly suggest a belief that readmissions indicate the quality of care provided by particular physicians and institutions.The validity of hospital readmissions as an indicator of quality of care depends on the extent that readmissions are avoidable. As the proportion of readmissions deemed to be avoidable decreases, the effort and expense required to avoid one readmission will increase. This de crease in avoidable admissions will also dilute the relation between the overall readmission rate and quality of care. Therefore, it is important to know the proportion of hospital readmissions that are avoidable.We conducted a systematic review of studies that measured the proportion of readmissions that were avoidable. We examined how such readmissions were measured and estimated their prevalence. Methods Literature searchWe consulted a local information scientist to develop a search strategy to identify studies that measured the proportion of readmissions deemed avoidable (Appendix 1, available at www .cmaj .ca /cgi /content /full /cmaj .101860 /DC1). We applied this strategy to search the MEDLINE and EMBASE databases for English-language papers published from 1966 to July 2010. Fulltext versions of citations were re trieved for complete review if they specified that hospital readmissions were counted; and the title or abstract used any term(s) indicating that re admissions were classified as avoidable (or "preventable,"

This study provides preliminary certification criteria for PDAs. Scoring and rating processes need to be tested and finalized. However, the process of appraising the quality of the clinical evidence reported by the PDA should be used to complement these criteria; the proposed standards are designed to rate the quality of the development process and shared decision-making design elements, not the quality of the PDA's clinical content.

ObjectivesTo describe the development, validation and inter-rater reliability of an instrument to measure the quality of patient decision support technologies (decision aids).DesignScale development study, involving construct, item and scale development, validation and reliability testing.SettingThere has been increasing use of decision support technologies – adjuncts to the discussions clinicians have with patients about difficult decisions. A global interest in developing these interventions exists among both for-profit and not-for-profit organisations. It is therefore essential to have internationally accepted standards to assess the quality of their development, process, content, potential bias and method of field testing and evaluation.MethodsScale development study, involving construct, item and scale development, validation and reliability testing.ParticipantsTwenty-five researcher-members of the International Patient Decision Aid Standards Collaboration worked together to develop the instrument (IPDASi). In the fourth Stage (reliability study), eight raters assessed thirty randomly selected decision support technologies.ResultsIPDASi measures quality in 10 dimensions, using 47 items, and provides an overall quality score (scaled from 0 to 100) for each intervention. Overall IPDASi scores ranged from 33 to 82 across the decision support technologies sampled (n = 30), enabling discrimination. The inter-rater intraclass correlation for the overall quality score was 0.80. Correlations of dimension scores with the overall score were all positive (0.31 to 0.68). Cronbach's alpha values for the 8 raters ranged from 0.72 to 0.93. Cronbach's alphas based on the dimension means ranged from 0.50 to 0.81, indicating that the dimensions, although well correlated, measure different aspects of decision support technology quality. A short version (19 items) was also developed that had very similar mean scores to IPDASi and high correlation between short score and overall score 0.87 (CI 0.79 to 0.92).ConclusionsThis work demonstrates that IPDASi has the ability to assess the quality of decision support technologies. The existing IPDASi provides an assessment of the quality of a DST's components and will be used as a tool to provide formative advice to DSTs developers and summative assessments for those who want to compare their tools against an existing benchmark.