" Computerized decision support systems (CDSS) which improve the quality of patient care are strong and necessary incentives for clinicians to use electronic medical records. We have noted previously that the logical path of CDSS design, which would be to determine the factors that predict success before the system is designed, appears rarely to have been followed. In this overview update of the literature on predictors of successful CDSS, we conclude that the predictors have not been adequately identified and the success of CDSS may improve when they are. "

" OBJECTIVE: To identify features of clinical decision support systems critical for improving clinical practice. DESIGN: Systematic review of randomised controlled trials. DATA SOURCES: Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews. STUDY SELECTION: Studies had to evaluate the ability of decision support systems to improve clinical practice. DATA EXTRACTION: Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature. RESULTS: Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations. CONCLUSIONS: Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate."

" BACKGROUND: Evaluation studies of clinical decision support systems (CDSS) have tended to focus on assessments of system quality and clinical performance in a laboratory setting. Relatively few studies have used field trials to determine if CDSS are likely to be used in routine clinical settings and whether reminders generated are likely to be acted upon by end-users. Moreover, such studies when performed tend not to identify distinct user groups, nor to classify user feedback. AIM: To assess medical residents' acceptance and adoption of a clinical reminder system for chronic disease and preventive care management and to use expressed preferences for system attributes and functionality as a basis for system re-engineering. DESIGN OF STUDY: Longitudinal, correlational study using a novel developmental trajectory analysis (DTA) statistical method, followed by a qualitative analysis based on user satisfaction surveys and field interviews. SETTING: An ambulatory primary care clinic of an urban teaching hospital offering comprehensive healthcare services. 41 medical residents used a CDSS over 10 months in their daily practice. Use of this system was strongly recommended but not mandatory. METHODS: A group-based, semi-parametric statistical modeling method to identify distinct groups, with distinct usage trajectories, followed by qualitative instruments of usability and satisfaction surveys and structured interviews to validate insights derived from usage trajectories. RESULTS: Quantitative analysis delineates three types of user adoption behavior: "light", "moderate" and "heavy" usage. Qualitative analysis reveals that clinicians of distinct types tend to exhibit views of the system consistent with their demonstrated adoption behavior. Drawbacks in the design of the CDSS identified by users of all types (in different ways) motivate a redesign based on current physician workflows. CONCLUSION: We conclude that this mixed methodology has considerable promise to provide new insights into system usability and adoption issues that may benefit clinical decision support systems as well as information systems more generally. "

" OBJECTIVES: The aim is to gain information on factors influencing success and failure for Health Informatics applications from a group of medical informaticians. METHODS: Based on the presentations at a special topic conference on success and failure in Health ICT and analysis of the proceedings, we conducted a Delphi study on success and failure aspects. RESULTS: A total of 110 success factors and 27 failure criteria were identified, distributed on categories like functional, organizational, behavioral, technical, managerial, political, cultural, legal, strategy, economy, education and user acceptance. These factors and criteria were rated for six different system types. Unanimously it was agreed that "collaboration and co-operation" and "setting goals and courses" are "essential for the success" of clinical systems, and "user acceptance" for educational systems. Similarly, the score "essential in order to avoid a failure" were given unanimously on clinical systems for "response rate and other performance measures" and on administrative systems for "not understanding the organizational context" with "not understanding or foreseeing the extent to which the new IT-system affects the organization, its structure and/or work procedures" as the highest scoring sub-item. CONCLUSIONS: All success factors and failure criteria were considered relevant by the Delphi expert panel. There is no small set of relevant factors or indicators, but success or failure of a Health ICT depends on a large set of issues. Further, clinical systems and decision support systems depend on more factors than other systems. "