Evidence-based Clinical Decision Support Research Articles

The Birth of InterQual

InterQual evidence-based clinical decision support criteria are used by thousands of organizations in healthcare. What industry trends led to their creation, and what accounts for their longevity and widespread acceptance? PRIMARY PRACTICE SETTINGS(S): Hospitals, health systems, and managed care organizations. Although the healthcare puzzle sometimes seems too complicated for anyone to solve, passionate people with good ideas have been able to effect substantive and relatively rapid change throughout healthcare history.

The Evidence-Based Clinical Decision Support Guide: Mucogingival/Esthetics: Making Clinical Decisions in the Absence of Strong Evidence

Although evidence-based decision-making in dentistry is quickly evolving, large gaps remain in our clinical knowledge base regarding every day decisions and procedures. Especially in the absence of strong evidence, as is the case with mucogingival conditions, risk assessment and identification are important components of the clinical decision-making process. Utilization of clinical decision support (CDS) guides, frameworks and systems enhances chairside decision-making and improves delivery of patient care. This article introduces an Evidence-Based Clinical Decision Support Guide for mucogingival/esthetic situations. This CDS guide delineates treatment strategies based upon evidence-based risk assessment and when possible, risk management. It provides the clinician with a framework that will support decision-making at the point of care. Recommendations for consultation, treatment and referral are reviewed.

Promoting evidence-based management of anemia in cancer patients: Background and scientific validation of RESPOND, a web-based clinical decision support system

19676 Background: The 2006 European Organisation for Research and Treatment of Cancer (EORTC) guidelines for erythropoietic proteins in cancer-related anemia provide the most up-to-date assessment of the evidence base. To promote clinicians’ adoption of evidence- based (EB) practice guidelines (EBPGs), it is critical to bring guidelines to the point of care. RESPOND is an EB clinical decision support system (CDSS) based on the EORTC guidelines. CDSSs are seldom validated. We describe the methodologies of two studies being conducted to validate RESPOND. Methods. Study 1: descriptive design - accuracy and content validity. Five experts are asked to rate the accuracy of algorithms derived from the guidelines; the objective being an intraclass correlation coefficient =0.90 for each of 27 algorithmic sets. Study 2: hybrid matched pre-post design - concurrent and discriminant validity. Two patient cohorts (n=33 each) matched by type of cancer and similarity of chemotherapy regimen ie, sample 1 (4 months prospective data after RESPOND [post]) and sample 2 (4 months retrospective data prior to RESPOND [pre]) were used to test concurrent validity (congruence scores [CS] of sample 1) and discriminant validity (difference between sample 2 and sample 1 CS). A score is calculated for each patient to quantify the extent to which treatment and outcomes are congruent with the EORTC EBPG. Table 1 . Calculation of the congruence score ESA, erythropoiesis-stimulating agent; Hb, hemoglobin, Q1W, once weekly, Q3W, once every three weeks Conclusions. These studies will provide the necessary validation for RESPOND as an evidence-based clinical support tool. [Table: see text] [Table: see text]

Applied health informatics: a multi-year international effort to implement and maintain evidence-based practice for chronic-wound treatment

This paper describes a multi-year attempt to develop, implement and maintain an evidence-based international clinical decision support system for preventing and treating chronic wounds. Incidental to implementation efforts, over 37,000 wound assessments, risk assessments and treatment events were accumulated. These data allowed further confirmation of the evidence on which protocols and algorithms were based as well as empirically based rationales for refinement or the protocols and algorithms. However, during the decade-long project many challenges were faced. Key points of learning are also presented. An iterative incidental model for applied health informatics is presented as a context for viewing the efforts.