Recommendations for selecting drug-drug interactions for clinical decision support

To recommend principles for including drug-drug interactions (DDIs) in clinical decision support

Accelerated Adoption of Advanced Health Information Technology in Beacon Community Health Centers.

BACKGROUND: To complement national and state-level HITECH Act programs, 17 Beacon communities were funded to fuel community-wide use of health information technology to improve quality. Health centers in Beacon communities received supplemental funding. METHODS: This article explores the association between participation in the Beacon program and the adoption of electronic health records. Using the 2010-2012 Uniform Data System, trends in health information technology adoption among health centers located within and outside of Beacon communities were explored using differences in mean t tests and multivariate logistic regression. RESULTS: Electronic health record adoption was widespread and rapidly growing in all health centers, especially quality improvement functionalities: structured data capture, order and results management, and clinical decision support. Adoption lagged for functionalities supporting patient engagement, performance measurement, care coordination, and public health. The use of advanced functionalities such as care coordination grew faster in Beacon health centers, and Beacon health centers had 1.7 times higher odds of adopting health records with basic safety and quality functionalities in 2010-2012. DISCUSSION: Three factors likely underlie these findings: technical assistance, community-wide activation supporting health information exchange, and the layering of financial incentives. Additional technical assistance and community-wide activation is needed to support the use of functionalities that are currently lagging

Utility of Capture-Recapture Methodology to Assess Completeness of Amyotrophic Lateral Sclerosis Case Ascertainment

Background: With the establishment of a national amyotrophic lateral sclerosis (ALS) registry in the United States, methods are needed to ascertain the completeness of case ascertainment, especially in view of the proposal to rely largely on existing data sources. Methods: Data about ALS patients residing in the 5-county metropolitan Atlanta area (within the State of Georgia) from 2001 to 2005 were categorized according to their source – ALS Association, clinical (Emory Healthcare, community neurologist, Veterans Health Administration, Veterans Benefits Administration), Medicare and death certificates. ALS diagnoses were verified using chart review. Capture-recapture analyses were carried out using log-linear modeling, stratified by age and race. Results: The final model (based on 798 cases), which included the 4 main sources and 3 two-way interaction terms, yielded an estimated total population of 880 (95% CI 816–965), indicating that the combination of case-finding methods identified about 90.7% of cases. The estimated 5-year period prevalence is 38.5/100,000 (95% CI 35.66–42.19). Conclusion: This study highlights gaps in data based on existing data sources and illustrates a method for combining data from multiple sources to help facilitate the successful establishment of a US national ALS registry

Consensus Recommendations for Systematic Evaluation of Drug–Drug Interaction Evidence for Clinical Decision Support

BackgroundHealthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations.ObjectiveThe aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents.MethodsA conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations.ResultsWe developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated?ConclusionEvidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs