Indication Alerts Intercept Drug Name Confusion Errors during Computerized Entry of Medication Orders

Background: Confusion between similar drug names is a common cause of potentially harmful medication errors. Interventions to prevent these errors at the point of prescribing have had limited success. The purpose of this study is to measure whether indication alerts at the time of computerized physician order entry (CPOE) can intercept drug name confusion errors. Methods and Findings: A retrospective observational study of alerts provided to prescribers in a public, tertiary hospital and ambulatory practice with medication orders placed using CPOE. Consecutive patients seen from April 2006 through February 2012 were eligible if a clinician received an indication alert during ordering. A total of 54,499 unique patients were included. The computerized decision support system prompted prescribers to enter indications when certain medications were ordered without a coded indication in the electronic problem list. Alerts required prescribers either to ignore them by clicking OK, to place a problem in the problem list, or to cancel the order. Main outcome was the proportion of indication alerts resulting in the interception of drug name confusion errors. Error interception was determined using an algorithm to identify instances in which an alert triggered, the initial medication order was not completed, and the same prescriber ordered a similar-sounding medication on the same patient within 5 minutes. Similarity was defined using standard text similarity measures. Two clinicians performed chart review of all cases to determine whether the first, non-completed medication order had a documented or non-documented, plausible indication for use. If either reviewer found a plausible indication, the case was not considered an error. We analyzed 127,458 alerts and identified 176 intercepted drug name confusion errors, an interception rate of 0.14±.01%. Conclusions: Indication alerts intercepted 1.4 drug name confusion errors per 1000 alerts. Institutions with CPOE should consider using indication prompts to intercept drug name confusion errors

A primary care, electronic health record-based strategy to promote safe drug use: study protocol for a randomized controlled trial

BackgroundThe Northwestern University Center for Education and Research on Therapeutics (CERT), funded by the Agency for Healthcare Research and Quality, is one of seven such centers in the USA. The thematic focus of the Northwestern CERT is ‘Tools for Optimizing Medication Safety.’ Ensuring drug safety is essential, as many adults struggle to take medications, with estimates indicating that only half of adults take drugs as prescribed. This report describes the methods and rationale for one innovative project within the CERT: the ‘Primary Care, Electronic Health Record-Based Strategy to Promote Safe and Appropriate Drug Use’.Methods/DesignThe overall objective of this 5-year study is to evaluate a health literacy-informed, electronic health record-based strategy for promoting safe and effective prescription medication use in a primary care setting. A total of 600 English and Spanish-speaking patients with diabetes will be consecutively recruited to participate in the study. Patients will be randomized to receive either usual care or the intervention; those in the intervention arm will receive a set of print materials designed to support medication use and prompt provider counseling and medication reconciliation. Participants will be interviewed in person after their index clinic visit and again one month later. Process outcomes related to intervention delivery will be recorded. A medical chart review will be performed at 6 months. Patient outcome measures include medication understanding, adherence and clinical measures (hemoglobin A1c, blood pressure, and cholesterol; exploratory outcomes only).DiscussionThrough this study, we will be able to examine the impact of a health literacy-informed, electronic health record-based strategy on medication understanding and adherence among diabetic primary care patients. The measurement of process outcomes will help inform how the strategy might ultimately be refined and disseminated to other sites. Strategies such as these are needed to address the multifaceted challenges related to medication self-management among patients with chronic conditions.Trial registrationClinicaltrials.gov NCT01669473

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Example Indication Alert for Levothyroxine.

<p>Example Indication Alert for Levothyroxine.</p

Association between Clinician Type, Location, Shift and Probability of an Intercepted Drug Name Confusion Error.

a<p>Resident physician, inpatient location and day shift were used as reference categories. Testing global null hypothesis for model with fluticasone, −2 log likelihood  = 2563.8, chi-square  = 94.9, p<.0001. For model without fluticasone, −2 log likelihood  = 1245.9, chi-square  = 24.6, p<.0001.</p>b<p>p<0.001.</p>c<p>p<0.05.</p

Distribution of Drug Pairs in Intercepted Errors.

a<p>The interception rate is the number of errors (confirmed by clinician chart review) divided by the total number of alerts for that drug.</p>b<p>In this pair, at the time of the alert, the branded names were most common, >90%.</p