Interpretability of the audiogram by audiologists and physician non-specialists.

OBJECTIVE: The audiogram is frequently used by hearing specialists communicate test results to non-specialists, such as physicians. However, it relies on uncommon terminology and interpreting unusual graphical elements to extract information. In this study, we examine whether the audiogram can be accurately interpreted by specialists and non-specialists. DESIGN: We used an online questionnaire to assess the interpretability of an audiogram by audiologists and primary-care physicians. Participants viewed a sample audiogram and submitted their answers via an online survey system. STUDY SAMPLE: We recruited actively practicing primary care physicians (n = 100) and actively practicing audiologists (n = 67). We only accepted respondents from the United States. RESULTS: The audiogram was not easily interpreted by physicians, with a median score of 4/9. Fewer than 25% could accurately report a threshold correctly. Audiologists were more accurate than physicians (median score 7/9, Wilcoxon two-sample p < 0.001, r = 0.648). CONCLUSIONS: The audiogram is difficult for non-specialists to interpret. Clinicians are advised to supplement or supplant the audiogram in interprofessional communication. The development of tools to facilitate interprofessional communication between audiologists and physicians could have positive effects on physician awareness of hearing loss, and even downstream influences on patient behaviour and outcomes.

A Modern Dilemma: How Experts Grapple with Ambiguous Genetic Test Results.

Objective. Clinicians regularly use panel genetic testing to identify hereditary breast cancer risk, but this practice increases the rate of receiving an ambiguous test result, the variant of uncertain significance (VUS). VUS results are a growing and long-term challenge for providers and have caused negative patient outcomes. The objective of this study was to elicit expert opinions about patients' decision making after receiving a VUS result to provide future guidance for VUS disclosure. Methods. Using an adapted mental models approach, experts (N = 25) completed an online survey and in-depth interview eliciting qualitative judgments of the factors relevant to informed patient decision making after receiving a VUS result. Content analysis of interview transcripts clarified the basis for these judgments. Results. Participants identified 11 decisions facing patients after receiving VUS results grouped into ambiguity management or risk management. The experts also identified 24 factors relevant to each decision, which reflected 2 themes: objective factors (e.g., clinical information, guidelines) and psychosocial factors (e.g., understanding or risk perception). Conclusion. This study presents an adaptation of the mental models approach for communication under conditions of ambiguity. Findings suggest providers who present VUS results from genetic testing for hereditary breast cancer should discuss decisions related to ambiguity management that focus on hope for future reclassification, and be directive when discussing risk management decisions. Objective and psychosocial factors should influence both ambiguity and risk management decisions, but especially risk management decisions.

Automated detection of wrong-drug prescribing errors.

BACKGROUND: To assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data. SETTING: Urban, academic medical centre, comprising a 495-bed hospital and outpatient clinic running on the Cerner EHR. We extracted 8 years of medication orders and diagnostic claims. We licensed a database of medication indications, refined it and merged it with the medication data. We developed an algorithm that triggered for LASA errors based on name similarity, the frequency with which a patient received a medication and whether the medication was justified by a diagnostic claim. We stratified triggers by similarity. Two clinicians reviewed a sample of charts for the presence of a true error, with disagreements resolved by a third reviewer. We computed specificity, positive predictive value (PPV) and yield. RESULTS: The algorithm analysed 488 481 orders and generated 2404 triggers (0.5% rate). Clinicians reviewed 506 cases and confirmed the presence of 61 errors, for an overall PPV of 12.1% (95% CI 10.7% to 13.5%). It was not possible to measure sensitivity or the false-negative rate. The specificity of the algorithm varied as a function of name similarity and whether the intended and dispensed drugs shared the same route of administration. CONCLUSION: Automated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Real-time error detection is not possible with the current system, the main barrier being the real-time availability of accurate diagnostic information. Further development should replicate this analysis in other health systems and on a larger set of medications and should decrease clinician time spent reviewing false-positive triggers by increasing specificity.

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

BACKGROUND: The 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/DESIGN: The 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). DISCUSSION: Through 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 REGISTRATION: Clinicaltrials.gov NCT01669473.

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.