Suzanne Falck

Effects of clinical decision support on venous thromboembolism risk assessment, prophylaxis, and prevention at a university teaching hospital

PURPOSE The implementation of a mandatory assessment of risk for venous thromboembolism (VTE) in a health systems electronic medical record (EMR) and clinical decision-support (CDS) system was evaluated to measure its effect on the use of pharmacologic prophylaxis and the occurrence of VTE and bleeding events. METHODS A commercially available CDS system was used in designing the automated CDS intervention. During computerized order entry, the system delivered alerts prompting clinician risk assessment and also delivered alerts under circumstances suggesting less-than-optimal prophylaxis. Rates of pharmacologic prophylaxis, clinically diagnosed hospital-acquired VTE, and hospital-acquired bleeding events were measured during one year before and one year after implementation. RESULTS After adjustment for patient age, sex, and high-risk comorbidities, the data showed a postimplementation increase in the percentage of patients who received pharmacologic prophylaxis at some time during their admission from 25.9% to 36.8% (p < 0.001). The rate of VTE for the entire hospital did not change significantly, but a significant reduction among patients on medical units was observed, from 0.55% to 0.33% (p = 0.02). There was no increase in either major or minor bleeding events. CONCLUSION Without increasing the risk of bleeding, a CDS system requiring clinicians to document VTE risk assessment in the EMR promoted improved rates of pharmacologic prophylaxis at any time during an admission and a decreased risk of VTE in general medical patients but not all adult patients.

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.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains

Background Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. Objectives We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Methods Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Results Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Conclusions Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors.

Automated detection of look-alike/sound-alike medication errors

PURPOSE The development and evaluation of an algorithm for detecting potential medication errors due to look-alike/sound-alike (LASA) drug names are described. SUMMARY A computer algorithm that detects potential LASA errors by analyzing medication orders and diagnostic claims data was developed. The algorithm flags a potential error when (1) a medication order is not justified by a diagnosis documented in the patients record, (2) another medication whose orthographic similarity to the index drug exceeds a specified threshold exists, and (3) the latter drug has an indication that matches an active documented diagnosis. A review of medication orders and diagnostic claims at a large health system identified cases in which cycloserine was ordered but cyclosporine was the intended treatment. Subsequent review of all cycloserine orders over a 7-year period indicated that 11 of 16 orders were erroneous, prompting placement of an alert regarding the potential for LASA errors involving cycloserine and cyclosporine in the electronic order-entry system. Automated detection and confirmation of LASA errors via chart review can be used retrospectively to identify problematic pairs of drug names and to assess associated error rates within a healthcare system. The same techniques can be used to prevent errors in real time through indication alerts if accurate diagnostic information is available at the time of order entry. CONCLUSION Automated methods involving the use of medication orders, diagnostic claims, and indications can be used to detect and prevent LASA 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.

Learning optimal opioid prescribing and monitoring: a simulation study of medical residents

Abstract Objective Hospitalized patients often receive opioids. There is a lack of consensus regarding evidence-based guidelines or training programs for effective management of pain in the hospital. We investigated the viability of using an Internet-based opioid dosing simulator to teach residents appropriate use of opioids to treat and manage acute pain. Materials and methods We used a prospective, longitudinal design to evaluate the effects of simulator training. In face-to-face didactic sessions, we taught 120 (108 internal medicine and 12 family medicine) residents principles of pain management and how to use the simulator. Each trainee completed 10 training and, subsequently, 5 testing trials on the simulator. For each trial, we collected medications, doses, routes and times of administration, pain scores, and a summary score. We used mixed-effects regression models to assess the impact of simulation training on simulation performance scores, variability in pain score trajectories, appropriate use of short- and long-acting opioids, and use of naloxone. Results Trainees completed 1582 simulation trials (M = 13.2, SD = 6.8), with sustained improvements in their simulated pain management practices. Over time, trainees improved their overall simulated pain management scores (b = 0.05, P < .01), generated lower pain score trajectories with less variability (b = −0.02, P < .01), switched more rapidly from short-acting to long-acting agents (b = −0.50, P < .01), and used naloxone less often (b = −0.10, P < .01). Discussion and conclusions Trainees translated their understanding of didactically presented principles of pain management to their performance on simulated patient cases. Simulation-based training presents an opportunity for improving opioid-based inpatient acute pain management.