Indication alerts intercept drug name confusion errors during computerized entry of medication orders

Psycholinguistic tests predict real-world drug name confusion error rates: a cross-sectional experimental study

BACKGROUND

Wrong-drug medication errors are common. Regulators screen drug names for confusability, but screening methods lack empirical validation. Previous work showed that psycholinguistic tests on pairs of drug names are associated with real-world error rates in chain pharmacies. However, regulators evaluate individual names not pairs, and individual names can be confused with multiple drugs (eg, hydroxyzine with hydralazine but also hydrocet, thorazine, hydrochlorothiazide). This study examines whether an individual drug name's performance on psycholinguistic tests correlates with that name's sum total error rate in the real world.

METHODS

Nineteen pharmacists and 18 pharmacy technicians completed memory and perception tests assessing confusability of 77 drug names. Tests involved presenting a drug name to participants in conditions that hindered their ability to see, hear or remember the name. Participants typed the name they perceived and selected that name from a menu of alternatives. Error rates on the tests were assessed in relation to real-world rates, as reported by the patient safety organisation associated with a national pharmacy chain in the USA.

RESULTS

Mean error rate on the psycholinguistic tests was positively correlated with the log-adjusted real-world error rate (r=0.50, p<0.0001). Linear and mixed effects logistic regression analyses indicated that the lab-measured error rates significantly predicted the real-world error rates and vice versa.

CONCLUSIONS

Lab-based psycholinguistic tests are associated with real-world drug name confusion error rates. Previous work showed that such tests were associated with error rates of specific look-alike sound-alike pairs, and the current work showed that lab-based error rates are also associated with an individual drug's overall error rate. Taken together, these studies validate the use of psycholinguistic tests in assessing the confusability of proposed drug names.

Compulsory Indications in Hospital Prescribing Software Tested with Antibacterial Prescriptions

The aim was to assess how making the indication field compulsory in our electronic prescribing system influenced free text documentation and to visualise prescriber behaviour. The indication field was made compulsory for seven antibacterial medicines. Text recorded in the indication field was manually classified as 'indication present', 'other text', 'rubbish text', or 'blank'. The proportion of prescriptions with an indication was compared for four weeks before and after the intervention. Indication provision increased from 10.6% to 72.4% (p<0.01) post-intervention. 'Other text' increased from 7.6% to 25.1% (p<0.01), and 'rubbish text' from 0.0% to 0.6% (p<0.01). Introducing the compulsory indication field increased indication documentation substantially with only a small increase in 'rubbish text'. An interactive report was developed using a live data extract to illustrate indication provision for all medicines prescribed at our tertiary hospital. The interactive report was validated and locally published to support audit and quality improvement projects.

Stakeholder views on requiring diagnosis or clinical indication on e-prescriptions

BACKGROUND

Medication safety organizations have been recommending the inclusion of diagnosis or clinical indication on prescription orders for decades. However, this information is typically not provided by prescribers and shared with pharmacists, despite the availability of data fields in the most commonly used standard for electronic prescriptions.

OBJECTIVE

To elucidate the views of selected industry stakeholders relative to perceived barriers to including diagnosis or indication on all electronic prescriptions.

METHODS

Semistructured concept elicitation interviews identified key issues. Survey items were refined iteratively by the research team. The final instrument consisted of 34 questions intended to elicit the importance and relative priority of perceived barriers and potential solutions. A link to the Internet survey was emailed to members of the National Council for Prescription Drug Programs in February 2023, with biweekly follow-up reminders.

RESULTS

A total of 139 surveys were analyzed for a response rate of 9.6%. On the importance of resolving issues related to the inclusion of diagnosis or indication on e-prescriptions, a majority of respondents indicated "extremely important" or "very important" for all items except one. On level of agreement with statements about how to implement such a requirement, a majority indicated "strongly agree" or "agree" for 10 of 17 items.

CONCLUSIONS

Although clearly exploratory, the results of our survey suggest industry stakeholder agreement that uniform inclusion of diagnosis or clinical indication on all e-prescriptions would improve patient safety and health outcomes. A number of important questions and potential barriers must be resolved for implementation to be successful.

Identification of Confusing Medicine Proprietary Names: Toward Safe Medicine Use-A Cross-Sectional Study in Sri Lanka

BACKGROUND

Look-alike sound-alike (LASA) medications have similar pronunciation (phonetic) and/or manifestation (orthographic), which could create confusion among users and challenge the safe use of medicines. The availability of foreign products in local markets aggravates the situation. This study was designed to examine the registered medicine proprietary names in Sri Lanka to discern the presence of similar medicine names in the industry.

METHODS

A cross-sectional study was conducted on the registered drug proprietary names in Sri Lanka. Using the RAND and RANK functions in Microsoft® excel® 365, a random sample of 385 proprietary names was selected. Two evaluators independently evaluated each proprietary name in the sample against the other registered proprietary names following a stepwise text filtering method. After each filter, the resulting proprietary names were manually examined for identical, similar-looking, and similar-sounding proprietary names to the name under evaluation. The observations were matched, categorized, and collated into ten groups.

RESULTS

Among the 385 names evaluated, 138 (35.84%) proprietary names had no similarity to existing other registered proprietary names. The rest of the names (n = 247, 64.15%) were found to be either identical (n = 03 pairs), look-alike (n = 91 pairs), or sound-alike (n = 80 pairs) to the registered proprietary names.

CONCLUSION

The findings revealed the presence of equal and similar proprietary names in the system. A multifactorial strategy led by the National Medicine Regulatory Authority (NMRA) is recommended to minimize the confusing names entering the system. Primarily the NMRA's call for action should include adequate industry guidance with specific guidelines, a significant pre-submission assessment process, and denying approval of LASA proprietary names.

Examining medication ordering errors using AHRQ network of patient safety databases

BACKGROUND

Studies examining the effects of computerized order entry (CPOE) on medication ordering errors demonstrate that CPOE does not consistently prevent these errors as intended. We used the Agency for Healthcare Research and Quality (AHRQ) Network of Patient Safety Databases (NPSD) to investigate the frequency and degree of harm of reported events that occurred at the ordering stage, characterized by error type.

MATERIALS AND METHODS

This was a retrospective observational study of safety events reported by healthcare systems in participating patient safety organizations from 6/2010 through 12/2020. All medication and other substance ordering errors reported to NPSD via common format v1.2 between 6/2010 through 12/2020 were analyzed. We aggregated and categorized the frequency of reported medication ordering errors by error type, degree of harm, and demographic characteristics.

RESULTS

A total of 12 830 errors were reported during the study period. Incorrect dose accounted for 3812 errors (29.7%), followed by incorrect medication 2086 (16.3%), and incorrect duration 765 (6.0%). Of 5282 events that reached the patient and had a known level of severity, 12 resulted in death, 4 resulted in severe harm, 45 resulted in moderate harm, 341 resulted in mild harm, and 4880 resulted in no harm.

CONCLUSION

Incorrect dose and incorrect drug orders were the most commonly reported and harmful types of medication ordering errors. Future studies should aim to develop and test interventions focused on CPOE to prevent medication ordering errors, prioritizing wrong-dose and wrong-drug errors.

Indication documentation and indication-based prescribing within electronic prescribing systems: a systematic review and narrative synthesis

BACKGROUND

Despite recommendations, documentation of indication on prescriptions and inpatient medication orders is not routinely practised. There has been a recent systematic review of indication documentation for antimicrobials, but not for interventions relating to indication documentation for medication more broadly. Our aims were to 1) identify, describe and synthesise the literature relating to effectiveness of interventions aimed at improving indication documentation and/or indication-based prescribing in both primary and secondary healthcare; 2) synthesise participant perspectives to identify barriers and facilitators to these interventions; and 3) make recommendations for both practice and research.

METHODS

A systematic literature search was conducted using Medline, Embase and CINAHL using two search concepts: electronic prescribing systems, and indication documentation and/or indication-based prescribing. Qualitative, quantitative and mixed-methods studies were included; outcome measures and results were extracted to produce a narrative synthesis. Quality appraisal by two independent reviewers was undertaken using the Mixed Methods Appraisal Tool.

RESULTS

We identified 21 studies evaluating interventions to aid indication documentation. Indication documentation was either via free-text, selection from a list, or by use of pre-defined indication-based order sentences for individual medications. For a number of outcomes, there was a mostly positive impact, including appropriateness of the medication order (6 of 8 studies), rates of prescribing error (2/2) and some less commonly reported clinical (2/4) and workflow-related outcomes (2/3). There was a less favourable impact on accuracy of indication documentation and rates of medication use, highlighting some unintended consequences that may occur when implementing new interventions. Participant insights from prescribers and other healthcare professionals complemented quantitative study results, highlighting both facilitators and barriers to indication documentation and the associated interventions. For example, barriers included long drop-down lists and the need to use workarounds to navigate approval systems due to time or knowledge constraints. Facilitating factors included the perceived benefits of indication documentation on communication among the healthcare team and with the patient.

CONCLUSION

Indication documentation has the potential to improve appropriate prescribing and reduce prescribing errors. However, further benefits to the prescriber, multidisciplinary team and patient may only be realised by developing methods of indication documentation that integrate more efficiently with prescriber workflows.

PROSPERO REGISTRATION NUMBER

CRD42021278495.

Top-Down Processing of Drug Names Can Induce Errors in Discriminating Similar Pseudo-Drug Names by Nurses

BACKGROUND

One factor that could cause medical errors is confusing medicines with similar names. A previous study showed that nurses who have knowledge about drugs faced difficulty in discriminating a drug name from similar pseudo-drug names. To avoid such errors, finger-pointing and calling (FPC) has been recommended in Japan.

OBJECTIVES

The present study had two aims. The first was to determine whether such difficulty was due to top-down processing, rather than bottom-up processing, being applied even for pseudo-names. The other was to investigate whether FPC affected error prevention for similar drug names.

METHOD

In two experiments, nurses and non-health care professionals performed a choice reaction time task for drug names and common words, with or without FPC. Error rate and reaction time were analyzed.

RESULTS

When drug names were used, nurses showed difficulty discriminating target names from distractors. Furthermore, the error prevention effect of FPC was marginally significant for drug names. However, nurses showed no significant differences when similar drug names were used. There was no significant difference regarding the error rate for words.

CONCLUSIONS

Nurses' knowledge of drug names activates top-down processing. As a result, the processing of drug names was not as accurate and quick as that for words for nurses, which caused difficulty in discriminating similar names. FPC may be applicable to reduce confusion errors, possibly by leading individuals to process drug names using bottom-up processing.

APPLICATION

The present study advances current knowledge about error tendencies with similar drug names and the effects of FPC on error prevention.

Indication alerts to improve problem list documentation

BACKGROUND

Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication.

METHODS

We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review.

RESULTS

Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%.

CONCLUSIONS

Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

Indication or diagnosis should be required on prescriptions

Although prospective drug utilization review and patient counseling have long been recognized as professional and ethical responsibilities of pharmacists, the implementation of the Omnibus Budget Reconciliation Act of 1990 made them legal responsibilities. Ensuring the safety and effectiveness of prescription pharmaceutical care requires that all members of the prescriber-patient-pharmacist triad are equally informed about the therapeutic plan for which the pharmacist is professionally, ethically, and legally responsible for properly implementing. Providing pharmacists with the clinical indication or diagnosis is an important and long overdue first step. DISCLOSURES: No funding was received for the writing of this article. Warholak has received grant funding through the University of Arizona from Sinfonia Rx, Pharmacy Quality Alliance, and the Arizona Department of Health Services, unrelated to this work. The other authors have nothing to disclose.

Risk factors associated with medication ordering errors

OBJECTIVE

We utilized a computerized order entry system-integrated function referred to as "void" to identify erroneous orders (ie, a "void" order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors.

MATERIALS AND METHODS

We collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors-based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems-based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety.

RESULTS

During the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors.

CONCLUSIONS

The void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.

Prescriber and pharmacist attitudes toward inclusion of diagnosis or clinical indication on prescription orders

BACKGROUND

Pharmacy and medication safety organizations have long recommended that diagnosis or clinical indication be required on medication orders to improve the safety and effectiveness of care.

OBJECTIVE

To assess attitudes of Arizona prescribers and pharmacists toward the inclusion of the clinical indication or the diagnosis on prescription orders and perceived barriers to its implementation in Arizona.

METHODS

Data were obtained by questionnaires from pharmacists and primary care prescribers after a continuing pharmacy education presentation on the value of including a clinical indication or a diagnosis on prescription orders. The survey was distributed to licensed pharmacists who attended the Arizona Pharmacy Association's Southwest Clinical Pharmacy Seminar. The survey was distributed to primary care providers with active Arizona licenses who attended the Arizona Osteopathic Medical Association Annual Convention and to nurse practitioners after an Arizona Nurse Practitioner Council educational webinar. Prescriber and pharmacist responses were compared using the Mann-Whitney U test. An a priori alpha of 0.05 was used, and in the cases of multiple comparisons, a Bonferroni correction was employed.

RESULTS

A total of 74 complete questionnaires were submitted by prescribers and 54 by pharmacists. Approximately 71% of the prescribers and 66% of the pharmacists agreed that they would support voluntary inclusion of a diagnosis or a clinical indication on prescription orders (P = 0.81). However, the 2 groups disagreed on whether the inclusion of the diagnosis or clinical indication should be a requirement (44% of prescribers agreed vs. 96% of pharmacists, P < 0.001). Two perceived barriers revealed statistically significant differences, with the prescribers being more concerned about possible insurance rejections than pharmacists (P = 0.005, whereas the pharmacists were more concerned about potential software transmission accuracy than prescribers (P < 0.001).

CONCLUSION

Arizona prescribers and pharmacists in our convenience sample supported the voluntary inclusion of a diagnosis or a clinical indication on prescriptions orders but disagreed as to whether it should be required. Prescribers especially indicated they have a variety of concerns that need to be overcome before they could support a statewide mandate.

A systematic literature review of LASA error interventions

AIMS

The aim of this systematic review was to explore and evaluate the efficacy of interventions to reduce the prevalence of look-alike, sound-alike (LASA) medication name errors.

METHODS

We conducted a systematic review of the literature, searching PubMed, EMBASE, Scopus and Web of Science up to December 2016, and re-ran the search in February 2020 for later results. We included studies of interventions to reduce LASA errors and included randomized controlled trials, controlled before-and-after studies, and interrupted time series. Details were registered in Prospero (ID: CRD42016048198).

RESULTS

We identified six studies that fulfilled our inclusion criteria. All were conducted in laboratories. Given the diversity in the included studies, we did not conduct a meta-analysis and instead report the findings narratively. The only intervention explored in RCTs was capitalization of selected letters ("Tall Man"), for which we found limited efficacy and no consensus.

CONCLUSIONS

Tall Man lettering is a marginally effective intervention to reduce LASA errors, with a number of caveats. We suggest that Tall Man gives rise to a "quasi-placebo effect", whereby a user derives more benefit from Tall Man lettering if they are aware of its purpose.

Computerized provider order entry-related medication errors among hospitalized patients: An integrative review

The Institute of Medicine estimates that 7,000 lives are lost yearly as a result of medication errors. Computerized physician and/or provider order entry was one of the proposed solutions to overcome this tragic issue. Despite some promising data about its effectiveness, it has been found that computerized provider order entry may facilitate medication errors.The purpose of this review is to summarize current evidence of computerized provider order entry -related medication errors and address the sociotechnical factors impacting the safe use of computerized provider order entry. By using PubMed and Google Scholar databases, a systematic search was conducted for articles published in English between 2007 and 2019 regarding the unintended consequences of computerized provider order entry and its related medication errors. A total of 288 articles were screened and categorized based on their use within the review. One hundred six articles met our pre-defined inclusion criteria and were read in full, in addition to another 27 articles obtained from references. All included articles were classified into the following categories: rates and statistics on computerized provider order entry -related medication errors, types of computerized provider order entry -related unintended consequences, factors contributing to computerized provider order entry failure, and recommendations based on addressing sociotechnical factors. Identifying major types of computerized provider order entry -related unintended consequences and addressing their causes can help in developing appropriate strategies for safe and effective computerized provider order entry. The interplay between social and technical factors can largely affect its safe implementation and use. This review discusses several factors associated with the unintended consequences of this technology in healthcare settings and presents recommendations for enhancing its effectiveness and safety within the context of sociotechnical factors.

Integrating patient-centric indications into the prescribing process: Experience at a tertiary academic medical center

PURPOSE

To describe the development of and implementation of a patient-centric clinical indications library (CIL) into the prescribing process and determine the operational and humanistic outcomes (from prescriber, pharmacist, and patient perspectives) of including indications on outpatient prescription labels.

METHODS

A descriptive retrospective data analysis was conducted. Multiple stakeholder groups were engaged to develop and integrate the CIL into the prescription package. After CIL integration, prescribers, pharmacists, and patients were surveyed. A focus group discussion consisting of Veterans and caregivers was held.

RESULTS

Following implementation of the CIL, the proportion of prescriptions associated with an indication increased from 88% to 96%. Surveyed clinicians responded that indications helped them better understand a patient's profile (61.1% of prescribers and 100% of pharmacists). Among surveyed pharmacists, 61.5% and 53.8%, respectively, believed that indications helped them catch instances of wrong medications and wrong doses ordered. Veterans surveyed found that indications on their prescription labels helped them know what their medications were for (91.0% of respondents) and why it is important to take their medications (70.7%). In focus group discussions, Veterans and family members and/or caregivers expressed a preference to see indications that describe how a medication works (eg, "to lower blood sugar" vs "for diabetes") because they felt that type of phrasing is measurable, action oriented (which was appealing due to Veterans' military background), provides surreptitious education, and tells the users what to expect.

CONCLUSION

Engaging multidisciplinary stakeholder groups, optimizing the electronic health record system, and authorizing pharmacists to add known indications to prescriptions increased the number of prescriptions with indications, decreased the perceived time spent on order entry and verification, and enabled better understanding of each medication's purpose by providers and patients.

The problem of look-alike, sound-alike name errors: Drivers and solutions

Look-alike or sound-alike (LASA) medication names may be mistaken for each other, e.g. mercaptamine and mercaptopurine. If an error of this sort is not intercepted, it can reach the patient and may result in harm. LASA errors occur because of shared linguistic properties between names (phonetic or orthographic), and potential for error is compounded by similar packaging, tablet appearance, tablet strength, route of administration or therapeutic indication. Estimates of prevalence range from 0.00003 to 0.0022% of all prescriptions, 7% of near misses, and between 6.2 and 14.7% of all medication error events. Solutions to LASA errors can target people or systems, and include reducing interruptions or distractions during medication administration, typographic tweaks, such as selective capitalization (Tall Man letters) or boldface, barcoding, and computerized physician order entry.

How often do prescribers include indications in drug orders? Analysis of 4 million outpatient prescriptions

PURPOSE

To examine the extent to which outpatient clinicians currently document drug indications in prescription instructions.

METHODS

Free-text sigs were extracted from all outpatient prescriptions generated by the computerized prescriber order entry system of a major academic institution during a 5-year period. Natural language processing was used to identify drug indications. The data set was analyzed to determine the rates at which prescribers included indications. It was stratified by provider specialty, drug class, and specific medications, to determine how often these indications were in prescriptions for as-needed (PRN) versus non-PRN medications.

RESULTS

During the study period, 4,356,086 prescriptions were ordered. Indications were included in 322,961 orders (7.41%). From these orders, 249,262 indications (77.18%) were written for PRN orders. Although internal medicine prescribers generated the highest number of medication orders, they included indications in only 6.26% of their prescriptions, whereas orthopedic surgery providers had the highest rate of documenting indications (33.41%). Pain was the most common indication, accounting for 30.35% of all documented indications. The drug class with the highest number of sigs-containing indications was narcotic analgesics. Non-PRN chronic medication prescriptions rarely included the indication.

CONCLUSION

Prescribers rarely included drug indications in electronic free-text prescription instructions, and, when they did, it was mostly for PRN uses such as pain.

Identification of potential drug name confusion errors in the Sentinel System

PURPOSE

In July 2015, the US Food and Drug Administration (FDA) published a drug safety communication regarding errors in prescribing and dispensing of the antidepressant Brintellix (vortioxetine) and the antiplatelet Brilinta (ticagrelor) that arose due to proprietary drug name confusion. Brintellix is indicated for major depressive disorder; Brilinta is indicated to reduce cardiovascular death, myocardial infarction, and stroke in patients with acute coronary syndrome or history of myocardial infarction. Brintellix was renamed to Trintellix in May 2016. Using Brilinta and Brintellix as a proof-of-concept feasibility use case, we assessed whether drug name confusion errors between the pair could be identified in electronic health care data via the combination of a claims-based algorithm and limited manual claims data review.

METHODS

Using data from the Sentinel System, we defined potential errors as Brintellix users without an on- or off-label indication for Brintellix, without a dispensing for a drug with the same indications as Brintellix, and with an on- or off-label indication for Brilinta between -365 and +30 days after index Brintellix dispensing; the reverse was done for Brilinta. We manually reviewed claims profiles of potential cases.

RESULTS

We identified 27 (0.1%) potential errors among 21 208 Brintellix users; 16 appeared to be likely errors based on claims profile review. Fifty-one (0.3%) of the 16 779 Brilinta users were identified as potential errors, and four appeared to be likely errors.

CONCLUSIONS

A claims-based algorithm combined with manual review of claims profiles could identify potential drug name confusion errors, and narrow down likely errors that warrant further investigation.

Categorization of free-text drug orders using character-level recurrent neural networks

BACKGROUND AND PURPOSE

Manual annotation and categorization of non-standardized text ("free-text") of drug orders entered into electronic health records is a labor-intensive task. However, standardization is required for drug order analyses and has implications for clinical decision support. Machine learning could help to speed up manual labelling efforts. The objective of this study was to analyze the performance of deep machine learning methods to annotate non-standardized text of drug order entries with their therapeutically active ingredients.

MATERIALS AND METHODS

The data consisted of drug orders entered 8/2009-4/2014 into the electronic health records of inpatients at a large tertiary care academic medical center. We manually annotated the most frequent order entry patterns with the active ingredient they contain (e.g. "Prograf"⟵"Tacrolimus"). We heuristically included additional orders by means of character sequence comparisons to augment the training dataset. Finally, we trained and employed character-level recurrent deep neural networks to classify non-standardized text of drug order entries according to their active ingredients.

RESULTS

A total of 26,611 distinct order patterns were considered in our study, of which the top 7.6% (2028) had been annotated with one of 558 distinct ingredients, leaving 24,583 unlabeled observations. Character-level recurrent deep neural networks achieved a Mean Reciprocal Rank (MRR) of 98% and outperformed the best representative baseline, a trigram-based Support Vector Machine, by 2 percentage points.

CONCLUSION

Character-level recurrent deep neural networks can be used to map the active ingredient to non-standardized text of drug order entries, outperforming other representative techniques. While machine learning might help to facilitate categorization tasks, still a considerable amount of manual labelling and reviewing work is required to train such systems.

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.

Detection of potential look-alike/sound-alike medication errors using Veterans Affairs administrative databases

PURPOSE

Results of a study to estimate the prevalence of look-alike/sound-alike (LASA) medication errors through analysis of Veterans Affairs (VA) administrative data are reported.

METHODS

Veterans with at least 2 filled prescriptions for 1 medication in 20 LASA drug pairs during the period April 2014-March 2015 and no history of use of both medications in the preceding 6 months were identified. First occurrences of potential LASA errors were identified by analyzing dispensing patterns and documented diagnoses. For 7 LASA drug pairs, potential errors were evaluated via chart review to determine if an actual error occurred.

RESULTS

Among LASA drug pairs with overlapping indications, the pairs associated with the highest potential-error rates, by percentage of treated patients, were tamsulosin and terazosin (3.05%), glipizide and glyburide (2.91%), extended- and sustained-release formulations of bupropion (1.53%), and metoprolol tartrate and metoprolol succinate (1.48%). Among pairs with distinct indications, the pairs associated with the highest potential-error rates were tramadol and trazodone (2.20%) and bupropion and buspirone (1.31%). For LASA drug pairs found to be associated with actual errors, the estimated error rates were as follows: lamivudine and lamotrigine, 0.003% (95% confidence interval [CI], 0-0.01%); carbamazepine and oxcarbazepine, 0.03% (95% CI, 0-0.09%); and morphine and hydromorphone, 0.02% (95% CI, 0-0.05%).

CONCLUSION

Through the use of administrative databases, potential LASA errors that could be reviewed for an actual error via chart review were identified. While a high rate of potential LASA errors was detected, the number of actual errors identified was low.

A Prescription For Enhancing Electronic Prescribing Safety

While electronic prescribing has been shown to reduce medication errors and improve prescribing safety, it is vulnerable to error-prone processes. We review six intersecting areas in which changes to electronic prescribing systems, particularly in the outpatient setting, could transform medication ordering quality and safety. We recommend incorporating medication indications into electronic prescribing, establishing a single shared online medication list, implementing the transmission of electronic cancellation orders to pharmacies (CancelRx) to ensure that drugs are safely and reliably discontinued, implementing standardized structured and codified prescription instructions, reengineering clinical decision support, and redesigning electronic prescribing to facilitate the ordering of nondrug alternatives.

Identifying Electronic Health Record Usability And Safety Challenges In Pediatric Settings

Pediatric populations are uniquely vulnerable to the usability and safety challenges of electronic health records (EHRs), particularly those related to medication, yet little is known about the specific issues contributing to hazards. To understand specific usability issues and medication errors in the care of children, we analyzed 9,000 patient safety reports, made in the period 2012-17, from three different health care institutions that were likely related to EHR use. Of the 9,000 reports, 3,243 (36 percent) had a usability issue that contributed to the medication event, and 609 (18.8 percent) of the 3,243 might have resulted in patient harm. The general pattern of usability challenges and medication errors were the same across the three sites. The most common usability challenges were associated with system feedback and the visual display. The most common medication error was improper dosing.

Evaluating the implementation and effectiveness of a multi-component intervention to reduce post-surgical opioid prescribing: study protocol of a mixed-methods design

INTRODUCTION

Opioids prescribed after surgery accounted for 5% of the 191 million opioid prescriptions filled in 2017. Approximately 80% of the opioid pills prescribed by surgical care providers remain unused, leaving a substantial number of opioids available for non-medical use. We developed a multi-component intervention to address surgical providers' role in the overprescribing of opioids. Our study will determine effective strategies for reducing post-surgical prescribing while ensuring adequate post-surgery patient-reported pain-related outcomes, and will assess implementation of the strategies.

METHODS AND ANALYSIS

The Minimising Opioid Prescribing in Surgery study will implement a multi-component intervention, in an Illinois network of six hospitals (one academical, two large community and three small community hospitals), to decrease opioid analgesics prescribed after surgery. The multi-component intervention involves four domains: (1) patient expectation setting, (2) baseline assessment of opioid use, (3) perioperative pain control optimisation and (4) post-surgical opioid minimisation. Four surgical specialities (general, orthopaedics, urology and gynaecology) at the six hospitals will implement the intervention. A mixed-methods approach will be used to assess the implementation and effectiveness of the intervention. Data from the network's enterprise data warehouse will be used to evaluate the intervention's effect on post-surgical prescriptions and a survey will collect pain-related patient-reported outcomes. Intervention effectiveness will be determined using a triangulation design, mixed-methods approach with staggered speciality-specific implementation for contemporaneous control of opioid prescribing changes over time. The Consolidated Framework for Implementation Research will be used to evaluate the site-specific contextual factors and adaptations to achieve implementation at each site.

ETHICS AND DISSEMINATION

The study aims to identify the most effective hospital-type and speciality-specific intervention bundles for rapid dissemination into our 56-hospital learning collaborative and in hospitals throughout the USA. All study activities have been approved by the Northwestern University Institutional Review Board (ID STU00205053).

Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs

Objective

To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs.

Methods

We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drug's association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications.

Results

Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR).

Conclusions

Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.

Preventing dispensing errors by alerting for drug confusions in the pharmacy information system-A survey of users

Introduction

Drug confusion is thought to be the most common type of dispensing error. Several strategies can be implemented to reduce the risk of medication errors. One of these are alerts in the pharmacy information system.

Objective

To evaluate the experiences of pharmacists and pharmacy technicians with alerts for drug name and strength confusion.

Methods

In May 2017, a cross-sectional survey of pharmacists and pharmacy technicians was performed in community pharmacies in the Netherlands using an online questionnaire.

Results

Of the 269 respondents, 86% (n = 230) had noticed the alert for drug name confusion, and 26% (n = 67) for drug strength confusion. Of those 230, 9% (n = 20) had experienced that the alert had prevented dispensing the wrong drug. For drug strength confusion, this proportion was 12% (n = 8). Respondents preferred to have an alert for drug name and strength confusion in the pharmacy information system. ‘Alert fatigue’ was an important issue, so alerts should only be introduced for frequent confusions or confusions with serious consequences.

Conclusion

Pharmacists and pharmacy technicians were positive about having alerts for drug confusions in their pharmacy information system and experienced that alerts contributed to the prevention of dispensing errors. To prevent alert fatigue, it was considered important not to include all possible confusions as a new alert: the potential contribution to the prevention of drug confusion should be weighed against the risk of alert fatigue.

A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care

Objective

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

Materials and Methods

We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken.

Results

A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and auto-population, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users' work processes, and clinical decision support systems. Displaying an incomplete list of a patient's medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users' misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors.

Discussion and Conclusions

Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users' workflow expectations.

Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders

OBJECTIVE

Medication voiding is a computerised provider order entry (CPOE)-based discontinuation mechanism that allows clinicians to identify erroneous medication orders. We investigated the accuracy of voiding as an indicator of clinician identification and interception of a medication ordering error, and investigated reasons and root contributors for medication ordering errors.

METHOD

Using voided orders identified with a void alert, we conducted interviews with ordering and voiding clinicians, followed by patient chart reviews. A structured coding framework was used to qualitatively analyse the reasons for medication ordering errors. We also compared clinician-CPOE-selected (at time of voiding), clinician-reported (interview) and chart review-based reasons for voiding.

RESULTS

We conducted follow-up interviews on 101 voided orders. The positive predictive value (PPV) of voided orders that were medication ordering errors was 93.1% (95% CI 88.1% to 98.1%, n=94). Using chart review-based reasons as the gold standard, we found that clinician-CPOE-selected reasons were less reflective (PPV=70.2%, 95% CI 61.0% to 79.4%) than clinician-reported (interview) (PPV=86.1%, 95%CI 78.2% to 94.1%) reasons for medication ordering errors. Duplicate (n=44) and improperly composed (n=41) ordering errors were common, often caused by predefined order sets and data entry issues. A striking finding was the use of intentional violations as a mechanism to notify and seek ordering assistance from pharmacy service. Nearly half of the medication ordering errors were voided by pharmacists.

DISCUSSION

We demonstrated that voided orders effectively captured medication ordering errors. The mismatch between clinician-CPOE-selected and the chart review-based reasons for error emphasises the need for developing standardised operational descriptions for medication ordering errors. Such standardisation can help in accurately identifying, tracking, managing and sharing erroneous orders and their root contributors between healthcare institutions, and with patient safety organisations.

Learning from errors: analysis of medication order voiding in CPOE systems

OBJECTIVE

Medication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors.

MATERIALS AND METHODS

We examined medication orders from an academic medical center for a 6-year period (2006-2011; n  = 5 804 150). We categorized orders based on status (void, not void) and clinician-provided reasons for voiding. We used multivariable logistic regression to investigate the association between order voiding and clinician, patient, and order characteristics. We conducted chart reviews on a random sample of voided orders ( n  = 198) to investigate the rate of medication ordering errors among voided orders, and the accuracy of clinician-provided reasons for voiding.

RESULTS

We found that 0.49% of all orders were voided. Order voiding was associated with clinician type (physician, pharmacist, nurse, student, other) and order type (inpatient, prescription, home medications by history). An estimated 70 ± 10% of voided orders were due to medication ordering errors. Clinician-provided reasons for voiding were reasonably predictive of the actual cause of error for duplicate orders (72%), but not for other reasons.

DISCUSSION AND CONCLUSION

Medication safety initiatives require availability of error data to create repositories for learning and training. The voiding function is available in several electronic health record systems, so order voiding could provide a low-effort mechanism for self-reporting of medication ordering errors. Additional clinician training could help increase the quality of such reporting.

Systematic Review of Medical Informatics-Supported Medication Decision Making

This systematic review sought to assess the applications and implications of current medical informatics-based decision support systems related to medication prescribing and use. Studies published between January 2006 and July 2016 which were indexed in PubMed and written in English were reviewed, and 39 studies were ultimately included. Most of the studies looked at computerized provider order entry or clinical decision support systems. Most studies examined decision support systems as a means of reducing errors or risk, particularly associated with medication prescribing, whereas a few studies evaluated the impact medical informatics-based decision support systems have on workflow or operations efficiency. Most studies identified benefits associated with decision support systems, but some indicate there is room for improvement.

Effect of implementation of a coded problem list entry subsystem

OBJECTIVES

Complete patient problem lists may improve the quality of care. To improve the completeness of the lists at our institution, we implemented the coded problem list entry subsystem (CPLES) in our electronic medical record system. Subsequently, physicians used the CPLES instead of handwritten notes to document coded problem lists and progress notes. We evaluated the effect of implementing the CPLES on the completeness of problem lists.

METHODS

We compared the completeness of coded problem lists input after CPLES implementation with that of problem lists handwritten before CPLES implementation and determined the differences. Moreover, the efficiency and usability of the CPLES were evaluated.

RESULTS

The efficiency and usability of CPLES were acceptable. However, the completeness of problem lists was reduced after CPLES implementation. The possible reasons for this reduction, namely system usability, efficacy, incentives, leadership, and education, were crucial for successful CPLES implementation and are discussed in the text.

CONCLUSION

CPLES implementation reduced the completeness of problem lists. Institutions may learn from our experience and carefully implement their own coded problem list systems to avoid this consequence.

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.

Evaluating the Potential Severity of Look-Alike, Sound-Alike Drug Substitution Errors in Children

OBJECTIVE

Look-alike, sound-alike (LASA) drug name substitution errors in children may pose potentially severe consequences. Our objective was to determine the degree of potential harm pediatricians ascribe to specific ambulatory LASA drug substitution errors.

METHODS

We developed a unified list of LASA pairs from published sources, removing selected drugs on the basis of preparation type (eg, injectable drugs). Using a modified Delphi method over 3 rounds, 38 practicing pediatricians estimated degree of potential harm that might occur should a patient receive the delivered drug in error and the degree of potential harm that might occur from not receiving the intended drug.

RESULTS

We identified 3550 published LASA drug pairs. A total of 1834 pairs were retained for the Delphi surveys, and 608 drug pairs were retained for round 3. Final scoring demonstrated that participants were able to identify pairs where the substitutions represented high risk of harm for receiving the delivered drug in error (eg, did not receive methylphenidate/received methadone), high risk of harm for not receiving the intended drug (eg, did not receive furosemide/received fosinopril), and pairs where the potential harm was high from not receiving the intended drug and from erroneously receiving the delivered drug (eg, did not receive albuterol/received labetalol).

CONCLUSIONS

Pediatricians have identified LASA drug substitutions that pose a high potential risk of harm to children. These results will allow future efforts to prioritize pediatric LASA errors that can be screened prospectively in outpatient pharmacies.

Patient Safety in Medication Nomenclature: Orthographic and Semantic Properties of International Nonproprietary Names

Background

Confusion between look-alike and sound-alike (LASA) medication names (such as mercaptamine and mercaptopurine) accounts for up to one in four medication errors, threatening patient safety. Error reduction strategies include computerized physician order entry interventions, and ‘Tall Man’ lettering. The purpose of this study is to explore the medication name designation process, to elucidate properties that may prime the risk of confusion.

Methods and Findings

We analysed the formal and semantic properties of 7,987 International Non-proprietary Names (INNs), in relation to naming guidelines of the World Health Organization (WHO) INN programme, and have identified potential for errors. We explored: their linguistic properties, the underlying taxonomy of stems to indicate pharmacological interrelationships, and similarities between INNs. We used Microsoft Excel for analysis, including calculation of Levenshtein edit distance (LED). Compliance with WHO naming guidelines was inconsistent. Since the 1970s there has been a trend towards compliance in formal properties, such as word length, but longer names published in the 1950s and 1960s are still in use. The stems used to show pharmacological interrelationships are not spelled consistently and the guidelines do not impose an unequivocal order on them, making the meanings of INNs difficult to understand. Pairs of INNs sharing a stem (appropriately or not) often have high levels of similarity (<5 LED), and thus have greater potential for confusion.

Conclusions

We have revealed a tension between WHO guidelines stipulating use of stems to denote meaning, and the aim of reducing similarities in nomenclature. To mitigate this tension and reduce the risk of confusion, the stem system should be made clear and well ordered, so as to avoid compounding the risk of confusion at the clinical level. The interplay between the different WHO INN naming principles should be further examined, to better understand their implications for the problem of LASA errors.

Tall Man lettering and potential prescription errors: a time series analysis of 42 children's hospitals in the USA over 9 years

BACKGROUND

Despite the widespread implementation of Tall Man lettering, little evidence exists regarding whether this technique has reduced drug errors due to look-alike sound-alike (LA-SA) drug names. This study evaluated rates of potential LA-SA drug errors in the drug management process through to the point of dispensing before and after implementation of Tall Man lettering in 2007.

METHODS

We used detailed pharmacy data for paediatric inpatients (<21 years old) from 42 children's hospitals in 2004-2012. After prespecifying a set of 8 potential LA-SA drug error patterns we searched within each hospitalisation for the occurrence of one of these patterns for a total of 12 LA-SA drug pairs deemed highly relevant to paediatric inpatients. To assess for potential change of error rates before and after Tall Man lettering implementation, we performed segmented regression analyses for each of 11 LA-SA drug pairs (because 1 pair had no detected potential errors) and for the overall total errors of all 11 LA-SA drug pairs.

RESULTS

Among 1 676 700 hospitalisations, no statistically significant change was detected for either the intercept or the slope of LA-SA error rate for each of the 11 drug pairs or for the combined error rate. In a sensitivity analysis of the moving average of the potential error rate over the entire study period, no downward trend in potential LA-SA drug error rates was evident over any time period 2004 onwards.

CONCLUSIONS

Implementation of Tall Man lettering in 2007 was not associated with a reduction in the potential LA-SA error rate. Whether Tall Man lettering is effective in clinical practice warrants further study.