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Purpose We examined the prevalence of, and factors associated with, serious opioid-related adverse drug events (ORADEs) that led to an emergency department (ED) visit or hospitalization among patients with chronic gastrointestinal (GI) symptoms and disorders dually enrolled in the Department of Veterans Affairs (VA) and Medicare Part D. Methods In this retrospective cohort study, we used linked national patient-level data (April 1, 2011, to October 31, 2014) from the VA and Centers for Medicare and Medicaid Services to identify serious ORADEs among dually enrolled veterans with a chronic GI symptom or disorder. Outcome measures included serious ORADEs, defined as an ED visit attributed to an ORADE or a hospitalization where the principal or secondary reason for admission involved an opioid. We used multiple logistic regression models to determine factors independently associated with a serious ORADE. Results We identified 3,430 veterans who had a chronic GI symptom or disorder; were dually enrolled in the VA and Medicare Part D; and had a serious ORADE that led to an ED visit, hospitalization, or both. The period prevalence of having a serious ORADE was 2.4% overall and 4.4% among veterans with chronic opioid use (≥90 consecutive days). Veterans with serious ORADEs were more likely to be less than 40 years old, male, white, and to have chronic abdominal pain, functional GI disorders, chronic pancreatitis, or Crohn’s disease. They were also more likely to have used opioids chronically and at higher daily doses. Conclusion There may be a considerable burden of serious ORADEs among patients with chronic GI symptoms and disorders. Future quality improvement efforts should target this vulnerable population.

Introduction Gastrointestinal (GI) symptoms and disorders affect an increasingly large group of veterans. Opioid use may be rising in this population, but this is concerning from a patient safety perspective, given the risk of dependence and lack of evidence supporting opioid use to manage chronic pain. We examined the characteristics of opioid prescriptions and factors associated with chronic opioid use among chronic GI patients dually enrolled in the DVA and Medicare Part D. Materials and Methods In this retrospective cohort study, we used linked, national patient-level data (from April 1, 2011, to December 31, 2014) from the VA and Centers for Medicare & Medicaid Services to identify chronic GI patients and observe opioid use. Veterans who had a chronic GI symptom or disorder were dually enrolled in VA and Part D and received ≥1 opioid prescription dispensed through the VA, Part D, or both. Chronic GI symptoms and disorders included chronic abdominal pain, chronic pancreatitis, inflammatory bowel diseases, and functional GI disorders. Key outcome measures were outpatient opioid prescription dispensing overall and chronic opioid use, defined as ≥90 consecutive days of opioid receipt over 12 months. We described patient characteristics and opioid use measures using descriptive statistics. Using multiple logistic regression modeling, we generated adjusted odds ratios and 95% CIs to determine variables independently associated with chronic opioid use. The final model included variables outlined in the literature and our conceptual framework. Results We identified 141,805 veterans who had a chronic GI symptom or disorder, were dually enrolled in VA and Part D, and received ≥1 opioid prescription dispensed from the VA, Part D, or both. Twenty-six percent received opioids from the VA only, 69% received opioids from Medicare Part D only, and 5% were “dual users,” receiving opioids through both VA and Part D. Compared to veterans who received opioids from the VA or Part D only, dual users had a greater likelihood of potentially unsafe opioid use outcomes, including greater number of days on opioids, higher daily doses, and higher odds of chronic use. Conclusions Chronic GI patients in the VA may be frequent users of opioids and may have a unique set of risk factors for unsafe opioid use. Careful monitoring of opioid use among chronic GI patients may help to begin risk stratifying this group. and develop tailored approaches to minimize chronic use. The findings underscore potential nuances within the opioid epidemic and suggest that components of the VA’s Opioid Safety Initiative may need to be adapted around veterans at a higher risk of opioid-related adverse events.

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.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.We recruited actively practicing primary care physicians (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-sampleThe 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.

BackgroundTo assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data.SettingUrban, 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.ResultsThe 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.ConclusionAutomated 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.

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.

BACKGROUND: Surgeons play a pivotal role in the opioid epidemic but it is unknown how different members of a surgical team vary in the way they prescribe opioids after surgical episodes. STUDY DESIGN: We conducted a retrospective cohort study of all inpatient discharges for 5 common surgeries. Total number of tablets and total milligram equivalents (MME) prescribed were calculated and differences in prescription patterns were determined for attending surgeons, surgical residents and advanced practice providers. Using a generalized ordered logistic regression, we examined factors associated with ordering a higher number of tablets or MME. RESULTS: The median number of tablets (range) prescribed by rank were attending surgeon 30 (6–72), surgical resident 20 (6–189) and advanced care practitioner 40 (5–1000); p

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 patient’s 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.

BACKGROUND. In a cohort of Veterans dually enrolled in the Department of Veterans Affairs (VA) and Medicare Part D, we sought to describe high-dose daily opioid use among Veterans with unexplained gastrointestinal (GI) symptoms & structural GI diagnoses and examine factors associated with high-dose use. METHODS. We used linked national patient-level data from the VA and Centers for Medicare & Medicaid Services (CMS). We grouped patients into 3 subsets: those with unexplained GI symptoms (e.g. chronic abdominal pain); structural GI diagnoses (e.g. chronic pancreatitis); and those with a concurrent unexplained GI symptom & structural GI diagnosis. High-dose daily opioid use levels were examined as a binary variable (≥100 morphine milligram equivalents (MME)/day) and as an ordinal variable (50-99 MME/day, 100-119 MME/day, or ≥120 MME/day). RESULTS. We identified 141,805 chronic GI patients dually enrolled in VA & Part D. High-dose opioid use was present in 11% of Veterans with unexplained GI symptoms, 10% of Veterans with structural GI diagnoses, and 15% of Veterans in the concurrent GI group. Compared to Veterans with only an unexplained GI symptom or structural diagnosis, concurrent GI patients were more likely to have higher daily opioid doses, more opioid days ≥100 MME, and higher risk of chronic use. Factors associated with high-dose use included opioid receipt from both VA & Part D, younger age, and benzodiazepine use. CONCLUSIONS. A significant subset of chronic GI patients in the VA are high-dose opioid users. Efforts are needed to reduce high-dose use among Veterans with concurrent GI symptoms and diagnoses.

Objective Low back pain accounts for nearly 4 million emergency department (ED) visits annually and is a significant source of disability. Physical therapy has been suggested as a potentially effective nonopioid treatment for low back pain; however, no studies to our knowledge have yet evaluated the emerging resource of ED-initiated physical therapy. The study objective was to compare patient-reported outcomes in patients receiving ED-initiated physical therapy and patients receiving usual care for acute low back pain. Methods This was a prospective observational study of ED patients receiving either physical therapy or usual care for acute low back pain from May 1, 2018, to May 24, 2019, at a single academic ED (>91,000 annual visits). The primary outcome was pain-related functioning, assessed with Oswestry Disability Index (ODI) and Patient-Reported Outcomes Measurement Information System pain interference (PROMIS-PI) scores. The secondary outcome was use of high-risk medications (opioids, benzodiazepines, and skeletal muscle relaxants). Outcomes were compared over 3 months using adjusted linear mixed and generalized estimating equation models. Results For 101 participants (43 receiving ED-initiated physical therapy and 58 receiving usual care), the median age was 40.5 years and 59% were women. Baseline outcome scores in the ED-initiated physical therapy group were higher than those in the usual care group (ODI = 51.1 vs 36.0; PROMIS-PI = 67.6 vs 62.7). Patients receiving ED-initiated physical therapy had greater improvements in both ODI and PROMIS-PI scores at the 3-month follow-up (ODI = −14.4 [95% CI = −23.0 to −5.7]; PROMIS-PI = −5.1 [95% CI = −9.9 to −0.4]) and lower use of high-risk medications (odds ratio = 0.05 [95% CI = 0.01 to 0.58]). Conclusion In this single-center observational study, ED-initiated physical therapy for acute low back pain was associated with improvements in functioning and lower use of high-risk medications compared with usual care; the causality of these relationships remains to be explored. Impact ED-initiated physical therapy is a promising therapy for acute low back pain that may reduce reliance on high-risk medications while improving patient-reported outcomes. Lay Summary Emergency department–initiated physical therapy for low back pain was associated with greater improvement in functioning and lower use of high-risk medications over 3 months.

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.

Importance More conservative prescribing has the potential to reduce adverse drug events and patient harm and cost; however, no method exists defining the extent to which individual clinicians prescribe conservatively. One potential domain is prescribing a more limited number of drugs. Personal formularies-defined as the number and mix of unique, newly initiated drugs prescribed by a physician-may enable comparisons among clinicians, practices, and institutions. Objectives To develop a method of defining primary care physicians' personal formularies and examine how they differ among primary care physicians at 4 institutions; evaluate associations between personal formularies and patient, physician, and practice site characteristics; and empirically derive and examine the variability of the top 200 core drugs prescribed at the 4 sites. Design, setting, and participants This retrospective cohort study was conducted at 4 US health care systems among 4655 internal and family medicine physicians and 4 930 707 patients who had at least 1 visit to these physicians between January 1, 2017, and December 31, 2018. Exposures Personal formulary size was defined as the number of unique, newly initiated drugs. Main outcomes and measures Personal formulary size and drugs used, physician and patient characteristics, core drugs, and analysis of selected drug classes. Results The study population included 4655 primary care physicians (2274 women [48.9%]; mean [SD] age, 48.5 [4.4] years) and 4 930 707 patients (16.5% women; mean [SD] age, 51.9 [8.3] years). There were 41 378 903 outpatient prescriptions written, of which 9 496 766 (23.0%) were new starts. Institution median personal formulary size ranged from 150 (interquartile range, 82.0-212.0) to 296 (interquartile range, 230.0-347.0) drugs. In multivariable modeling, personal formulary size was significantly associated with panel size (total number of unique patients with face-to-face encounters during the study period; 1.2 medications per 100 patients), physician's total number of encounters (5.7 drugs per 10% increase), and physician's sex (-6.2 drugs per 100 patients for female physicians). There were 1527 unique, newly prescribed drugs across the 4 sites. Fewer than half the drugs (626 [41.0%]) were used at every site. Physicians' prescribing of drugs from a pooled core list varied from 0% to 100% of their prescriptions. Conclusions and relevance Personal formularies, measured at the level of individual physicians and institutions, reveal variability in size and mix of drugs. Similarly, defining a list of commonly prescribed core drugs in primary care revealed interphysician and interinstitutional differences. Personal formularies and core medication lists enable comparisons and may identify outliers and opportunities for safer and more appropriate prescribing.

Background Benzodiazepine-opioid combination therapy is potentially harmful due to the risk of synergistic respiratory depression, and the rate of death due to benzodiazepine-opioid overdose is increasing. Little is known about the prevalence and characteristics of benzodiazepine-opioid co-prescribing from the ED setting. Methods Secondary analysis of data from the National Hospital Ambulatory Medical Care Survey, using sample weights to generate population estimates. The primary objective was to describe the annual prevalence of benzodiazepine-opioid co-prescribing from 2006 to 2012, using 95% confidence intervals (95% CI) to compare adjacent years. The secondary objective was to compare characteristics of ED encounters receiving a benzodiazepine-opioid co-prescription versus those receiving an opioid prescription alone, using a multivariable logistic regression. Results The prevalence of benzodiazepine-opioid co-prescribing did not significantly change from 2006 to 2012. During this period, 2.7% (95% CI: 2.5–2.8%) of ED encounters prescribed an opioid were also prescribed a benzodiazepine. Relative to encounters receiving an opioid prescription alone, encounters receiving a co-prescription were more likely to represent a follow-up rather than initial visit (Odds Ratio [OR] 1.52), receive more medications (OR 1.41) and fewer procedures (OR 0.48) while in the ED, and more likely to have a diagnosis related to mental disorder (OR 20.60) or musculoskeletal problem (OR 3.71). Conclusions From 2006 to 2012, almost 3% of all ED encounters receiving an opioid prescription also received a benzodiazepine co-prescription. The odds of benzodiazepine-opioid co-prescribing were significantly higher in ED encounters representing a follow-up visit and in diagnoses relating to a mental disorder or musculoskeletal problem.

Objective To test if a 2006 communication-and-resolution program to address unexpected adverse outcomes was associated with changes in cost and use trajectories. Data source Records of patients discharged with a principal diagnosis of chest pain from 44 nonfederal general hospitals in Cook County, Illinois, between January 2002 and December 2009. Study design Propensity-score matched discharges from the intervention and comparison hospitals before computing difference-in-differences estimates of quarterly growth rates. Data collection methods We used discharge records submitted to a central statewide repository. Principal findings Relative to the comparison hospitals and to pre-implementation trends, and consistent with reduced testing at presentation, the intervention hospital recorded an increase in the number of patients with a principal diagnosis of chest pain. Among admitted patients, quarterly growth rates of clinical laboratory and radiology charges at the intervention hospital declined by 3.8 and 6.9 percentage points. Conclusions Among patients with chest pain, the implementation of a comprehensive communication-and-resolution program was associated with substantially reduced growth rates in the use of diagnostic testing and imaging services. Further research is needed to establish to what extent these changes were attributable to the program and clinically appropriate.

Purpose An ethnographic study of 3 pharmacy and therapeutics (P&T) committees was conducted to characterize the process by which evidence is weighted and decisions are made. Methods We recorded discussions and conducted an analysis of the transcripts from the 3 P&T committees for a 1-year period. We examined the content and ideas expressed during deliberations and synthesized themes to give a broader picture of the issues arising. Committee discussions were transcribed and the segments of each meeting that addressed any new formulary additions were then analyzed. Using constant comparison method, we generated a series of topic codes to characterize and classify that portion of the discussion. Results At 26 meetings across the 3 sites, 24 new drug formulary additions were discussed. A total of 1,093 discussion segments were identified and mapped to 7 broad categories related to discussion of evidence of need, efficacy/indications, safety, misuse potential, cost issues, committee decision-making issues, and discussion related to operationalizing use and implementation at the local institution. Overall, the leading category of discussion was efficacy/indications followed by evidence of need, operational issues, and cost issues, with some variation by site. The committees devoted substantially greater portions of their discussion to the logistics of using the drugs in their institutions than they did safety issues. We identified wide variations in specific drugs being considered and the relative amount of time devoted to various issues related to these drugs being discussed. We found discussions generally did not follow a systematic, standardized, rigorous, and reproducible approach. Discussions tended to be more idiosyncratic, individualized, varying from drug to drug, and at times devoted to a variety of tangential issues raised by committee members. Conclusion P&T committee discussions at all 3 sites tended to be idiosyncratic and individualized, varying from drug to drug, and at times devoted to a variety of issues more tangentially raised by committee members. All spent less time talking about drug safety, in each case roughly half the time that they devoted to discussions of efficacy.

Objective Physical therapy (PT) is commonly cited as a non-opioid pain strategy, and previous studies indicate PT reduces opioid utilization in outpatients with back pain. No study has yet examined whether PT is associated with lower analgesic prescribing in the ED setting. Methods This was a retrospective cohort study of discharged ED visits with a primary ICD-10 diagnosis relating to back or neck pain from 10/1/15 to 2/21/17 at an urban academic ED. Visits receiving a PT evaluation were matched with same-date visits receiving usual care. We compared the primary outcomes of opioid and benzodiazepine prescribing between the two cohorts using chi-squared test and multivariable logistic regression. Results 74 ED visits received PT during the study period; these visits were matched with 390 same-date visits receiving usual care. Opioid prescribing among ED-PT visits was not significantly higher compared to usual care visits on both unadjusted analysis (50% vs 42%, p = 0.19) and adjusted analysis (adjOR 1.05, 95% CI 0.48–2.28). However, benzodiazepine prescribing among ED-PT visits was significantly higher than usual care visits on both unadjusted (45% vs 23%, p Conclusions In this single center study, ED back and neck pain visits receiving PT were no less likely to receive an opioid prescription and were more likely to receive a benzodiazepine than visits receiving usual care. Although prior studies demonstrate that PT may reduce opioid utilization in the subsequent year, these results indicate that analgesic prescribing is not reduced at the initial ED encounter.

Wrong-drug errors, thought to be caused primarily by drug names that look and/or sound alike, occur at a rate of about one error per thousand dispensed prescriptions in the outpatient setting and one per thousand orders in the inpatient setting.1 ,2 Most are relatively benign, but some cause severe or even fatal harm.3–5 One of the best known attempts to reduce drug name confusion has been the use of mixed case or ‘Tall Man’ lettering.6 The idea is to use capital letters to maximise the visual perceptual difference between two similar drug names. Thus, vinblastine and vincristine become vinBLAStine and vinCRIStine. If some look-alike/sound-alike (LASA) mix-ups are caused by errors in visual perception, the reasoning goes, then making the names more visually distinct should reduce the probability of confusion and error. After being endorsed by the US Food and Drug Administration (FDA),6 the Institute for Safe Medication Practices (ISMP),7 The Joint Commission8 and others, the practice has become widespread.9 However, apart from limited evidence of effectiveness in laboratory settings, no evidence shows that this technique prevents drug name confusion errors in clinical practice. Zhong et al 10 attempted to assess the effect of Tall Man lettering on drug name confusion errors in a large scale, longitudinal, observational study. They conclude that this widely disseminated error-prevention strategy had no measurable effect on the rate of drug name confusions in 9 years of data from 42 children's hospitals in the USA. Below we comment on methodological issues in the Zhong et al study, review laboratory research on Tall Man lettering and consider policy implications. The authors are to be commended for conducting a large-scale, empirical test of the effect of Tall Man lettering on the drug name confusion error rate in real-world clinical settings. The …

Confusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.

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 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.

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.

Objective Benzodiazepines and opioids are prescribed simultaneously (i.e. “co-prescribed”) in many clinical settings, despite guidelines advising against this practice and mounting evidence that concomitant use of both medications increases overdose risk. This study sought to characterize the contexts in which benzodiazepine-opioid co-prescribing occurs and providers’ reasons for co-prescribing. Methods We conducted focus groups with ED providers (resident and attending physicians, advanced practice providers, and pharmacists) from three hospitals using semi-structured interviews to elicit perspectives on benzodiazepine-opioid co-prescribing. Discussions were audio-recorded and transcribed. We performed qualitative content analysis of the resulting transcripts using a consensual qualitative research approach, aiming to identify priority categories that describe the phenomenon of benzodiazepine-opioid co-prescribing. Results Participants acknowledged co-prescribing rarely and reluctantly, and often provided specific discharge instructions when co-prescribing. The decision to co-prescribe is multifactorial, often isolated to specific clinical and situational contexts (e.g. low back pain, failed solitary opioid therapy) and strongly influenced by a provider's beliefs about the efficacy of combination therapy. The decision to co-prescribe is further influenced by a self-imposed pressure to escalate care or avoid hospital admission. When considering potential interventions to reduce the incidence of co-prescribing, participants opposed computerized alerts but were supportive of a pharmacist-assisted intervention. Many providers found the process of participating in peer discussions on prescribing habits to be beneficial. Conclusions In this qualitative study of ED providers, we found that benzodiazepine-opioid co-prescribing occurs in specific clinical and situational contexts, such as the treatment of low back pain or failed solitary opioid therapy. The decision to co-prescribe is strongly influenced by a provider's beliefs and by self-imposed pressure to escalate care or avoid admission. This article is protected by copyright. All rights reserved.

Physicians must navigate a balance between under- and overdiagnosis, both of which may harm patients. The authors discuss core principles to help find this balance and foster a thoughtful, patient-...

Importance Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation. Objective To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently. Design, Setting, and Participants This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings. Interventions Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687). Main Outcomes and Measures The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient). Results Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings ( P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions. Conclusions and Relevance A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors. Trial Registration clinicaltrials.gov Identifier:NCT02876588

Purpose The variations in how drug names are displayed in computerized prescriber-order-entry (CPOE) systems were analyzed to determine their contribution to potential medication errors. Methods A diverse set of 10 inpatient and outpatient CPOE system vendors and self-developed CPOE systems in 6 U.S. healthcare institutions was evaluated. A team of pharmacists, physicians, patient-safety experts, and informatics experts created a CPOE assessment tool to standardize the assessment of CPOE features across the systems studied. Hypothetical scenarios were conducted with test patients to study the medication ordering workflow and ways in which medications were displayed in each system. Brand versus generic drug name ordering was studied at 1 large outpatient system to understand why prescribers ordered both brand and generic forms of the same drug. Results Widespread variations in the display of drug names were observed both within and across the 6 study sites and 10 systems, including the inconsistent display of brand and generic names. Some displayed drugs differently even on the same screen. Combination products were often displayed inconsistently, and some systems required prescribers to know the first drug listed in the combination in order for the correct product to appear in a search. It also appeared that prescribers may have prescribed both brand and generic forms of the same medication, creating the potential for drug duplication errors. Conclusion A review of 10 CPOE systems revealed that medication names were displayed inconsistently, which can result in confusion or errors in reviewing, selecting, and ordering medications.

To reduce the risk of wrong-patient errors, safety experts recommend limiting the number of patient records providers can open at once in electronic health records (EHRs). However, it is unknown whether health care organizations follow this recommendation or what rationales drive their decisions. To address this gap, we conducted an electronic survey via 2 national listservs. Among 167 inpatient and outpatient study facilities using EHR systems designed to open multiple records at once, 44.3% were configured to allow ≥3 records open at once (unrestricted), 38.3% allowed only 1 record open (restricted), and 17.4% allowed 2 records open (hedged). Decision-making centered on efforts to balance safety and efficiency, but there was disagreement among organizations about how to achieve that balance. Results demonstrate no consensus on the number of records to be allowed open at once in EHRs. Rigorous studies are needed to determine the optimal number of records that balances safety and efficiency.

Pain care for hospitalized patients is often suboptimal. Representing pain scores as a graphical trajectory may provide insights into the understanding and treatment of pain. We describe a 1-year, retrospective, observational study to characterize pain trajectories of hospitalized adults during the first 48 hours after admission at an urban academic medical center. Using a subgroup of patients who presented with significant pain (pain score >4; n = 7762 encounters), we characterized pain trajectories and measured area under the curve, slope of the trajectory for the first 2 hours after admission, and pain intensity at plateau. We used mixed-effects regression to assess the association between pain score and sociodemographics (age, race, and gender), pain medication orders (opioids, nonopioids, and no medications), and medical service (obstetrics, psychiatry, surgery, sickle cell, intensive care unit, and medicine). K-means clustering was used to identify patient subgroups with similar trajectories. Trajectories showed differences based on race, gender, service, and initial pain score. Patients presumed to have dissimilar pain experiences (eg, sickle vs obstetrical) had markedly different pain trajectories. Patients with higher initial pain had a more rapid reduction during their first 2 hours of treatment. Pain reduction achieved in the 48 hours after admission was approximately 50% of the initial pain, regardless of the initial pain. Most patients' pain failed to fully resolve, plateauing at a pain score of 4 or greater. Visualizing pain scores as graphical trajectories illustrates the dynamic variability in pain, highlighting pain responses over a period of observation, and may yield new insights for quality improvement and research.

BACKGROUND Medication guides are required documents to be distributed to patients in order to convey serious risks associated with certain prescribed medicines. Little is known about the effectiveness of this information to adequately inform patients on safe use.