Analysis of National Drug Code Identifiers in Ambulatory E-Prescribing

Dhavle, Ajit A.; Ward-Charlerie, Stacy; Rupp, Michael T.; Amin, Vishal P.; Ruiz, Juan Ignacio Marí

doi:10.18553/jmcp.2015.21.11.1025

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…However, FDBM is widely used as the knowledge base for many health care information and prescribing systems. 35 Future work should repeat our study with different knowledge bases. Second, we utilized only 1 source of SALA case pairs, and this source is based on self-reports that include medication pairs that vary in nature, severity, and clinical importance, which may compromise the validity and generalizability of our study findings.…”

Section: Discussionmentioning

confidence: 95%

Sound-Alike Look-Alike Confusion and Matching Medication Product Attributes: Simulated Case-Control Studies

Her

Toh

2019

Ann Pharmacother

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Background: Sound-alike look-alike (SALA) medication confusions harm as many as 250 000 Americans annually. Most preventive strategies focus on medication name similarities. Objectives: To evaluate the association between matching medication product attributes and SALA confusion. Methods: We simulated 20 000 case-control studies using the Institute for Safe Medication Practices’ List of Confused Drug Names as case pairs and 4 randomly selected control pairs per case pair from the First DataBank MedKnowledge (FDBM) database. We extracted 7 product attributes for each medication from the FDBM database. We used logistic regression models to estimate the associations between matching product attributes and SALA confusion. The models included a series of univariate (unadjusted) models, a model that adjusted for all product attributes, and a model that further adjusted for medication name similarity measures. Results: Medications with a matching product attribute had increased odds of SALA confusion in the univariate analyses (odds ratio [OR] = 4.2 to 55.5). When we simultaneously adjusted for all attributes, the associations of matching package unit, package unit size, formulation, strength, therapeutic class, and manufacturer with SALA confusion were attenuated but remained elevated (OR = 1.5 to 26.5), whereas the direction of association between matching route and SALA confusion reversed (OR = 0.8). These associations persisted on adjustment for medication name similarity measures. Conclusions and Relevance: This study is the first to evaluate the association between matching medication product attributes and SALA confusion with a control group. Having matching product attributes increased the odds of SALA confusion. SALA risk reduction strategies should consider product attributes.

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Section: Discussionmentioning

confidence: 95%

Sound-Alike Look-Alike Confusion and Matching Medication Product Attributes: Simulated Case-Control Studies

Her

Toh

2019

Ann Pharmacother

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“…Within the dosing cohorts, patients were categorized into once-daily or twice-daily dosing groups using administration instructions extracted from the prescription signatura ("the sig") stored in dispensing records. Because the sig contains unstructured free-text data [11][12][13][14], a text-processing tool was developed to extract the daily dosing frequency from the sig [15]. Briefly, the tool first standardizes all numbers, fractions, and characters (e.g., "two" was converted to "2", the ampersand symbol [&] was converted to "and").…”

Section: Assessment Of Daily Dosing Frequencymentioning

confidence: 99%

Twice-daily versus once-daily lisinopril and losartan for hypertension: Real-world effectiveness and safety

et al. 2020

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Background Lisinopril and losartan manufacturer labels recommend twice-daily dosing (BID) if once-daily (QDay) is insufficient to lower blood pressure (BP). Methods and results Retrospective cohort study of patients taking QDay lisinopril and losartan who experienced a dose-doubling (index date). A text-processing tool categorized BID and QDay groups at the index date based on administration instructions. We excluded: pregnant/hospice, regimens other than BID/QDay, and without BP measurements -6 months/+12 months of the index date. The most proximal BP measurements -6 months and +2 weeks to 12 months of the index date were used to evaluate BP differences. Propensity scores were generated, and differences in BP and adverse events (angioedema, acute kidney injury, hyperkalemia) between BID/QDay groups were analyzed within dosing cohorts using inverse propensity of treatment-weighted regression models. Of 11,210 and 6,051 patients who met all criteria for lisinopril and losartan, 784 (7.0%) and 453 (7.5%) were taking BID, respectively. BID patients were older and had higher comorbidity and medication burdens. There were no differences in systolic/diastolic BP between BID and QDay, with absolute differences in mean systolic BP ranging from -1.8 to 0.7 mmHg and diastolic BP ranging from -1.1 to 0.1 mmHg (all 95% confidence intervals [CI] cross 0). Lisinopril 10mg BID was associated with an increased odds of angioedema compared to lisinopril 20mg QDay (odds ratio 2.27, 95%CI 1.13–4.58). Conclusions Adjusted models do not support improved effectiveness or safety of BID lisinopril and losartan.

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“…ealth informatics applications, such as e-prescribing, have the capability to improve the quality of care while reducing the cost of health care, 1 improve prescriber and pharmacy productivity and work flow, 2 and result in lower prescription medication costs. 3 However, much of the research to date does not identify all stakeholders and all benefits, including quality and cost considerations and whether benefits are direct or indirect, tangible or intangible, or financial or otherwise.…”

mentioning

confidence: 99%

How Stakeholder Assessment of E-Prescribing Can Help Determine Incentives to Facilitate Management of Care: A Delphi Study

DeMuro

Ash

Middleton³

et al. 2017

JMCP

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This research was supported by the National Library of Medicine of the National Institutes of Health under Award Number T15LM007088. The authors declare no conflicts of interest in the research. Study concept and design were contributed by DeMuro, Ash, Middleton, and Fletcher. DeMuro took the lead in data collection, along with Ash, and data interpretation was performed by DeMuro, Ash, Madison, Middleton, and Fletcher. The manuscript was written primarily by DeMuro, along with Ash and Middleton, and revised by DeMuro, Madison, and Ash, along with Middleton and Fletcher.

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Analysis of National Drug Code Identifiers in Ambulatory E-Prescribing

Cited by 6 publications

References 14 publications

Sound-Alike Look-Alike Confusion and Matching Medication Product Attributes: Simulated Case-Control Studies

Sound-Alike Look-Alike Confusion and Matching Medication Product Attributes: Simulated Case-Control Studies

Twice-daily versus once-daily lisinopril and losartan for hypertension: Real-world effectiveness and safety

How Stakeholder Assessment of E-Prescribing Can Help Determine Incentives to Facilitate Management of Care: A Delphi Study

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