PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.

OBJECTIVES: Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study. METHODS: We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview. RESULTS: Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice. CONCLUSION: Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.

Iodine allergy: Common misperceptions.

PURPOSE: The current evidence regarding iodine-containing compounds and iodine allergy cross-reactivity is reviewed. SUMMARY: Iodine is an essential human nutrient found in the thyroid gland. It is used in the synthesis of the thyroid hormones thyroxine and triiodothyroxine. Patients who report having adverse reactions to iodine-containing substances are often labelled as having an "iodine allergy," which can result in delays in care or patients being denied essential iodinated contrast media (ICM) or other iodine-containing drugs. A literature review was conducted to evaluate the evidence regarding iodine allergy and iodine-containing drugs. Of 435 articles considered potentially appropriate for full review (plus 12 additional articles included on the basis of references from the eligible articles), 113 could not be obtained. After exclusion of 353 articles that did not meet all inclusion criteria, the remaining 81 articles were included in the review. The results of the literature review indicated that iodine has not been shown to be the allergen responsible for allergic reactions to iodinated contrast media, amiodarone, povidone-iodine, and other iodine-containing compounds. CONCLUSION: There is a lack of evidence to support cross-reactivity between iodine-containing compounds in so called iodine-allergic individuals.

Using Medicare Data to Assess the Proarrhythmic Risk of Non-Cardiac Treatment Drugs that Prolong the QT Interval in Older Adults: An Observational Cohort Study.

INTRODUCTION: Serious cardiac arrhythmias caused by QT-prolonging drugs are difficult to predict based on physiological measurement and pre-approval clinical trials. Post-marketing surveillance and monitoring are important to generate safety data. OBJECTIVES: To assess whether an observational study using Medicare claims data can detect the arrhythmogenic risk of QT-prolonging drugs. METHODS: We identified 17 QT-prolonging drugs with known risk of torsades des pointes (TdP) that were not used to treat cardiac arrhythmias. Amoxicillin and four serotonin-norepinephrine reuptake inhibitors (SNRIs) were used as controls. De-identified claims data of 1.2 million Medicare beneficiaries were accessed. Two separate Cox regressions were done for short-term and chronic-use drugs. The primary outcome was a composite of ventricular arrhythmias and/or sudden death, identified by ICD diagnostic codes. We explored the independent effect of each study drug on the outcomes. Other covariates included patient demographics, comorbidities, and known risk factors for drug-induced cardiac arrhythmia. RESULTS: We were able to detect increased risk in 14 of 17 study drugs (82.3%), and none of the control drugs. Among the fluoroquinolones, ciprofloxacin was the safest. Azithromycin and clarithromycin were relatively safe compared to erythromycin. Compared to SNRIs, both citalopram and escitalopram had increased risk, more so with escitalopram than citalopram. Comorbidities associated with increased risk included ischemic heart disease, electrolyte imbalance, bradycardia, acute myocardial infarction, heart failure, and chronic kidney and liver disease. CONCLUSION: Medicare data can be utilized for post-marketing surveillance and monitoring of the proarrhythmic risk of QT-prolonging drugs in older adults.

Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions.

OBJECTIVE: The study sought to develop a criteria-based scoring tool for assessing drug-disease knowledge base content and creation of a subset and to implement the subset across multiple Kaiser Permanente (KP) regions. MATERIALS AND METHODS: In Phase I, the scoring tool was developed, used to create a drug-disease alert subset, and validated by surveying physicians and pharmacists from KP Northern California. In Phase II, KP enabled the alert subset in July 2015 in silent mode to collect alert firing rates and confirmed that alert burden was adequately reduced. The alert subset was subsequently rolled out to users in KP Northern California. Alert data was collected September 2015 to August 2016 to monitor relevancy and override rates. RESULTS: Drug-disease alert scoring identified 1211 of 4111 contraindicated drug-disease pairs for inclusion in the subset. The survey results showed clinician agreement with subset examples 92.3%-98.5% of the time. Postsurvey adjustments to the subset resulted in KP implementation of 1189 drug-disease alerts. The subset resulted in a decrease in monthly alerts from 32 045 to 1168. Postimplementation monthly physician alert acceptance rates ranged from 20.2% to 29.8%. DISCUSSION: Our study shows that drug-disease alert scoring resulted in an alert subset that generated acceptable interruptive alerts while decreasing overall potential alert burden. Following the initial testing and implementation in its Northern California region, KP successfully implemented the disease interaction subset in 4 regions with additional regions planned. CONCLUSIONS: Our approach could prevent undue alert burden when new alert categories are implemented, circumventing the need for trial live activations of full alert category knowledge bases.

Variation in Generic Drug Manufacturers' Product Characteristics.

OBJECTIVES: Studies suggest appearance may be an important factor in medication nonadherence. This study was undertaken to characterize the range of appearances and costs of 16 oral solid generic medications in four major chronic diseases/conditions. METHODS: We identified frequently prescribed medications in four therapeutic classes-antidiabetics, 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins), beta blockers, and heart failure drugs-and verified that each had at least three generic manufacturer sources in 2016. The color, shape, scoring, and size for each formulation were compared. Prices were determined based on manufacturers' self-reported wholesale acquisition costs effective December 31, 2016. RESULTS: We identified 40 unique manufacturers for the antidiabetics, 35 for the statins, 38 for the beta blockers, and 71 for the heart failure agents. For all 16 drugs across all four disease states, there was an average of three colors, two shapes, 11 manufacturers, and four appearances when color and shape together are considered. The cost variance per drug ranged from 2% to more than 62,253%. CONCLUSION: Substantial appearance variation among generically equivalent products raises the strong possibility that patients may experience product switches that could increase the likelihood of nonadherence. Our data support the need to further study drug appearance changes and interventions as a potential factor affecting chronic disease adherence outcomes.

Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors.

Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support.

OBJECTIVE: To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice. METHODS: Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support. RESULTS: The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions. CONCLUSION: The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.

Introducing the Big Knowledge to Use (BK2U) challenge.

The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited. We describe summarization and visualization techniques that capture the big picture of a BK knowledgebase, possibly created from Big Data. In this research, we distinguish between assertion BK and rule-based BK (rule BK) and demonstrate the usefulness of summarization and visualization techniques of assertion BK for clinical phenotyping. As an example, we illustrate how a summary of many intracranial bleeding concepts can improve phenotyping, compared to the traditional approach. We also demonstrate the usefulness of summarization and visualization techniques of rule BK for drug-drug interaction discovery.

Depression screening for prescribed medications with mental health risk: Considerations for clinical decision support, workflow redesign, and health information exchange arrangements.

OBJECTIVE: Depression screening should be increased when prevailing knowledge underscoring medication-associated mental health risk is highest. Depression screening in primary care practices when medications with mental health risk were prescribed was estimated while considering the absence and presence of clinical decision support systems. MATERIALS AND METHODS: A cross-sectional, descriptive study using the National Ambulatory Medical Care Survey (NAMCS) data from 2008 to 2010 was conducted. Primary care physician visits were classified based on whether a medication prescribed had a contraindication, severe warning, moderate warning, adverse event only, or no documented mental health risk. Adjusted odds of depression screening for each risk warning level were estimated while controlling for important sociodemographic factors and presence of computerized systems for medication warnings and guideline recommendations. RESULTS: Depression screening at primary care practice visits when medications were prescribed was 2.1% and increased to 2.8% or higher when medications had a moderate or severe mental health risk warning or medication-disease contraindication. Depression screening was increased at visits when at least one medication was prescribed that had a contraindication (AOR = 6.31, P < 0.001), severe warning (AOR = 2.04, P = 0.003), or moderate warning (AOR = 2.50, P = 0.012) for mental health risk, but not for mental health adverse event only warnings alone (AOR = 1.54, P = 0.074). DISCUSSION: Depression screening is increased when medications were prescribed with a documented mental health risk. Presence of clinical decision support systems may help discern between minor and major medication-associated mental health risks. CONCLUSIONS: Appropriately, positioned warning systems with targeted content, workflow redesign, and health information exchange may improve depression screening in at-risk patients.

Recommendations to improve the usability of drug-drug interaction clinical decision support alerts.

OBJECTIVE: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. MATERIALS AND METHODS: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to improve decision support for DDIs. We addressed three key questions: (1) what, how, where, and when do we display DDI decision support? (2) should presentation of DDI decision support vary by clinicians? and (3) how should effectiveness of DDI decision support be measured? RESULTS: Our recommendations include the consistent use of terminology, visual cues, minimal text, formatting, content, and reporting standards to facilitate usability. All clinicians involved in the medication use process should be able to view DDI alerts and actions by other clinicians. Override rates are common but may not be a good measure of effectiveness. DISCUSSION: Seven core elements should be included with DDI decision support. DDI information should be presented to all clinicians. Finally, in their current form, override rates have limited capability to evaluate alert effectiveness. CONCLUSION: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.

Drug-drug Interaction Discovery Using Abstraction Networks for "National Drug File - Reference Terminology" Chemical Ingredients.

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology. NDF-RT provides important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles. It is difficult to comprehend large, complex terminologies like NDF-RT. In previous studies, we introduced abstraction networks to summarize the content and structure of terminologies. In this paper, we introduce the Ingredient Abstraction Network to summarize NDF-RT's Chemical Ingredients and their associated drugs. Additionally, we introduce the Aggregate Ingredient Abstraction Network, for controlling the granularity of summarization provided by the Ingredient Abstraction Network. The Ingredient Abstraction Network is used to support the discovery of new candidate drug-drug interactions (DDIs) not appearing in First Databank, Inc.'s DDI knowledgebase.

Prevalence and Therapeutic Classifications of FDA-Approved Prescription Drugs With Boxed Warnings.

PURPOSE: To characterize and determine the prevalence of drugs with boxed warnings (BXWs) based on a review of structured product labels (SPLs) available on the National Library of Medicine (NLM) DailyMed website. METHODS: A cross-sectional review was conducted of SPLs with BXWs for human prescription drugs on the NLM DailyMed website in July 2012. The presence of a BXW in the DailyMed version of the SPL was validated by cross-referencing a corresponding label on the FDA website. The SPLs were organized into drug groups, and descriptive statistics were used to determine the proportion of SPLs and drug groups associated with a validated BXW. The top therapeutic classes of drugs with BXWs were determined as well as the percentage of the top 100 BXW-associated drugs used in US hospitals and retail settings in 2012. RESULTS: Findings revealed that 35% (n = 4940/14,264) of drug labels on DailyMed and 35% (n = 650/1848) of the drug groups created were associated with a validated BXW. Central nervous system agents, antineoplastic agents, and cardiovascular drugs were the most common therapeutic classes. In 2012, 39% of the top 100 drugs were associated with a BXW.

Identifying knowledge gaps in the labeling of medications for geriatric patients.

The authors found that two-thirds of drugs approved by the FDA in recent years lacked adequate efficacy and safety information for use in older patients. With an expected doubling of the elderly population by 2040, it is time for pharmaceutical manufacturers to incorporate more robust prescribing information into their product labels of drugs used in this patient population.

An approximate matching method for clinical drug names.

OBJECTIVE: To develop an approximate matching method for finding the closest drug names within existing RxNorm content for drug name variants found in local drug formularies. METHODS: We used a drug-centric algorithm to determine the closest strings between the RxNorm data set and local variants which failed the exact and normalized string matching searches. Aggressive measures such as token splitting, drug name expansion and spelling correction are used to try and resolve drug names. The algorithm is evaluated against three sets containing a total of 17,164 drug name variants. RESULTS: Mapping of the local variant drug names to the targeted concept descriptions ranged from 83.8% to 92.8% in three test sets. The algorithm identified the appropriate RxNorm concepts as the top candidate in 76.8%, 67.9% and 84.8% of the cases in the three test sets and among the top three candidates in 90-96% of the cases. CONCLUSION: Using a drug-centric token matching approach with aggressive measures to resolve unknown names provides effective mappings to clinical drug names and has the potential of facilitating the work of drug terminology experts in mapping local formularies to reference terminologies.

Methods for managing variation in clinical drug names.

OBJECTIVES: To develop normalization methods for managing the variation in clinical drug names. METHODS: Manual examination of drug names from RxNorm and local variants collected from formularies led to the identification of three types of drug-specific normalization rules: expansion of abbreviations (e.g., tab to tablet);reformatting of specific elements (e.g., space between number and unit); and removal of salt variants (e.g., succinate from metoprolol succinate). RESULTS: After drug-specific normalization, recall of 3397 previously non-matching names from formularies reaches 45% overall (70% of some subsets), compared to 10-20% after generic normalization. Ambiguity has not increased significantly in the RxNorm dataset. CONCLUSIONS: A limited number of drug-specific normalization operations provide significant improvement over general language normalization.