Evaluation of adverse drug reaction formatting in drug information databases

Abstract

Objective: The research evaluated the differences in formatting of adverse drug reaction (ADR) information in drug monographs in commonly used drug information (DI) databases.Methods: A cross-sectional analysis of formatting of ADR information for twenty commonly prescribed oral medications in seven commonly used DI databases was performed. Databases were assessed for presentation of ADR information, including presence of placebo comparisons, severity of ADR, onset of ADR, formatting of ADRs in percentile (quantitative) format or qualitative format, whether references were used to cite information, whether ADRs are grouped by organ system, and word count of the ADR section. Data were collected by two study investigators and discrepancies were resolved via consensus. Chi-square analyses and one-way analysis of variance (ANOVA) were used to evaluate for mean group differences in categorical and continuous data, respectively.Results: The seven DI databases varied significantly on each analyzed ADR variable, including variables known to impact interpretation such as placebo comparisons and qualitative versus quantitative formatting. Placebo comparisons were most common among monographs in Micromedex In-Depth Answers (70%) but were absent among monographs in Epocrates, Lexicomp, and Micromedex. Quantitative information was commonly used in most databases but was absent in Epocrates. Average word counts were higher in Clinical Pharmacology and Micromedex In-Depth answers compared to other databases.Conclusion: Substantial variation in ADR formatting exists between the most common DI databases. These differences may translate into alternative interpretations of medical information and, thus, impact clinical judgment. Further studies are needed to assess whether these differences impact clinical practice.

Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting

“…The first step is a computational and semantic comparison of case history to the medications the patient is taking. This is powered by DrugBank [ 25 ], Epocrates [ 26 ], and Lexicomp [ 27 ] to tally the subset of symptoms that are present and known to occur as side effects of the medications the patient is taking. At a clinical informatics level, cough with fever might be connected/mapped to curated ontologies such as SNOMED (Systematized Nomenclature of Medicine [ 28 ]) about SARS-CoV-2, or pneumonia in a weighted fashion, by subject matter experts who then have the capability to markedly enhance this highly navigable information.…”

Evaluation of adverse drug reaction formatting in drug information mobile phone applications

“…Similar to a previous study of DI desktop applications, we identified variability across DI mobile applications in a variety of formatting parameters that are known to influence clinical decision-making [ 24 ]. Mobile applications varied substantially with regard to the presence of comparative placebo information, ADR severity information, referencing, organization by organ system, and both quantitative and qualitative formatting.…”

“…This study has a number of limitations. First of all, this project, similar to the previous analysis of DI databases, was limited to an assessment of twenty medications [ 24 ]. However, by evaluating the most widely prescribed medications in the United States, including drugs that were approved over disparate periods of time (and thus under varying degrees of regulatory approval by the Food and Drug Administration), we believe the information drawn from the monographs within the analyzed mobile applications is likely a good representation of ADR formatting more generally [ 32 ].…”

“…The twenty most highly prescribed medications in the United States, as listed by ClinCalc.com (as of December 1, 2020), were chosen to compose the sample drug monograph population, as was done in a previous study [ 24 ]. A list of the DI mobile applications and assessed medications is available in Table 1.…”

“…Other formatting differences, such as the use of qualitative versus quantitative formatting, can also influence comprehension of medication risk depending on an individual's numeracy [ 22 , 23 ]. A recent analysis found numerous differences in ADR formatting between DI applications that are available on desktop computers with regard to many criteria that are used by clinicians in causality assessments, including information regarding onset of ADR, severity of ADR, the presence of placebo comparison data, and frequency formatting [ 24 ]. However, there is no research available on the formatting of ADRs in DI mobile applications, which are used by a growing number of clinicians.…”