Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews

Abstract

BackgroundDrug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews.MethodsStarting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified.ResultsReview of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems.ConclusionsWe identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations. Effective information models and system designs will be needed to maximize the utility of this information.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0419-3) contains supplementary material, which is available to authorized users.

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey (Preprint)

“…The survey provided optional responses for resources, keywords, and study types that were identified from the team's prior research on the information needs of professionals who search and synthesize PDDI evidence [11]. Questions about PDDI information search were organized according to the range of PDDI topics important for clinical decision making recently published by Payne et al [29].…”

“…A list of drug interaction experts was assembled starting with individuals our team has worked with on past PDDI projects, including the aforementioned information needs study [11] and a prior workgroup on PDDI evidence assessment. Experts were contacted and asked to provide names and contact information for other colleagues that would be appropriate to send the survey.…”

“…In prior work, we described the workflow of individuals who maintain PDDI information resources used by clinicians [11]. We refer to these individuals as compendium editors.…”

“…We refer to these individuals as compendium editors. The workflow of compendium editors generally involves topic identification, evidence search, evidence synthesis, and generating recommendations [11]. The evidence search step is non-trivial because there is no single repository housing data on PDDIs.…”

Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System

“…Studies focusing on specific subsets of individuals rely on other sources of data, such as electronic health records and published literature from clinical trials, where information about the individuals' medication intake is explicit (e.g., Akbarov et al, 2015;Zhou et al, 2016;Romagnoli et al, 2017). Harnessing social media for studying the effects of medications on specific cohorts would require developing systems that can automatically distinguish posts that express personal intake from those that do not.…”