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<!DOCTYPE html>
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<head>
<meta charset='utf-8'>
<title>A Minimum Representation of Potential Drug-Drug Interaction Knowledge and Evidence - Technical and User-centered Foundation</title>
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<section id='abstract'>
<p>
Ensuring medication therapy occurs safely and to the maximum benefit
for any given patient is of great interest to clinicians
[[institute-medicine-2006]]. One possible threat to patient safety comes
from exposure to two or more drugs that are known to interact (i.e.,
potential drug-drug interactions or PDDIs), and could therefore lead
to a clinically observable effect on the patient (i.e., an actual
drug-drug interaction). New information regarding
PDDIs is published every day in primary sources such as drug product
labeling and the scientific literature. However, there are currently
no broadly accepted standards to guide these experts in the
organization and presentation of PDDI information for clinical decision support. These shortcomings suggest
the need for harmonized approaches for documenting and sharing PDDI
information. The purpose of this Community Group Note is to
provide a technical and user-centered foundation for a minimum
information model for PDDI information. The resulting common
representation of PDDI summaries would facilitate curation and
information exchange. Downstream applications would process these
representations into forms amenable for clinical decision support,
drug product label enhancement, cohort identification, and other
pharmacovigilance activities.
</p>
</section>
<section id='sotd'>
This draft is currently open for editing suggestions by all interested stakeholders.
</p>
<p>
This document is governed by the <a href="https://www.w3.org/community/reports/reqs/">W3C requirements of the Community and Business Group Process related to deliverables</a>.
</p>
</section>
<section>
<h2>Introduction</h2>
<p>
Ensuring medication therapy occurs safely and to the maximum benefit for of any given patient is of great
interest to clinicians [[institute-medicine-2006]]. One possible threat to patient safety comes from exposure
to two or more drugs that are known to interact (i.e., potential drug-drug interactions or PDDIs), and could therefore
lead to a clinically observable effect on the patient (i.e., an actual drug-drug interaction). While the effects
that may occur due to exposure to some PDDIs can benefit patients (e.g., some HIV therapies use a low-dose of ritonavir
to increase plasma concentrations of co-administered protease inhibitors by inhibiting their metabolism), PDDIs are
more often a patient safety concern. Clinically important events that are attributable to PDDI exposure occur in
5.3% - 14.3% of inpatients, and are responsible for up to 231,000 emergency department visits that occur each year in
the United States alone [[magro-2012]][[cdc-faststats]]. A recent systematic review and meta-analysis of 13 studies conducted
on 3 continents found the median rate of PDDI associated hospital admissions to be 22.2%
(interquartile range 16.6 - 36.0%) [[dechanont-2014]]. The potential for harm from PDDIs is an international concern reflected in
guidance documents of regulatory agencies around the world [[rekic-2017]][[european-medicines-2012]][[usdhhs-2017]]. Moreover, in the United States, PDDI alerting is a criteria included in the so-called Meaningful Use criteria
for Electronic Health Records [[cms-2013]][[ridgely-2012]]), and population-based strategies for tracking exposure
are promoted by organizations such as the Pharmacy Quality Alliance [[ahrq-drug-drug]].
</p>
<p>
Clinicians often face barriers to the effective and appropriate management of PDDI exposures [[nabovati-2017]].
Barriers include PDDI alerts with poor specificity and incomplete personal PDDI knowledge [[abarca-2004]][[van-der-sijs-2006]]
. An awareness of the need for PDDI decision support prompts clinicians to use various drug
knowledge resources including print or online drug information references, drug interaction checking tools, and alerting systems.
Unfortunately, poor specificity leads clinicians to be overwhelmed by PDDI information that is “difficult to retrieve, sort and
digest into clinical decision making” [[bottiger-2009]]. PDDI alerts are often criticized for “over-alerting” that
obfuscates the most important information, hinders the usability of the decision support system, and leads to alert fatigue
and clinician dissatisfaction [[bottiger-2009]][[payne-2015]]. Moreover, while many compilations of
PDDI evidence exist to help improve prescriber and pharmacist knowledge, they are not concordant in their coverage,
accuracy, and agreement [[wang-2010]][[saverno-2011]][[ayvaz-2015]][[fung-2017]]. Together, these
shortcomings suggest the need for harmonized approaches for documenting and sharing PDDI information.
</p>
<section>
<h2>Need, envisioned workflow, and high-level requirements</h2>
<p>New information regarding PDDIs is published every day in primary sources such as drug product labeling and the scientific
literature. A PubMed search for publications indexed with the Medical Subject Headings keyword “Drug interactions” shows an
average of 3,970 publications per year from 2000 through 2016. This suggests that the body of evidence about PDDIs is
overwhelming and dynamic. As it is impossible for clinicians to keep up with the PDDI evidence base, drug
experts generate summaries of PDDI evidence from primary sources. These summaries bring PDDI knowledge to clinicians
in the form of published drug information compendium, clinical decision support rules, and interaction checking applications.
<strong>However, there are currently no broadly accepted standards to guide these experts in the organization and presentation of PDDI
information that would be most effective for clinical decision support.</strong>
</p>
<p><a href="#PDDI-MI-APPLICATIONS"></a> provides an overview of
the roles envisioned for a PDDI minimal information model. Drug
experts would generate summaries of PDDI evidence from primary
sources using the information elements from the PDDI minimal
information model. The information elements would cover the
minimum set of information required for the effective clinical
management of PDDI exposure. The resulting common representation
of PDDI summaries would facilitate curation and information
exchange. Downstream applications would process these
representations into forms amenable for clinical decision
support, drug product label enhancement, cohort identification,
and other pharmacovigilance activities. To achieve the
envisioned roles, the minimal information model must be flexible
and computable. Where possible, model elements should draw upon
accepted biomedical taxonomies and ontologies to represent
medications, diagnoses, and descriptions of potential adverse
reactions. Preferring the use of ontologies over free-text
descriptions will reduce ambiguity associated with free-text,
thus supporting comparison and computational
analyses. Representations in commonly-used formats (JSON,XML/RDF
,etc.) will support ease of construction and parsing of models,
particularly through shared libraries and APIs.
</p>
<figure id="PDDI-MI-APPLICATIONS">
<img src="Presentations/images/info-model-value-proposition.png"