Our mission

This project seeks to provide a necessary bridge between electronic health records and healthcare providers in the clinical decision making process. Providing information to clinicians about natural product-drug interaction warnings based on known attributes of the medications involved and patient specific factors is our goal. In essence, we seek to get the right information, at the right time, through the right channel, and the right format to clinicians. The construction of meaningful NPDI algorithms will permit healthcare providers, organizations, and systems to provide useful decision support to reduce patient harm due to these natural product-drug interactions.

• Click to view our current NPDI algorithms

Objectives

Individualize natural product-drug interaction alerts to individual patient circumstances so that physicians, pharmacists, and other healthcare providers will receive contextualized alerts, leading to greater attention to alerts when the patient is at risk for harm due to a NPDI.

Provide a comprehensive assessment of the evidence for NPDIs and factors that affect the risk of harm from specific natural product and drug combinations.

The necessity for contextual NPDI warnings

NPDIs have the potential of posing a major risk to patient health, but are preventable because of proposed consequences found from in-vitro studies and case reports.

Over the past two decades, the increasing use of natural products including dietary supplements, vitamins, and botanicals alongside pharmaceutical medications has raised concerns about possible adverse events and decreased efficacy of pharmaceutical products. In addition, older adults, who often take multiple medications and natural products simultaneously (up to 88% in the US), may face greater risks than the rest of the population. Generation of evidence-based CDS is important in order to mitigate adverse events resulting from NPDIs.

Need for greater specificity for warnings based on drug attributes

Advances in electronic health records are opening up new possibilities for alerts that account for dose, route of administration, duration of treatment, care setting.

Need to incorporate patient-level factors in decision making

Because patient data is being captured in real-time, algorithms can be constructed to query the most recent laboratory tests, physiological status, commodities, and other risk factors to assess the likelihood of harm at the time of prescribing.

Contact Information

Email us at info@ddi-cds.org