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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 drug-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 DDI algorithms will permit healthcare providers, organizations, and systems to provide useful decision support to reduce patient harm due to these drug-drug interactions.


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

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

The necessity for contextual DDI warnings

DDIs can pose a major risk to health, but are preventable because of known consequences from exposure to interacting medications. DDIs are responsible for 5-14% of adverse drug reactions among hospitalized patients and occur in up to 13% of elderly ambulatory patients.

Alert fatigue

Excessive exposure to irrelevant alerts is thought to decrease the users’ sensitivity to alerts; over 90% of DDI alerts seen by prescribers are overridden. The lack of specificity may to be due to alerts failing to account for contextual information, leading prescribers to bypass the alert and/or search for the relevant data if needed. Alternatively, there are many situations in which a particular alert might not be clinically relevant to a patient or situation.

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.


We recently published a paper testing a range of high priority contextualized drug interaction clinical decision support algorithms on real-world data: https://doi.org/10.1093/jamiaopen/ooab023

Contact Information

Email us at info@ddi-cds.org