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Predict drug adverse effects with Artificial Intelligence, increasing patient health and satisfaction.
Every year, Adverse drug reactions (ADRs) – unintended, harmful events attributed to intended medicine use – result in more than 750,000 inpatient injuries or deaths, affect nearly two million hospital stays, and directly result in over one million ED visits and 125,000 hospitalizations. ADRs are estimated to cause at least 10% of all admissions in older adults.
Every year, adverse drug reactions (ADRs) – unintended, harmful events attributed to intended medicine use – result in more than 750,000 inpatient injuries or deaths, affect nearly two million hospital stays, and directly result in over one million ED visits and 125,000 hospitalizations (1,2). Older adults are more vulnerable to ADRs due to aging-related kidney and liver changes that create increased sensitivity and exposure to pharmaceuticals. They experience ADRs twice as frequently as their younger counterparts, and are four times as likely to be hospitalized (3). ADRs are estimated to cause at least 10% of all admissions in older adults, and between 10–39% of hospitalized older adults will experience an ADR (4,5). They are also more likely to die from ADRs; a recent study found that fatal outcomes were reported approximately three times more often for older adults (6).
One reason for increased risk of ADRs is the use of Potentially Inappropriate Medications (PIMs), particularly for older adults who may be taking multiple medications. Polypharmacy, commonly defined as regular use of five or more medications, and the prevalence of PIMs are strongly associated with increased risk of ADRs in older adults. Alarmingly, the prevalence of PIMs ranges from 20–60% of all older adults depending on healthcare setting and criteria used to define inappropriate prescribing (7). PIM use is associated with a 10–30% increased risk of hospitalization, and older adults with polypharmacy are roughly 80% more likely to be hospitalized within a year relative to equivalent patients without polypharmacy (7,8).
PIMs and polypharmacy can result in considerable cognitive impairment consistent with dementia and may lead to misdiagnosis and further prescriptions, potentially adding to an already-elevated ADR risk. Despite this, opportunities for medication reconciliation and deprescribing are frequently missed. A recent study found that 66% of hospitalized older patients had at least one PIM prescribed at discharge, 49% continued a previously prescribed PIM, 31% were prescribed a new PIM during hospitalization, and ultimately 36% visited the ED, were rehospitalized, or died within 30 days of discharge (9).
Up to two-thirds of ADRs in hospitalized and multi-morbid older adults are considered preventable, and AI-based models are ideal to help Healthcare organizations (HCOs) identify, anticipate, and avoid these adverse outcomes (7). Predictive analytics enable HCOs to integrate patient-specific data (e.g., conditions, comorbidities, physiologic vulnerabilities, and medications) with drug burden indices, support continuous monitoring of health and behaviors, and predict individuals at the greatest risk of ADRs. This insight provides care teams with the ability to proactively initiate tailored interventions. For example, interventions designed around deprescribing and reconciling medication use have been shown to reduce ADR risk (7,10).
