The use of psychotropic drugs—such as antidepressants and benzodiazepines—is a significant risk factor for falls, increasing the risk by about 50 percent. These drugs are often prescribed off-label for older adults to treat insomnia or pain, despite a lack of strong research evidence.
Computerized drug alerts are often used to draw attention to drug interaction risks, but are underutilized and do not fully take advantage of current computing power. A group of researchers in Québec, Canada tested a new model of computer drug alerts over a large sample of patients and physicians to see if an innovative system could lead to a reduction in high-risk prescribing.
The current focus on individualized medicine—paired with improvements in technology— presents researchers and practitioners with many new opportunities to assess and study the effects of medication. Currently, commercial drug alert systems do not factor in many patient-specific risk factors. They review drugs for potential interactions and allergies, cumulative toxicity, and disease-specific therapeutic relevance.
For this study, researchers developed a system that gives physicians patient-specific risk alerts that factor in age, sex, medical history, cognitive well-being, as well as gait and balance information. The system also included an innovative visual interface that provided physicians with visual representations of prescription risks.
Researchers randomly assigned clusters of patients and physicians either to an intervention group (with the innovative patient-specific system)or to a control group (which used already existing commercial drug alerts). Eighty-one physicians agreed to participate, who represented a total of 5,628 older-adult patients. The physicians in the study were predominantly French-speaking and male, and patients were predominantly female with an average age of 75 years. Physicians in the control group used a typical commercial system, while the intervention group received the patient-specific system and a five-minute training program on its use.
Physicians in the intervention group used the new system to review drug treatment in over 80 percent of visits, and changed the treatment in about one-fourth of the visits. This is a greater response than those found in previous studies on current risk alert systems. There was a statistically significant reduction in injury risk for patients in the intervention group, particularly among patients at higher baseline injury risk. Off-label use of antipsychotic and anticonvulsants saw the most significant reductions in use.
Further research will have to determine if such innovations in drug alert systems are perceived as useful in clinical practice, however, this study suggests that alert systems that use patient-specific information—presented in an effective visual interface—might reduce falls and other injury risks.
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