An electronic medical record (EMR)-based intervention to reduce polypharmacy and falls in an ambulatory rural elderly population.

摘要

BACKGROUND: Falls are the leading cause of injury-related deaths in the aging population. Electronic medical record (EMR) systems can identify at-risk patients and enable interventions to decrease risk factors for falls. OBJECTIVE: The objectives of this study were to evaluate an EMR-based intervention to reduce overall medication use, psychoactive medication use, and occurrence of falls in an ambulatory elderly population at risk for falls. DESIGN: Prospective, randomized by clinic site. PATIENTS/PARTICIPANTS: Six-hundred twenty community-dwelling patients over 70 at risk for falls based on age and medication use. INTERVENTIONS: A standardized medication review was conducted and recommendations made to the primary physician via the EMR. MEASUREMENTS AND MAIN RESULTS: Patients were contacted to obtain self reports of falls at 3-month intervals over the 15-month period of study. Fall-related diagnoses and medication data were collected through the EMR. A combination of descriptive analyses and multivariate regression models were used to evaluate differences between the 2 groups, adjusting for baseline medication patterns and comorbidities. Although the intervention did not reduce the total number of medications, there was a significant negative relationship between the intervention and the total number of medications started during the intervention period (p < .01, regression estimate -0.199) and the total number of psychoactive medications (p < .05, regression estimate -0.204.) The impact on falls was mixed; with the intervention group 0.38 times as likely to have had 1 or more fall-related diagnosis (p < .01); when data on self-reported falls was included, a nonsignificant reduction in fall risk was seen. CONCLUSIONS: The current study suggests that using an EMR to assess medication use in the elderly may reduce the use of psychoactive medications and falls in a community-dwelling elderly population.