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

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.