The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial

Robyn Tamblyn, Tewodros Eguale, David L Buckeridge, Allen Huang, James Hanley, Kristen Reidel, Sherry Shi, Nancy Winslade, Robyn Tamblyn, Tewodros Eguale, David L Buckeridge, Allen Huang, James Hanley, Kristen Reidel, Sherry Shi, Nancy Winslade

Abstract

Context: Computerized drug alerts for psychotropic drugs are expected to reduce fall-related injuries in older adults. However, physicians over-ride most alerts because they believe the benefit of the drugs exceeds the risk.

Objective: To determine whether computerized prescribing decision support with patient-specific risk estimates would increase physician response to psychotropic drug alerts and reduce injury risk in older people.

Design: Cluster randomized controlled trial of 81 family physicians and 5628 of their patients aged 65 and older who were prescribed psychotropic medication.

Intervention: Intervention physicians received information about patient-specific risk of injury computed at the time of each visit using statistical models of non-modifiable risk factors and psychotropic drug doses. Risk thermometers presented changes in absolute and relative risk with each change in drug treatment. Control physicians received commercial drug alerts.

Main outcome measures: Injury risk at the end of follow-up based on psychotropic drug doses and non-modifiable risk factors. Electronic health records and provincial insurance administrative data were used to measure outcomes.

Results: Mean patient age was 75.2 years. Baseline risk of injury was 3.94 per 100 patients per year. Intermediate-acting benzodiazepines (56.2%) were the most common psychotropic drug. Intervention physicians reviewed therapy in 83.3% of visits and modified therapy in 24.6%. The intervention reduced the risk of injury by 1.7 injuries per 1000 patients (95% CI 0.2/1000 to 3.2/1000; p=0.02). The effect of the intervention was greater for patients with higher baseline risks of injury (p<0.03).

Conclusion: Patient-specific risk estimates provide an effective method of reducing the risk of injury for high-risk older people.

Trial registration number: clinicaltrials.gov Identifier: NCT00818285.

Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
(A) Screenshot of the user interface for the ‘risk of injury alert’ showing current risk and lowest possible risk. (B) Screenshot of the user interface for the risk of injury alert showing a reduction in risk due to modified treatment. (C) Screenshot of the user interface for the risk of injury alert showing risk increase with modified treatment.
Figure 2
Figure 2
Formula for estimating the risk of injury based on individual patient characteristics. NB Baseline risk was specified at 2.06%, representing the incidence of injury among 65-year-old men without any other risk factors. SSRI, selective serotonin reuptake inhibitors; SSNRI, selective serotonin-norepinephrine reuptake inhibitors.
Figure 3
Figure 3
Consort diagram of physicians and patients eligible and enrolled in the trial.
Figure 4
Figure 4
Risk of injury at the end of follow-up in the intervention and control groups and modification of the effect of the intervention by the magnitude of the baseline risk. The model used to estimate the change in the effect of the intervention by baseline risk of injury was: follow-up risk (y)=intercept (0.3696) + baseline risk (0.8427) + intervention (0.9585) + baseline risk × intervention (−0.2584) (p=0).

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Source: PubMed

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