Optimizing Electronic Health Record Prompts With Behavioral Economics to Improve Prescribing for Older Adults

Prescribing of potentially unsafe medications for older adults is extremely common; benzodiazepines and sedative hypnotics are, for example, key drug classes frequently implicated in adverse health consequences for vulnerable older adults, such as confusion or sedation, leading to hospitalizations, falls, and fractures. Fortunately, most of these consequences are preventable. Physicians' lack of awareness of alternatives, ambiguous practice guidelines, and perceived pressure from patients or caregivers are among the reasons why these drugs are used more than might be optimal. Reducing inappropriate use of these drugs may be achieved through decision support tools for providers that are embedded in electronic health record (EHR) systems. While EHR strategies are widely used to support the informational needs of providers, these tools have demonstrated only modest effectiveness at improving prescribing. The effectiveness of these tools could be enhanced by leveraging principles of behavioral economics and related sciences.

This is an adaptive cluster randomized control trial (RCT) to evaluate whether newly designed EHR-based tools designed using behavioral principles reduce inappropriate prescribing and adverse outcomes among older adults. This study will be conducted in outpatient and acute care practices of Atrius Health, a large integrated delivery network in eastern and central Massachusetts, which uses the Epic EHR system.

In Stage 1, approximately 200 primary care providers at Atrius Health will be randomized to receive usual care or an active intervention. Providers randomized to the active intervention will be randomly assigned to one of 15 active intervention arms. They will then be followed for 6 months. Providers randomized to one of the 15 active intervention arms will receive a newly-designed EHR tool to guide their care of eligible patients. Providers randomized to usual care will receive no newly-designed EHR tool. Providers will receive these EHR tools for their patients who meet the following criteria: 1) older adults (aged 65 years or more) and 2) who have been prescribed at least 90 pills of benzodiazepine or sedative hypnotic in the last 180 days.

At the end of Stage 1 follow-up, the 15 active intervention arms will be ranked based on their observed effectiveness at reducing prescribing high-risk medications and select up to the 5 more promising arms for Stage 2. In Stage 2, the providers assigned in Stage 1 to usual care will be randomized with equal probability to be assigned to one of the 5 most promising treatment arms identified or usual care. Providers randomized to one of up to the 5 selected treatment arms will receive an EHR tool to guide their care of eligible patients. After this analysis, the Stage 1 providers in the "winning" arms (i.e., the promising arms) will be randomly assigned to continue to receive their original treatment assignments or to usual care. Similarly, the Stage 1 providers assigned to treatment arms determined to be statistically inferior will be randomly assigned in equal proportions to one of the winning arms or to usual care.

After Stage 2, we will evaluate the effectiveness of the tools by comparing the effectiveness of the behavioral principles contained within the tools on outcomes, combining data across both Stages. This is our primary analytic approach.

No participant (patient or provider) provided consent for participation, as this trial received a waiver of informed consent and authorization for use of study data.

The Mass General Brigham trial described in the protocol is described in another clinicaltrials.gov record (NCT05538065).

Eligibility Criteria:
Inclusion Criteria:

* Primary care provider at Atrius Health

Providers will receive these EHR tools for their patients who meet the following criteria:

1. older adults (aged 65 years or more)
2. who have been prescribed at least 90 pills of benzodiazepine or sedative hypnotic in the last 180 days.

Exclusion Criteria:

* NA