Evidence-Based Nudges for Better Patient Outcomes

Best practices for leveraging SMS, Voice, and IVR across diverse patient populations


No matter how effective a medication, protective a vaccine, or beneficial a lifestyle modification, clinicians must recommend them and patients must engage with them to achieve improvements in health. Our decisions and behaviors are heavily influenced by the environment in which they occur. For example, small changes to the way choices are presented and information is framed within the electronic health record (EHR) can lead to significant differences in the way clinicians order tests and treatments. Social networks and norms influence how individuals eat and exercise. In many cases, these influences go unnoticed or are not given much thought. A concerted effort can help develop systematic approaches to designing interventions that better align our decisions and behaviors with long-term goals.


The concepts of remote monitoring, telemedicine, and, as noted by The New England Journal of Medicine, automated hovering have been around for a while now. In the United States, health care funding promotes a reactive, visit-based model in which patients are seen when they become sick, usually during hospitalizations and outpatient visits. The care model fails not only because it is costly and often fails to prevent disease, but also because individual behaviors account for so much of health outcomes. Even patients with chronic illnesses may only spend a few hours a year with a doctor or nurse, but they spend 5,000 waking hours a year determining whether to take prescribed drugs or obey other medical advice, deciding what to eat and drink, whether to smoke, and other things that can have a huge effect on their health.


Meanwhile, clinicians are being asked to do more with less time. They often run behind schedule and suffer from decision fatigue. As noted by The Journal of the American Medical Association, a cumulative burden of making choices leads to worse decisions over time, and even the most knowledgeable and well-resourced doctors deliver care with gaps, such as overusing surveillance imaging in a cancer patient, failing to prescribe statins to a patient who meets the criteria, or prescribing an expensive medication when a less expensive alternative is accessible. Usually, these differences emerge not from a lack of judgement, experience, or poor intentions, but rather because doing it right all of the time is difficult, and practice environment design unwittingly makes guideline-concordant treatment harder — or at least not as easy as it should be. This is where nudges come into play.

Enabling personalized and measurable engagements through the Nudge Framework

True patient engagement should have observable performance metrics that can be measured over time. In general, this is still a concern. However, data being collected continues to demonstrate that patient engagement can be highly beneficial when performed in a coordinated manner. With the advent of devices to track activity, heart rate, and much more, the rise of AI to predict health issues from that flood of data, automation to “hover” over patients and so on, healthcare is on the cusp of putting patients where they belong — at the center of their own health (with some assistance).

It is possible to provide amazing care to any individual but it can come at a significant cost. Concierge care for all? Sure, but at what cost and who will pay that price? This is where advances in technology and “automated hovering” will play an increasingly important role. Programs such as Heart Safe Motherhood have been shown to reduce automated hovering costs significantly and reduce readmissions while not increasing clinician workloads.

Nudge Framework
The Nuffield Council of Bioethics has created an intervention ladder that helps to balance the impact of interventions with their ethical considerations. It can be adapted to form a nudge intervention ladder. As one moves up the ladder, nudges become both more paternalistic and more effective.
Nudge intervention ladder

Guide choice through default options

This is often the most effective option where making cancer screening the path of least resistance is implemented. Defaults are already in place in other industries. Companies like Netflix and Amazon do this already by playing the next episode in the series. You have to explicitly make a choice to exit out. 

Enable choice

This approach increases the options made available and simultaneously makes it more convenient to complete the cancer screening. Approaches could include easy and immediate scheduling or other such approaches.

Prompt implementation intention

This asks the individual to pre-commit to completing the cancer screening in a timely manner. This approach could include selecting a particular date or even committing to a pledge.

Frame information

This approach delivers feedback in a manner that motivates completing cancer screening. This could be either by delivering peer comparisons to indicate when one is an outlier to the norm, or by increasing transparency on the costs of tests and treatments.

Provide Information

This is often only slightly better than doing nothing. The presumption is that simply providing information will cause someone to act in their best interest, such as offering education on the benefits of cancer screening or quitting smoking.

Do nothing

An example being to simply monitor cancer screening rates. This doesn’t provide much value except to establish a baseline. Most reports might fall into this category, i.e. there is rarely a “so what” question being answered by the report.

While it might be tempting to jump directly to the top of the ladder, it is important to remember that, the higher on the ladder, implementing those nudges requires access to more data from other systems, in addition to EHR data, or in the context of clinicians, access to the EHR. Each of those are possible but the implementation and on-the-ground effort will be non-trivial.
Examples of patient nudges
  • In a randomized trial with overweight and obese adults from 40 US states, we used a behaviorally designed gamification intervention with competition to significantly increase physical activity during a 6-month period with sustained effects in the 3 months after the intervention stopped. Participants in the competition arm walked about 100 miles more than control during the study.
  • In a randomized trial of elderly patients that recently had a heart attack or other ischemic event, we used loss-framed financial incentives and personalized goal-setting to increase physical activity during a 4-month intervention (1368 steps per day more than control) with sustained effects in the 2 month follow-up period (1154 steps per day more than control).
  • In a randomized trial of adolescents and young adults with type 1 diabetes, we used loss-framed financial incentives to significantly increase glucometer adherence from 19% in control to 50% in the intervention group.
Examples of clinician nudges
  • In a randomized trial of primary care physicians, the trial used peer comparison feedback on clinician performance with automated patients lists to triple the prescribing of statin medications for patients that were at high-risk of cardiovascular events. This led to the health system wide adoption of these types of nudges for statin prescribing.
  • In a randomized trial of radiation oncologists, the trial used nudges in the electronic health record to reduce the rate of unnecessary imaging in palliative cancer patients from 68% to 32%. This saved more than 3,000 unnecessary imaging tests per year.
  • In a study of primary care practices, the study used nudges in the electronic health record to increase influenza vaccination rates by 9.5 percentage points. Across the entire health system, this led to 5000 more patients being vaccinated.

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