Econs vs Patients: Why Behavioral Science could be the secret sauce of Digital Health

Sofia Martin Giron (*) & Jose Antonio Martin MD-MBA (**)

(*) Behavioral and Social Sciences student at IE University (4th year)

(**) Healthcare Consultant & Advisor | Digital Health

November 2022

Towards the end of a traditional, in-person medical consultation, the doctor prescribes the treatment along with lifestyle recommendations (on physical activity, diet, etc.) and closes with a “will see you again in X months”. At that point, the (rational) doctor expects the (rational) patient to follow these instructions to the letter… but unfortunately that is not how things play out in real life.

Patients are Humans, not Econs

Traditional Economics assumes that people, as consumers, are fully rational; they are ideal decision-makers with complete information who act in a consistent manner to pursue self-interested goals. Behavioral Economics (a young branch that has already produced five Nobel prize winners) highlights that this Homo economicus is a mythical creature that only exists in textbooks. Real people in real life don´t behave as “Econs” but as “Humans”, and their decisions and actions are influenced by context, emotions, and cognitive biases. Thinking fast and slow(Daniel Kahneman) and Nudge (Richard Thaler and Cass Sunstein) are two books that have served to popularize these new concepts in Economics.

Traditional Medical textbooks and clinical protocols also implicitly assume this idealized version of patients as rational agents. However, unsurprisingly, in the real world, patients don't behave as Econs but as plain Humans.

This simple realization could have profound implications for how healthcare delivery is designed, both in traditional settings and -as we will later discuss- in new digital health models. Everyone involved in this industry (doctors but also hospital managers, health insurers, pharma and medtech companies, etc.) should be aware of their role as “choice architects” (Thaler & Sunstein): by designing the context in which patients will make decisions, they have a marked impact on issues like adherence to treatment and lifestyle behaviors.

Healthcare´s digital opportunity

Digital Health, also a recent development, offers the potential to complement traditional in-person Medicine and augment its impact through remote interventions. A quantum leap, as the delivery of healthcare services is no longer tied to particular “locations”, and as it will no longer be episodic and reactive but rather (potentially) a continuous, 24x7 activity.

Digital health care delivery models include (Jose A. Martin: La oportunidad digital de la sanidad, 2016) three main groups: Teleconsultations, Remote chronic disease management (now including remote patient monitoring and Digital Therapeutics or DTx), and Self-care and connected care (e.g., consumer health apps, devices, and search engines).

The first wave of telehealth initiatives was based on providing access to synchronous teleconsultations for acute minor ailments. But increasingly we are witnessing a shift towards the use of asynchronous communications, algorithm-based automated interventions, and a renewed focus on chronic disease management, which is the top health concern globally (NCDs account for approximately 75% of deaths and of health spend). Additionally, we begin to realize that the future of healthcare delivery is hybrid: framing the battle as “digital vs brick-and-mortar” healthcare is myopic.

In this emerging, digitally enhanced, hybrid version of healthcare, Behavioral Science (BS) has much to offer.

Digital Health meets Behavioral Science

BS has been shown to have an impact on several health decisions (classic examples include how to increase organ donations or how to facilitate the choice of health insurance), but it may find a particularly fertile ground in digital health models. The application of BS principles and tools in this area may yield impact on a large scale -specially in improving the management and self-management of chronic diseases- and, due to the economics of digital, in a highly cost-effective manner.

There are many aspects of healthcare delivery that could be rethought with BS in mind. But probably the most impactful areas are the promotion of healthier behaviors and the improvement in adherence to the prescribed treatment. To do that, we need to be aware of the more prevalent cognitive biases in this context to then design the appropriate interventions.

Some cognitive biases (systematic or non-random errors in thinking) may be particularly relevant in the case of patients:

• Optimism bias: The propensity to think positively and overestimate successful outcomes. Patients may underestimate the frequency of adverse occurrences, which may alter their assessment of their vulnerability to a disease or condition

• Ostrich effect: This is linked to optimism bias, and as the term implies, patients may act like ostriches by burying their heads in the sand to deny or downplay any threat and choose to block out or restrict their thoughts to prospective medical problems to prevent the suffering

• Paradox of choice: “More is less.” The complexity and consequent cognitive work increase as the number of alternatives increases. Consequently, there is a decline in motivation to do anything. Offering patients an ever-increasing range of treatment alternatives might cause people to feel overburdened, which could foster mistrust, discontent, and frustration

• Hyperbolic discounting or present bias: The propensity for people to favor more immediate rewards over those that would come later. The longer a solution/treatment takes to become apparent/work, the less likely they are to choose it

• Social influence: The tendency to base decisions on the opinions of others. The patient’s environment might impact their judgement and choices (e.g., advice from friends, family, and peers)

• Status quo: Tendency for things to stay the way they have always been. Patients can be averse to change, perceiving it as something risky and scary and preferring to maintain circumstances as they are

Being aware of these biases, we can (as choice architects) design nudges and plug them into the overall design and algorithms of health apps and other digital health models. A nudge (Thaler & Sunstein) is “any aspect of the choice architecture that alters people´s behavior in a predictable way without forbidding any options or significantly changing their economic incentives”.

Examples of nudging applicable to promoting healthier habits and improving treatment adherence include (a non-comprehensive list) the following ones:

• Setting up defaults: Make it easy by harnessing the power of defaults - automatically setting up reminders (e.g., medication uptake, exercise time, breaks) with no effort from the user’s part

• Setting short-term, realistic goals: “A goal without a plan is just a wish”. Start by taking smaller steps every day. Setting up SMART goals (specific, measurable, achievable, relevant, and time-bound) – to make the goal clearer and easier to manage

• Social norms: “When in doubt, follow the crowd”. Make it social by providing data on what other patients/users do – in comparison with them

• Rewards and recognition: Establish a series of incentives and rewards to promote behaviors. Successful incentive programs can combat present bias by providing prizes that make the healthier or ideal choice more alluring in the here and now (e.g., gaining a sense of accomplishment by congratulating the behavior, making their status noticeable, unlocking premium features, etc.)

• Framing effect: The agony of losing is two times stronger than the joy of winning. The way a decision is presented might affect how we feel about it; for example, framing decisions as a loss will make you avoid them

• Commitment bias: Encourage commitment to finish tasks. Create commitment with others and with oneself to raise the likelihood that a behavior will be carried through (e.g., StickK and the use of Commitment Contracts)

• Simplification: The easier you make it, the more likely it is it will be completed. Avoid choice overload by simplifying messages (avoiding jargon), highlighting the most important information and using visual cues to make it easier to understand and follow

• Gamification: Can be used both as education (using quizzes, competitions) and as incentives interventions

What´s next?

Behavioral Science and Digital Health are nascent areas with significant potential synergies. As both continue to expand and grow in impact in the coming years, we see many opportunities for fruitful collaboration between them.

This opinion piece is just meant as an invitation to the communities of these two disciplines to further their dialogue, and jointly design nudges that direct patients to take actions that will improve their lives.

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ps: "If we all make systematic mistakes in our decisions... then why not develop new strategies, tools, and methods to help us make better decisions and improve our overall well-being?"