Nudges Aren’t One-Size-Fits-All. Here’s How Personalizing Them Could Change Health Care
In the last decade, health care has been moving away from a traditional one-size-fits-all approach and towards personalized medicine. While the concept of precision medicine usually brings to mind things like genetic profiling for targeted therapeutic interventions (for example, tailored drug dosages), it can also be applied to facilitating more health-promoting behaviors among patients.1
In a recent article for Harvard Business Review, Shirley Chen and Mitesh Patel discuss how the rise in digital health tools such as mobile health apps, wearable technologies, and other remote patient-monitoring tools offers new channels for personalized care.2 Such tools provide the opportunity to collect a plethora of patient data, which can, in turn, provide insight into health behaviors, preferences, personality traits, motivations, barriers, area of residence, etc.
A lot of these data points are not exactly “clinical,” but can still complement the patient’s medical history. This allows for the creation of what Chen and Patel call “behavioral phenotypes,” which can then pave the way for personalized nudging in health care.
What are personalized nudges?
Personalized nudging is nudging that is optimized based on a patient’s behavioral phenotype, to maximize the effectiveness of the intervention. Personalized nudging can take two forms: choice personalization and delivery personalization.3 Choice personalization concerns the options presented to the patient, and delivery personalization refers to the method of nudging. Patient-centered nudging is powerful because nudges don’t work the same for everyone, and what works for a particular patient may not be effective for another.
Nudging in health care can be applied towards improving medication adherence, physical activity, program engagement, and can even mental health. Artificial intelligence (AI) and machine learning algorithms allow for nudging interventions to be personalized and delivered at scale by automatically stratifying patients, incrementally learning from patient activity, and delivering personalized messages and recommendations. In essence, AI can learn and predict the most effective nudges for a particular patient and deliver them accordingly.
Here are some digital health startups that have already leveraged this technology to overcome challenges associated with patient behavior modification.
References
1. Jethwani K, Kvedar J, Kvedar J. Behavioral phenotyping: a tool for personalized medicine. Per Med. 2010;7: 689–693.
2. Chen XS, Patel MS. Digital Health Tools Offer New Opportunities for Personalized Care. 18 Nov 2020 [cited 27 Dec 2021]. Available: https://hbr.org/2020/11/digital-health-tools-offer-new-opportunities-for-personalized-care
3. Mills S. Personalized nudging. Behavioural Public Policy. 2020. pp. 1–10. doi:10.1017/bpp.2020.7
About the Author
Sanketh Andhavarapu
Sanketh is an undergraduate student at the University of Maryland: College Park studying Health Decision Sciences (individual studies degree) and Biology. He is the co-Founder and co-CEO of Vitalize, a digital wellness platform for healthcare workers, and has published research on topics related to clinical decision-making, neurology, and emergency medicine and critical care. He is also currently leading business development for a new AI innovation at PediaMetrix, a pediatric health startup, and previously founded STEPS, an education nonprofit. Sanketh is interested in the applications of behavioral and decision sciences to improve medical decision-making, and how digital health and health policy serve as a scalable channel to do so.
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