Digital Phenotyping and Digital Twin- Where’s the Precision?

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Precision medicine has become a buzz world as digital health matures and is more deeply embedded into traditional care models. A working definition suggests, “An emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” The promise is we can potentially move from generalized clinical treatment protocols for managing chronic conditions, usually based on Randomized Control Trial (RCT) data – the highest standard of clinical evidence to personalized care.

A new viewpoint by Guy Fagherazzi from the Luxemburg Institue of Health published in the Journal of Medical Internet Research (JMIR) takes stock of the current state of precision medicine. Fagherazzi points out that cancer has been an early focus on precision medicine. Still, while some progress has been made, few patients have benefited from a more personalized approach, which has primarily relied on genetic data.

To truly deliver on the promise of precision medicine, the extensive real-time data collection that occurs with apps and sensors needs to be explored; digital phenotyping offers a useful framework to develop new approached. With the majority of Americans owning smartphones, we are walking live data streams, and we may not be aware of the data we are transmitting or who is tracking it. Still, it is present, and we each have a “digitosome” – a collection of all digitally derived data from our online activity, smartphone, senors, and device use. Early work on digital phenotyping has been applied in psychiatry. In essence, useful contextual information is shared with patients so they can observe how their mood, sleep quality, and activity may contribute to their symptoms. To date, these data streams have been under-utilized and present an excellent opportunity to support more optimal management of clinical conditions. A much more patient-friendly approach that fits into a people’s lives could be shaped by looking at continuous data versus the more infrequent touchpoints of doctors’ visits. The author points out that someone with diabetes may spend six hours a year with their care team, but they spend 600 hours managing the condition in between those appointments. Why are we ignoring this data? The visual below outlines all the potential data streams to support deeper digital phenotyping.

Screenshot 2020-03-03 17.26.30

Source: DOI: 10.2196/16770

Enter the digital twin- imagine replicating your health data and leveraging your patterns to explore different choices to improve health. A digital twin would replicate your data set and then compare your characteristics to other patients like you. The potential is exciting. Some more significant issues need to be addressed, namely the ethical and transparent use of data, an agreement on who owns the data you generate, and clarity on who gets to see and act on the data.

We are in a new era of digital health, and this next decade will see tremendous progress in how complex real-time data can be leveraged transparently to improve health. New models of care will emerge, and patients will have a more in-depth view of how their moment to moment decisions can help and support them in managing their health.

Thanks for reading – Trina



Definition of Precision Medicine- Psaty BM, Dekkers OM, Cooper RS. Comparison of 2 Treatment Models: Precision Medicine and Preventive Medicine. JAMA 2018 Aug 28;320(8):751-752. [CrossRef] [Medline]

Fagherazzi G. Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper J Med Internet Res 2020;22(3):e16770 DOI: 10.2196/16770


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