In the summer of 2018, a US a teenager’s story about a lifesaving Apple Watch alert made it to the national headlines. The seemingly healthy 18 years old girl was sitting in church when the watch alerted her: the heart rate is extremely high. Her parents rushed her to the hospital, where they found out that she had kidney failure, with both of her kidneys functioning below 30%. Doctors diagnosed her with chronic kidney disease. Her caregiver admitted that the alert led the girl to the hospital, where doctors discovered her condition before dialysis or kidney transplant was needed to save her life.

These headlines help the marketing of fitness devices, but they also pave the way for personalized, evidence-based medicine. It uses stored health data, namely of patient diagnoses, laboratory work, insurance claims, and demographic information among others. This information allows to move beyond the reactive, one-size-fits-all approach of the 20th century’ method of treating illness, allowing healthcare provider to predict and prevent future diseases. (Harvey, A., Brand, A., Holgate, S.T., Kristiansen, L.V., Lehrach, H., Palotie, A., Prainsack, B.: The future of technologies for personalised medicine, 2012)

Personalized medicine offers a new approach to manage the patient’s health and target therapies better to achieve the best outcome for that particular patient. Data is collected and stored to control an existing, known disease of a patient or even a predisposition to a specific disease. Just like in our example: early detection of symptoms made possible for the teenage girl to discover her chronic disease before things turned worse.

We can take this one step further, and combine sensor provided health data with genomic technologies, that are already used widely to offer better outcomes to patients. Beside prediction and prevention of particular diseases data allows healthcare providers to form a more precise diagnose. The cause of shared symptoms could be different for each patient. If a doctor can pinpoint the exact cause of the symptom, he can form a more accurate diagnose.

Personalized medicine offers the possibility to move away from the “trial-and-error” method, prescribing the optimal therapy first time around. The current pharmaceutical interventions are effective in only 30-60% of patients due to differences in individual reactions and responses. If we map out the genetic variants responsible for personal drug response, we can use that knowledge to create a person’s pharmacogenomic profile and to identify optimal treatment.

For example, doctors treat a newly diagnosed Type 1 diabetes with regular insulin injections. However, there are some forms of diabetes, that have different underlying causes, but clinically looks like a classic Type 1 diabetes. A simple genetic test can identify some patients who can be better treated with tablets or who are best managed by no treatment at all – preventing all risks associated with poorly controlled diabetes.

When data is available the patient has a more participatory role in forming the diagnose and deciding what therapy may help. Healthcare professionals and patients discuss and interpret together information about individual genomic characteristics, lifestyle, and environmental factors, personal data from wearable technology. They consider lifestyle changes together and decide when treatments might not be necessary at all. Patients consider preventative measures, even when there is only a likelihood of a disease developing. A real doctor-patient dialogue opens, with one goal: better health.

Author: Laszlo Varga