The End of One-Size-Fits-All Health Advice
Why the Future of Medicine is Personalized—and How It Will Change Everything
Vive les différences
A new health study has found that the classic recommendation of eight hours of sleep may not apply to everyone. Some need more, some need less—it depends on sleep quality and, most importantly, who you are.
This is just the latest reminder that when it comes to health, one size does not fit all. We are all unique, and that fact is about to revolutionize healthcare, creating new industries and jobs.
The traditional recommendations you receive from experts—who base their advice on sample population studies—may or may not work for you.
We can do better.
Think about the recommendations you’ve received from trained experts, regulatory agencies, activist organizations, Hollywood movie stars, your sister, your friend, your mother, or even your twin. At best, these recommendations are probabilities, not guarantees, that something will work for you.
This applies to every one of the eight billion people on this planet.
Dr. Seuss said it best:
“Today you are You, that is truer than true.
There is no one alive who is Youer than you.”
Because you are unique, there is no such thing as a universal health recommendation.
This is true for exercise, sleep, medicine, diet, and even meditation.
Research
The first randomized double-blind placebo-controlled trial was done in the United Kingdom in 1943-1944.
Neither the patients nor doctors knew who was receiving treatment and who was receiving a placebo. Seventy-six years later, these studies are still considered the gold standard for determining causation.
However, while it remains the gold standard for understanding causation for populations, the “platinum standard” will emerge when we can analyze information about both the test and placebo groups.
Knowing these individual characteristics in both groups will allow us to get personalized results.
Although a test subjects’ characteristics are not fully known before a study begins, the results should reveal key differences of both test and control group subpopulations.
This will allow us to determine how various factors—such as health conditions (e.g., health biomarkers, cancer, diabetes), age, preferences, environment, stress levels, diet, drugs (legal and illegal), sleep, exercise—affect outcomes. Even genes can change over time (epigenetics), which means we must continually monitor these factors.
By integrating individual characteristics with population studies, we can provide more precise, personalized treatments. Whether it’s diet, reactions to medicine, or sleep recommendations, future studies will offer probabilities of what will work for each individual.
Treatments
Incorporating data from population studies alongside an individual’s unique characteristics will be essential for effective treatment. Medical professionals will need both an initial snapshot of a patient and ongoing monitoring to optimize care. Rather than definitive cause-and-effect conclusions, health outcomes will likely be reported as probabilities.
To improve accuracy, researchers will need to collect and analyze a lot of participant data, using machine learning and artificial intelligence.
For example, future recommendations may state:
“There is a 72-91% probability that a medicine or diet will work for you.”
A 50% probability is essentially a coin toss, meaning it’s just as likely to fail as to succeed. If you’re desperate, you will settle for less than 50%. If you have options, you will select the one with the highest probability—unless there are concerning side effects.
New Industries
Precision health will likely create entirely new industries and job opportunities.
Personal Monitoring Devices: We are already seeing early versions of wearable technology that track individual health metrics, and this sector will expand significantly.
Data Analysis & AI Integration: Researchers will use machine learning and AI to analyze information from animal and human studies, as well as real-time data from personal health monitors.
Privacy Protection: As data collection increases, privacy specialists will be needed to prevent misuse of personal health information.
Scientific Oversight: Independent third parties will be necessary to ensure that companies remain grounded in scientific principles and do not use data for profit.
The Decline of Traditional Health Guidance
Public health institutions, non-profits, companies, and other organizations dispensing public health recommendations are likely to fade.
We may have already experienced some of that with the COVID-19 recommendations, where we learned that, for example, the vaccines were important for the elderly but not so much for children. This was particularly true as they were hardly vaccines—neither preventing people from getting sick nor preventing the spread of the virus.
Blanket public health guidance was no more accurate than a universal prescription for a gluten-free diet.
In short, paraphrasing (badly) Martin Luther King:
You will be treated by the content of your characteristics, not by an average population response.