A good article in MIT Sloan Review about patient innovation mentions the rise of n-of-1 trials: Fortunately, very low-cost approaches exist and are being developed to make it practical for patients — both individuals and groups — to carry out high-quality, ethically appropriate trials. Many of them involve a trial design called “n of 1,” […]
Medicine’s history is often portrayed as a sequence of discoveries, all made in laboratories. In fact, though, the biggest changes in US medicine over the last 200 years were propelled by forces beyond medicine, specifically, in media. How medicine’s stakeholders communicated in different eras — in formats including medicine shows, newspapers, cars, telephones, medical journals, and TVs — determined what, and how much, was communicated. Where information flows, medicine follows: now social media, biomonitors and AI are ushering in a new age, one of patient generated medicine.
An n-of-1 trial is a experiment conducted for a single person in which treatment blocks are randomly rotated, symptoms are systematically logged, and results are statistically analyzed. Since many treatments work differently for individuals, n-of-1 trials help determine a treatment’s efficacy for a specific individual. N-of-1 trials are typically used for chronic conditions and are not considered appropriate for acute illnesses.
In an article in Harvard Business Review, author Sarah Peck sums up the relative success of four strategies she tested to break her own social media addiction. No social media for 30 days, which was ‘easier than expected.’ Result: after the month was over, Peck discovered her phone was her addiction enabler. Allowed social sites […]
Moore’s law finally arrives for the people with impaired hearing. New FDA regulations may bring hearing aids to tens of millions of Americans, disintermediating audiologists and cutting into sales for makers of traditional hearing aids.
Though the effectiveness of many treatments varies widely across individuals, treatments are rarely rigorously evaluated on a personal basis. Instead, pressed for time, physicians rely on trial and error testing. But protocols for personal experiments are simple, and their benefits are well documented.
Because many treatments’ effects vary depending on the individual, some researchers argue that effectiveness should be evaluated per patient. Yet a simple evaluation protocol that’s been tested and refined for 30 years, called an n-of-1 trial, is rarely used. Why?
Though doctors and drug makers tout “average” effects, many treatments deliver a smorgasbord of results—substantial benefits for some people, little benefit for many, and harm for a few. Why don’t we hear more about this variability?
Most people’s tests of potential treatments for chronic conditions involve haphazard cycling through doses and brands, spotty symptom diaries and no statistical analysis of results. This lack of rigor introduces numerous cognitive biases.
In the spring of 2019, we had been talking for nearly a year about building a software toolkit to help automate what many people already do informally—track symptoms and statistically evaluate the effects of various treatments. As the idea started to jell, we started casting about for names for the toolkit. Though there are a […]