#713 2006 · PatientsLikeMe (Heywood brothers) · Healthcare / patient data
Patients logging their own symptoms online did in months what a randomized clinical trial would have taken years to attempt — and it refuted a real drug claim
the problem
A rare disease produces too few patients and too slow a trial pipeline for any single doctor or study to see the real pattern across cases
background
Stephen Heywood was diagnosed with ALS in 1998, a disease with no cure and, at the time, no practical way for a patient or their family to see how their disease course compared to anyone else's — doctors saw only their own small caseload, and clinical trials moved far too slowly to help an individual patient make real-time treatment decisions. His brothers Jamie and Ben Heywood, with a former MIT classmate, watched this information vacuum firsthand and set out to build something doctors and trials weren't providing: a way for patients to see their disease in the context of thousands of others like them.
The standard tools for understanding a rare, slow-moving disease were the standard tools of clinical research generally — small trials, peer-reviewed publication, years-long timelines — none of which could answer an urgent question an individual patient actually needed answered quickly, like whether a promising new treatment showing up in early research was actually working for people taking it right now.
what everyone would do
Wait for a formal randomized controlled trial to test whether lithium slowed ALS progression -- the rigorous, standard evidentiary path in medicine, and the only one most patients and doctors would consider valid, but one that takes years an ALS patient making today's treatment decision doesn't have.
what they saw
No single patient, or even a single doctor's caseload, could ever see whether lithium was working across the ALS population -- but the platform didn't need a formal trial to see it either, because thousands of patients were already self-experimenting with the drug and logging their own symptoms. The pattern that a trial exists to reveal was already being generated by real patients making real treatment decisions; it just needed to be captured in a comparable, structured format instead of scattered across individual medical records nobody was aggregating.
the move
PatientsLikeMe, founded in 2006, had patients themselves log structured symptom, treatment and side-effect data over time, building a dataset out of exactly the kind of unremarkable, individually invisible records that only reveal a pattern in aggregate. When a small 2008 Italian study suggested lithium might slow ALS progression, roughly 10% of PatientsLikeMe's ALS users started taking it on their own rather than wait for a formal trial — and the platform's researchers built an algorithm matching 149 lithium-treated patients against 447 non-treated controls with similar disease trajectories, using the patients' own logged data to run what amounted to an informal observational trial in real time.
why it works
Structured self-reported logging turns each patient's individually unremarkable data point into a row in a dataset large enough to reveal a population-level pattern no single case could show; because roughly 10% of the platform's ALS patients had already started taking lithium on their own initiative, the natural variation in who did and didn't take it created two comparable groups without needing to recruit or randomize anyone into a trial arm. Matching lithium-takers to similar-trajectory non-takers using the same logged data let researchers approximate a controlled comparison from data that already existed, compressing years of trial timeline into an analysis of information patients were generating anyway.
the payoff
The matched-cohort analysis found no effect of lithium on ALS disease progression at 12 months, refuting the earlier published claim years before a formal randomized trial would have reached the same conclusion — the study, published in Nature Biotechnology in 2011, was among the first demonstrations that patient-reported data collected online could accelerate drug evaluation. By the time of that publication PatientsLikeMe had grown to more than 100,000 patients tracking over 500 conditions.
where it breaks
It depends on having enough patients logging comparable, structured data for a matched-cohort comparison to be statistically meaningful -- a rare enough condition, or a platform too small to reach critical mass, leaves the same information vacuum the method is meant to fill. It also can't fully replace randomization: patients who self-select into taking an unproven treatment may differ from those who don't in ways a matching algorithm can't fully control for, so the method is faster and more responsive than a formal trial but carries a higher risk of hidden confounding than genuine randomization would.
what came after
PatientsLikeMe's lithium study is cited as a landmark early proof that patient-generated real-world data can meaningfully contribute to clinical research, and the platform's model — patients as active data contributors rather than passive trial subjects — influenced the broader real-world-evidence movement in healthcare research and regulatory science that followed.
references
- [1]Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithmNature Biotechnology (via PubMed), 2011pubmed.ncbi.nlm.nih.gov
- [2]Data from Patient Social Network Refutes Lithium for ALSMIT Technology Review, 2011technologyreview.com