Ramez Naam on Twitter / X

What Patients Say Works (And Doesn't) for Migraines

For the live-updated, fully-labelled, interactive version of this infographic, click here. Eight of the top ten patient-reported treatments for Migraine are simple lifestyle changes, not drugs. CureTogether – a free resource owned by 23andMe that allows people to share information about their health and treatments – surveyed more than 6,000 people who self-identify as having […]

Two Research Papers Published on PatientsLikeMe - SPM Blog

Two research papers were published this month on the Health 2.0 website, PatientsLikeMe. PatientsLikeMe is arguably the only “real” health social network online today, because it lets patients share actual […]

Jason Crawford on Twitter / X

It's hard to find information that all the world's top scientists and doctors have missed.But it's not that hard, if you're intelligent, thoughtful, and diligent, to find information that the medical system has not yet caught up to—information that is not yet incorporated into… pic.twitter.com/HrgGzCUdX2— Jason Crawford (@jasoncrawford) September 22, 2024

Patrick Collison on Twitter / X

Observing some people close to me with chronic health conditions, it's striking how useful Reddit frequently ends up being. I think a core reason is because trials aren’t run for a lot of things, and Reddit provides a kind of emergent intelligence that sits between that which any… pic.twitter.com/Eto23uQMDb— Patrick Collison (@patrickc) September 14, 2025

Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem

Background: Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0 / health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. Objective: The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. Methods: Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. Results: Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23andMe are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. Conclusions: Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change… | Samuel G. Rodriques | 491 comments

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we. | 491 comments on LinkedIn

Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data

Background: This project investigates the ways in which patients respond to the shared use of what is often considered private information: personal health data. There is a growing demand for patient access to personal health records. The predominant model for this record is a repository of all clinically relevant health information kept securely and viewed privately by patients and their health care providers. While this type of record does seem to have beneficial effects for the patient–physician relationship, the complexity and novelty of these data coupled with the lack of research in this area means the utility of personal health information for the primary stakeholders—the patients—is not well documented or understood. Objective: PatientsLikeMe is an online community built to support information exchange between patients. The site provides customized disease-specific outcome and visualization tools to help patients understand and share information about their condition. We begin this paper by describing the components and design of the online community. We then identify and analyze how users of this platform reference personal health information within patient-to-patient dialogues. Methods: Patients diagnosed with amyotrophic lateral sclerosis (ALS) post data on their current treatments, symptoms, and outcomes. These data are displayed graphically within personal health profiles and are reflected in composite community-level symptom and treatment reports. Users review and discuss these data within the Forum, private messaging, and comments posted on each other’s profiles. We analyzed member communications that referenced individual-level personal health data to determine how patient peers use personal health information within patient-to-patient exchanges. Results: Qualitative analysis of a sample of 123 comments (about 2% of the total) posted within the community revealed a variety of commenting and questioning behaviors by patient members. Members referenced data to locate others with particular experiences to answer specific health-related questions, to proffer personally acquired disease-management knowledge to those most likely to benefit from it, and to foster and solidify relationships based on shared concerns. Conclusions: Few studies examine the use of personal health information by patients themselves. This project suggests how patients who choose to explicitly share health data within a community may benefit from the process, helping them engage in dialogues that may inform disease self-management. We recommend that future designs make each patient’s health information as clear as possible, automate matching of people with similar conditions and using similar treatments, and integrate data into online platforms for health conversations.

Patient Led Research Collaborative  for Long COVID

About the Patient-Generated Hypotheses JournalPatient-Led Research Collaborative

Patient Led Research Collaborative  for Long COVID

About the Patient-Generated Hypotheses JournalPatient-Led Research Collaborative

How To Prompt on Twitter / X

scientist tested a 3.5 billion dollar medical AI against regular chatGPT and it lost on every single one.they just published a paper in nature medicine, and the results are actually mindblowing.they took specialized clinical ai tools and tested them against general-purpose… pic.twitter.com/P7ePLunrDj— How To Prompt (@HowToPrompt__) July 6, 2026

Launching openRxiv Labs - openRxiv

Over the last thirteen years, bioRxiv and medRxiv have grown into widely used infrastructure for rapid research sharing in biology and medicine. The reliability and researcher-first values that have defined these platforms since their founding remain central to how we operate, and building on the trust, partnerships, and community relationships researchers worldwide depend on is…

Chris Beiser on Twitter / X

it'd be interesting to quantify the rate at which unaffiliated reddit users have improved on the state of the art for treatment protocols for diseases. my guess is that for 75% of diseases, they're responsible for a greater QoL increase than pharmaceutical companies over 10 years— Chris Beiser (@ctbeiser) June 7, 2021

Co-creation process of an app for people with rare diseases - a citizen science approach

Background Rare diseases affect a small percentage of the population, leading to challenges such as delayed diagnoses and limited treatment options. Mobile health technologies offer solutions to improve patient outcomes, yet their application in rare diseases remains underexplored. The German citizen science project SelEe created a customizable app for the self-management of rare diseases through a co-creation process that involved patients with such conditions. Methods The project consisted of three phases. In Phase 1, 9 to 68 patients or relatives of patients participated in workshops to define research topics and app requirements. Phase 2 involved a core research team of nine patients and researchers who iteratively developed the app, released in March 2023. Phase 3 focused on evaluating the app’s usage and usability through an in-app survey conducted from March 2023 to February 2024. We utilized descriptive statistics to evaluate app usage and employed the mHealth App Usability Questionnaire to assess usability. Results The SelEe app offers the possibility to create and store data in a personalized health diary. Patients can create their own templates or use templates which were defined by the core research team. Users can record findings (e.g. blood test results) and export data using different graphs and formats. Furthermore, the app supports blind users. The app was downloaded 3040 times and 1456 users registered, with 1967 unique diseases entered. 50.7% of the diseases were rare, 30.5% non-rare, and 18.8% were classified as suspected, undefined, or symptoms. A total of 1223 valid user profiles were analyzed for app usage and demographics. Furthermore, 432 users qualified for the in-app survey by making at least one health diary entry, and 117 participated. The app was rated with an overall usability score of 5.19 out of 7. While the app’s health diary function was frequently used, other functionalities like findings and data export were less utilized. Feedback highlighted the need for improved usability and additional features. Conclusions The study highlights active patient engagement in developing a mobile health app for individuals with rare diseases. Although improvements are necessary for broader acceptance, the app is promising for the management of rare diseases. Supplementary information The online version contains supplementary material available at 10.1186/s13023-025-04140-1.

Meta’s New AI Asked for My Raw Health Data—and Gave Me Terrible Advice

Meta’s Muse Spark model offers to analyze users’ health data, including lab results. Beyond the obvious privacy risks, it’s not a capable stand-in for a real doctor.

Precision Medicine in Neuroscience: Tools, Translation, and Implementation: A Workshop

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