In the United States, fifteen new cases of ALS are discovered every day, an estimated 1 million people suffer from multiple sclerosis, and an estimated additional 1 million people suffer from Parkinson’s disease.  These are the three main disease categories that PatientsLikeMe sought to address when they created an online platform for patients to share personal health information. Founded in 2004, PatientsLikeMe created a platform to crowdsource self-reported patient medical data to in an effort to 1) educate patients within disease-specific communities and empower them in their health journey and 2) advance the pace of medical research.
PatientsLikeMe (PLM) has built an online social network for patients to exchange information within their disease-specific communities. Patients upload demographic information to their personal profile and share information on their disease, treatment plan, and symptoms. They can post and comment on articles, discuss new clinical trials, and direct message other patients for more in-depth conversations. Since 2004, the PLM network has grown to over 650,000 patients covering 2,800 different conditions. 
Figure 1: Visualization tools offered on PLM platform for patients with fibromyalgia. Patients can research information about the disease as well as compare treatment plans and reported side effects.
Educating and Empowering Patients – “Learn From Patients Like You”
The PLM platform offers an opportunity for patients to learn, connect, and own their health. Patients can review information to understand their best path given their condition and “Doctor Visit Templates” are available to empower patients to have more effective conversations with their doctor.  A 2010 study of PLM showed that 57% of patients found the site helpful for understanding treatment side effects and that 12% changed their physician as a result using the site.  In addition to providing information, PLM also provides an outlet for social and emotional relief. Patients can connect with a community of peers suffering from the same disease. In the same 2010 study, 41% of HIV patients and 22% of mood disorder patients reported community-specific benefits. 
Figure 2. Patient biography on PLM platform.
Collecting Data to Advance Medical Research – “We’re more than a disease. Together, we’re the answer”
In creating a space for information-sharing, PLM has been able to collect large amounts of data and share them with 80 plus partners spanning biotech, pharmaceutical, and research.  They have partnered with the Journal of Medical Internet Research on numerous occasions. In one instance, it was to study the impact of two drugs (amitriptyline and modafinil) on patients within 5 disease categories (multiple sclerosis, Parkinson’s disease, mood conditions, fibromyalgia/chronic fatigue syndrome, and amyotrophic lateral sclerosis).  More recently, PLM data was used to observe the patient perspective on neuromyelitis optica spectrum disorders. 
A core competency in this process is PLM’s ability to de-identify patient information and convert it into structured, medically-accepted data. In 2018, an evaluative study showed that 97.09% of PLM-assigned codes, generated from their patient reported database, were compatible with FDA coding requirements.  This suggests that PLM reported data is reliably being converted into medically-accepted data. Managing this process and increasing coding reliability will continue to be essential to PLM’s model.
Crowdsourcing patient-data to improve the patient experience and accelerate medical research continues to generate enthusiasm within the healthcare community. Just last week, FDA Commissioner Dr. Scott Gottlieb announced that the FDA is launching a new app to gather patient-reported data for clinical trials.  PLM has built a strong platform to benefit from this trend, but there are some important questions that still need to be addressed:
- Can PLM overcome patient and population bias and leverage data to make meaningful medical advances?
- Patient bias – patients may report data incorrectly when submitting information.
- Population bias – PLM might be attracting patients that are not representative of the underlying population
- While patient reported data research is significantly less expensive than traditional clinical trials, it seems more appropriate as a supplement to clinical trials rather than as an alternative.
- Is PLM doing enough to help patients get better?
- Interactions are solely between patients. Should PLM incorporate advice from medical professionals? Should they leverage their database to identify highly skilled doctors and match them with patients?
 Brubaker, Jed; Lustig, Caitlin and Hayes, Gillian. “PatientsLikeMe: Empowerment and Representation in a Patient-Centered Social Network”. Semantic Scholar. 2009. http://www.gillianhayes.com/wp-content/uploads/2011/01/CnP11_PatientsLikeme.pdf, accessed November 2018.
 Wicks, Paul; Massagli, Michael; Frost, Jeana; Brownstein, Catherine; Okun, Sally; Vaughan, Timothy; Bradley, Richard and Heywood, James. “Sharing health data for better outcomes on PatientsLikeMe”. Journal of Medical Internet Research. June 14, 2010. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2956230/, accessed November 2018.
 Frost, Jeana; Okun, Sally; Vaughan, Timothy; Heywood, James and Wicks, Paul. “Patient-reported Outcomes as a Source of Evidence in Off-Label Prescribing: Analysis of Data from PatientsLikeMe”. Journal of Medical Internet Research. January 21, 2011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3221356/, accessed November 2018.
 Eaneff, Stephanie; Wang, Victor; Hanger, Morgan; Levy, Michael; Mealy, Maureen; Brandt, Alexander; Eek, Daniel; Ratchford, John; Nyberg, Fredrik; Goodall, Jonathan and Wicks, Paul. “Patient perspectives on neuromyelitis optica spectrum disorders: Data from the PatientsLikeMe online community”. Multiple Sclerosis and Related Disorders Journal. October 2017. https://www.msard-journal.com/article/S2211-0348(17)30164-5/fulltext, accessed November 2018.
 Brajovic, Sonja; Blaser, David; Zisk, Meaghan; Caligtan, Christine; Okun, Sally; Hall, Marni and Pamer, Carol. “Validating a Framework for Coding Patient-Reporting Health Information to the Medical Dictionary for Regulatory Activities Technology: An Evaluative Study”. Journal of Medical Internet Research. August 18, 2018. https://medinform.jmir.org/2018/3/e42/, accessed November 2018.
 Arndt, Rachel. “FDA releases open-source app to collect patient-reported data”. Modern Healthcare. November 7, 2018. https://www.modernhealthcare.com/article/20181107/NEWS/181109936?mc_cid=cbc74122a0&mc_eid=04b2d4daeb, accessed November 2018.