Clinical trials and patient records have been the main information sources for clinical research. While well- designed clinical trials can produce high quality data, they are generally very expensive and time consuming. Prior studies have also shown that patients enrolled in clinical trials are not necessarily representative of the general patient population. Chart reviews, which rely on the patient records, avoid some of the drawbacks of the clinical trials approach. Although chart review studies are more labor intensive, new developments in structured data entry and natural language processing (NLP) are helping to automate the process. However, studies which use chart reviews are limited by the accuracy and completeness of the data in the records. In the past decade, online social networks have grown exponentially. Some health-focused social network sites have attracted large numbers of users and begun accumulating large quantities of detailed clinical information. The PatientsLikeMe site, for instance, has about 3,200 amyotrophic lateral sclerosis (ALS) patients worldwide, and includes about 5% of the ALS population in the US. Information gathered by online social networks is primarily intended for patients to share with each other. Such information has also begun to attract the attention of medical researchers.[3, 4] Because using information from online social networks for medical research is a fairly new phenomenon, the value and limitation of this type of information source have not been systematically examined. To do so, we propose to conduct a comparison study of patient-contributed information from PatientsLikeMe and records from a large medical record data repository - the Research Patient Data Registry (RPDR) of the Partners Healthcare Systems. The proposed study will focus on ALS, multiple sclerosis (MS), and Parkinson's disease (PD). The general goal is to explore how the medical record and online networking data differ, and if and how online networking data could complement the medical record data.
The specific aims are: 1) Extract symptom and treatment information from the two different data sources. 2) Compare the prevalence of symptoms and treatments from the two information sources and analyze the difference. 3) Extract treatment response of prescription medications from PatientsLikeMe and analyze the confounding effect of the misunderstanding of medication indication.

Public Health Relevance

The proposed project will investigate an emerging data source for clinical research: online social network. This data source may complement and supplement the data from clinical trials and medical records, with a unique emphasis on patients'experience and perspectives.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Exploratory/Developmental Grants (R21)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Odenkirchen, Joanne
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University of Utah
Schools of Medicine
Salt Lake City
United States
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Nakamura, Carlos; Bromberg, Mark; Bhargava, Shivani et al. (2012) Mining online social network data for biomedical research: a comparison of clinicians' and patients' perceptions about amyotrophic lateral sclerosis treatments. J Med Internet Res 14:e90
Liao, Katherine P; Cai, Tianxi; Gainer, Vivian et al. (2010) Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken) 62:1120-7