Online health communities

Neal, Lisa; Lindgaard, Gitte; Oakley, Kate; Hansen, Derek L.; Kogan, Sandra; Leimeister, Jan Marco; Selker, Ted

doi:10.1145/1125451.1125549

Cited by 14 publications

(6 citation statements)

References 2 publications

(3 reference statements)

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“…This shift is attributed mostly to changes in the healthcare system (including decreased access to healthcare professionals and higher costs of healthcare) and increased technological literacy in the patient population. 1 While PAT may not contain scientifically accurate or systematic data, it comprises rich descriptions of hundreds of patients’ experiences over a wide range of conditions, in real time. Already, projects such as Google Flu 2 and HealthMap 3 have shown that PAT is a reliable data source for tracking disease trends; moreover, novel insights into co-morbidities and drug-treatment effects have been discovered on sites like CureTogether 4 and PatientsLikeMe.…”

Section: Objectivementioning

confidence: 99%

Identifying medical terms in patient-authored text: a crowdsourcing-based approach

MacLean

Heer

2013

J Am Med Inform Assoc

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Background and objectiveAs people increasingly engage in online health-seeking behavior and contribute to health-oriented websites, the volume of medical text authored by patients and other medical novices grows rapidly. However, we lack an effective method for automatically identifying medical terms in patient-authored text (PAT). We demonstrate that crowdsourcing PAT medical term identification tasks to non-experts is a viable method for creating large, accurately-labeled PAT datasets; moreover, such datasets can be used to train classifiers that outperform existing medical term identification tools.Materials and methodsTo evaluate the viability of using non-expert crowds to label PAT, we compare expert (registered nurses) and non-expert (Amazon Mechanical Turk workers; Turkers) responses to a PAT medical term identification task. Next, we build a crowd-labeled dataset comprising 10 000 sentences from MedHelp. We train two models on this dataset and evaluate their performance, as well as that of MetaMap, Open Biomedical Annotator (OBA), and NaCTeM's TerMINE, against two gold standard datasets: one from MedHelp and the other from CureTogether.ResultsWhen aggregated according to a corroborative voting policy, Turker responses predict expert responses with an F1 score of 84%. A conditional random field (CRF) trained on 10 000 crowd-labeled MedHelp sentences achieves an F1 score of 78% against the CureTogether gold standard, widely outperforming OBA (47%), TerMINE (43%), and MetaMap (39%). A failure analysis of the CRF suggests that misclassified terms are likely to be either generic or rare.ConclusionsOur results show that combining statistical models sensitive to sentence-level context with crowd-labeled data is a scalable and effective technique for automatically identifying medical terms in PAT.

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Section: Objectivementioning

confidence: 99%

Identifying medical terms in patient-authored text: a crowdsourcing-based approach

MacLean

Heer

2013

J Am Med Inform Assoc

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“…Online health communities serve as a common learning environment that enhances social interactions and sharing of experiences which facilitates learning. However, online health communities raise challenges because of the differences of members, health literacy, and medical knowledge (Neal et al, 2007).…”

Section: Social Cognitive Theory and Online Learningmentioning

confidence: 99%

The Power of Peers: Applying User-Generated Content to Health Behaviors “Off-Line”

Willis

2018

Qual Health Res

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Many patients seek and share information online regarding health, especially those diagnosed with chronic disease. The social cognitive theory is used as the theoretical framework for analyzing how members learn within online health communities. This study conducted in-depth interviews with members from online health communities related to arthritis to understand their motivation for participating in the community and how the content exchanged therein is applied to managing their disease. Four themes were identified: processing disease diagnosis, collaborating to solve problem, recognizing personal limitations, and appreciating that health is variable. Topical communication within the online communities was often tailored to members' situational needs with the ultimate goal being better self-management. The findings demonstrate the online health community is an environment that invites members who share common characteristics to engage with one another and with the shared content for the purpose of learning arthritis self-management strategies. Theoretical and practical implications are discussed.

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“…Health “communities” can develop online, established independent of time and geographical space (Douglas et al, 2008). People can connect with others in similar circumstances (Neal et al, 2007; Broom & Tovey, 2008; Hinton, Kurinczuk, & Ziebland, 2010) and can learn to better manage long-term problems or conditions. Posting on YouTube is likely to have different rewards for the posters, such as gaining credibility, becoming a spokesperson for a stigmatized group, and/or attaining hero status (Fixsen, 2015).…”

Section: Introductionmentioning

confidence: 99%

Stories of Hell and Healing: Internet Users’ Construction of Benzodiazepine Distress and Withdrawal

Fixsen

Ridge

2017

Qual Health Res

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Benzodiazepines are a group of drugs used mainly as sedatives, hypnotics, anti-epileptics and muscle relaxants. Consumption is recommended for 2-4 weeks only (NICE CKS 2013) due to fast onset of dependency and potentially distressing withdrawal symptoms (Ashton, 2005;Lader, 2012). Few peer review studies have drawn on the user experiences and language to appreciate first hand experiences of benzodiazepine withdrawal or discontinuation syndrome (Fixsen, 2015). We looked extensively at patient stories of benzodiazepine withdrawal and recovery on Internet support sites and YouTube. Our analysis indicated that users employ rich metaphors to portray the psychologically disturbing and protracted nature of their suffering.We identified seven major themes: hell and isolation; anxiety and depression; alienation; physical distress; anger and remorse; waves and windows; and healing and renewal. By posting success stories, ex-users make known that "healing" can be a long, unpredictable process, but distress does lessen and recovery can happen.

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Online health communities

Cited by 14 publications

References 2 publications

Identifying medical terms in patient-authored text: a crowdsourcing-based approach

Identifying medical terms in patient-authored text: a crowdsourcing-based approach

The Power of Peers: Applying User-Generated Content to Health Behaviors “Off-Line”

Stories of Hell and Healing: Internet Users’ Construction of Benzodiazepine Distress and Withdrawal

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