The Internet is expected to innovate healthcare, in particular patient-centredness of care. Within fertility care, information provision, communication with healthcare providers and support from ...peers are important components of patient-centred care. An online infertility community added to an in vitro fertilisation or IVF clinic's practice provides tools to healthcare providers to meet these. This study's online infertility community facilitates peer-to-peer support, information provision to patients and patient provider communication within one clinic. Unfortunately, these interventions often fail to become part of clinical routines. The analysis of a first introduction into usual care can provide lessons for the implementation in everyday health practice. The aim was to explore experiences of professionals and patients with the implementation of an infertility community into a clinic's care practice. We performed semi-structured interviews with both professionals and patients to collect these experiences. These interviews were analyzed using the Normalisation Process Model. Assignment of a community manager, multidisciplinary division of tasks, clear instructions to staff in advance and periodical evaluations could contribute to the integration of this online community. Interviews with patients provided insights into the possible impact on daily care. This study provides lessons to healthcare providers on the implementation of an online infertility community into their practice.
To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying ...automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.
ABSTRACT
Due to the social oppressions from the mainstream society, social support becomes fundamental for people who are D/deaf and hard of hearing (D/hh) to exchange information within their own ...community. We aim to examine the support content of messages and how support processes become established by exploring the interactions within a D/hh discussion forum, AllDeaf.com, which is one of the leading online communities for D/hh. In this pilot study, we first employed content analysis to code discussion posts using the Social Support Behavior Code. Two authors collaboratively coded a random sample of 25 threads and 632 posts. Informational support was observed as the most frequently exchanged type of social support for people who are D/hh. Then we identified social support features used for future text classification task. Our preliminary qualitative findings indicated that the linguistic and lexical features have the potential to support automated feature identification and social support classification typology with text mining techniques
Health 2.0 is a benefit to society by helping patients acquire knowledge about health care by harnessing collective intelligence. However, any misleading information can directly affect patients' ...choices of hospitals and drugs, and potentially exacerbate their health condition.
This study investigates the congruence between crowdsourced information and official government data in the health care domain and identifies the determinants of low congruence where it exists. In-line with infodemiology, we suggest measures to help the patients in the regions vulnerable to inaccurate health information.
We text-mined multiple online health communities in South Korea to construct the data for crowdsourced information on public health services (173,748 messages). Kendall tau and Spearman rank order correlation coefficients were used to compute the differences in 2 ranking systems of health care quality: actual government evaluations of 779 hospitals and mining results of geospecific online health communities. Then we estimated the effect of sociodemographic characteristics on the level of congruence by using an ordinary least squares regression.
The regression results indicated that the standard deviation of married women's education (P=.046), population density (P=.01), number of doctors per pediatric clinic (P=.048), and birthrate (P=.002) have a significant effect on the congruence of crowdsourced data (adjusted R²=.33). Specifically, (1) the higher the birthrate in a given region, (2) the larger the variance in educational attainment, (3) the higher the population density, and (4) the greater the number of doctors per clinic, the more likely that crowdsourced information from online communities is congruent with official government data.
To investigate the cause of the spread of misleading health information in the online world, we adopted a unique approach by associating mining results on hospitals from geospecific online health communities with the sociodemographic characteristics of corresponding regions. We found that the congruence of crowdsourced information on health care services varied across regions and that these variations could be explained by geospecific demographic factors. This finding can be helpful to governments in reducing the potential risk of misleading online information and the accompanying safety issues.
The Internet has become a valuable and convenient mechanism for supporting evidence-based medicine, healthcare delivery, and healthcare decision making. The utilization of online resources to seek ...healthcare information and support is rapidly growing, and the Internet and social media have become leading resources for information and interactive applications for both professionals and consumers. The diffusion of Internet services and social media has transformed how individuals view and make their healthcare decisions. Health-related Internet use, including the use of social media and social networking, has many important implications for caregivers, as this widely utilized resource will likely continue to support family caregivers in the twenty-first century.
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