A new approach to capturing pain disclosure in daily life in-person and online.

This feasibility study employed a new approach to capturing pain disclosure in face-to-face and online interactions, using a newly developed tool. In Study 1, 13 rheumatoid arthritis and 52 breast cancer patients wore the Electronically Activated Recorder to acoustically sample participants' natural conversations. Study 2 obtained data from two publicly available online social networks: fibromyalgia (343,439 posts) and rheumatoid arthritis (12,430 posts). Pain disclosure, versus non-pain disclosure, posts had a greater number of replies, and greater engagement indexed by language style matching. These studies yielded novel, multimethod evidence of how pain disclosure unfolds in naturally occurring social contexts in everyday life.

Mental Health-Related Behaviors and Discussions Among Young Adults: Analysis and Classification.

BACKGROUND: There have been recurring reports of web-based harassment and abuse among adolescents and young adults through anonymous social networks. OBJECTIVE: This study aimed to explore discussions on the popular anonymous social network Yik Yak related to social and mental health messaging behaviors among college students, including cyberbullying, to provide insights into mental health behaviors on college campuses. METHODS: From April 6, 2016, to May 7, 2016, we collected anonymous conversations posted on Yik Yak at 19 universities in 4 different states and performed statistical analyses and text classification experiments on a subset of these messages. RESULTS: We found that prosocial messages were 5.23 times more prevalent than bullying messages. The frequency of cyberbullying messages was positively associated with messages seeking emotional help. We found significant geographic variation in the frequency of messages offering supportive vs bullying messages. Across campuses, bullying and political discussions were positively associated. We also achieved a balanced accuracy of over 0.75 for most messaging behaviors and topics with a support vector machine classifier. CONCLUSIONS: Our results show that messages containing data about students' mental health-related attitudes and behaviors are prevalent on anonymous social networks, suggesting that these data can be mined for real-time analysis. This information can be used in education and health care services to better engage with students, provide insight into conversations that lead to cyberbullying, and reach out to students who need support.

Classification of Health-Related Social Media Posts: Evaluation of Post Content-Classifier Models and Analysis of User Demographics.

BACKGROUND: The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. OBJECTIVE: The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. METHODS: We analyzed two different types of health-related social media: (1) health-related online forums-WebMD and DailyStrength-and (2) general online social networks-Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. RESULTS: We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users' WebMD posts were more likely to ask for medical advice than female users' posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. CONCLUSIONS: We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message.

Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms.

BACKGROUND: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment. OBJECTIVE: This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features. METHODS: We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences. RESULTS: We evaluated several state-of-the-art text classification algorithms as well as our dependency tree-based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect. CONCLUSIONS: We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria.

Correlating Ratings of Health Insurance Plans to Their Providers' Attributes.

BACKGROUND: There is a push towards quality measures in health care. As a consequence, the National Committee for Quality Assurance (NCQA) has been publishing insurance plan quality measures. OBJECTIVE: The objective of this study was to examine the relationship between insurance plan quality measures and the participating providers (doctors). METHODS: We collected and analyzed provider and insurance plan data from several online sources, including provider directories, provider referrals and awards, patient reviewing sites, and hospital rankings. The relationships between the provider attributes and the insurance plan quality measures were examined. RESULTS: Our analysis yielded several findings: (1) there is a moderate Pearson correlation (r=.376) between consumer satisfaction insurance plan scores and review ratings of the member providers, (2) referral frequency and provider awards are negligibly correlated to consumer satisfaction plan scores (correlations of r=.031 and r=.183, respectively), (3) there is weak positive correlation (r=.266) between the cost charged for the same procedures and consumer satisfaction plan scores, and (4) there is no significant correlation between member specialists' review ratings and specialty-specific insurance plan treatment scores for most specialties, except a surprising weak negative correlation for diabetes treatment (r=-.259). CONCLUSIONS: Our findings may be used by consumers to make informed choices about their insurance plans or by insurances to understand the relationship between patients' satisfaction and their network of providers.

Provider attributes correlation analysis to their referral frequency and awards.

BACKGROUND: There has been a recent growth in health provider search portals, where patients specify filters-such as specialty or insurance-and providers are ranked by patient ratings or other attributes. Previous work has identified attributes associated with a provider's quality through user surveys. Other work supports that intuitive quality-indicating attributes are associated with a provider's quality. METHODS: We adopt a data-driven approach to study how quality indicators of providers are associated with a rich set of attributes including medical school, graduation year, procedures, fellowships, patient reviews, location, and technology usage. In this work, we only consider providers as individuals (e.g., general practitioners) and not organizations (e.g., hospitals). As quality indicators, we consider the referral frequency of a provider and a peer-nominated quality designation. We combined data from the Centers for Medicare and Medicaid Services (CMS) and several provider rating web sites to perform our analysis. RESULTS: Our data-driven analysis identified several attributes that correlate with and discriminate against referral volume and peer-nominated awards. In particular, our results consistently demonstrate that these attributes vary by locality and that the frequency of an attribute is more important than its value (e.g., the number of patient reviews or hospital affiliations are more important than the average review rating or the ranking of the hospital affiliations, respectively). We demonstrate that it is possible to build accurate classifiers for referral frequency and quality designation, with accuracies over 85 %. CONCLUSIONS: Our findings show that a one-size-fits-all approach to ranking providers is inadequate and that provider search portals should calibrate their ranking function based on location and specialty. Further, traditional filters of provider search portals should be reconsidered, and patients should be aware of existing pitfalls with these filters and educated on local factors that affect quality. These findings enable provider search portals to empower patients and to "load balance" patients between younger and older providers.