The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management.

BACKGROUND: Body listening, described as the act of paying attention to the body's signals and cues, can be an important component of long-term health management. OBJECTIVE: The aim of this study was to introduce and evaluate the Body Listening Project, an innovative effort to engage the public in the creation of a public resource-to leverage collective wisdom in the health domain. This project involved a website where people could contribute their experiences of and dialogue with others concerning body listening and self-management. This article presents an analysis of the tags contributed, with a focus on the value of these tags for knowledge organization and incorporation into consumer-friendly health information retrieval systems. METHODS: First, we performed content analysis of the tags contributed, identifying a set of categories and refining the relational structure of the categories to develop a preliminary classification scheme, the Body Listening and Self-Management Taxonomy. Second, we compared the concepts in the Body Listening and Self-Management Taxonomy with concepts that were automatically identified from an extant health knowledge resource, the Unified Medical Language System (UMLS), to better characterize the information that participants contributed. Third, we employed visualization techniques to explore the concept space of the tags. A correlation matrix, based on the extent to which categories tended to be assigned to the same tags, was used to study the interrelatedness of the taxonomy categories. Then a network visualization was used to investigate structural relationships among the categories in the taxonomy. RESULTS: First, we proposed a taxonomy called the Body Listening and Self-Management Taxonomy, with four meta-level categories: (1) health management strategies, (2) concepts and states, (3) influencers, and (4) health-related information behavior. This taxonomy could inform future efforts to organize knowledge and content of this subject matter. Second, we compared the categories from this taxonomy with the UMLS concepts that were identified. Though the UMLS offers benefits such as speed and breadth of coverage, the Body Listening and Self-Management Taxonomy is more consumer-centric. Third, the correlation matrix and network visualization demonstrated that there are natural areas of ambiguity and semantic relatedness in the meanings of the concepts in the Body Listening and Self-Management Taxonomy. Use of these visualizations can be helpful in practice settings, to help library and information science practitioners understand and resolve potential challenges in classification; in research, to characterize the structure of the conceptual space of health management; and in the development of consumer-centric health information retrieval systems. CONCLUSIONS: A participatory platform can be employed to collect data concerning patient experiences of health management, which can in turn be used to develop new health knowledge resources or augment existing ones, as well as be incorporated into consumer-centric health information systems.

Diagnosis of Life-Threatening Alpha-Gal Food Allergy Appears to Be Patient Driven.

OBJECTIVE: Patients exhibiting life-threatening symptoms associated with the alpha-gal food allergy (delayed urticaria or anaphylaxis due to mammalian meat) are frequently undiagnosed, causing unnecessary emergency department (ED) and health care visits, and extensive pain and suffering. This study aimed to determine the path to diagnosis experienced by alpha-gal patients. METHODS: Semistructured interviews were conducted from March to June 2016 with a chronological systematic sample of approximately 10% of patients diagnosed with alpha-gal and treated by the University of North Carolina Allergy and Immunology Clinic (n = 28). Main outcome measures included average length of time between first symptoms' appearance and diagnosis, number and type of health care encounters en route to diagnosis, and typical symptom severity. RESULTS: Six interviewees (21%) were diagnosed within a year of experiencing symptoms, of the remaining 22, mean time to diagnosis was 7.1 years. In over 100 medical encounters (including 28 ED visits and 2 urgent care) the correct diagnosis or effective diagnosing referral occurred less than 10% of the time. Seventy-one percent (20/28) described their first symptoms as severe. More patients found the allergist specializing in this condition on their own (n = 12; 43%) than those who were formally diagnosed or received referrals (n = 10; 36%) through the health care system. CONCLUSIONS: The medical community is challenged to stay abreast of emerging and newly uncovered illnesses through traditional medical literature communication channels. Presently, patients more often discover a diagnosis of alpha-gal allergy by using information resources on their own than by presenting to the ED with anaphylaxis.