Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health AppsFindings from the 2017 Yearbook Section on Education and Consumer Health Informatics
11 September 2017 (online)
Objective: To summarize the state of the art during the year 2016 in the areas related to consumer health informatics and education with a special emphasis in secondary use of patient data.
Methods: We conducted a systematic review of articles published in 2016, using PubMed with a predefined set of queries. We identified over 320 potential articles for review. Papers were considered according to their relevance for the topic of the section. Using consensus, we selected the 15 most representative papers, which were submitted to external reviewers for full review and scoring. Based on the scoring and quality criteria, five papers were finally selected as best papers
Results: The five best papers can be grouped in two major areas: 1) methods and tools to identify and collect formal requirements for secondary use of data, and 2) innovative topics highlighting the interest of carrying on “secondary” studies on patient data, more specifically on the data self-expressed by patients through social media tools. Regarding the formal requirements about informed consent, the selected papers report a comparison of legal aspects in European countries to find a common and unified grammar around the concept of “data donation”. Regarding innovative approaches to value patient data, the selected papers report machine learning algorithms to extract knowledge from patient experience and satisfaction with health care delivery, drug and medication use, treatment compliance and barriers during cancer disease, or acceptation of public health actions such as vaccination.
Conclusions: Secondary use of patient data (apart from personal health care record data) can be expressed according to many ways. Requirements to allow this secondary use have to be harmonized between countries, and social media platforms can be efficiently used to explore and create knowledge on patient experience with health problems or activities. Machine learning algorithms can explore those massive amounts of data to support health care professionals, and institutions provide more accurate knowledge about use and usage, behaviour, sentiment, or satisfaction about health care delivery.
Education and Consumer Health Informatics
Daniulaityte R, Chen L, Lamy FR, Carlson RG, Thirunarayan K, Sheth A. “When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets. JMIR Public Health Surveill 2016 Oct 24;2(2):e162 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5099500/
Freedman RA, Viswanath K, Vaz-Luis I, Keating NL. Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment. Breast Cancer Res Treat 2016 Jul;158(2):395-405 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537590/
Hawkins JB, Brownstein JS, Tuli G, Runels T, Broecker K, Nsoesie EO, McIver DJ, Rozenblum R, Wright A, Bourgeois FT, Greaves F. Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf 2016 Jun;25(6):404-13 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878682/
Kondylakis H, Koumakis L, Hänold S, Nwankwo I, Forgó N, Marias K, Tsiknakis M, Graf N. Donor’s support tool: Enabling informed secondary use of patient’s biomaterial and personal data. Int J Med Inform 2017 Jan;97:282-92 https://linkinghub.elsevier.com/retrieve/pii/S1386-5056(16)30234-9
Massey PM, Leader A, Yom-Tov E, Budenz A, Fisher K, Klassen AC. Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter. J Med Internet Res 2016 Dec 5;18(12):e318 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5168526/