Extracting Quality of Life Information of Patients Diagnosed With Breast Cancer From Health Care Online Forum Posts:...

Quality of life (QoL) questionnaires are used in many disease areas to measure the burden that a disease causes for patients, which help provide insights into disease impact, identify unmet medical needs, and inform patient-centered drug development and value assessment for treatments. The collection of data imposes both a significant burden on patients as well as effort on health care personnel, thus incurring high costs for the health care system. Given that patients share detailed information about their condition and treatment experiences on social media and patient forums, an important research question is to what extent information about QoL can be obtained from patients' online forum posts to potentially complement information obtained from questionnaires.

This study aimed to assess how much QoL information can be gained from the analysis of posts by patients in online health care communities and whether this information is rich enough to estimate individual patient's QoL based on their posts. We conducted this feasibility study in the context of breast cancer as it is the most prevalent cancer in the female population.

We recruited 134 female patients diagnosed with breast cancer on the Inspire patient online forum, who voluntarily participated in our feasibility study. They filled in the EORTC (European Organisation for Research and Treatment of Cancer) QLQ-C30 and QLQ-BR23 questionnaires consisting of 30 general questions and 23 additional breast cancer-specific questions and provided consent to analyze their posts and comments on the online forum (756 posts and 19,478 comments). Posts were coded manually to identify parts of the text providing answers to 1 of the aforementioned 53 questions.

The data annotation yielded a substantial agreement (mean Fleiss κ of 0.5, SD 0.28). Overall, we found answers in the coded data for 50 out of 53 EORTC QLQ-C30 and QLQ-BR23 questions. The information coded in the posts reliably predicted the answers given in the questionnaires (F1-score=0.7), with even better results when grouping similar questions (F1-score=0.8 for fine-grained and 0.9 for coarse-grained grouping). The 5 questions that were most frequently answered on the basis of the coded posts were "Did you feel ill or unwell?" (304 of 2683 annotated posts and comments), "Did you worry?" (105 posts and comments), "Have you had pain?" (104 posts and comments), "Did you feel tense?" (85 posts and comments), and "Were you limited in doing either your work or other daily activities?" (77 posts and comments).

Our feasibility study shows that there is valuable QoL-related information in posts of online patient communities, which can potentially serve as an innovative low-burden QoL monitoring approach. Future research should consider how these insights can be used to complement existing QoL instruments and whether the process of extracting QoL-related information can be automated.