Novel Data Capture Method for the Identification of Potential Flare Events in Chronic Diseases Via Patient Self-Tracking: Insights from Use Case in Sickle Cell Disease (SCD)
Author(s)
Luo N1, Anwar H1, Healey A1, Kean J2, Zhang C3
1Folia Health, Boston, MA, USA, 2University of Utah, Salt Lake City, UT, USA, 3Folia Health, Kansas City, MO, USA
Presentation Documents
OBJECTIVES: To describe and test the feasibility of a new method for identification and characterization of flare events.
METHODS: Within the Folia home-reported outcomes platform for self-tracking of chronic diseases, a flare tracking system was developed on the basis of 5 years of participant and researcher feedback. The flare tracking system identifies potential events by flagging changes in symptom burden as they are reported, resulting in a prompt to enter additional information. The flag-enabled symptom names, flag thresholds, and follow-up questions are developed via an iterative development process with patients and experts. This method was implemented in Sickle Cell Disease for identification of potential pain crises, with three flag-enabled symptoms: “chronic pain”, “acute pain”, and “pain crisis”. The thresholds were set for change in severity of 2+ points (out of 10) over patient-identified baseline severity for each symptom, or any tracking of these symptoms utilizing the “lightning bolt” feature on the app. When the threshold was met, users received a prompt to answer 5 additional questions about the potential event.
RESULTS: Data in this analysis was collected from 73 participants living with SCD in September - November 2022. During this time, the cohort recorded 20,317 symptom tracks. Of these, there were 2954 tracks for chronic pain, 250 for pain crisis, and 95 for acute pain. A total of 241 (7.3%) of these tracks were flagged as potential flare events and triggered follow-up questions. On average, there were 80 unique potential flare events per month. When users were asked when the potential event happened, 85% indicated they were tracking in real-time.
CONCLUSIONS: This novel framework promotes patient self-identified, real-time or short-recall flare tracking, which expands upon current data capture methods used for pain crisis. Further studies should be conducted to increase sensitivity of flare tracking and eventual early intervention for potential flare events in chronic disease.
Conference/Value in Health Info
Value in Health, Volume 26, Issue 6, S2 (June 2023)
Code
MT38
Topic
Medical Technologies, Methodological & Statistical Research, Patient-Centered Research
Topic Subcategory
Artificial Intelligence, Machine Learning, Predictive Analytics, Instrument Development, Validation, & Translation, PRO & Related Methods
Disease
Rare & Orphan Diseases
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