36 Feasibility Testing of Online Health Symptom Trackers for Children with Medical Complexity at Home
Bibliographic record
Abstract
Abstract Background Children with medical complexity (CMC) are a highly medicalized population of children due to the complexity of their clinical presentations, various diagnoses, and multiple care providers. Real-time health information can inform clinicians to make better recommendations and improve clinical outcomes. In other populations, such as children with Type 1 diabetes, online health symptom trackers (HST) were used to facilitate clinic visits and track symptoms longitudinally. To-date, HSTs have not yet been examined in a clinical setting with CMC and their families. Objectives The aims of our study were to create a standardized online tool, which supports the creation of online HSTs, and to assess their utility in clinical care from a parent and health care provider (HCP) perspective. Design/Methods Parents of CMC were invited to use a standardized online care platform called Connecting2gether for 6-months and create online HSTs that could be shared with their HCPs. Online HSTs could be added by parents from a prepopulated list with an open notes section, and the ‘Signs and Symptoms’ trackers could be customized by parents. A demographic survey was completed at baseline. At 6-months a tracker acceptability survey and in-depth semi-structured qualitative interviews were completed to assess the utility and usability of HSTs. HST usage data were also collected. Interviews were analyzed via thematic analysis whereby codes were generated to inform themes, and surveys were analyzed using descriptive methods. Results Thirty-six parents enrolled on the platform and 21 (57%) created at least one online HST during the study period. The most used HSTs were ‘Signs and Symptoms’, ‘Sleep’, and ‘How I Feel’. Majority of parents (86%) reported finding the trackers useful. Only 55% of HCPs viewed the HSTs and of those, 36% reporting using them in clinical care. Qualitative interviews revealed three themes: 1) HST Usability: HSTs were used in different settings such as in clinic and school, they were used to guide conversation as a visual over time, and they used in decision making; 2) Enhancement to HST Usage: suggestions included having more options of different symptoms; and 3) Challenges and Barriers to HST Usage: including personal preference and medical stability of the patient. Conclusion The ability of online HSTs to visually depict the change in symptoms over time in CMC was found to be a benefit from a parent and HCP perspective. HSTs were identified as a tool used in clinic to guide conversation and decision making, and as a visual to track symptoms longitudinally. However, HCPs need more guidance on how to use trackers. Future directions for online HSTs include integration into the electronic health record to increase accessibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".