Challenges in Developing a Patient-Reported Symptom-Based Risk Stratification System for Suspected Head and Neck Cancer: Protocol for a Qualitative Case Study
Bibliographic record
Abstract
Background: The Symptom Input Clinical (SYNC) system is being developed to enhance the timely reporting of head and neck cancer symptoms and ensure that high-risk patients receive faster diagnoses. A key feature of the system is a digital questionnaire co-designed with patient representatives to accommodate varying levels of digital literacy. The system integrates a validated algorithm that assigns risk scores to categorize cases as low or high risk and a dashboard that supports clinicians by providing them with patient reports. However, the development process has encountered challenges that necessitate a systematic evaluation of the process, roles, and experiences of team members. Objective: This study aims to identify challenges faced during development, how these challenges were addressed, and the implications for future digital health innovations. Methods: A qualitative single-case study approach will be used following the Standards for Reporting Qualitative Research guidelines focusing on individuals involved in the SYNC system's development. Participants will be selected using a combination of purposive and snowball sampling to ensure diverse perspectives, using meeting minutes and recommendations from key stakeholders. A total of 8 to 12 participants will be interviewed, representing clinical, research, and IT roles. Data collection will involve semistructured interviews, which will be conducted through Microsoft Teams. The interviews are expected to last between 40 and 60 minutes each. These interviews will be audio recorded, transcribed, and analyzed using framework analysis in Dedoose. The actor-network theory will guide the analysis by mapping interactions between human and nonhuman actors, such as developers, clinicians, and technological tools, to understand how they influenced the project's outcomes. Participant confidentiality will be maintained through data encryption, deidentification, and secure storage. Results: We anticipate identifying key barriers and facilitators in the SYNC system's development, including technical, organizational, and collaboration-related challenges. The findings are expected to provide a detailed account of the challenges encountered, such as delays, security concerns, and coordination issues; an insight into how these challenges were mitigated; and lessons learned and recommendations for improving digital health technology development, including best practices for co-design, technical integration, and stakeholder engagement. Conclusions: This case study will provide valuable insights into the complexities of developing digital health technologies, particularly in collaborative, multistakeholder environments. Documenting the challenges encountered in the SYNC system's development will contribute to best practices in digital health innovation.
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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 | 0.064 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".