Development of a Digital Patient Assistant for the Management of Cyclic Vomiting Syndrome: Patient-Centric Design Study
Bibliographic record
Abstract
BACKGROUND: Cyclic vomiting syndrome (CVS) is an enigmatic and debilitating disorder of gut-brain interaction that is characterized by recurrent episodes of severe vomiting and nausea. It significantly impairs patients' quality of life and can lead to frequent medical visits and substantial health care costs. The diagnosis for CVS is often protracted and complex, primarily due to its exclusionary diagnosis nature and the lack of specific biomarkers. This typically leads to a considerable delay in accurate diagnosis, contributing to increased patient morbidity. Additionally, the absence of approved therapies for CVS worsens patient hardship and reflects the urgent need for innovative, patient-centric solutions to improve CVS management. OBJECTIVE: We aim to develop a digital patient assistant (DPA) for patients with CVS to address their unique needs, and iteratively enhance the technical features and user experience on the initial DPA versions. METHODS: The development of the DPA for CVS used a design thinking approach, prioritizing user needs. A literature review and Patient Advisory Board shaped the initial prototype, focusing on diagnostic support and symptom tracking. Iterative development, informed by the design thinking approach and feedback from patients with CVS and caregivers through interviews and smartphone testing, led to significant enhancements in user interaction and artificial intelligence integration. The final DPA's effectiveness was validated using the System Usability Scale and feedback questions, ensuring it met the specific needs of the CVS community. RESULTS: The DPA developed for CVS integrates an introductory bot, daily and weekly check-in bots, and a knowledge hub, all accessible via a patient dashboard. This multicomponent solution effectively addresses key unmet needs in CVS management: efficient symptom and impacts tracking, access to comprehensive disease information, and a digital health platform for disease management. Significant improvements, based on user feedback, include the implementation of artificial intelligence features like intent recognition and data syncing, enhancing the bot interaction and reducing the burden on patients. The inclusion of the knowledge hub provides educational resources, contributing to better disease understanding and management. The DPA achieved a System Usability Scale score of 80 out of 100, indicating high ease of use and relevance. Patient feedback highlighted the DPA's potential in disease management and suggested further applications, such as integration into health care provider recommendations for patients with suspected or confirmed CVS. This positive response underscores the DPA's role in enhancing patient engagement and disease management through a patient-centered digital solution. CONCLUSIONS: The development of this DPA for patients with CVS, via an iterative design thinking approach, offers a patient-centric solution for disease management. The DPA development framework may also serve to guide future patient digital support and research scenarios.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".