Co-Creation With TickiT : Designing and evaluating a clinical eHealth platform for youth
Bibliographic record
Abstract
Background: All youth are susceptible to mental health issues and engaging in risky behavior, and for youth with chronic health conditions, the consequences can be more significant than in their healthy peers. Standardized paper-based questionnaires are recommended by the American Academy of Pediatrics in community practice to screen for health risks. In hospitals, psychosocial screening is traditionally undertaken using the Home Education, Eating, Activities, Drugs, Depression, Sex, Safety (HEEADDSS) interview. However, time constraints and patient/provider discomfort reduce implementation. We report findings from an eHealth initiative undertaken to improve uptake of psychosocial screening among youth. Objective: Youth are sophisticated “technology natives.” Our objective was to leverage youth’s comfort with technology, creating a youth-friendly interactive mobile eHealth psychosocial screening tool, TickiT. Patients enter data into the mobile application prior to a clinician visit. Response data is recorded in a report, which generates alerts for clinicians, shifting the clinical focus from collecting information to focused management. Design goals included improving the patient experience, improving efficiency through electronic patient based data entry, and supporting the collection of aggregated data for research. Methods: This paper describes the iterative design and evaluation processes undertaken to develop TickiT including co-creation processes, and a pilot study utilizing mixed qualitative and quantitative methods. A collaborative industry/academic partnership engaged stakeholders (youth, health care providers, and administrators) in the co-creation development process. An independent descriptive study conducted in 2 Canadian pediatric teaching hospitals evaluated the feasibility of the platform in both inpatient and ambulatory clinical settings, evaluating both providers and patient responses to the platform. Results: The independent pilot feasibility study included 80 adolescents, 12-18 years, and 38 medical staff-residents, inpatient and outpatient pediatricians, and surgeons. Youth uptake was 99% (79/80), and survey completion 99% (78/79; 90 questions). Youth found it easy to understand (92%, 72/78), easy to use (92%, 72/78), and efficient (80%, 63/79 with completion rate < 10 minutes). Residents were most positive about the application and surgeons were least positive. All inpatient providers obtained new patient information. Conclusions: Co-creative design methodology with stakeholders was effective for informing design and development processes to leverage effective eHealth opportunities. Continuing stakeholder engagement has further fostered platform development. The platform has the potential to meet IHI Triple Aim goals. Clinical adaptation requires planning, training, and support for health care providers to adjust their practices.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".