Patient-driven learning and symptom monitoring using handheld technology: A new perspective on education and counseling in the multidisciplinary Pediatric Inflammatory Bowel Disease Team
Bibliographic record
Abstract
When faced with a life-changing event such as a diagnosis of inflammatory bowel disease (IBD), young patients and their families are frequently overwhelmed by the volume and complexity of the information given to them. A recent audit conducted within our Division has identified important deficits in the knowledge of patients and parents with regards to disease location and previous investigation results (Benchimol et al. IBD 2010). Reporting of IBDrelated symptoms and compliance with medication are particularly troublesome in the teenage years (Hommel et al. IBD 2009), thus impeding the delivery of adequate IBD care. Our aims were to design a novel way of empowering young patients and their families to come to terms with the diagnosis of IBD, to enable patient-driven learning by engaging children/teenagers and to allow the contemporaneous symptom monitoring and documentation of adherence to prescribed medication. We have developed an application, for use on a handheld device such as iPod/iPad or Android Smartphone, containing an IBD-video-academy, a dedicated IBD-educational game and a real time recording feature of disease activity and compliance with medication, which will be beta-tested during the Summer of 2011 and presented at the meeting. Until now, IBD-related information was most often delivered to young patients and their families at the time of diagnosis or during disease flares using printed material. Consequently, IBD-education was mostly directed at parents/guardians with children/teenagers often too unwell to make full use of the provided counseling, in spite of the increased time commitment by particularly IBD Nurse Specialists worldwide. This application has given our young patients and their families the opportunity to preview/review the information given during the face-to-face meeting with a member of our IBD-team. Thus, the time spent with the health professional can be more focused on answering questions. Within the same app, we have included a feature to monitor disease activity and treatment compliance in real time. This has allowed our young patients to take control of their symptom reporting, to generate a clinical summary-pdf prior to follow-up in the IBD clinic and to actively prepare for a transition to adult care. Our innovative approach to pediatric IBD-care has resulted in the development of a dedicated application for use on handheld devices. This app has enabled ongoing patient-driven learning and real time recording of disease activity and compliance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".