Treatment of Infantile Hemangioma in Regional Hospitals With eHealth Support: Evaluation of Feasibility and Acceptance by Parents and Doctors
Bibliographic record
Abstract
BACKGROUND: Since beta blockers became the preferred treatment for infantile hemangiomas (IH), the number of patients eligible for treatment is increasing. Currently treatment of IH with beta blockers is mainly reserved for expert centers, where wait times are lengthening. This demonstrated the need for development of a more efficient and accessible way of providing care for children needing treatment for IH. An eHealth intervention, Hemangioma Treatment Plan (HTP), was developed to treat IH in regional hospitals with online support from an academic doctor. OBJECTIVE: Our goal was to evaluate the feasibility of the eHealth intervention by determining its use, acceptance, and usability. By evaluating the feasibility, usage can be predicted and points for improvement can be defined, thereby facilitating implementation of the intervention. METHODS: Parents of children with an IH, presenting between October 2012 and November 2013 at the tertiary expert Center for Congenital Vascular Anomalies Utrecht, requiring treatment with a beta blocker, were asked to participate in the digital HTP. Both parents and regional doctors were sent a study questionnaire. Acceptance and usability of the HTP were evaluated by using the modified Technology Acceptance Model. RESULTS: A total of 31 parents and 22 regional doctors participated in the eHealth intervention and received the questionnaire, and 25 parents and 15 doctors responded (response rates respectively 81% and 68%). A majority of the parents (96%, 24/25) and the regional doctors (87%, 13/15) considered the eHealth intervention useful in the care for IH. Most parents (76%, 19/25) and over half of the regional doctors (53%, 8/15) found the HTP easy to use. Technical problems using the HTP were reported by 28% (7/25) of the parents and 73% (11/15) of the doctors. The majority of parents (92%, 23/25) felt positive about usage of the HTP during treatment of their child. All regional doctors (100%, 15/15) felt positive about transition of treatment from the tertiary expert center to them, and 93% (14/15) felt positive about using the HTP. CONCLUSIONS: Our eHealth intervention shows good feasibility, especially among parents. Improvement with respect to technical problems, training of regional doctors, and achieving organizational support might be needed for successful implementation in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".