Improving Diagnostic Efficiency in Children Aged 12-39 Months Referred for Autism Spectrum Disorder (ASD)
Bibliographic record
Abstract
Abstract BACKGROUND: Early diagnosis and intervention for ASD is important. The increasing prevalence of ASD in Canada is challenging teams performing ASD diagnostic evaluations to keep pace with demand. In October 2013, the ASD clinic at our Pediatric Tertiary Care Centre (PTCC) faced a waitlist of more than twelve months for children under 39 months of age; it became necessary to engage in a quality improvement with the aim of looking for efficiencies with a focus of reducing our waitlist. OBJECTIVES: To present a) two years of experience (Jan. 2014-Dec. 2015) using our diagnostic assessment model for evaluation of children aged 12-39 months referred for ASD evaluation to PTCC, and b) psychometric performance of RITA-T (Rapid Interactive Test for Autism in Toddlers-developed by Choueiri/Wagner – Boston). DESIGN/METHODS: This quality improvement project incorporated evidence-based practice with process improvement methodology. Our team utilized a Plan-Do-Study-Act (PDSA) approach in the development of a ‘new’ ASD standardized diagnostic process. Our new model included: a) an initial mandatory parent education session followed one week later by b) a child visit using the face-to-face ‘level 2 screening tool’ (RITA-T) + completion of M-CHAT (questionnaire) followed in 7-10 days by c) an ASD diagnostic evaluation appointment, and finally 5-7 days after evaluation d) an “After ASD Diagnosis” parent group session. RESULTS: We assessed a total of 173 patients (81% male, mean age 30.74±5.53 mo., interval 15.4-39.0 mo.). The diagnostic process was completed within a max 30-day cycle (previously a period >4 months) and required less hours/child (12 vs. 20 hours – overall 40% net gain or 1384 hours). Waitlist was reduced to <1 month (compared to >12 mo. in Oct. 2013). A total of 143 children (82.7% of total cohort; 116 male/27 female) were diagnosed with ASD. The discriminative properties of RITA-T were calculated: Sensitivity: 0.99; Specificity 0.53; Positive Predictive Value 0.91; Negative Predictive Value: 0.94. CONCLUSION: By following a combination of quality improvement methodology with evidence-based practice, we successfully reduced wait and cycle times at our PTCC for children aged 12-39 months referred for ASD diagnostic assessment. RITA-T showed very good discriminative properties and was instrumental in the overall process improvement. This sustainable diagnostic approach promoted practice innovation. Consequently, patients are now able to access critical community supports and resources in a timely manner.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".