AI-Assisted Triage

1. Hypothesis

AI-supported prioritisation reviewed by qualified staff will reduce delay while preserving safety better than autonomous triage.

2. Experiment design

Design: prospective early-intervention pathway study from identification through referral, service uptake and outcome follow-up, focused on AI-Assisted Triage; developmental-stage cohorts, active comparison, child and family acceptability, service or platform pathway testing, subgroup analysis and longitudinal follow-up Methods: prospective screening validation, service-pathway mapping, referral audit, implementation trials, time-to-support analysis and multidisciplinary case review; adapted specifically to AI-Assisted Triage; child-appropriate participatory methods; caregiver and practitioner input; age-stratified analysis; validated developmental measures; service-pathway testing; safeguarding review; delayed or longitudinal follow-up; implementation-fidelity assessment Independent variables: model recommendation; human review; urgency; data completeness; caseload Dependent variables: priority accuracy; wait time; missed risk; reviewer burden Confounders: age; developmental stage; disability; socioeconomic conditions; culture and language; family structure; prior exposure; service access; implementation fidelity Measures: triage concordance; adverse events; time-to-review; override quality; child agency; developmental appropriateness; family burden; safeguarding events; service continuity; subgroup equity; acceptability; recovery; sustained flourishing Success criteria: Developmentally and practically meaningful benefit; child and family acceptability; preserved child agency and family relationships; effective safeguarding and human escalation; no disproportionate subgroup harm; manageable burden; durable benefit. Failure conditions: No meaningful benefit; developmental, relational, privacy or safeguarding harm exceeds benefit; normal variation is pathologised; burden shifts to families; child agency is reduced; service handoff fails; benefits do not persist.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports AI-Assisted Triage as a testable research proposition. Problem basis: Current approaches to ai-assisted triage often optimise a narrow operational outcome while overlooking developmental stage, family relationships, child agency, service capacity or long-term effects. Directional expectation: If supported, the proposed approach should improve priority accuracy; wait time; missed risk; reviewer burden while preserving child agency, family trust, equity and access to human support. Proposed observations: triage concordance; adverse events; time-to-review; override quality; child agency; developmental appropriateness; family burden; safeguarding events; service continuity; subgroup equity; acceptability; recovery; sustained flourishing. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 36/100; Novelty 76/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. AI-Assisted Triage warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “AI-supported prioritisation reviewed by qualified staff will reduce delay while preserving safety better than autonomous triage.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Prevention science; developmental screening; stepped care; triage; integrated care; family-centred practice and implementation research. It also records: Australian Early Development Census — https://www.aedc.gov.au/; WHO Nurturing Care — https://www.who.int/; Harvard Center on the Developing Child — https://developingchild.harvard.edu/; AIHW — https://www.aihw.gov.au/. Evidence register status: “Seeded; authoritative child-rights and developmental source register refreshed; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Prevention science; developmental screening; stepped care; triage; integrated care; family-centred practice and implementation research. References: Australian Early Development Census — https://www.aedc.gov.au/; WHO Nurturing Care — https://www.who.int/; Harvard Center on the Developing Child — https://developingchild.harvard.edu/; AIHW — https://www.aihw.gov.au/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.