Better Outcomes for ABA Therapy by using ConnectCare AI™

Transform Your ABA Practice with ConnectCare AI™. Empower your team to deliver more effective, data‑driven therapy—faster, smarter, and with less paperwork.

Meet ConnectCare AI™, the AI assistant chatbot designed specifically for ABA providers. From IEP goal breakdowns to real‑time coaching and automated reporting, ConnectCare AI™ supercharges every step of your treatment process. ABA therapy features were built into ConnectCare AI™ by behavioral therapists, for behavioral therapists, who serve all ages of individuals on the autism spectrum.

More treatment options (a selectable matrix) — breaks therapists out of linear A→B→C thinking

What it does: ConnectCare AI replaces single-path playbooks with a clinician-controlled matrix of evidence-informed treatment options — letting ABA therapists choose, combine, and adapt the best strategies for each child quickly, confidently, and with clear evidence behind every choice.

Rather than forcing a linear “A→B→C” approach, ConnectCare AI presents a curated menu of viable, ranked treatment options tailored to such data as a child’s age, comorbidities, medication timeline, and local outcomes. Each option includes an easy-to-read summary, expected timeline, caregiver impact, and anonymized supporting evidence — so therapists spend less time guessing and more time delivering effective care.

Why this matters:

·         Faster clinically-sound decisions: get high-quality, ranked options in seconds instead of hours of chart review.

·         Personalized, not prescriptive: recommendations adapt to the child’s profile (age, meds, co-diagnoses) and the clinic’s realities.

·         Mix & match flexibility: combine primary, secondary, and booster strategies to fit families and settings.

·         Actionable outputs: one-click plans, session templates, and parent scripts reduce administrative burden.

·         Safety built in: medication and comorbidity signals are flagged so clinicians can review before changing care.

·         Real-world evidence: every recommendation links to anonymized examples and outcome statistics for clinician confidence.

·         Improves caregiver carry-over: ready-made home plans and brief coaching scripts increase family engagement.

·         Operational insight: optional integrations (e.g., Epic) can surface cost and staff-efficiency data alongside clinical choices.

Example: instead of following the next step in a linear playbook, an ABA therapist picks a primary discrete trial, a concurrent naturalistic strategy, and a caregiver coaching mini-protocol — the AI shows expected overlap and how other teams scheduled them.

How to measure: number of unique strategy combinations used per therapist per 6 weeks; % reduction in repeated unsuccessful linear attempts.

Better caregiver communication & home carry-over

What it does
Automatically converts clinician plans and progress-note insights into short, parent-friendly summaries, daily/weekly home practice prompts, and bite-sized coaching scripts; supports two-way secure messages so caregivers know what to practice, why, and how. The AI also adapts language level and suggested homework to caregiver capacity (time, preferred format).

Improved care-team coordination (therapists, BCBAs, prescribers, billing)

What it does
Creates a single, shareable “care snapshot” per goal that includes treatment plan, session-level progress, medication timeline + flagged signals, billing/cost indicators (optional Epic integration), and action items — viewable by authorized clinicians and, with consent, by prescribers or admin. It timestamps decisions and captures clinician feedback so team members can pick up where others left off.

Much faster, evidence-informed decisions at the point of care

What it does: instantly surfaces the most relevant treatment options and short summaries of outcomes from similar anonymized cases so therapists don’t have to dig through paper charts or one linear playbook.

Truly personalized plans that account for demographics, comorbidities, and medications

What it does: combines age/sex/zip (as proxy for local trends), comorbid diagnoses, and anonymized med histories to filter and rank options that worked for similar profiles — so interventions aren’t one-size-fits-all.

Early detection of safety signals and medication-related side effect patterns

What it does: analyzes text progress notes and medication timelines to surface patterns (e.g., behavioral regressions temporally associated with med changes) and flags items needing medical review. This helps clinicians coordinate with prescribers and adjust behavioral plans safely.

Streamlined documentation, training support, and knowledge transfer

What it does: auto-summarizes relevant prior progress notes into a concise “what worked/what didn’t” box for each goal, generates suggested IEP wording, and provides quick “how to implement” coaching tips for less experienced staff. This reduces repetitive admin and flattens the learning curve.

Continuous learning & local adaptation (human-in-the-loop feedback loop)

What it does: Clinicians give quick thumbs up/down + short reason on recommendations; feedback is logged and used to adjust local rankings and inform periodic model retraining.

Improved generalization & retention tracking

What it does: Schedules short probes across settings (clinic/home/classroom), collects probe results, compares trends across contexts and flags failures-to-generalize or retention drops.

Resource optimization & smarter scheduling

What it does: Estimates clinician hours, parent time, equipment and staff skill needs for each plan and suggests lower-resource schedules that meet target mastery probability.

Program-level analytics for QI and research

What it does: Provides de-identified dashboards and cohort builders (by age, zip, diagnosis, meds) with effect sizes and time-to-mastery visualizations for QI and research exports.