AI Therapy Note Automation for Behavior Analysts: The Case of Jason Omoto, BCBA

Abstract

This case study explores how Jason Omoto, a behavior analyst and Executive Director at Omoto Consulting, integrated CliniScripts’ AI therapy note system into his applied behavior analysis (ABA) practice. As a clinician who supervises 5 to 6 children per day across homes, schools, and clinical offices, Jason faced significant documentation challenges due to frequent travel and inconsistent internet connectivity. Through the adoption of CliniScripts’ mobile and offline-enabled AI documentation tools, he achieved consistent time savings, improved note accuracy, and enhanced workflow reliability. His experience demonstrates how AI-driven note automation can support efficiency and clinical quality in behavior analysis.

Introduction

Behavior analysts are responsible for comprehensive documentation that supports individualized treatment planning, progress monitoring, and insurance reporting. These requirements are essential for clinical accountability but contribute to increasing administrative workloads. For mobile practitioners who provide ABA therapy across multiple locations, the combination of documentation volume and network instability creates inefficiencies that can compromise both timeliness and accuracy.

Advances in artificial intelligence (AI) and natural language processing have introduced new opportunities to automate this process. AI therapy note systems such as CliniScripts can extract structured data from natural clinician–client dialogue, generating formatted progress notes while ensuring compliance with documentation standards. This study presents the experience of Jason Omoto, a behavior analyst with extensive clinical and administrative responsibilities, who implemented CliniScripts to streamline his ABA documentation workflow.

Background

The literature on clinical documentation emphasizes that behavior analysts spend approximately one third of their workday on administrative tasks that are not directly billable. These duties include writing treatment summaries, categorizing goals, and generating insurance-compliant progress reports. Research on AI applications in behavioral health suggests that automation can reduce note-taking time by 40 to 70 percent, while maintaining clinical validity. CliniScripts, an AI-driven documentation platform, has been developed to address these challenges by combining real-time transcription, offline recording, and automatic goal categorization. This case situates Jason Omoto’s experience within this emerging field of applied digital transformation.

Methods

Participant and Setting
Jason Omoto, BCBA, MS, serves as Executive Director of Omoto Consulting, a behavioral health organization providing applied behavior analysis services to children in both educational and home-based settings. His role includes conducting assessments, supervising registered behavior technicians, and ensuring treatment fidelity across multiple clients each day.

Pre-Implementation Challenges
Before CliniScripts, Jason’s workflow required extensive manual note entry after each session, averaging 10 to 20 minutes per progress note. Internet limitations across client sites further delayed real-time documentation, forcing him to rely on handwritten or offline notes that required later transcription. These factors not only extended his administrative hours but also introduced inconsistencies in data formatting and goal tracking.

Intervention
CliniScripts was introduced as an AI-powered platform for automated documentation. The system was customized to fit Omoto Consulting’s ABA framework, including session recording, automatic note generation, and customizable templates for behavioral goals and progress measurement. The mobile and offline features allowed Jason to maintain full functionality during home and school visits without dependency on continuous Wi-Fi connectivity.

Results

Quantitative findings revealed a significant improvement in efficiency and consistency.

Quantitative Analysis

Statistical review of Jason’s time logs showed a reduction of approximately 9 minutes per session in documentation time. Across five to six sessions per day, this translated into a daily savings of 45 to 60 minutes. Data consistency improved through the automated categorization of client goals, reducing the frequency of human input errors. Standard deviation in completion times narrowed from 5.3 to 1.7 minutes, reflecting a more predictable and standardized documentation pattern.

User Experience and Qualitative Feedback

The user feedback highlighted both practical and cognitive benefits. Jason reported that CliniScripts simplified his documentation workflow and reduced the mental effort associated with repetitive data entry.

“CliniScripts transformed how I write ABA progress notes. My custom template now runs automatically, categorizing goals and progress straight from natural conversation.”
— Jason Omoto, BCBA, MS, Executive Director, Omoto Consulting

He noted that the ability to record sessions offline was essential for consistency, as some client locations lacked stable Wi-Fi. This flexibility allowed him to maintain continuity in note creation and ensure compliance with documentation timelines.

Implications and Future Work

The results have several implications for the field of applied behavior analysis. First, AI documentation tools may enhance administrative efficiency across multiple service models, including home-based, school-based, and telehealth interventions. Second, the structured templates generated by CliniScripts could facilitate data-driven supervision and more transparent quality audits. Future studies should include larger samples of behavior analysts and longitudinal tracking of both clinician and client outcomes. Integration with electronic health records could further extend the benefits of automated documentation.