Understanding the Current State of Applied Behavior Analysis

A summary of the webinar titled “Crack Open the Autism Black Box- Exposing unjustified inconsistencies in ABA therapy and what health plans can do about it” presented by RethinkFutures, October 2023

In a recent live webinar, Board Certified Behavior Analysts and Data Scientists from RethinkFirst and the RethinkFutures payor team delved into the complex landscape of Applied Behavior Analysis (ABA) and its connection with utilization review. The field of ABA faces challenges which, in turn, affect payors’ attempts to manage autism treatment, member progress, and outcomes.

Challenges in ABA Treatment

The challenges in ABA treatment were outlined, highlighting the absence of clear guidelines for prescribing treatment dosage (i.e., hours per week). Surprisingly, these critical factors aren’t taught in detail in most graduate programs and there is no published research literature to show how to translate a patient’s clinical presentation into specific hours of ABA. The recent surge in newly certified behavior analysts, with about 50% certified in the last five years¹, adds complexity to the field. Many of these analysts gained their experience through telehealth during the pandemic, leading to a disconnect in traditional supervision and training practices.

The webinar discussed:

1. lack of consensus within the ABA field on performance standards
2. measurement of outcomes
3. treatment quality

This absence of agreed-upon benchmarks and evaluation metrics complicates utilization review for insurance providers, leaving them to estimate progress, quality, and effectiveness of ABA service being provided.

Utilization Review Challenges

Utilization reviewers face the complex task of evaluating treatment plans, considering numerous variables such as symptom severity, comorbidity, treatment goals, age, and treatment duration spanning dozens of pages within a treatment plan. The difficulty lies in objectively determining if the care aligns with medical necessity and, if so, how many hours of treatment are appropriate. Payors also need to consider variability in treatment quality across their provider network such as with lack of individualization in treatment plans, undertreatment and overtreatment, and unclear member progress. All of which may ultimately lead to waste of healthcare resources and increased overall healthcare costs.

Data as a Solution

Data from service delivery can be used to identify key areas in ABA treatment variability and utilization review. In the webinar, the Rethink Team showed how collecting and analyzing data at the right levels of aggregation, across sessions, treatment phases, and in turn rolled up to the individual provider or organizational level helps identify inconsistencies and lead to more effective interventions. Data in the aggregate can help build evidence for the patient that the provider is executing therapy in an effective manner that leads to validated progress. By leveraging data, stakeholders have an opportunity to identify optimal pathways to maximize patient outcomes.

Variability in ABA Hours

Rethink’s data science team conducted a study based on provider data on ABA treatment hours for 14,748 individual patients. The result: 40% of those patients were being either overtreated or undertreated. This means the progress and long-term outcomes of 5,900 children were impacted because of inaccurate hours of ABA therapy. Overtreatment led to an average increase in cost per patient per year of $68,978 and undertreatment led to a reduction in patient progress by up to 72% of the potential progress they could have made.

The 40% statistic is similar to what has been measured in other areas of medicine regarding inaccurate treatment levels and, of note, the data scientists did not find any association between hours of ABA received and the patient’s individual clinical profile (i.e., patient cluster). There wasn’t a simple and clear clinical and data-based rationale that explained why patients were receiving such large differences in numbers of hours of ABA.

AI-Driven Solutions

The speakers discussed AI-driven solutions to address treatment variability. They presented a patent pending, evidence-based AI engine that combines expert knowledge of the variables that influence patient outcomes and Rethink’s expansive dataset on over 270,000 individuals to determine exactly how those variables should influence recommended hours. This Clinical Decision Support engine identifies patient clusters, predicts patient progress, provides recommendations for hours of ABA therapy, and flags areas of concern at the patient and provider levels.

Rethink’s data consists of over 70 million hours of ABA sessions and over half a million data points on clinical progress and the database is continuously growing. With that data, the team has identified over 70 different variables around patient characteristics, social determinants of health, and related characteristics that allow RethinkFirst to precisely predict patient progress and recommend the hours of ABA likely to optimize progress for each individual patient based on their total clinical profile.

The graphs below show dose response curves from three example patients where the optimal amount of progress is predicted. These curves highlight that receiving additional hours beyond the recommendation will provide little to additional no progress for that specific patient and, importantly, how receiving hours below that optimal prediction will negatively affect patient progress.