Development and Preliminary Validation of the Patient Outcome Planning Calculator (POP-C)
Introduction
Applied Behavior Analysis (ABA) is a widely used therapy for individuals with Autism Spectrum Disorder (ASD). One of the most significant challenges in ABA is determining the appropriate treatment dosage for each patient. Currently, dosage recommendations rely heavily on clinical judgment, which can vary among practitioners. Furthermore, insurance policies differ, often influencing approved therapy hours.
To address this issue, researchers have developed the Patient Outcome Planning Calculator (POP-C), a standardized tool designed to assist Board Certified Behavior Analysts (BCBAs) in determining treatment dosages. In this blog post, we will explore the POP-C, how it was developed, and its potential impact on clinical practice.
Current Challenges in ABA Treatment Dosage Determination
ABA therapy plays a crucial role in improving communication, social skills, and daily functioning for individuals with ASD. However, deciding the appropriate number of therapy hours can be complex. Some common challenges include:
- Lack of Standardization: Different practitioners may recommend varying dosages based on their own experience and interpretation.
- Risk of Under- or Over-Prescribing: Without a structured framework, some individuals may receive too little or too much therapy.
- Insurance Variability: Different insurance companies have varying guidelines on what they consider “medically necessary.”
- Patient Complexity: Individuals with ASD have diverse needs, including differences in social skills, functional abilities, and comorbid conditions.
Given these challenges, a well-defined, research-backed tool like the POP-C is necessary to guide decision-making.
What is the POP-C?
The Patient Outcome Planning Calculator (POP-C) is a structured tool used to determine medically necessary treatment dosage based on key patient factors. It was developed as a standardized decision-making aid for BCBAs and aims to improve consistency in determining appropriate therapy hours.
Key Features of the POP-C
- Provides an objective framework for assessing treatment needs.
- Balances clinical judgment with research-based recommendations.
- Categorizes treatment intensity into three levels:
- Low Intensity (Up to 20 hours/week): Recommended for individuals who require only moderate support.
- Medium Intensity (20–30 hours/week): Suitable for those needing a structured program in home or center-based settings.
- High Intensity (30–40 hours/week): Designed for individuals requiring comprehensive, one-on-one ABA therapy.
By synthesizing multiple patient characteristics, the POP-C helps practitioners make informed recommendations based on individual needs.
Methodology Behind the POP-C
The POP-C was developed by Lauryn M. Toby, Kristin M. Hustyi, Breanne K. Hartley, Molly L. Dubuque, Erica E. Outlaw, and Jesse J. Logue (Toby et al., 2024). The development process included expert consultation and a review of existing research on treatment outcomes.
Evaluation Criteria in the POP-C
The POP-C consists of two primary scoring areas:
- Treatment Dosage – Determines recommended therapy hours based on symptom severity.
- Treatment Setting – Identifies appropriate environments for therapy (e.g., home, clinic, school).
Practitioners rate various patient characteristics, including:
- Social Communication & Interaction: Evaluates an individual’s ability to engage with others.
- Adaptive & Functional Skills: Assesses daily living abilities.
- Challenging Behaviors: Considers safety concerns, aggression, or self-injury.
- Comorbid Conditions: Accounts for additional diagnoses such as intellectual disabilities.
- Sensory Sensitivities: Addresses how sensory processing issues impact learning.
By systematically scoring these factors, the POP-C provides treatment recommendations based on objective data rather than subjective judgment alone.
Key Findings from the Pilot Study
To determine the effectiveness of POP-C, researchers conducted a pilot study with 77 participants between the ages of 2 and 20 diagnosed with ASD. The study examined the reliability and validity of the tool by comparing its scoring system to established clinical assessments.
Key Results
- Interrater Reliability: 88% agreement between POP-C recommendations and independent BCBAs' clinical judgments.
- Convergent Validity: Strong correlations with widely used assessments, including:
- Vineland Adaptive Behavior Scales – Measures communication and daily living skills.
- Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) – Assesses language and learning ability.
- Essential for Living (EFL) – Evaluates functional communication.
- Discriminant Validity: No significant correlation based on patient age, ensuring the tool is not biased toward any specific age group.
These findings suggest that the POP-C is both reliable and valid in guiding treatment dosage decisions.
Implications for Clinical Practice
For BCBAs and other professionals working in the field of ABA, the POP-C provides a structured approach to treatment planning. Some of the key benefits include:
- Consistency Across Practitioners: Reduces variability in dosage recommendations.
- Improved Justification for Insurance Authorization: Uses data-driven scoring to support treatment necessity.
- Personalized Treatment Planning: Helps ensure that individuals receive therapy tailored to their needs.
By integrating the POP-C into clinical practice, BCBAs can enhance the quality of care while improving their ability to advocate for appropriate therapy hours.
Conclusion
The Patient Outcome Planning Calculator (POP-C) is a promising tool in the field of ABA therapy. By providing a standardized framework for determining treatment dosage, it helps BCBAs make more consistent, data-driven decisions while addressing the diverse needs of individuals with ASD.
If you are a BCBA or an ABA practitioner, consider integrating the POP-C into your practice to enhance treatment planning and improve patient outcomes.
To learn more about the research and validation behind this tool, read the full study by Toby et al. (2024) at https://doi.org/10.1007/s40617-023-00861-6.
