Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

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11th International Conference; Dublin, Ireland; 2022

Event Details


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Symposium #23
Chill for a SEQ: A Discussion on Service Efficiency and Quality
Friday, September 2, 2022
10:30 AM–12:20 PM
Meeting Level 2; Ecocem Room
Area: AUT; Domain: Basic Research
Chair: Sara Gershfeld Litvak (Behavioral Health Center of Excellence)
Discussant: Ellie Kazemi (California State University, Northridge)
Abstract:

What patients, their parents/guardians, insurance providers, and the community at large have in common with direct care and supervisory-level staff and ABA service organizations is that each stakeholder cares about quality services. To assure all stakeholders that quality services are being provided, a neutral entity must set standards to determine what is considered quality. Such standards are typically based on the scientific literature, and where research may fall short, subject matter experts provide guidance based on best practice. Then, to determine if services meet these standards, an objective entity conducts thorough evaluations using reliable assessment methods. In this symposium, we will share how Behavioral Health Center of Excellence (BHCOE) has developed a quality assurance system with a focus on our accumulated data collected over the past six years. The first presenter will discuss best practices in quality assurance systems for the field of ABA and how organizations perform when they are evaluated on BHCOE’s standards. The second presenter will discuss factors that can predict changes in VB-MAPP and Vineland scores for individuals with autism spectrum disorder. The third and fourth presenters will share the results of evaluations with patients’ guardians and employees. All presenters will discuss the implications of their findings and future steps.

Instruction Level: Intermediate
 
Exploring Indicators of Healthy Business Practices and High-Quality Applied Behavior Analysis Services
Nichole Williams (The Behavioral Health Center of Excellence ), David J. Cox (Behavioral Health Center of Excellence; Endicott College)
Abstract: Essential components of the quality assurance process include collecting, analyzing, and reporting data. One way to evaluate quality assurance processes is establishing, monitoring, and evaluating key performance indicators developed around clinical and administrative outcome data. Over the past six years, the Behavioral Health Center of Excellence has evaluated over 500 ABA service providers serving cumulatively 42,000 patients and employing approximately 25,000 staff. In this presentation, we will provide information regarding how these organizations perform when we evaluate them on BHCOE’s standards, which are indices of their clinical and administrative outcomes. We will describe best practices in quality assurance systems for the field of ABA. For example, we share how organizations are doing with competency-based training around diversity, equity, and inclusion, and employees’ satisfaction with how diverse their organizations’ employees are. We review data surrounding supervisor caseload, percent utilization of direct therapy, percentage of supervision hours utilized, and percent utilization of parent/guardian training. Overall, attendees will learn various methods to measure and monitor quality-related KPI’s, ways to make comparisons using industry-average benchmarks on quality-related metrics, and how to become involved in future areas of research and collaboration.
 
Organizational Outcome Data: What’s Ya bench…mark?
SCOTT PAGE (Behavioral Health Center of Excellence), Ellie Kazemi (California State University, Northridge), David J. Cox (Behavioral Health Center of Excellence; Endicott College)
Abstract: A limited, but increasing, number of organizations have begun to collect and monitor data on patient outcomes using skill-based and adaptive assessments. These data allow organizations to track how well the services they provide translate to patient improvement. Last year, we provided descriptions about the number of organizations that collect outcome data, monitor patient progress, and how they identify areas of strengths and deficits based on patient outcomes. In this presentation, we use advanced analytic techniques to identify the factors that allow us to predict changes in VBMAPP and Vineland scores. To do this, we used data from 700+ patients and 40+ ABA organizations on factors such as patient demographics, ABA utilization percentages (e.g., direct, parent training), staff credentials, turnover rates, and characteristics of service delivery (e.g., home versus center-based settings). After reviewing the results of this analysis, we discuss how organizations can use patient outcomes to benchmark themselves against the industry, target areas of weakness and identify the organizational characteristics wherein improvement would likely have the greatest impact on patient outcomes. Finally, we discuss how transparency around patient outcomes has led some organizations to report growth in the number of patients seeking their services.
 
Understanding Guardian Satisfaction with ABA Treatment
ELLIE KAZEMI (California State University, Northridge), Paul Friddle (Behavioral Health Centers of Excellence), David J. Cox (Behavioral Health Center of Excellence; Endicott College)
Abstract: Measuring patients’ and their guardians’ satisfaction with services are critical to ensure that interventions based on behavior analysis are socially valid. However, we currently do not have a thorough understanding of the variables that have the greatest impact on patient satisfaction within behavior analysis service delivery settings. The purpose of this study was to determine the most likely factors that influence the satisfaction of patients and their guardians. To help ABA organizations focus their limited time and attention on only the most important variables that predict guardian satisfaction, we analyzed data collected during a multi-modal quality assurance evaluation of over 500 ABA service organizations. This dataset contained several parent/guardian satisfaction measures as well as several objectively defined metrics of service delivery. In this presentation, we provide general quantitative descriptions of these variables and also share how well we can predict guardian Net Promoter Score when we analyze the parent/guardian satisfaction relative to various stratifications of our data such as staff’s interpersonal skills, specific components of service delivery, and severity of patient impact. These results will help organizations determine how to identify areas of need for their organization that will maintain high levels of guardian satisfaction.
 

Predicting Employee Satisfaction from Quality Assurance Systems

Melissa Cottengim (BHCOE)
Abstract:

Employee satisfaction directly impacts organizational success. With the Great Resignation of 2021, many ABA service providers are prioritizing initiatives that directly address employee satisfaction and retention efforts. The purpose of this symposium is to demonstrate how those quality assurance systems might allow you to predict and improve organizational key performance indicators related to employee satisfaction and retention. Specifically, we discuss the results of analyses using a data set from over 500 organizations that: establish industry-average benchmarks for technician and supervisor-level employee satisfaction and turnover; and how prioritizing diversity, equity, & inclusion efforts, as well as retention, hiring, and recruitment efforts influence levels of employee satisfaction. We also discuss the results of analyses that evaluate the predictive relationships among employee satisfaction and turnover, patient satisfaction, and employee compensation to provide organizations with insight on what to prioritize for employee satisfaction initiatives. Finally, we close by discussing the limitations to our dataset as well as future directions for research.

 

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