|Shaping Behavior Analysis Through Technology: The Road to Optimal Outcomes|
|Saturday, May 28, 2022|
|11:00 AM–11:50 AM |
|Meeting Level 1; Room 104A|
|Area: CBM/PCH; Domain: Service Delivery|
|Chair: Ian M. Santus (Springtide Child Development)|
|Discussant: Nathan Allen Albright (The Cedar Group)|
|CE Instructor: Nathan Allen Albright, M.S.|
The field of behavior analysis has grown exponentially in the last decade. With growth, comes growing pains. There is a systematic inconsistency in our field’s applied therapeutic practice. While there are published ethical codes of conduct from the Behavior Analysis Certification Board (BACB) and practice care guidelines outline by the Council of Autism Service Providers (CASP), this is a limited set of parameters, which leaves a practicing behavior analyst to rely on their coursework and individual supervised experience for decision making. Given the vast number of different graduate programs and options for supervision, certified behavior analysts have varying degrees of experience and areas of competence. The response to this disconnect has been to lean on those more experienced, which has resulted in a strain of resources. This is not a viable or practical long-term solution to this pervasive problem. Clinical decision support systems are an interactive algorithmic decision making technology that offer clinicians the ability to identify variables that affect various clinical decisions, the resources to guide the decision making process, and action plans for best outcomes - regardless of the clinician’s experience or background.
|Instruction Level: Intermediate|
|Keyword(s): applied-behavior-analysis, decision-modeling, technology|
|Target Audience: |
Participants should possess a basic understanding of decision making processes, as well as an understanding of where and how to access current research articles and systems. In addition, participants should be able to explain basic processes (ie, intake, assessment) of the client life cycle to understand where clinical decision modeling fits within that practice.
|Learning Objectives: At the conclusion of the presentation, participants will be able to: (1) Describe and define decision making; (2) Describe and define Clinical Decision Support Systems: (3) Begin to develop or enhance current clinical practices using the general Clinical Decision Support Systems process.|