I love data analysis and graphing. I was trained separately on group statistics and single-case experimental design, but not trained on objective supplements for visual inspection of single-case designs. After much searching I finally found this novel approach (machine learning/artificial intelligence) that is being used in many other fields such as medicine (e.g., data-driven COVID-19 care at Johns Hopkins). This approach was developed by Dr. Marc Lanovaz by training computer models to “learn” to make visual inspection decisions. The conceptual appeal is the ability to take valuable, subjective human expertise, clinical experience and practice; objectify and quantify it; and transfer it to be stored and used easily via a trained computer model that has had experience and practice from thousands of data sets, saving the time of another person. The analysis can be easily and quickly performed by a practitioner without any statistical training or software via a simple free web app. This paper presents a real clinical case as an applied real-world example of how it can be used, and collaboration between a quantitative researcher and a clinician working in home settings. https://doi.org/10.1177/01454455211038208
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