The transition from pre-clinical to clinical rotations is often described as a “steep learning curve” without referring to any specific data or metrics. Learning curves are a visual representation of performance. Experience curves add in a component of deliberate practice and feedback that allows the community to declare a student competent. (Pusic 2012, 2015)
The pre-clinical apprenticeship course for Tufts is competency-based, tracking student achievement in basic history, physical exam, counseling, office procedures, etc. Many schools utilize similar competency-based approaches to education and assessment. Completion of these various checklists as an end-user can feel like busy-work, but a thoughtful and organized approach to data management can become a powerful curriculum evaluation tool.
We will describe how using a big data analytics approach can help transform your competency-based programs in four key ways.
1) We will describe a methodology for generating competency curves using our longitudinal apprenticeship course as an example.
2) A student dashboard provides context for individual students to keep pace with their peers in their developmental milestones.
3) Competency curves also allows faculty to identify laggards at risk for remediation, “falling off the growth chart” in a manner similar to a pediatric growth chart, in relevant ACGME competencies like communication and patient care.
4) Data analytics can provide future direction for faculty development and skill workshops in key areas.
Pusic MV, Kessler D, Szyld D, Kalet A, Pecaric M, Boutis K.Experience curves as an organizing framework for deliberate practice in emergency medicine learning. Acad Emerg Med. 2012 Dec;19(12):1476-80
Pusic MV, Boutis K, Hatala R, Cook DA. Learning curves in health professions education. Acad Med. 2015 Aug;90(8):1034-42.#BigData #ExperienceCurve #ConferenceonMedicalStudentEducation #Competency #2017 #CompetencyLearningCurve #evaluation #MedicalEducation #LearningAnalytics #LearningCurve