An Individualized Approach to Remediating Skill Decay: Framework and Applications

Physicians predominantly use self-monitoring to assess and maintain skill proficiency, and to determine when refresher training is required. However, strikingly low correlations exist between physician self-assessments and observer-expert ratings. In addition, in many military and civilian positions, training and education schedules are often standardized and rigid, potentially leading to wasted resources on training that is not needed for those that remain proficient at needed skills. In order to optimize training, there is a critical need for adaptive learning systems that can objectively measure, and preemptively or timely refresh knowledge and support skill maintenance. This paper outlines challenges associated with objectively quantifying skill decay within the medical domain. Requirements for a skill decay framework are summarized based on identified challenges, and a preliminary Skill- DETECT (Degradation Evaluation Toolkit for Eliminating Competency-loss Trends) framework is presented. This Skill-DETECT framework uses objective data to tailor an education and training program to a user’s specific needs. The current application of the Skill-DETECT framework is developed within a medical environment, and utilizes electronic medical records generated by a physician, as well as real-time cognitive assessment data to suggest recommendations on individualized, optimized retraining regimens to reduce the likelihood of skill decay.