Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment

Introduction: Precision medicine aims to focus on meeting patient requirements accurately, optimizing patient outcomes, and reducing under-/overdiagnosis and therapy. We aim to offer a fresh perspective on accuracy driven “age-old precision medicine” and illustrate how newer case-based blended learning ecosystems (CBBLE) can strengthen the bridge between age-old precision approaches with modern technology and omics-driven approaches. Methodology: We present a series of cases and examine the role of precision medicine within a “case-based blended learning ecosystem” (CBBLE) as a practicable tool to reduce overdiagnosis and overtreatment. We illustrated the workflow of our CBBLE through case-based narratives from global students of CBBLE in high and low resource settings as is reflected in global health. Results: Four micro-narratives based on collective past experiences were generated to explain concepts of age-old patient-centered scientific accuracy and precision and four macro-narratives were collected from individual learners in our CBBLE. Insights gathered from a critical appraisal and thematic analysis of the narratives were discussed. Discussion and conclusion: Case-based narratives from the individual learners in our CBBLE amply illustrate their journeys beginning with “age-old precision thinking” in low-resource settings and progressing to “omics-driven” high-resource precision medicine setups to demonstrate how the approaches, used judiciously, might reduce the current pandemic of over-/underdiagnosis and over-/undertreatment.