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

1
Department of Internal Medicine, Tairunnessa Memorial Medical College, Gazipur 1704, Bangladesh
2
Division of Hematology/Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
3
Department of Internal Medicine, Kamineni Institute of Medical Sciences, Narketpally 508254, India
4
Department of Neurology, Rajagiri Hospital, Chunanangamvely, Aluva 683112, India
5
Department of Internal Medicine, Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation, Chinaoutapalli 521101, India
6
Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine 0000, Trinidad and Tobago
*
Author to whom correspondence should be addressed.
Healthcare 2018, 6(3), 78; https://doi.org/10.3390/healthcare6030078
Received: 31 May 2018 / Revised: 3 July 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
(This article belongs to the Special Issue Precision Public Health and Genomic Medicine)
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Abstract

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. View Full-Text
Keywords: overdiagnosis; overtreatment; CBBLE (case-based blended learning ecosystem); case studies; precision medicine; omics driven; low resource setting; high resource setting Healthcare overdiagnosis; overtreatment; CBBLE (case-based blended learning ecosystem); case studies; precision medicine; omics driven; low resource setting; high resource setting Healthcare
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Podder, V.; Dhakal, B.; Shaik, G.U.S.; Sundar, K.; Sivapuram, M.S.; Chattu, V.K.; Biswas, R. Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment. Healthcare 2018, 6, 78.

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