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

Healthcare

卷:6

Madhava Sai Sivapuram¹  Gousia Ummae Salma Shaik²  Rakesh Biswas²  Vivek Podder³  Kaushik Sundar⁴  Vijay Kumar Chattu⁵  Binod Dhakal⁶ 

[1] Department of Internal Medicine, Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation, Chinaoutapalli 521101, India;

[2] Department of Internal Medicine, Kamineni Institute of Medical Sciences, Narketpally 508254, India;

[3] Department of Internal Medicine, Tairunnessa Memorial Medical College, Gazipur 1704, Bangladesh;

[4] Department of Neurology, Rajagiri Hospital, Chunanangamvely, Aluva 683112, India;

[5] Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine 0000, Trinidad and Tobago;

[6] Division of Hematology/Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;

关键词: overdiagnosis;    overtreatment;    CBBLE (case-based blended learning ecosystem);    case studies;    precision medicine;    omics driven;    low resource setting;    high resource setting Healthcare;   

DOI  :  10.3390/healthcare6030078

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown