Teaching Data Science through an Interactive, Hands-On Workshop with Clinically Relevant Case Studies

Abstract

Background In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

Objectives Addressing the limited exposure of health care providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.

Methods The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.

Results Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.

Conclusion Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven health care landscape, offering a template for integrating data science education into health care informatics programs or continuing professional development.

Keywords

machine learning - professional training - data analysis - decision support algorithm - forecasting

Protection of Human and Animal Subjects

We received a determination from the Vanderbilt University Medical Center's Institutional Review Board that our evaluation approach did not qualify as research (IRB number: 241036).

Publication History

Received: 25 March 2024

Accepted: 29 August 2024

Accepted Manuscript online:
30 August 2024

Article published online:
11 December 2024

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