New Titan Series paper in the Health Systems Journal

New Titan Series paper in the Health Systems Journal

Health Systems publishes high-quality articles about the idea that all aspects of health and healthcare delivery can be viewed from a systems perspective. The journal is moving towards greater visibility, impact, and reach, not only within the academic community but also engaging clinicians and healthcare managers in a meaningful way. To achieve this, there have been several exciting initiatives. One such initiative is the “Titan Series” .

The Titan Series will spotlight prominent academics in their fields of Information Systems and Operational Research. The Editors will invite distinguished scholars to write reflective papers, often taking the form of authoritative position papers. These papers will revisit and evaluate previously published work while proposing new directions and associated synergies leading to innovation.

The inaugural Titan Paper is available here.

Summary:

Title: Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

Authors: William R. Hersh, Robert E. Hoyt, Steven Chamberlin, Jessica S. Ancker, Aditi Gupta & Tara B. Borlawsky-Payne

Who is this relevant for?

This research discusses strategies for developing curricula that provide access to data science engaging a variety of stakeholders. 

Background

Teaching data science, artificial intelligence in general, or machine learning approaches typically involves a lot of technical skills, such as coding or non-standard statistical and mathematical analyses. Hence, for students with technical background, such courses are more accessible than for, e.g., healthcare professionals who would like to learn more.

While AI/ML or data science enjoys growing popularity, the technical details of such courses limit the audience. The authors of this work, therefore, pose the question how to lower the hurdles for healthcare professionals who are eager to use such models and methods in practice. For them, learning the necessary skills and tools would require different approaches and curricula. This work explores and discusses such alternative approaches to make AI and the like more accessible to a variety of stakeholders.