December 15, 2017: Does Machine Learning Have a Place in a Learning Health System?


Michael Pencina, PhD
Professor of Biostatistics and Bioinformatics, Duke University
Director of Biostatistics
Duke Clinical Research Institute


Does Machine Learning Have a Place in a Learning Health System?


Machine Learning; Artificial Intelligence; AI; Learning Health Systems

Key Points

  • Machine learning has many different applications for generating evidence in meaningful ways in a learning health system (LHS).
  • Although other industries are using machine learning, the health care industry has been slow to adopt artificial intelligence (AI) methodologies.
  • The Forge Center was formed under the leadership of Dr. Robert Califf and uses team science—biostatisticians, engineers, computer scientists, informaticists, clinicians, and patients collaborate to develop machine learning solutions and prototypes to improve health.
  • In a learning health system, the process is to identify the problem, formulate steps to solve it, find the right data and perform analysis, test the proposed solution (by embedding randomized experiments in a LHS), and implement or modify the solution.
  • Machine learning is a small piece of a LHS, but an important one, and methods are characterized by the use of complex mathematical algorithms trained and optimized on large amounts of data.

Discussion Themes

Demonstrating enhanced value of machine learning over existing algorithms will be an important next step. An ongoing question is how do models get translated into clinical decision making? Machine learning is a tool to develop a model, but implementation of the findings will require team science.

Prediction models can be calibrated to work across health systems to an extent, but there are many unique features of individual health systems, so large health systems should use their own data to optimize the information and learning in a specific setting.  

There are key issues related to accurate ascertainment of data, especially with relation to completeness. For example, inpatient data collected during a hospital stay are likely to yield models that have value. If data rely on events that happen outside the system, it can be harder to get the complete picture.

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