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Systematic Literature Review on machine learning

Asc Academics performed a systematic literature review (SLR) to identify all relevant articles on machine learning use in health economics and/or market access within the indicated disease context.

What we did

Project background

With a Phase III trial almost ready and an oncology landscape being actively transformed by machine learning, our global HEOR client needed machine learning insights to identify hurdles and opportunities for their product. The project needed to recognize the impact of machine learning on both health economics and market access and put the client at the forefront of machine learning knowledge in the health economics and market access spaces, particularly concerning their disease area and product.

Challenges faced

Despite the potential benefits of implementing machine learning considering available data for their product and the disease area in general, the novelty and rapid development of machine learning have obscured its fit-for-purpose uses and best practices, hindering its appropriate use in the health economics and market access industries.

Our solution

We performed a systematic literature review (SLR) to identify all relevant articles on machine learning use in health economics and/or market access within the indicated disease context. This was followed by a quantitative analysis of the included articles. Additionally, we conducted a qualitative analysis of the findings of the SLR by reviewing selected articles based on the SLR outcomes, matching the characteristics and criteria with potential client use cases. After completing this process, we created a slide deck reporting the most valuable insights from the SLR, focusing on the use of machine learning in health economics and market access related to the client’s disease area. This comprehensive slide deck highlighted general trends and provided actionable use cases and recommendations for the use of machine learning.

Our impact

Our comprehensive slide deck streamlined the client’s work by providing them with instructions on understanding the application and extent of machine learning, informing decision-making around its implementation, formulating the best use of this technology, and providing them with actionable use cases and recommendations.

Meet the experts

Sebastiaan Fuhler, MSc

Sebastiaan Fuhler, MSc

Services used

Evidence synthesis
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