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Abstract:
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A recent trend in medical research is to develop prediction models aiming to improve patient care and health outcomes. While statisticians and data scientists are well-trained in the methods and process of developing a prediction model, their role post-model-development is less clear. This poster covers the critical scientific reasoning step in the prediction pipeline after a model is developed. Working collaboratively with domain experts, statisticians and data scientists should critically evaluate models, carefully implement models into practice, and assess the model’s impact in real world settings. Constructs from implementation science are discussed in the context of prediction modeling. The poster focuses on clinical prediction models, but these ideas apply to other domains as well.
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