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. 2019 Dec 1;26(12):1645-1650.
doi: 10.1093/jamia/ocz145.

Prognostic models will be victims of their own success, unless…

Matthew C Lenert  1 , Michael E Matheny  2   3   4   5 , Colin G Walsh  2   4   6
Affiliations

Prognostic models will be victims of their own success, unless…

Matthew C Lenert et al. J Am Med Inform Assoc. .

Abstract

Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model's predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.

Keywords: learning health system; model updating; predictive modeling.

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Figures

Figure 1.
Figure 1.
Model life cycle.
Figure 2.
Figure 2.
Model interactions with outcome.
Figure 3.
Figure 3.
Consequences of model success.
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