Achieving Individualized Precision Prevention (IPP) through Scalable Infrastructure Employing the USPSTF Recommendations in Computable Form (Michigan)

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The U.S. Preventive Services Task Force (USPSTF) provides evidence-based recommendations to improve health. As the number and complexity of the recommendations increase, it becomes more challenging for primary care physicians to personalize and prioritize preventive services for their patients. Learning Health System (LHS) methods have the potential to systematically scale USPSTF’s recommendations. An LHS “learns” through a cyclic process including assembly and analysis of data relevant to an important problem, which leads to discovery of new knowledge from the data. The learning cycle is completed by direct application of that knowledge to change practice. Changed practice generates new data, driving the next iteration of the learning cycle, which leads to improvement over successive iterations.

Researchers at the University of Michigan will apply LHS methodology with the goal of advancing individualized precision prevention (IPP) for Grade A and B USPSTF recommendations, or those recommended for all patients. The Knowledge Grid (KGrid), a broadly applicable platform created by the research team, will be used to support the knowledge-to-practice aspect of the LHS. KGrid includes knowledge objects, a digital library to hold and manage learning objects, and an activator to deploy the objects as digital services. Enhancing this infrastructure, the Substitutable Medical Applications & Reusable Technologies (SMART) initiative supports an application ecosystem to extend the capabilities of electronic health records (EHRs). The project will use KGrid, with support from SMART, to build and test a new application capable of automatically personalizing and prioritizing preventive measures to achieve individualized precision prevention (IPP).

The specific aims of the project are as follows:

  • Using KGrid, design and develop computable knowledge objects for USPSTF A and B recommendations, along with an executive object capable of applying computable risk and benefit models, forming a core knowledge object collection for IPP (KOs-4-IPP). 
  • Design, develop, and test a shareable SMART IPP application that can be integrated with various EHRs and which draws on KOs-4-IPP services from KGrid to provide individualized precision prevention information. 
  • Conduct a study, with primary care providers as subjects, to assess the potential utility of IPP information. 

To assess the potential utility of IPP information in practice, the project team will create a set of fictional but realistic preventive service scenarios reflecting actual patient care experiences with adult patients. The KOs-4-IPP services from KGrid will be used to generate individualized lists of prioritized preventive services, and these will be compared to priority lists generated by providers based on their personal knowledge for the same scenarios. The hypothesis is that, in many cases, the IPP information will advise physicians to take valid preventive actions that their personal knowledge would not have motivated.

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