Only 12.7% to 82.9% of the U.S. population receives recommended prevention services, and more specifically, between 70.7% to 91.9% of U.S. children aged 19-35 months receive recommended immunizations (Centers for Disease Control and Prevention, CDC). The utilization of clinical decision support (CDS) can help to increase these rates. Meta-analyses have shown that CDS, as a component of electronic health records (EHRs), is effective in increasing preventive care services. The rules for a CDS involve the knowledge needed to decide a CDS?s behavior in clinical tasks. Continuous rule maintenance is necessary to keep a CDS updated, useful, and at its full potential. Outdated rules can lead to missing alerts for preventive services or even to a patient?s death due to outdated drug-drug interaction alerts. Currently, there are no publicly accessible, reusable, generic, and machine-interpretable CDS rules for immunization schedules. Historically, CDS has been utilized successfully in large academic institutions. In the United States, however, small practices provide healthcare services to a majority of the population, with the volume of physician office visits at about 7.4 times that of hospital visits. In view of rapidly increasing EHR adoption rates in the United States, CDS usage rates have reached 68.5% to 100% in office-based primary care settings, indicating that CDS currently plays an important role in small practices. To be able to regularly update CDS rules is critical to maintaining a CDS. CDS rule management and maintenance have been recognized as challenging in large institutions. Thus, we anticipate that CDS rule management and maintenance will be an obstacle for smaller primary care practices, especially those without in-house IT support. Ontology is the enabling technology of the Semantic Web. Ontology has the potential to improve the interoperability, reusability, and sharability of ontology-based CDS rules, which will reduce duplicate efforts by multiple stakeholders. We also propose to enable primary care providers, especially in settings without in- house IT support, to manage and maintain CDS rules independently. The output of the investigation will be beneficial to small primary care practices in the long term. Our efforts will contribute to more consistent preventive services, including improved immunization recommendation rates for the large population served by these practices. We propose to (1) build and validate an upper-level CDS ontology; (2) develop portable, reusable, and machine-executable CDS rules based on ontology for CDC-recommended immunization schedules; (3) develop implementation scripts for CDS rules; (4) implement CDS rules and evaluate their reuse, use, and maintenance in simulated primary care settings, and (5) revise ontology, CDS rules, and implementation scripts. The long-term goal is to achieve interoperable EHR across platforms seamlessly by utilizing individuals? immunization records. The experience gained from this proposed investigation will provide a critical foundation for our long-term goal and help to solve ?curly braces problem? in the reuse of CDS rules.
The proposed investigation will develop publicly accessible, reusable, upper-level clinical decision support (CDS) ontology and machine-interpretable CDS rules for CDC-recommended immunization schedules, which will save duplicate effort by multiple stakeholders of CDS rules and build a critical foundation for achieving interoperability for individuals? immunization records. Our investigation also aims to enable primary care providers, especially in small primary care practices, to manage and maintain CDS rules independently to keep CDS updated, useful, and working to its full potential and to benefit the large population served by such practices. This investigation has the potential to provide more consistent preventive services and improve CDC-recommended immunization rates in primary care settings.
