Healthcare organizations must achieve mastery of high-quality data and analytics to thrive within value-based care models. Today’s IT professionals are challenged to design systems that improve data exchange with industry stakeholders as well as acquire more complete and accurate patient information for quality measures reporting. Without a strategy in place that addresses each of these key areas, hospitals and health systems face significant barriers to achieving their overall population health or financial goals.
cohort rules management,
If you’ve followed the previous steps outlined in this blog series, you should have a good idea of how to pursue data normalization within your healthcare organization. But challenges will continue to arise once you get underway. One of the decisions a healthcare provider or payer will face in the course of a project is whether to use an existing industry standard or create a local terminology. As it turns out, the answer isn’t cut and dried, and will depend to a large degree on the use case involved.
Payer-based care and utilization management programs are essential for managing member health, closing care gaps, and managing risk. Research data indicates that capturing members’ vast healthcare histories and normalizing that information for consumption can enable a care management program that enhances the effectiveness of provider-based care initiatives.
So far we have outlined the steps from securing executive buy-in to establishing a governance process—you should be ready to start your first normalization project (I know I am!).
A project team that has gone through an impact analysis and prioritized its data normalization projects will probably have a short list of likely project candidates. The two critical factors to consider when selecting the initial project are size and impact. The project must be small enough to be manageable and likely to be completed in a reasonable time, but large enough to demonstrate a meaningful early win to key stakeholders. Resistance to changes (such as formal data governance processes) can be minimized if participants readily see how they will benefit.