As you monitor and evaluate your health equity strategy, a continual improvement approach with the right tools will help you adapt and accelerate progress and impact.
The Disparities Impact Statement worksheet implies that evaluating progress is simply a matter of selecting success metrics, tracking these metrics, and reporting them to CMS.
But what happens if you’re not making progress, or your progress isn’t fast enough?
While your health equity goals remain constant, your health equity strategy may have to adapt as your tools and interventions play out in real-world conditions.
Maximizing your success will require constantly refining your health equity strategy as you identify which activities are driving the most value, how they should be performed for maximum impact, and which intermediate measures show you’re on the right path to meeting your overall goals.
In order to adapt as you see real-world results and create new workflows and metrics to improve them, you’ll need systems that can adapt as quickly as you can plan.
Succeeding with complex interventions in a real-world healthcare environment requires purpose-built data science infrastructure that’s adaptable and flexible. Your systems should enable you to ingest new data types and quickly iterate on testing new approaches and evaluating their impact.
To support an adaptive health equity strategy, the right data science infrastructure will:
Many tools will allow you to monitor and evaluate your initial strategy. But in a complex, real-world healthcare environment, you’ll need the right tools if you want to adapt and accelerate your progress and impact.
