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In a health care system where all quality measures are digital and automated, the goal is a standardized set of data flows that feed those measures. This is possible in theory today, given developments in legislation, regulations, standards and technology, but is it feasible?
I do think it is feasible, assuming that all stakeholders do their part - driven by compelling motivators: operational streamlining and cost savings, taking advantage of incentives and avoiding penalties to name a few. Let’s explore how health plans can best approach the transition to a standardized data flow. There are two key steps toward accomplishing this:
1. Define the desired end-state. The definition should be detailed enough to paint a clear picture but flexible enough to accommodate changes (in network, entering/leaving markets and so on). Generically speaking, a plan should use all digital quality measures and take full advantage of automation and standardized data feeds and interfaces.
2. Create a roadmap. This is a multi-step, strategic effort. Once you know where you want to end up—and where you are (through an inventory of current capabilities, data sources, flows and such)—you can map the journey.
With that planning framework in place, remember to execute the transition in meaningful, well defined phases that align with the macro-context (e.g., measure evolution, interoperability rules and technology developments, availability of APIs, aggregator services, seasonality of measure operations). Track each phase of the transition on expected business metrics, including ROI (which should be expected, given that interoperability and dQM move us much closer to streamlined, standardized and automated data operations).
Keep in mind that many initiatives on the data-operations side will overlap with use cases other than quality measurement. This not only creates a need for broader coordination, it also provides an opportunity for more strategically aligned and streamlined clinical data initiatives for the entire payer organization. As we’ve discussed in a previous post, the benefit s of transitioning to a digital quality data ecosystem are strategic and compelling.
As we collectively mature toward a standardized, automated clinical data ecosystem, we have the opportunity to move our entire industry forward even faster by developing and sharing standardized best practices tools and solutions and getting software vendors to adopt them.
We also need to make sure we don’t lose sight of our goals. Details will change, but our destination will remain the same. If we continuously orient ourselves toward that destination, we have a good shot at making an all-digital quality ecosystem a reality.
- Is your organization developing and updating a “desired end-state” and roadmap?
- What information and tools do you want/need to execute this transition?
- Do you have a good understanding of the operational benefits?
- Do you have a good understanding of the penalties and incentives around the adoption of interoperability?
Michael Klotz, Healthcare IT Entrepreneur, MK Advisory Services
Michael is a Healthcare IT entrepreneur, consultant and expert in the flow of data between patients, providers and payers (the healthcare data ecosystem), healthcare interoperability, digital quality and digital prescriptions. He applies his understanding of technology and standards, including HL7 FHIR, the regulatory environment, NCQA’s quality measures, the emerging digital quality standards (QDM-CQL, FHIR-CQL) and NCPDP standards to simplifying and automating secure data exchanges between patients, providers and payers.
Michael has built three successful companies, brought the first SaaS platforms for Medical Records Review to market and has been delivering strategic solutions for 120+ consulting clients for over 20 years.