Regulations mandating the use of new standards and specifications present a double-edged sword. On the one side, there is the promise of a cure-all for the interoperability challenges in the healthcare system. The bitter pill comes in the technological implementation of these rules. That said, when stakeholders leverage technology in use cases—like quality measurement and prior authorizations—the potential game-changing benefits outweigh any short-term pain.
HEDIS scores provide the information that regulators and consumers need to assess and compare plan value—and hold plans accountable for their performance.
These circumstances are about to change. The CMS has adopted Clinical Quality Language (CQL), a human-readable language standard structured enough to simplify the electronic sharing and manipulation of healthcare data. CQL logic is valuable in Clinical Decision Support (CDS) and Electronic Quality Measures (eCQM) reporting, as it standardizes and integrates data across different systems. As a result, data is not open to as much interpretation, making it more transparent and consistent. In turn, reliable data improves HEDIS audit precision, and helps determine accurate plan value. Furthermore, by writing a query in CQL and disseminating it to everyone who has data in FHIR, implementers can automate processes to reduce the administrative burden placed on measurement professionals and providers. Plus, the substantial dollar values associated with a top 5-Star rating can motivate payers to provide data in a way that skews measurement.
CAQH estimated $13.3 billion in possible savings—$9.9 billion of which stands to be saved by plans and providers.
Automated access to quality data has significant financial implications. In addition to more efficient and cost-effective workflows, payers can act on insights quickly to realize far greater returns on their Value-Based Payments. The Council for Affordable Quality Healthcare (CAQH), a non-profit alliance of health plans and related associations, has gone so far as to quantify the benefit of automation. CAQH estimated $13.3 billion in possible savings—$9.9 billion of which stands to be saved by plans and providers.
While the deadline for organizations to adopt the standard is still on the horizon, some HEDIS metrics are already drafted in CQL and piloted. The move paves the way for efficiencies in the measurement process, monetary benefits, and greater accountability and value in healthcare.
The prior authorization process, where providers submit requests for approval to payers before rendering a medication, service, or supply, is another application where CQL can make a difference. Evidence-based checks by insurance plans may help determine the most cost-effective choices but often use labor-intensive methods, like phones, faxes, and portals, to exchange requests and medical records with providers. It is time-consuming and inefficient. Rule checking will always need to happen, but having the rules encoded in CQL automates the process. Additionally, thanks to the standardized clinical terminology of CQL, there is greater consistency in the data, with less latitude for miscommunication, unnecessary expenditures, and abuse of the system.
By enabling immediate notification based on clinical and user events, CDS Hooks allow payers and providers to share and process data seamlessly.
The introduction of Clinical Decision Support (CDS) Hooks is an opportunity to further streamline prior authorization. CDS Hooks, an open-source specification that builds on FHIR, offers providers the functionality to interact in near real-time with the Electronic Health Records (EHR) of multiple organizations operating on different platforms. By enabling immediate notification based on clinical and user events, CDS Hooks allow payers and providers to share and process data seamlessly. For example, if a physician sends a prescription to a plan for approval, the system can respond straight away with an alternative drug recommendation that can save money or maybe even provide a better patient outcome. Alerts also can suggest helpful apps, like one that uses information in a patient's EHR to adjust their dosage. Clinicians could actually be supported with relevant evidence-based information they need…when they need it.
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