Health Language Blog

Webinar Recap: How Quality Data is Key to Delivering Value

Posted on 06/13/19

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As hospitals, health systems, and health plans implement value-based care programs, managing the quality of an organization’s data continues to be key to driving down costs and improving the value that patients receive from healthcare providers.

In conversations I’ve had with our clients, I often hear that it’s not an issue with the quality of care, it’s the amount of quality data available to inform care decisions.  This statement provides us an opportunity to look at how we can leverage the data assets at our disposal to make an impact in the way care is delivered.

In a recent webinar that we hosted with Fierce Healthcare, I talked about how the regulatory guidance we’ve seen from the Center for Medicaid and Medicare (CMS) and the Office of the National Coordinator (ONC) has been focused on how we can free the data for consumers, reduce the administrative burden on clinicians, and innovate the payment model.  With the recent proposed ruling by ONC and CMS under the 21st Century Cures Act, focused on enabling access to electronic health information via open APIs, it’s clear that the availability of data is going to be the next horizon for healthcare.

The challenge is that data isn’t captured in a standardized way. Common terminologies such as SNOMED CT®, ICD-10, CPT®, HCPCS, LOINC®, and RxNorm are always changing.  To create that 360-degree view of the patient, you also need clues trapped in a variety of different unstructured formats.  In 10 years working in Diagnostic Imaging I have developed an appreciation for the depth of information locked in the interpretive report dictated by the radiologist or cardiologist.  Incidental findings are common in certain CT chest and abdominal studies and the task of extracting the quantitative results (e.g., size and location of a lesion or mass) is often accomplished through manual review of these reports.  If you can do this effectively, it can have a positive impact across many key areas for hospitals, health systems, and health plans including quality metrics, reimbursement, and how member management programs are mobilized to steer patients to low-cost, high-quality alternatives.

For our Health Plan partners, Cheryl Mason and I discussed where data exists and how to optimize its quality by effectively managing reference data, mapping data to appropriate standards, and leveraging technologies such as natural language processing to tap into unstructured text.  By optimizing data quality in this way, health plans can streamline how they manage claims operations, optimize care and disease management programs, and leverage unstructured text to inform risk adjustment for quality reporting and pay-for-performance programs.  This approach can be effective in improving the quality of data and can change the way healthcare is delivered. What’s exciting is that a lot of this data is already available to us--we just need the tools to extract it and transform the data into information.

If you missed it, you can watch the Fierce Healthcare webinar on demand.   

If you’re interested in talking about how your organization is tackling the challenges around quality data, register your comments below or contact a Health Language solutions expert directly.

CPT® is a registered trademark of the American Medical Association (AMA).
LOINC® is a registered trademark of Regenstrief Institute, Inc.
SNOMED CT® is a registered trademark of the International Health Terminology Standards Development Organisation (IHTSDO).

Topics: data normalization, interoperability, mapping, quality reporting, NLP, Natural Language Processing, Reference Data Management, artificial intelligence, Machine learning, clinical decision support, quality measure reporting, value-based care, clinical and claims data, enabling interoperability

About the Author

Brian Diaz is the Senior Director of Strategy, Health Language, part of Wolters Kluwer, Health. Brian has over 17+ years of leading product and marketing teams for SaaS-based healthcare companies focused on interoperability, data quality, and diagnostic imaging. Brian has a computer engineering degree with the University of Minnesota.