DH Blog

Interoperability between Clinical and Claims Data, Standard Supplemental Data, and a (Very) Brief History of Clinical Quality Measurement

05/23/2018

Tom Gaither
Vice President of Marketing

Mike Baillie, Vice President Clinical Integration and Interoperability at United Healthcare and Diameter Health’s own Jonathan Niloff recently presented a fascinating (for those of us in the clinical data interoperability world!) webinar (replay available elsewhere on diameterhealth.com) describing the emerging alignment between Providers and Payers created by the emergence of value-based payment models.

While chatting with John D’Amore and our friends and customers Peg Eichner and Adam Rossbach at CliniSync, Ohio’s Health information Exchange, following the webinar, John remarked that the history of systematic quality measurement in healthcare is relatively short.  Which led me to think, “How short is short?”

Shorter than you might think.  While historians point to Florence Nightingale[i] and other healthcare pioneers who focused on patient outcomes, the foundational framework for evaluating medical care quality (i.e. structure, process, outcomes) was not published until 1966.[ii]  The Healthcare Employer Data Information Set (HEDIS) from the National Committee on Quality Assurance (NCQA) was implemented only in the early 1990s.  And, not until 1998 did the “Advisory Commission on Consumer Protection and Quality in the Health Care Industry” recommend standardizing measures of healthcare nationally.  “At that time, little systematic information on the quality of health care was available and no commonly held ‘rules of the road’ for quality measurement and reporting existed.[iii]   The National Quality Forum (NQF), established in 1999, established the Strategic Framework Board to design a strategy for national quality measurement.  Only since 2012 has the NQF advised the government on the selection of measures for federal reporting and value-based care plans.

Compared to most other industries, healthcare is late to the quality measurement party.  For example, even US automobile manufacturers implemented statistical process control by the 1980s, and Japanese manufacturers started twenty years before that.[iv]  It’s also interesting to note that, with NCQA’s HEDIS program, Payers (using claims data primarily) have been in the quality measurement business for 20-30 years already.

Claims data have advantages and disadvantages for quality reporting.  Claims data are widely available in electronic form, support a consolidated view of the patient, and have a degree of consistency which makes them usable for analytic purposes.  Clinical data contains detailed patient information missing from claims data but, until the last ten years, were not widely available in digital form.  Even today, while most clinicians are working within certified EHR systems, the incompleteness, syntactical inaccuracy and lack of standardization of clinical data spells doom for realizing a single patient view, population health analytics, and quality reporting.

However, the divide between claims data and clinical data is beginning to close.  Diameter Health, for example, developed technology which transforms clinical data from any certified EHR into standardized, normalized, enriched substrate data fit for analytic and quality measurement purposes. The NCQA has established e-Clinical Quality Measures (you can look it up, Diameter Health is certified on more of these measures than any other vendor) which are approved as “standard supplemental data” for HEDIS reporting in calculating quality measures at the health plan level without the need for audit.

So what?

If, like Mike Baillie at United, you’re responsible for acquiring clinical data, interoperable, standard supplemental data saves money chasing individual charts, simplifies data acquisition, and improves the data available for HEDIS reporting and patient analytics.

If, like CliniSync, you’re responsible for adding value for members and improving care for patients in your state, standard supplemental data represents a new revenue stream for HIEs, provides a valuable service to Payers by aggregating standard clinical data on large patient populations, and enables analytic insight to improve patient care (which is what this is all about, anyway).

Healthcare hasn’t been at this quality measurement thing very long.  But, if you look carefully, there’s a Whole Lotta Shaking Goin’ On.

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[i] The evolution of healthcare quality measurement in the United States. H. Burstin, S. Leatherman & D. Goldmann
[ii] National Healthcare Quality and Disparities Report. 2014.http://www.ahrq.gov/research/findings/nhqrdr/nhqdr14/2014nhqdr.pdf
[iii] The evolution of healthcare quality measurement in the United States. H. Burstin, S. Leatherman & D. Goldmann
[iv] http://daaam.info/Downloads/Pdfs/science_books_pdfs/2012/Sc_Book_2012-051.pdf

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