Monday, 25 January 2016 02:53

Essential Building Blocks for Linking Medical and Dental Outcomes Research and Analytics

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This past Christmas, my 28-year-old son walked himself into the emergency department complaining of blurry vision, severe thirst, and headache. His blood glucose was off the scale at 1350. After a costly 13-day hospital stay, which included two days in the ICU, we came home with a discharge diagnosis of Type I diabetes. Having no family history of this on either side, I started wondering what triggered this auto-immune condition for my now insulin-dependent son. 

Just like Shirley MacLaine, who played the controlling and over-protective mother in the movie Terms of Endearment (and told her adult daughter with a lump in her armpit that it was “probably just a swollen gland…you never learned how to properly use a washcloth while bathing”) I resorted to a “mom-ism” inspired by years of subtle observations as both a nurse and a mother. This has to be connected to my son’s poor state of dental and oral hygiene, I thought, as he never did learn how to brush and floss right. Little did I know then that my maternal intuition was actually based in medical research, which shows a link between periodontal disease and diabetes. While periodontal disease is not known to be causative in nature, routine dental exams may lead to earlier diagnosis and treatment for some diabetic patients, which ultimately could reduce healthcare costs.

As I reviewed the literature, I was surprised to discover that dentists are often the first to recognize leukemia and other hematologic disorders such as acquired neutropenia, from the presence of gingivitis. In fact, at least 16 systemic diseases have been linked to periodontitis, including coronary heart disease, cerebrovascular disease, and erectile dysfunction. These diseases are thought to be associated with periodontal disease because they generally contribute to either a decreased host resistance to infections or to dysfunction in the connective tissue of the gums, increasing patient susceptibility to inflammation-induced destruction.

The literature was abundantly clear that more research in this field is needed, and I became excited about the “big data” analytics opportunities in this seemingly untapped field of research. If researchers and data scientists could merge large medical claims and outcomes data with dental claims data, it theoretically would be possible to discover correlations between periodontal disease states and other illnesses and medical disorders. We also could begin to incorporate other types of data into the analysis, including socioeconomic determinants and environmental factors. But that realization caused me to wonder why others haven’t embarked on this endeavor. 

Dentistry is one of the major healthcare professions and as such, it includes a number of subspecialties. It has its own educational institutions, governing bodies (the American Dental Association, for example), and specialty boards, such as those for periodontics and orthodontics. However, despite the fact that periodontal disease has been recognized and treated for at least 5,000 years, dentistry historically has been practiced in isolation from medicine. 

Rarely do medical researchers include aspects of periodontal disease in their basic and clinical research. This is not because there isn’t a recognition that there are potentially important interactions between orofacial systems and other systems in the body. Rather, it is related in part to the fact that diagnostic data from dentistry simply does not exist.

Dentists in private practice and in healthcare institutions use billing systems based on the Current Dental Terminology (CDT) taxonomy, which is specifically designed for dental procedures; however, codes for diseases and diagnostics are not currently used in the dental industry. In 2003, the U.S. Department of Health and Human Services (HHS) purchased rights to SNOMED-CT (Systematized Nomenclature of Medicine – Clinical Terms) from the College of American Pathologists. SNOMED-CT includes more than 6,000 embedded terms to describe dental diagnostics, within a taxonomy known as SNODENT (Systematized Nomenclature of Dentistry). However, much work remains in order to establish an accurate, efficient, and reliable ontology for dentistry. For example 618 SNODENT terms (9.52 percent) are either retired, duplicates, or ambiguous. Another 1,203 of SNODENT terms (18.53 percent) have slightly different descriptions than SNOMED descriptions, and 437 (6.73 percent) have different meanings entirely. 

While there has been some efforts to establish an ontology for dental diagnoses, few incentives exist today that would encourage rapid and broad adoption by dentistry professionals. Following the medical model, such incentives can come in the form of mandated quality reporting to assist the consumer in selection of quality dental providers. The National Quality Forum (NQF) offers a handful of endorsed measures stewarded by the American Dental Association on behalf of the Dental Quality Alliance. However, most of these metrics are geared toward children and prevention of dental issues, such as fluoride treatments and frequency of exams, and are not designed for outcomes research.

Another impediment to the adoption of diagnostic coding methodologies for dental practice is that it will cost money. Unlike the meaningful use electronic medical record (EMR) incentive programs, which have provided financial incentives to medical providers to implement EMR technologies that adhere to a defined set of standards, dentistry lacks both national standards and financial incentives. Whether diagnostic codes are captured through provider documentation and chart review by designated medical record coders, or captured using computerized systems at the point of care, financial incentives will be a critical component in the adoption of dentistry coding practices.

Emerging technologies such as voice recognition software and natural language processing (NLP) can be used to turn spoken or written language into structured codes able to be consumed by an analytics engine to create predictive analytics. However, for these technologies to be successful, they must be incorporated into the provider’s workflow as well as affordable.


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Last modified on Wednesday, 27 January 2016 01:15

Vicky Mahn-DiNicola is Vice President of Research and Market Insights at Midas+ Xerox, where she serves as a speaker, author and clinical consultant in the areas of healthcare analytics, quality improvement, regulatory reporting and healthcare transformation. A Certified Lean Six Sigma Black Belt, Ms. Mahn completed her undergraduate and post graduate studies at the University of Arizona, where she continues to serve as Adjunct Faculty.