• Subscribe

Feature - Clarity in clinical care

Feature - Clarity in clinial care: achieving fully interoperable databases


Werner Ceusters
Image courtesy of W. Ceusters.

Werner Ceusters, director of the Ontology Research Group at the NYS Center of Excellence in Bioinformatics & Life Sciences, spoke at the Bio-IT World Conference in Boston this past April on the topic of "Do Healthcare IT Standards Hamper the Advance of Science?"


"Swimming is healthy and contains eight letters." Absurd? Of course - the real thing is confused with its symbolic representation. Absurdity, then, abounds in current Electronic Health Records (EHRs) according to Werner Ceusters of the New York State Center of Excellence in Bioinformatics and Life Sciences and the University at Buffalo.

A polyp by any other name…

A significant and universal problem with legacy EHRs, Ceusters says, is that software programs that access them cannot adequately differentiate between diagnoses for a given patient and link them properly to the disorders in the patient's body - i.e. they cannot distinguish between representations of a disorder and the disorder itself. Separate diagnoses for a patient can correspond to distinct disorders, to different opinions about one disorder, or to the evolution of a single disorder over time. Furthermore, a single, standardized code may not describe a given disorder appropriately.

For example, on successive occasions a colon polyp might be referenced in a database as an intestinal polyp, or just a polyp, and be assigned different codes. If the polyp becomes malignant, it will likely get the code for malignant neoplasm of colon. The polyp - the actual disorder in the patient - may undergo changes, but its identity does not. A single disorder, then, may have multiple codes, potentially entered by different caregivers at different times; furthermore, a single patient may have multiple disorders.

Input needed from the research community

Ceusters and others have argued that many of the medical errors and ineffective treatments that continue to plague the industry are consequences of poor information capabilities and standards. The clinical research community's input to health IT standards is paramount, he says, if the standards are to serve this community. So far, industry has taken the lead in devising and implementing standards, largely because funding is scarce for these activities in the nonprofit sector.

The traditional approach to data integration, Ceusters says, tries to use middleware to unite disparate databases so that applications can draw data from them, unaware of their underlying differences. He argues that in a world of heterogeneous and distributed data sources, this only accomplishes a partial integration. Semantic interoperability, i.e. the ability of two or more communicating parties to understand the same message in exactly the same way, both syntactically and contextually, requires additional measures.

(Click here or on image for full picture.)
In a Referent Tracking-based data model, instances of diseases, their causes, and their manifestations are represented by means of unique identifiers (shown here as numbers). The relationships amongst them, as well as the classes of which they are instances, are defined in an ontology. Disease X has a unique identifier #105. The disease is caused by something (unnamed) that is a form of aberrant DNA molecule (a type of pathological anatomical structure). This disease manifested itself as a hepatoblastoma tumor (a type of pathological formation) in John Doe. John Doe's tumor gets the unique identifier #3.

Image courtesy of Werner Ceusters.

Track disorders uniquely and directly

Ceusters proposes a new reference-based paradigm for data integration, that he calls Referent Tracking, in addition to current code-based efforts.

Under Referent Tracking, a unique identifier that he calls an IUI (for Instance Unique Identifier), applies to each concrete individual entity (for example, a disorder as diagnosed by a particular caregiver at a given time) relevant to the accurate description of a patient's condition, therapies, and outcomes.

"IUIs refer to the real entities themselves, and not to data about these entities," says Ceusters, "and are the means for representing constellations of entities that are relevant to clinical care in an EHR in the same direct way as (disorder) classes are currently represented by clinical coding systems."

Ceusters feels confident that once the right infrastructure is in place, the data-entry burden on caregivers will increase only slightly if at all. The benefits, on the other hand - in the areas of patient management, cost containment, epidemiology and disease control, as well as for the advancement of biomedicine in general, he says - can be enormous.

-Anne Heavey, iSGTW

Read more about referent tracking

Join the conversation

Do you have story ideas or something to contribute? Let us know!

Copyright © 2018 Science Node ™  |  Privacy Notice  |  Sitemap

Disclaimer: While Science Node ™ does its best to provide complete and up-to-date information, it does not warrant that the information is error-free and disclaims all liability with respect to results from the use of the information.

Republish

We encourage you to republish this article online and in print, it’s free under our creative commons attribution license, but please follow some simple guidelines:
  1. You have to credit our authors.
  2. You have to credit ScienceNode.org — where possible include our logo with a link back to the original article.
  3. You can simply run the first few lines of the article and then add: “Read the full article on ScienceNode.org” containing a link back to the original article.
  4. The easiest way to get the article on your site is to embed the code below.