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3 Ways Semantic Interoperability Improves Your View of Patient Data

Posted on 10/03/14

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Semantic interoperability has long been a key missing piece of the healthcare industry’s data-sharing puzzle. Semantic Interoperability is the ideal state where the meaning of data can be effectively shared between systems despite the numerous ways the data may be represented in any individual system [1, 2].

This ideal state is not too far out of reach. We are well on our way to conquering several major obstacles along the way to Semantic Interoperability. First, the widespread adoption of electronic health record (EHR) systems among healthcare providers has greatly increased the volume of available patient data in electronic format. Second, the Health Level 7 (HL7) series of standards provides guidance on how messages should be structured to share information between systems. This enables Syntactic Interoperability [2], or technical interoperability, which is a critical step on the journey. Unfortunately, this still falls short of a complete solution because no single standard exists for the content of the HL7 messages, so the meaning can be lost in translation.  

Semantic interoperability aims to create a common vocabulary that will pave the way for accurate and reliable communication among computers. Indeed, a semantically normalized information model can contribute to the success of a number of forward-thinking healthcare initiatives, such as accountable care organizations, patient-centered medical homes, and health information exchanges.

Here are three ways semantic interoperability will help improve a healthcare organization’s view of patient data.

1. Clarifying Drug Data

Healthcare entities studying prescription drug use among patients or reconciling medication need to work with a clean, normalized set of data. Unfortunately, when a health plan wants to know how many of its providers prescribe a Codeine 100 MG Extended Release Tablet, for instance, they may obtain some confusing results. Provider A may represent a local drug code as “cod ER tab .1 gm.” But if the health plan wants to aggregate that data point into a report, it must know all the different ways that the drug could be represented. Data normalization solutions, optimized for clinical data, can automatically map local data to standard codes (in this case, to RxNorm 248550), quickly enabling semantic interoperability.

2. Sorting Lab Data

Similarly, local medical testing facilities often use proprietary lab data, often lacking any standardization, as opposed to the industry standard LOINC codes, which are required for reporting under the federal government’s Meaningful Use program. To qualify for meaningful use incentives, a provider must be able to report, for example, Clinical Quality Measure CMS122v2, “The percentage of patients 18–75 years of age with diabetes (type 1 or type 2) who had HbA1c >9.0%” [3].  Identifying patients with diabetes is relatively easy but when “HbA1c” can be represented over 100 different ways, it is difficult to query and ensure accuracy of the measure. Automated mapping solutions can streamline the mapping process to enable semantic interoperability and make it easy to make meaningful use of the patient data.  

3.  Rationalizing Disparate EHRs

Healthcare providers who want to share patient data must first confront the incompatibilities among EHRs. The same healthcare system may use different in-patient and ambulatory EHRs. Exchanging data among unrelated providers is all the more difficult, since they will most likely use different systems, with different terminologies and representations of data.

A key to enabling semantic interoperability is to create a common vocabulary, and then automatically normalize data to the common vocabulary so that the meaning is non-ambiguous. This is particularly important for health information exchanges and accountable care organizations, where data sharing is a critical element of the technical architecture and business model.

Leveraging Patient Data

Patient data has been freed from paper forms, but the healthcare industry needs to achieve semantic interoperability among systems to make full use of it. Are you struggling with patient data sharing and aggregation? Leave your comments below.

1 "What Is Interoperability?" HIMSS. HIMSS, n.d. Web. <http%3A%2F%2Fwww.himss.org%2Flibrary%2Finteroperability-standards%2Fwhat-is>.

2 "Semantic Interoperability of Health Information." EN 13606 Association. N.p., n.d. Web. <http%3A%2F%2Fwww.en13606.org%2Fthe-ceniso-en13606-standard%2Fsemantic-interoperability>.

3 Research, Mathematica Policy. Clinical Quality Measures for 2014 CMS EHR Incentive Programs for Eligible Professionals (n.d.): n. pag. Centers for Medicare & Medicaid Services. Web. <www.cms.gov>.

 

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Topics: semantic interoperability

About the Author

Sarah Bryan is Director of Product Management with Health Language, part of Wolters Kluwer Health.