Health Language Blog

5 Benefits of Data Normalization with MediSpan, RXNorm and NDC

Posted on 09/12/14


Prescription drug nomenclature over the years has been embedded in myriad terminologies and coding systems.

The lack of standards has made it difficult to share and aggregate drug-related data. But that situation is changing. Terminology standards such as RxNorm and NDF-RT are being adopted for the use in HIT applications dealing with drug information. Many existing systems already use NDCs as well as proprietary drug terminologies such as Medispan.  

As data is aggregated from disparate systems across the continuum, one of the first steps in using this data rests with the ability to normalize clinical and claims data into standardized terminologies to ensure an accurate picture of health across identified patient populations. Data normalization is required to 1) standardize local content to terminology standards and 2) semantically translate data between standards to eliminate ambiguity of meaning.  

Standardizing Local Content

Still, many healthcare systems have their own local content and differences in terminology usage still exists between legacy vendors. Particularly with drug terminologies, providers, pharmacies, and payers often use disparate drug terminologies including proprietary ones, NDCs, and locally added terms. For example, one HIT system may use a local code and the text string “cod ER tab .1gm” to represent a prescription order for Codeine. A drug to drug alert analytic engine would miss this prescription if it was looking for the RxNorm code. HIT systems need to be able to take cryptic text strings such as the one represented above and normalize it to the terminology standard, in this case "RxNorm 248559 Codeine 100MG Extended Release Tablet" in order to remove any ambiguity of clinical meaning.  

Semantic Translation Between Standards

Even if some of your systems are using RxNorm, we still see a need for drug terminology normalization. For example, your payer systems may only have NDCs and these codes would need to be mapped over to RxNorm. HL7 messages received by providers and payers often only have drug text strings and not associated drug codes. Semantic translation between these standards is necessary to sort, categorize, and label medication data to make it useful for secondary purposes such as drug interaction alerts, allergy checks,  avoiding medication contraindications, verifying appropriate dosage levels, removing/collapsing duplicative medication items in a longitudinal provider portal, or linking terminology to compute Clinical Quality Measures (e.g. HEDIS). Even further, Health Information Exchanges need to aggregate the disparate drug information to provide a single relevant list of active and inactive patient medications.

For example, you may desire to group medications into drug classes to create a clinically-friendly view of patient data – e.g. Captopril and Benazapril within an ACE inhibitor list. Let’s say that your clinical data repository is being fed by two systems. System A feeds a medication list that includes Benazepril 10mg, NDC code 54868235002 and System B feeds a medication list that includes Captopril 25mg, RxNorm code 197436. In order to group these two medications, you must be able to map between NDC and RxNorm as well as RxNorm and NDF-RT. From Benazepril, you would need to walk from NDC to RxNorm to NDF-RT, and go up the hierarchy in NDF-RT to “Angiotensin Converting Enzyme Inhibitor”. From Captopril, the mapping process begins with a walk from RxNorm to NDF-RT, and moving up the hierarchy to “Angiotensin Converting Enzyme Inhibitor”. Terminology services can automate this process for you.  

This is hard - is it worth the trouble?

From being able to correct the 50 different misspellings of the drug “lisinopril” and correctly classifying it as an ACE inhibitor to understanding that Captopril and Benazepril are also ACE inhibitors, standardizing the concepts that those terms represent ensures that analytic results include all the data contained within the healthcare system. Benefits of using data normalization include:

1. Improved data exchange

Data normalization across Medi-Span, RxNorm and NDC lets systems using the different nomenclatures to share data with less effort. This exchange is more efficient since it’s less likely that manual intervention and interpretation will be required.

2. Meaningful use compliance

RxNorm is on the list of criteria for Stage 2 Meaningful Use qualification. It’s a required terminology for communicating medication data. Data normalization will make it easier for physicians and hospitals to meet this requirement. A data normalization solution can help healthcare organizations map their current pharmacy management codes to RxNorm.

3. Facilitation of Emerging Healthcare Delivery Models

Accountable care organizations established under the Centers for Medicare and Medicaid Services are required to share medication data across facilities in a CMS-compliant fashion. Required actions such as medication reconciliation are more readily accomplished if drug data is normalized. A kludge of drug vocabularies would make it extremely difficult to keep a consistent set of information on a patient’s drug use.

4. Improved Support For Clinical Data Tasks

Data normalization and use of a standard drug terminology can support a range of clinical data activities. The Journal of the American Medical Informatics Association cites several including the “creation of electronic medical records (EMR), automated decision support, quality assurance, healthcare research, reimbursement, and mandatory reporting.”

5. Ease of Use

Data normalization, especially NDC-to-RxNorm mapping, puts drug data into a form that is more provider-friendly. NDC codes are perceived as being overly complicated and not particularly intuitive.

Simplifying Drug Data

Data normalization in the drug data space is well underway. RxNorm incorporates a number of drug vocabularies including Medi-Span, while NDC-to-RxNorm mapping provides additional clarity. A common drug nomenclature provides a number of advantages, from data exchange to compliance with major healthcare initiatives.

Are you benefiting from data normalization and vocabularies such as RxNorm? Do you need help mapping your pharmacy codes to a standard terminology? Leave your comments below.

data normalization

Topics: data normalization

About the Author

Dr. Brian Levy, MD is Vice President and Chief Medical Officer with Health Language, part of Wolters Kluwer Health. He holds an MD and BS from the University of Michigan. Go Blue!