Patient data, scattered across the healthcare community, is notoriously difficult to pull together.
Disparate systems obstruct the ability to aggregate data from multiple sources or to share data among healthcare organizations. Data normalization, however, seeks to harmonize data from different sources into standard terminologies. A data normalization solution can provide a shared vocabulary that can ease data exchange and help improve data analytics-driven initiatives such as population health management.
Laboratory information systems present one use case for data normalization. In this field, organizations face the task of mapping different proprietary lab codes into industry standard LOINC codes. That the federal Meaningful Use program, which incentivizes the adoption of electronic health records (EHRs), requires the use of LOINC, makes this normalization endeavor all the more important.
Labs may be the worst offenders when it comes to having clean, structured data. Many lab orders and results are cryptic to read, make use of proprietary coding paradigms, or implement LOINC codes with very subtle differences (e.g. different systems, also known as specimen source). This makes it very difficult to trend lab results over time, de-dup lab lists so that they are easier to view, or use lab information for downstream analytics such as quality of care measurement.
As an example, a patient may have two lab results for a sodium test. Both are mapped to LOINC, but one has a system of serum, the other blood. Hence, there are two different LOINC codes. A data normalization solution must be able to make use of LOINC hierarchies to group these two lab items next to each other in a clinical system.
As another example, a healthcare system may be attempting to trend A1C hemoglobin levels for diabetes. The organization may receive a local code from Lab A—1234/Hgb A1c Blood. Another code coming from Lab B may be a standardized LOINC code—LOINC 17855-8. Both of these represent lab results of A1C, but they must be normalized to one standard for trending to be accurate.
LOINC provides a vocabulary that standardizes the names of tests, observations, panels and assessments. According to the Regenstrief Institute, which created LOINC, most labs and clinical services use Health Level 7 (HL7) to electronically dispatch the results from their reporting systems to the systems physicians and hospitals operate. Tests embedded in the electronic messages, however, are identified “by means of their internal, idiosyncratic code values,” the institute noted.
As a consequence, provider or payer systems can’t interpret and fully make use of the lab results. But health organizations that pursue data normalization can map internal lab codes to LOINC. LOINC becomes the common language that enables data sharing and aggregation.
How It Works
LOINC mapping may be unfamiliar to the typical healthcare administrator or clinician. To clarify, here are a few considerations a healthcare entity should think about before mapping a lab catalog to LOINC:
- Understand the scope of the mapping project
- Determine whether you will need to consider proprietary system identifiers
- Understand your source data elements as they are from disparate sources (LIS, EMR).
- Ascertain whether you have the people, tools and time to map and analyze the data
- Decide whether you can commit personnel full time to analyze and map
- Determine whether your staff has the appropriate combination of lab domain knowledge, system knowledge and LOINC knowledge to map in an accurate and expedient way
Using data normalization to map differing lab codes to LOINC provides a number of benefits for healthcare enterprises and organizations. The enhanced ability to share and pull together information -- a consequence of standardization -- will impact several areas including:
- Data warehousing and data mining/analytics
- Regulatory compliance (Meaningful Use, for example)
- Quality measures and reporting
- Reimbursement optimization
- Integration of administrative and clinical data
There are plenty of reasons to embark on a data normalization project focused on LOINC code mapping. The use case is straightforward, but getting there will require a concerted effort on the part of the healthcare entity. The right staff, tools and methods will need to be in place to ensure the success of the data normalization project.
Do you have a LOINC mapping project in your future? Do you have a data normalization platform in place or plan to acquire one? Leave your comments below.