5 July 2020
Electronic health record (EHR), as the name infers, is software built to gather and manage patients’ information in healthcare organizations. EHRs collect a variety of clinical data such as patient’s history, diagnoses, medicines, lab results, allergies, treatment trends and so on. Basically, all medically-relevant available information about a patient.
EHRs are required to catalog and uniquely identify all clinical data. To do so effectively, EHRs use clinical coding standards for identifying different types of data. Most code standards are international and used worldwide, but some healthcare organizations choose to customize these codes as per their specific needs. Integrating with EHRs that use customized clinical codes requires data mapping prior to any such integration. This is where our journey begins.
What is a clinical data mapping?
Clinical data such medications, lab results, allergy, diagnosis, etc., all have unique identifiers, allowing relevant software to record, access and analyze it. In its essence, clinical data mapping is all about matching data models and various parameters (codes, names, schemes) to enable such analysis and usage.
To enable translating one code model to another in cases where code concepts vary, a meticulous mapping process is required for matching a code data-point in a specific model. This is the cornerstone which allows different clinical software to “speak” with one another and exchange data. Data mapping is an ongoing process, which requires constant updating, as new clinical data accumulates, or properties change (becomes excluded or edited). Keeping data and data sources mapped and updated demands a great deal of effort from any healthcare organization.
The complexity of clinical data mapping
When a healthcare organization is using a certain EHR, it communicates and sends clinical data in real time to a decision support software which monitors, analyzes and searches the data for Drug-Related Problems (DRPs, the 4th leading cause of death in the US). If a doctor prescribes a certain medication, both systems need to have a pre-defined unique identifier for that specific medication. You can easily imagine what the result of any mismatched drugs between the two systems could be, i.e.an incident which could potentially harm the patient.
Furthermore, clinical data coding is divided into multiple domains, with different coding standards for each. For example, diagnosis (ICD10/9, SNOMED, etc.), lab results (LONIC, etc.), medication (RxNorm, NDC, and others). Different organizations use different standards per each domain, adding another layer of intricacy. The problem worsens in EHR systems which are installed in multiple sites – although using the same software, each health organization’s specific coding, which is embedded in the EHR, results in very complex and hard to maintain data structures.
Though there is an abundance of mapping challenges, one rises above all – the brand name mapping. Medications are based on active ingredients and are classified under a generic name but in many cases a single generic drug is sold under several brand names, as pharmaceutical companies brand their own medications. Various healthcare organizations store their medication data using both generic and brand names. Localization also takes effect, as in different parts of the world there are unique brand names of medications, all needing to be mapped to their underlying generic name. In such instances, the mapping process becomes even more complex, as interoperability between systems in different parts of the world requires translating a given brand name to its counterpart’s generic name in a different language. Now that’s a daunting problem to solve…
The challenge of integrating EHRs with 3rd party solutions
The centricity of the EHR in patient care and the growing amount of clinical data collected by them, make EHRs a perfect fit for integration with 3rd party applications. Yet, the data mapping process could have a great impact on such integrations’ process in terms of the time and resources required. Many healthcare organizations find themselves investing many months and much effort in getting all data properly mapped and tested, prior to interfacing.
Healthcare organizations are constantly seeking solutions to overcome mapping challenges. To reduce integration efforts, some organizations use proprietary integration engines, which automatically convert and format data. Although advanced integration engines are available, they are yet to handle all data aspects and standards, leaving a big portion of the data mapping unattended.
To increase the data similarity to other systems, healthcare organizations around the world are adopting common data coding standards and conventions, but still maintaining a portion of custom codes as needed. The industry has leaped forward by introducing integration standards like FHIR, aimed to unify interfaces between parties, however coding standards domain is still stalling.
Seegnal simplifies clinical data mapping
Seegnal supports the world’s leading data coding industry standards as well as custom data coding. Seegnal utilizes a battle-tested, home-grown AI reinforced mapping engine, which reduces mapping efforts by relying on large mapping databases – both generic and brand. Seegnal enables tailor-made mapping processes and custom fit to the needs of any healthcare organization. This, combined with automated mapping processes, makes Seegnal a highly flexible and easy to integrate platform.