Only when providers can merge clinical data, claims data, and patient-collected data can they generate a holistic view of the patient and the patient’s care over time.
Imagine a day when providers can customize care effectively by patient or by community because the information is all there, understood, and aggregated across patient populations. This will certainly enable robust population health analytics and empower patients to make more informed choices. Today the industry is advancing with evidence-based medicine, epidemiologic research, cost-containment solutions, and many other clinical effectiveness initiatives, but one thing still stands in our way: a common language.
As the meaningful-use timeline matures, more rigorous requirements related to health information terminology standards are introduced with each stage, thereby positioning the industry for accurate and consistent exchange of patient information. The adoption of industry-accepted clinical vocabularies provides the needed infrastructure for improving communication between disparate IT systems, which leads to better sharing of patient data, better decision-making, and ultimately, better outcomes.
Even though guidelines help advance the industry, health information technology rarely captures information in the same way. Often, the data is not codified in its use of standard terminology, and much of the otherwise rich clinical data is free text with syntactic variations.
Consider how many ways a provider can say hypertension. What exactly is going on with the patient diagnosed with Other specified heart block? Is it A1C test or Hemoglobin A1C? Data normalization provides a means to codify all of that data by mapping it to nationally recognized standards.
Once normalized, data can be effectively exchanged between information systems in a contextual, clinically appropriate manner without losing the meaning of the message, and semantic interoperability is achieved.
Unfortunately, this is not currently common procedure for healthcare organizations. In a study published in 2014 in the Journal of Biomedical Informatics, less than 17% of the laboratory data sent to a public health entity in Indiana was coded in LOINC. Even worse is that a similar organization in Wisconsin receives only 13% of its clinical data using the standard terminology of Systematized Nomenclature of Medicine–Clinical Terms (SNOMED). The remainder of the information being exchanged is not normalized to a standard terminology. Therefore, the receiving institution is left to make sense of this disparate data in order to monitor and respond to public health concerns.
Industry standards are an important step toward changing this type of scenario and achieving the goals of interoperability and information sharing. But healthcare organizations still face serious challenges to laying the best frameworks for normalizing data to those standards.
Whether it’s translating a physician’s unstructured progress notes to SNOMED or procedures for a patient case into ICD-10 codes, these are important steps that connect directly to the tracking of clinical care provided to patients, the efficacy of those practices over time and the level of reimbursement due to hospitals or clinics.
Think about the complexity of language in healthcare and then add to that Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT); the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10); the Logical Observation Identifiers Names and Codes (LOINC) database; and the National Library of Medicine’s RxNorm naming system for generic and branded drugs.
Because there is no one standard that covers all healthcare information, clinical and financial data must be cleaned and appropriately mapped to a single source of truth to make them meaningful. Once normalized, data can be exchanged effectively between information systems in a contextual, clinically appropriate manner without losing the meaning of the message – and semantic interoperability is achieved, achieving the true promise of electronic health records in sharing insights between providers to benefit patients and clinicians.
Need a better understanding of the standard terminologies to meet the increasing need for semantic interoperability? Read our white paper.