With healthcare costs reaching an all-time high, payer and provider organizations are hyper-focused on initiatives that improve patient outcomes, control costs and increase profitability. Clinical and financial leaders are expected to abide by the latest regulatory requirements, report accurate system-wide metrics for quality measures, and produce reliable data analytics, all of which depend on having accurate and complete access to healthcare data.
Yet, reaching the holy grail of having enterprise data readily available, accurate and reliable continues to be an industry-wide challenge. In the age of value-based care, industry stakeholders are trying to fully leverage vast and growing amounts of data, drawing from a variety of data types including labs, medications, and even social determinants of health. Yet, data management is complex. Healthcare organizations not only collect information in a variety of ways—through electronic health records, patient-generated information, and unstructured notes—but also in varying formats, such as clinical, claims, and reference data.
Today, it is common for healthcare data to live in disparate data silos, which creates the first significant challenge. To overcome this obstacle, many healthcare organizations have put initiatives in place to centralize the storage of the data into an enterprise repository. This is an excellent first step; however, due to the variety of data types, formats and standards that exist, data must be normalized, or translated into a common language, to be used effectively.
Healthcare organizations can achieve a framework of data normalization by mapping disparate data elements, along with their own local or proprietary medical terminologies, to appropriate, recognized industry standards. This process bridges the gap between disparate systems by establishing semantic interoperability of data across the healthcare enterprise.
Over the next couple months, the Health Language Solutions experts will dig into four distinct data mapping services in a four-part blog series. Be sure to catch all four to learn: 1) What data needs to be mapped and the problems that can occur if it is not; 2)What the solution is and how is it achieved; and 3) Examples of how this kind of exercise has helped organizations like yours.