By Dr. Barbara Antuna is the Medical Informatics Specialist at Health Language
Four Reasons Dirty Data Destroys Healthcare Analytics
A goldmine of data exists across the healthcare continuum. Hospitals and health systems, by and large, recognize the opportunity. Many just don’t know how to overcome the complexities of data management to make it work for them.
Consider the challenges: Staggering amounts of patient information are regularly accumulated from disparate systems, many with their own terminology frameworks. Even when data is converted to industry standards like SNOMED CT or LOINC, many healthcare organizations find that the tools they use produce duplicate records or were not set up to capture data correctly.
Given the complexity and variety of information systems needed to manage a healthcare enterprise, it’s understandable that data degenerates over time. Unfortunately, faulty data results in inaccurate analytics initiatives and negative downstream impacts, touching everything from patient care and regulatory reporting to revenue cycle and the bottom line.