The Evolution of Coding and How We Got Here
In years past, medical coders were primarily hospital-based. They worked with the facility’s chargemaster and patient charts (usually populated by handwritten notes) to cull codable details of the care encounter, then referenced coding manuals to identify the proper codes before entering them manually into the facility’s database or billing system.
It was a paper-based, error-prone, inefficient system with few real alternatives as PCs were financially out of reach for all but the largest healthcare facilities. It all changed with the technology boom of the 1980s, followed by the years-long transition to ICD-10-CM/PCS, both of which forever altered how and how much patient information was collected and managed.
Technology on the Scene
The first major impact of the 1980s tech boom was the emergence of more affordable, powerful, and compact hardware. PCs, local area networks (LANs), and the Internet became more prevalent, providing faster and easier access to medical information. This was followed by the rise of encoder technologies – some with early versions of computer-assisted coding (CAC) – which accelerated searches, increased accuracy and streamlined the overall coding process.
By 2015, EHRs were becoming mainstream. Physicians began documenting electronically and inpatient orders were linked to the EHR. Patient data management systems enabled automated management of patient records, and departments including pharmacy, laboratory, surgery, radiology, respiratory therapy, and infectious diseases were interfaced with records systems. Cloud-based systems were also growing in popularity.
These advances brought with them tools that allowed coders to work directly in the patient’s electronic record, aggregating data and quickly looking up chart information to populate billing and procedure codes. While EHRs and associated tools made the coding process light-years faster than its manual predecessor, it was not without challenges. Third-party modules on legacy EHRs forced coders to switch between multiple systems to collect and compare information to the chargemaster, reference code books and other information sources to arrive at the appropriate clinical codes for the case.
The Technology Tipping Point
The tipping point that brought us to the coding technologies of today was the 2015 transition to ICD-10-CM/PCS. Technologically, the switch required system upgrades and enhancements to ensure they were compliant with the code set and capable of operating in a dual coding environment.
ICD-10 also exposed weaknesses in documentation processes, which gave rise to demands for tools that would make the coding process easier and more efficient while also ensuring compliant documentation capable of supporting ICD-10’s higher specificity levels. The answer was markedly enhanced CAC software and the addition of Clinical Documentation Integrity (CDI) tools.
Today’s NLP-enhanced CAC solutions enable a level of automation that makes it possible to achieve higher coder productivity without increasing staff. CDI software has also grown in importance under value-based care models, while the cloud is being leveraged more frequently to integrate CAC, CDI, and auditing tools into a single cohesive platform.
The addition of NLP and AI technologies enhances these benefits by automating the analysis of chart contents and prioritizing for CDI specialists those with the highest likelihood of requiring clinician queries. When these tools are embedded into encoder and CAC software, it allows coders to focus on validating or adjusting recommendations based on their review of appropriate chart elements.
The result of these advances is a coding environment characterized by coder-CDI collaboration, which generates a higher degree of quality, accuracy and, subsequently, reimbursement. Coder and CDI staff are more efficient and able to focus more on complex cases while the software manages the routine cases and more easily automated tasks.
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