Tune in to this monthly online coding column, facilitated by AHIMA’s coding experts, to learn about challenging areas and documentation opportunities for ICD-10-CM/PCS.
By Erika Bailey, MBA, RHIA
The implementation of ICD-10-CM/PCS has diversified the role of coding professionals, resulting in emerging trends in productivity standards. Previous standards used for benchmarking productivity required a focus on transactional coding, whereas current standards call for more of a relational approach. The profitability of the organization hinges upon coders’ ability to stay productive, accurate, and efficient in their code selection. A coding environment is not linear and therefore cannot be defined by just the number of charts completed or accounts finalized, because that fails to capture the coding professionals’ true efforts.
Defining and tracking coder productivity can seem like we are trying to measure the unmeasurable. It can prove difficult to measure all factors and variables that go into the coding process because coding is more of an art than a science. Critical thinking skills are the foundation of each coding professional, and coder decisions are based on developing the best conclusions from the information presented. With that being said, consideration for the thought processes involved in the coding professionals’ decision-making is rarely accounted for when measuring productivity.
The coding process lives quietly in the middle of the revenue cycle. When coding is performed accurately and completely, charges get out the door quickly and the claim is adjudicated appropriately and in a timely manner. However, every stage of the healthcare revenue cycle has an impact on the next. In the new era of value-based reimbursements, the process of medical coding has an impact on revenue cycle performance like never before. The champion of the revenue cycle often gets overlooked, until there is an obtrusive problem, and then the coder becomes the center of attention.
Establishing coding productivity standards can be difficult because you must take into consideration various factors and there is no apples-to-apples comparison on which you can base your own requirements. Multiple variables influence productivity, with the depth of the coding and the patient population being major considerations. With the emphasis on complete and comprehensive coding, there is a direct correlation between the efforts to appropriately assign all codes a case and coding quality. Best practices for HIM directors and physician practice managers can be established by developing coder productivity standards, but they must take into account their own account data and drill down into the factors that affect their individual coders in terms of productivity.
In the past, productivity for coders was more easily benchmarked due to the process being more transactional, meaning the diagnosis and procedure codes were primarily used to collect data and for reimbursement. In today’s realm of coding, however, there is more of a relational approach between coded data and quality of care, risk adjustment, hierarchical condition categories, and regulatory audit focuses in addition to data collection and reimbursement to name a few. There used to be a simple formula for optimizing productivity, and as long as your baseline was greater than your target, you were on track. With the shift from transactional coding to relational coding and the addition of big data, the grey areas of tracking coding productivity are becoming more evident.
With the emphasis on complete and comprehensive coding, there is a direct correlation between the efforts to appropriately assign all codes and coding quality, which must factor into productivity standards. All coders know that quantity does not equal quality when it comes to accuracy. The bottom line is that coder roles have changed, resources have changed, and overall productivity has changed, resulting in the need for evolving productivity standards. Is your organization measuring the unmeasurable?
Erika Bailey (firstname.lastname@example.org) is assistant professor, health information management, College of Health Professions at Grand Valley State University.