There’s no question the transition to ICD-10 will bring with it a significant loss in coder productivity. If left unaddressed, this loss of productivity will cause hospitals to get bills out more slowly. And if addressed incompletely, it will cause hospitals to incur excessive expense in increased coder headcount. Fortunately, there is still time to greatly narrow the productivity gap to protect revenue and to minimize the need to add coding resources that are increasingly difficult to secure. The key is to move quickly and with precision in addressing the root causes of productivity loss.

ICD-10 productivity loss stems from both the coding system’s newness and complexity. The fact that coders must master a completely different coding system means that experienced coders face a steep learning curve until they hit peak productivity, and those entering the profession have much more to master than in the past to become fully productive. Even when coders reach peak performance with ICD-10, they will perform at a slower pace than in ICD-9 simply because of the tremendous increase in complexity.

The short- and long-term impact of ICD-10 on coder productivity poses two questions of HIM departments if they are to achieve ICD-10 readiness efficiently and effectively:

• Do you know precisely what aspects of ICD-10 are slowing down your coders so that you can address them head-on to minimize productivity loss?
• Can you quantify the remaining productivity loss so that you can know precisely what resources you need to add?

The most common approach to ICD-10 readiness is to train coders with e-learning and dual-coding, but that won’t provide an answer to either of these questions. It also won’t necessarily lead to coder performance improvement unless accompanied by productivity tracking and extensive coding review and feedback as the basis for continuing coder education. The shortcoming of dual-coding is that it doesn’t direct attention to what is actually slowing down the individual. Random, untargeted dual-coding is also inefficient, with coders spending time on aspects of ICD-10 that aren’t causing issues as well as those that are.

To more precisely and efficiently get coders up to speed requires pinpointing, at the DRG level for individual coders, the root causes of productivity loss—and that requires an analytical approach to training. Data-based, analytical ICD-10 readiness tools can establish coder-specific baselines for performance in the ICD-10 environment compared to ICD-9 and reveal which DRGs affect productivity, by how much, for each coder. This in turn enables the focusing of coder training attention specifically on problem areas to bring up productivity by addressing core issues.

HIM departments that employ analytics in ICD-10 training invariably uncover initial productivity losses of between 50 and 70 percent—with less than a dozen DRGs causing up to 90 percent of that loss. Because analytics can reveal not just the problem DRGs but the amount of productivity loss associated with each, they can be attacked in order of impact for the fastest return on training investment. Analytic ICD-10 readiness tools typically include productivity tracking to provide certainty of progress.

HIM directors who take these measures to identify and address issues immediately have a year to focus coders for optimal improvement in advance of the transition, often minimizing productivity loss to the long-term target of 20 percent realized in countries that adopted ICD-10 years ago. They can also realize these improvements more efficiently by focusing specifically on areas where individuals are struggling, rather than simply having coders practice in ICD-10 overall.

Applying these techniques across an entire coding group to raise everyone’s performance levels will not only reduce the number of new coders that must be added, but will also quantify precisely what resources are required to fill the remaining productivity gap. Regardless of gains made in identifying issues and addressing them, ICD-10 complexity means that some productivity loss compared to ICD-9 will be long-term. If productivity loss is quantifiably reduced to 20 percent, 20 percent more coders are required for the group to perform at the same level as before. 

It’s very important to quantify coder resource requirements well in advance of October 2015. There will inevitably be a shortage of skilled coders as the transition date approaches, and preparing a thorough resource plan early will be essential to doing the necessary hiring. The longer the delay, the harder it will be to get those resources, as those who plan best will have already hired many of them.

After the October 2015 transition, it will be critical to continue focusing on productivity and quality. An ongoing auditing plan can reveal opportunities for additional training efforts and uncover documentation issues that can be addressed with new policies and procedures.