Improving Coding Productivity Standards

The medical coding process exists directly in the middle of the revenue cycle. When accurate coding productivity standards are upheld, charges for care are sent out quickly and the claim is processed properly and in a reasonable time frame. However, every step of the healthcare revenue cycle has an effect on the next. In the progression to value-based reimbursement, coding productivity standards have a direct impact on revenue cycle performance more than ever. 

Establishing professional coding productivity standards can be difficult because you must take into consideration various factors, and nearly every coding need is unique, so there is typically no comparable on which you can base your own requirements. Multiple variables influence what measures can you implement to increase coding accuracy, with the depth of the coding and the complexity of the patient population being major factors to take into account.

Many healthcare organizations have been inputting data into electronic health record systems for nearly a decade, resulting in lots of data, and most likely, inaccurate problem lists. Ensuring professional coding productivity standards includes maintaining an accurate problem list - removing duplicative and inactive diagnoses, identifying key unstructured data within the EMR, and using a diagnosis preference list to include HCC codes and RAF scores as well.

The Hierarchical Condition Category (HCC) risk adjustment model is used by CMS to estimate predicted costs for Medicare Advantage beneficiaries, and therefore a healthcare organization’s coding productivity standards directly impact the reimbursements they are eligible to receive. The HCC risk adjustment model was initially formed in 2004, but is becoming more widespread as value-based payment models gain acceptance.

Medicare Advantage growth has continued to rise over the past ten years, with roughly one-in-three Medicare beneficiaries now enrolled in a Medicare Advantage plan. The HCC model issues a risk adjustment factor, commonly referred to as RAF, to each Medicare patient as a measurement of predicted costs. That predicted cost is then used to regulate capitation payments for patients entered into Medicare Advantage plans. 

According to the American Academy of Family Physicians, hierarchical condition category coding helps communicate patient complexity and paint a picture of the whole patient,” this helps to appropriately measure cost performance and quality of care. Subsequently, accurate HCC risk adjustment coding using the right coder tools can have a considerable impact on a healthcare organization's financial sustainability and effective delivery of care.

For inpatient coding productivity standards as well as outpatient coding productivity standards CMS requires all provider organizations to identify qualifying conditions each calendar year. One of the biggest points to drive home to clinicians is not what Medicare risk adjustment score they should be aiming for, but the significance of accuracy. Documentation associated with a non-specific diagnosis, or documentation that is not evidence-based and incomplete can significantly affect reimbursements in a negative way. 

Educating providers and following AAPC coding productivity standards is necessary to improve coding accuracy, but it’s also important to build appropriate coding into the daily encounter workflow. Potential strategies to increase coding productivity standards include having decision-support tools, at the point of care, that integrate with the provider's EHR system using the latest FHIR standards. Also, some HCC coding software platforms can even suggest appropriate codes based on evidence from the patient chart, and diagnosis alerts for previously under coded conditions.

A good question to ask is, can your healthcare organization identify patients with chronic illnesses that have not been seen during the calendar year? If so, the next step is to do so, and properly document and code the encounter, which may be easier said than done. Adhering to coding productivity standards does require adjusting the way healthcare organizations are documenting and coding chronic conditions, but by doing so, can help the organization capture more complete diagnoses, resulting in improved care delivery for serving complex patient populations. Organizations that utilize the latest artificial intelligence in healthcare like NLP technology and medicine machine learning can enable better coding productivity standards and documentation of care for patients with chronic diseases, which leads to more accurate coding, and more appropriate reimbursement for quality care.

Measuring Medical Coding Productivity Standards

The sustainability of a healthcare organization depends a great deal upon coders’ ability to be efficient and accurate, but determining how to calculate coder productivity can be complicated. Coding landscapes vary and in-turn cannot be defined by just the number of charts concluded or accounts completed, because that doesn’t truly encompass the efforts of the coder. Many variables make up the coding environment like interruptions, the ability for departments to work in unison, and the functionality of the EHR system are just a few of the headaches a coder may confront. In reality, the coder often gets ignored until there’s a problem, and then they become a point of scrutiny.

In the past, the coding process was more transactional, making coding productivity standards more easily measurable. Diagnosis and procedure codes were mostly used to gather data and to process provider compensation. In the coding world of today however, there’s more of a relational method between coded data and quality of care, risk adjustment and HCC, in addition to data collection and compensation. Previous models for optimizing coding productivity were simple. If your baseline was higher than your target, your coding productivity spreadsheet was on track. With the evolution from transactional coding to relational coding, combined with massive amounts of big data, the ambiguity of tracking coding productivity has become increasingly apparent.

When considering how to increase medical coding productivity there’s many different factors to consider, and the tough part is there’s no real comparable factors similar to your own to base your requirements. Many different variables dictate coding productivity, with the scope of the coding and the complexity of the patient population being the biggest factors. When the focus is complete and thorough coding, there is a direct link between the efforts to accurately designate all appropriate codes and the objective of coding quality. Best practices for directors and managers is to clearly define coder productivity standards, but they must also consider elements unique to their environment that affect their own coding teams performance measures.

Trying to track and improve coder productivity can seem like an impossible task. All the factors that go into coding can be difficult to measure as the coding process can at times seem more or an art form than a straightforward system. At the core of medical coding is the coder's ability to think critically and find the best conclusions based on the evidence available. The fact of the matter is, the thought process involved in the medical coding process is hardly ever considered when measuring coder productivity, and when it comes to accuracy, as the saying goes, quantity does not equal quality. 

Coder roles have vastly changed over the years and revisions to coding productivity standards are long belated. In the coding landscape of today a coder may perform a variety of different duties including charge reviews, navigating various systems, auditing of computer assisted coding diagnoses to ensure the right and relevant codes are captured, generating queries, corresponding to directors, managers, coworkers, reviewing and redirecting accounts, etc. On top of that, mandated education, scheduled or impromptu meetings, and as well as a multitude of other issues that always seem to come up. I think we can all relate. 

With diagnosis and procedural coding becoming more complicated everyday, applying skills such as reasoning, along with clinical knowledge have become more valuable in coding than ever. The medical field is always producing new technology so diagnoses, procedures, and coding will undoubtedly be evolving as well. As long as the outdated coding productivity standards of yesterday are in place, coding productivity will always be a challenge to measure. Coders will remain disadvantaged until more current standards are updated to account for all the tasks involved with the coding process.

The upside is that accuracy and efficiency in coding productivity standards is very possible and it doesn’t have to be an exhausting process for your coding team. Knowing what measures can you implement to increase coding accuracy like acquiring coder tools that leverage sophisticated healthcare analytics and AI in healthcare technology can help ensure organizations more accurately document the complexity of their patients and be eligible for greater CMS reimbursements.

ForeSee Medical Can Help

ForeSee Medical was built to encourage and facilitate collaboration between the coding community and the provider, because we don’t think that software replaces coders. It extends the use of coders across more providers by making it faster for coders to review clinical documentation. Our HCC risk adjustment coding software makes it possible for you to link the clinical documentation that’s already in the chart with the recommendations that our software makes. Doctors are evidence based beings. We need to show them the evidence, and be able to point it out to them, on the interface within seconds, so the original documentation that exists in the patient chart gets promoted into the interface in such a way that it’s easy for the physician to see why the coder and software is recommending that code. 

Blog by: The ForeSee Medical Team