Additionally, the final DRG and supporting codes that are submitted for billing will impact how each encounter is reflected in numerous metrics that measure performance and drive revenue. Quality Indicators (e.g., PSI, PDI, and IDI), HACs, and HAIs are just a few measures impacted. In an outpatient setting, professional fee billing, ACO performance metrics, and MIPS scores for MACRA are all influenced by the coding of the encounter.
As you can see, there are seemingly endless opportunities for coding and quality measure accuracy to go off the rails due to poor communication, lack of integration and other issues.
An accurately coded chart represents a series of complex interdependencies among different stakeholders of mid-revenue cycle management. Like a row of dominoes, a change in one element can cascade into a series of others but the siloed approach to managing these changes reduces visibility and collaboration between teams. In an inpatient setting, CDI teams are usually the first to focus on coding accuracy by establishing a working set of codes and coordinating activities to refine the clinical documentation that support the codes. At the same time, QI teams are looking for potential patient safety issues such as HAIs, and HACs.
Additional teams, like Case Management, Utilization Review and various HIM roles, also leverage this data for various purposes. This influx of activity makes concurrent review of the data (and subsequent coding) a seemingly impossible task. As a result, many organizations wait until after the proverbial dust has settled, the case is coded and submitted for payment, then they perform coding audits.
While certainly easier, post billing audits come with their own pitfalls. After a bill has been sent to CMS for reimbursement, organizations only have 60 days to correct errors and resubmit for the additional revenue they deserve. However, when it comes to overcoding for services, CMS has no limitation on how long it has to review and request any overbilled funds be returned, often with penalties and the specter of future audits.
Coding audits require a great deal of expertise, which is often in short supply. As a result, many organizations can only audit a small percentage of cases, usually chosen at random. This means the majority of revenue leakage and compliance exposure goes undetected.
For greater accuracy, begin with the end in mind
To address these issues, begin with the end in mind and develop a technology-enabled, concurrent and repeatable auditing system that can assess each case in real time and prior to submission for billing. Such a system would offer the best of both worlds: the complex understanding of interrelated variables that comprise accurate documentation and coding, while also providing the bandwidth to review 100 percent of cases, not just a small sample, and do so prior to billing when there’s maximum opportunity for optimization.
For example: for each day of an inpatient encounter, the system would review the available data and estimate all elements of the patient bill, such as diagnoses, procedures, charges, the DRG, and all relevant performance metrics to determine what may need more attention. If attention is warranted, routing criteria can be established to push the case to the appropriate resource.
How would you develop such auditing technology? The answer is Artificial Intelligence (AI) and Machine Learning (ML), both of which “learn” from a team of expert auditors. The accuracy AI journey begins with over a thousand expert rules that are defined and validated by veteran auditors with experience auditing cases that represent virtually every encounter possible. Encounters with potential accuracy problems are flagged by one or more rules, then returned for review along with detailed, proscriptive advice on how to correct and complete.
Imagine a repository of auditing data from nearly 100,000 cases, including the original coding, the optimized coding and the supporting rationale. ML can be leveraged to identify patterns in the coding data that either triggered a recommendation to change the codes or not. Once patterns are identified, a multivariate algorithm can augment the expert rules to estimate accuracy of a given DRG and/or other metrics driving revenue and reimbursement.
As additional data becomes available, all rules leveraging it can be automatically reassessed and adjusted accordingly. And this powerful analysis can drive accuracy earlier in your revenue cycle for greater financial and operational results.