Compliance and the New ICD-10 Rules

Our nation’s healthcare coding system will receive a massive overhaul Oct. 1, 2014, as it transitions to ICD-10. At stake for all providers during this transition is not only millions of dollars in lost revenues, but also compliance implications for improper coding and billing. The irony is that while the government has mandated a switch to ICD-10 to improve quality reporting, that very switch may lead to an initial increase in coding and reporting errors.

“During the first six months or so after implementation, error rates may rise to 6-10 percent compared with the typical 3 percent error rate that occurs for a few months each year with the annual ICD-9 updates,” according to the Department of Health and Human Services.1 These errors could potentially put providers at risk of noncompliance and accusations of fraud and abuse.

Successful migration to ICD-10 will require intensive planning and an integrated, enterprise-wide technology approach that minimizes compliance risk. Parallel to developing new policies and procedures, implementing new workflows, and intensive staff training, providers must implement information technology that addresses both the financial and compliance challenges resulting from the ICD-10 transition. Computer-assisted coding (CAC) software augmented with Natural Language Comprehension™ (NLC) is one technology that should be embraced as healthcare organizations adjust to the level of detail and complexity required in the migration to ICD-10.

Computer-assisted Coding: A Promising Solution
One of the key strategies for healthcare organizations to minimize risk in this incredibly complex new environment would be to find new processes and technologies to ensure that appropriate diagnosis codes are consistently applied. In this regard, CAC tools equipped with NLC are promising. If clinical documentation is not done correctly, then coding cannot be done correctly, and if coding cannot be done correctly, healthcare providers will not be reimbursed correctly. Providers may also be at risk with the government view that improper coding is a basis of fraud. NLC technology makes CAC tools the most comprehensive and appropriate choice to help coding professionals better manage the eightfold increase and complexity of codes.

CAC is a transformational technology that will yield results now and in the future if appropriately deployed. The success of this technology relies heavily on the quality of the data sources. In spite of major investments in EHRs, most healthcare organizations still function with a hybrid record, and because of that, an innovative coding platform must be able to accept patient data from various source systems and consolidate that data from disparate systems into a single view for coders and clinical documentation improvement specialists. This is the first step toward improving coder productivity.

The advantages of CAC include:

Increased coding productivity and efficiency: Early studies have shown that for some CAC applications, there is a net increase in coding speed. This is largely because the key point of CAC software is that codes have already been selected by the software, and the coding professional need only review the preselected codes and make whatever changes are necessary. Certainly some medical domains lend themselves to higher speeds than others. Domains where the documentation tends to be highly repetitive (e.g., screening mammogram radiology reports) or where procedural techniques are fairly predictable (e.g., gastroenterology endoscopies) realize the greatest speeds.

Of course, the high speeds are offset when a code is found to be incorrect and editing needs to be done, so there may be a tradeoff between accuracy and speed (which is always the dilemma in coding).

Productivity may also be affected by increased efficiency in the coding workflow itself. Using the CAC tool, different medical report or chart types can be routed to particular work queues so that coding professionals can get into a rhythm by coding all the reports or charts of one type, physician, location or any other attribute the tool is designed to recognize. An added consideration – unique to the structured input-based CAC method – is that in the near future, the system may be able to prompt more complete documentation so that delays caused by missing details needed to assign a precise code may be minimized, delivering more compliant and timely coding.

Increase in coding consistency: It is difficult to make organizational improvements in coding when coding is done inconsistently from one day to the next or from one coder to the next. CAC is consistent – even when it is not right. This consistency makes for a compelling case. Codes will be assigned in the same manner each time. In a structured input-based tool, codes linked to structured input are assigned once at the time the input is created. NLC rules-based software does not forget a rule one day and remember it another. Even mistakes would be consistently generated, an advantage being that trends in problem coding can be identified sooner and resolved faster.

Availability of coding audit trail: All code assignments made by the CAC software and the human coder are auditable. Furthermore, the reason a particular code was added, deleted, or modified is tracked and available for future reference. System designers or developers may reconstruct such audit trails as needed. Having these audit functions is imperative, as it allows you to identify problem areas faster and execute improvement strategies sooner to minimize the impact on possible compliance risks.

Data query ability: The use of CAC data for such purposes as Joint Commission auditing, quality assurance measures, performance studies, credentialing, and research is an attractive feature of this technology. Many CAC systems offer different ways to query data from their systems, including prewritten “canned” reports, ad-hoc queries, and the use of structured query language (SQL) to access the data. CAC ensures consistency in queries and reporting, as well as increased staffing efficiencies.

Potential for more comprehensive code assignment: The use of ICD-10 has great implications for clinical care and global health monitoring. Public health diseases are generally able to be captured in a more specific way when using ICD-10.

In the case of outpatient claims, CMS-1500 forms have a limited number of fields for ICD-9-CM codes. Because of production demands, often only the codes necessary for reimbursement and/or reporting to third-party payers are captured. This often has to do with minimizing impact on productivity more than anything else. An advantage of CAC software is that it can code a chart with 10 diagnoses as quickly and easily as it can code a chart with only two diagnoses. CAC technology can make it easier to capture all relevant diagnoses and procedure codes for better reporting that positively affects patient outcomes and overall global healthcare monitoring.

Potential increase in coding accuracy: Coding rules are a moving target, with clarifications offered quarterly in the case of ICD-9-CM and HCPCS Level II codes and monthly in the case of CPT® codes. Revisions to the CPT, HCPCS, and ICD-9-CM code sets are regularly made, and payer rules such as NCCI edits are frequently updated. Official clarification on proper use of the ICD-9-CM code set comes from the Central Office for ICD-9-CM (either via its quarterly “Coding Clinic for ICD-9-CM” publication or via questions posed directly to the Central Office for ICD-9-CM). Similarly, official clarification on proper use of the CPT code set comes from the American Medical Association (AMA) through its “CPT Assistant” publication.

Even the most skilled coding professional struggles with keeping up with the rapid and frequent changes. Frequently updated CAC software will help ensure consistent compliance.

Furthermore, the axiom, “If it wasn’t documented, it wasn’t done,” may apply to CAC software better than to human coders. Both structured input-based and NLC-based CAC applications are incapable of assuming, leaping to conclusions, or even “reading between the lines;” the software will automatically assign codes based on the documentation available. If the documentation is ambiguous or incomplete, the software should not return any assigned codes.

Potential decrease in coding costs: CAC engines can read through documentation at a much higher rate of speed in order to assign relevant codes. As a result, the coder only needs to focus on the relevant documentation that should be highlighted by the software and associated with the CAC-assigned code. As with any major change, the return on investment should take into consideration all relevant factors, including the initial investment in the system, how the tool is implemented, ongoing costs, as well as the financial and non-financial benefits of risk avoidance and compliance.

Use of free text for recording documentation: Physicians can continue to document health record information using their preferred terms, reducing the obstacles of system implementation and user acceptance of process and technology changes, which in turn will support more accurate and complete documentation.

• System improvements through feedback: CAC with NLC can learn from coder behavior by accepting feedback on the action taken by the coder on each code. This ongoing feedback and learning loop will result in continuous improved performance by the CAC engine.

Mitigate the Risks Associated With ICD-10
ICD-10 promises to provide much-needed improvement in our nation’s healthcare system, but to survive the transition, providers must take a strategic, organization-wide approach to preparing for ICD-10. The identification of vulnerabilities and opportunities is crucial to planning and implementation. And when these efforts are coupled with the power of CAC technology that is equipped with NLC, organizations will not only mitigate the risks associated with ICD-10, but will perhaps emerge as even better, more financially sound organizations in the long run.

Cindy Doyon, RHIA, is vice president of coding and client audit services with Precyse, which provides services and technologies that capture, organize, secure, and analyze healthcare data and transform it into actionable information, supporting the delivery of quality patient care and optimizing operating performance.

Reference
1. Department of Health and Human Services, August 22, 2008, 45 CFR Parts 160 and 162, HIPAA Administrative Simplification: Modification to Medical Data Code Set Standards to Adopt ICD-10-CM and ICD-10-PCS Proposed Rules. Federal Register Volume 73, Number 164.

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