Revolutionizing Medical Coding Auditing with Dexur's AI-Powered Medical Coding Auditor Software for Quality and Revenue Cycle
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Medical coding audits play a crucial role in ensuring that healthcare providers maintain compliance with industry regulations and receive appropriate reimbursement for their services. However, current focuses of medical coding have several issues:
- Medical coding auditors are exclusively focused on Revenue Cycle (e.g. CC or MCC coding). While focusing on revenue cycle is one part of medical coding, they miss the implications of quality measure reporting. For example, even if a DRG is coded for a CC or MCC, if there are no other codes that are part of CMS risk factors (e.g. Obesity, Renal Failure etc), the Hospitals seem like they are treating lower risk patients and the measure is risk adjusted, i.e. Hospitals have a higher readmission or mortality rates.
- Medical coding auditors scope is mostly focused on the gap between coding and clinical documentation. However, there are often times, that the Physicians themselves may not have captured the right clinical information. Hospitals try and solve this by training physicians but there are significant gaps that still remain.
In addition to the above issues of the focus of Medical coding auditing, the current process is mainly done manually.
Challenges of Manual Medical Coding Auditing
Manual auditing of medical coding can be a daunting task, as auditors must meticulously review each claim to identify discrepancies and errors. Some of the key challenges associated with manual medical coding auditing include:
- Human error: Manual audits are susceptible to mistakes, as auditors may overlook critical details or misinterpret documentation, potentially leading to incorrect assessments of compliance and revenue.
- High Cost & Inefficiency: The manual review of medical records and claims is a time-consuming process, often requiring significant resources and delaying feedback to medical coders and healthcare providers.
- Inconsistency: Different auditors may interpret coding guidelines and clinical documentation differently, resulting in inconsistent audit outcomes.
- Limited scope: Manual audits typically focus on a small sample of claims, leaving room for undiscovered errors and inaccuracies in the larger dataset. Further, AI can analyze the millions of permutations and combinations that may lead to finding coding issues.
- Difficulty adapting to change: As coding guidelines and regulations evolve, manual auditing processes may struggle to keep pace with updates, potentially leaving healthcare providers vulnerable to noncompliance.
Impact of Inaccurate Medical Coding on Quality Outcome Reporting
Inaccurate medical coding can have a significant impact on hospital quality outcome measure reporting, as these measures rely heavily on accurate coding to reflect the true performance of a healthcare organization. The quality outcome measures serve as indicators of the care provided to patients, and they are used to compare healthcare providers, inform payment models, and guide quality improvement initiatives. When medical coding errors occur, the data used to calculate these measures may be skewed or misrepresented, leading to an inaccurate portrayal of a hospital's performance. This can result in misinformed decision-making and negatively affect patient care, as healthcare providers may not be aware of the areas that require improvement or may implement changes based on faulty information.
Moreover, inaccurate medical coding can lead to financial repercussions for healthcare organizations, as quality outcome measures are often tied to reimbursement rates and incentive programs. For instance, value-based purchasing programs use these measures to determine payment adjustments for hospitals, with higher-performing facilities receiving increased reimbursements and underperforming facilities facing financial penalties. As a result, coding inaccuracies can directly impact a hospital's bottom line, as well as its reputation among patients, payers, and regulatory agencies. In this context, ensuring the accuracy and reliability of medical coding is of paramount importance for healthcare providers to maintain their credibility, optimize their financial performance, and, most importantly, deliver high-quality patient care.
The below table shows examples of various hospitals whose observed rates are below risk adjusted rates for COPD CMS quality measures. The # symbol implies that Observed Rate is lower than the risk-adjusted rate. This implies that the Hospital is treating a lower-risk population or may not be coding appropriately to show the true risk of the population.
Example COPD Mortality Observed Vs Risk Adjusted Rates
| Example Hospital | Observed Rates | Risk Adjusted Rate | Difference in Risk Adjusted Vs Observed |
|---|---|---|---|
| Hospital 1 | #9.12% | 9.90% | 0.78% |
| Hospital 2 | #3.39% | 8.20% | 4.81% |
| Hospital 3 | #3.57% | 7.90% | 4.33% |
| Hospital 4 | #5.00% | 7.60% | 2.60% |
| Hospital 5 | #5.26% | 7.40% | 2.14% |
The Dexur Solution: AI-Powered Medical Coding Auditor Software
Dexur's AI revenue cycle and medical coding auditor software offers an innovative and efficient solution to the challenges of manual coding. The software utilizes machine learning and AI algorithms to analyze primary and secondary ICD codes and CPT codes, identifying missing or potentially incorrect codes for both revenue cycle AND quality outcomes impact.
- Revenue Cycle + Quality Outcomes Audit: Codes that drive revenue are different from codes that drive in risk in quality measures. Dexur does both.
- Clinical Documentation Improvement: Dexur can suggest specific clinical documentation that may have been missed based on clinical conditions therefore improving documentation.
- Enhanced accuracy: By leveraging a database of hundreds of million claims, Dexur's software can identify patterns and trends, significantly reducing the likelihood of errors in coding.
- Improved efficiency: Automated auditing processes significantly reduce the time and resources required to conduct comprehensive coding audits, enabling healthcare providers to receive timely feedback and make necessary adjustments.
- Consistency: AI algorithms ensure a uniform approach to interpreting coding guidelines and clinical documentation, minimizing the risk of inconsistent audit outcomes.
- Comprehensive analysis: AI-powered auditing solutions can analyze entire datasets rather than a limited sample, offering a more thorough assessment of coding accuracy and compliance.
- Adaptability: As coding guidelines and industry regulations evolve, AI-powered solutions can learn and adapt to changes, ensuring that healthcare providers remain compliant with the latest requirements.
The below table has examples of illustrative claims that could have potential missing codes that impact both revenue cycle and quality outcomes.
The integration of Dexur's AI revenue cycle and medical coding auditor software into healthcare organizations' billing processes has the potential to revolutionize medical coding. By leveraging advanced technology, this solution can significantly lower cost, improve accuracy, efficiency, and consistency, ultimately enhancing revenue cycle management and reducing the burden on medical coders. As the healthcare industry continues to evolve, Dexur's innovative software promises to play a vital role in streamlining medical coding and improving overall financial performance for healthcare providers.