In a value-based healthcare environment, the importance of accurate medical coding cannot be overstated. Proper coding ensures patient records are accurate, care team members have necessary data and documentation available in real-time, and the revenue cycle is healthy and strong. Incorrect coding will almost always result in claim denials and reduced reimbursements from payers. However, as coding protocols have become more complex, healthcare organizations have struggled to maintain the efficacy of the medical coding process.
With the introduction of the ICD-11 coding classification, coding and documentation processes are expected to increase in complexity. Furthermore, CMS code changes bring an additional level of complication. As a result, hospitals are fighting an uphill battle to improve their coding framework and maintain consistent revenue streams. According to the 2019 report “Medicare Fee-for-Service Supplemental Improper Payment Data” by the Centers for Medicare & Medicaid Services (CMS), 80% of improper payments are the result of incorrect coding.
While revenue loss is the most prominent and direct consequence of incorrect coding, the consequences of incorrect coding extend far beyond disrupting revenue cycles.
The Repercussions of Improper Coding: Beyond Revenue Loss
Quality of care is the most important aspect of the value-based care model. The goal of accurate coding, among other things, is to ensure that higher-quality care is delivered. Inaccurate or improper coding works counter to this goal and may result in irreversible damage. Assigning incorrect diagnosis codes, for example, results in incorrect procedures that could be damaging to the patient.
The specifics of each diagnosis influence the codes. As a result, it's one of the most common mistakes during the coding process. Such errors can cause chaos and confusion, leading to the breakdown of long-standing relationships between patients and hospital staff.
Incorrect coding can also result in fraud and medical abuse charges. Medical abuse occurs when medical codes, intentionally or unintentionally, misrepresent treatment, resulting in monetary gains for hospitals. Such circumstances are considered fraud and are punishable under the Federal Civil False Claims Act (FCA). Violations of the FCA can result in fines of up to three times the amount of the false claim. Furthermore, a history of such transgressions may result in a loss of reputation.
Getting Ahead of it with AI- and NLP-based CAC
The financial and reputational consequences of improper billing are terrifying, and they often take a long time and much effort to recover from. But hospitals are not entirely helpless in the aftermath of these incidents. With the introduction of cutting-edge technology, hospitals are constantly striving to improve their processes and avoid revenue loss due to medical billing and coding errors.
This is where artificial intelligence (AI)-powered computer-assisted coding (CAC) software comes into play. These systems employ the linguistic algorithms of natural language processing (NLP) to extract clinical indicators from unstructured data, including notes and prescriptions. This data can then be used to assign appropriate clinical codes.
CAC powered by AI and clinical NLP automates the clinical coding process, allowing organizations to reduce denials and improve care quality. Additionally, the speed with which these tools operate increases the coding team’s overall productivity. As a result, hospitals can eliminate inaccuracies in their code and do so quickly. In addition, the CAC tool detects the pattern and likelihood of claims denials. This can direct the billing team's attention to specific areas to maximize payment returns.
CAC Software powered by AI can enhance hospital coding programs and improve revenue streams, which allows hospitals to maintain a solid coding framework. CAC-AI also eases resource pressures, which could allow the organization to push forward with innovations to position them as forerunners in the modernization of healthcare.
For hospitals, coding tools such as AGS Health’s CAC Software that leverages clinical NLP will provide relief against the struggles of improper coding. AGS offers a comprehensive service platform that enables hospitals to extract critical insights from unstructured data and organize that data into a structured format for clinical analysis. This software as a service reduces the cost, time, error margins, and effort required for processing and clinical coding, allowing hospitals to receive quick and accurate healthcare expertise in near-real-time.
To learn more about AGS Health’s AI-based, mid-revenue cycle management solutions, visit AGSHealth.com or request a live demo.
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