Computer-Assisted CDI: Your Secret Weapon to Revenue Generation

The market size of the global Clinical Documentation Improvement market is predicted to reach $7,181.66 million by the year 2028 at a 7.40% CAGR during the forecast period. And for good reason. With 23 states embracing a value-based care model to enhance their healthcare systems, the spotlight is strongly on clinical documentation.

The lifeblood of healthcare revenue streams is accurate clinical documentation. Payers may delay, deny, or partially reimburse you if you have missing or incomplete documentation of diagnosis or treatment. The accumulation of such cases not only makes it difficult for hospitals to maintain their emergency medical services but also damages their financial, legal, and reputational standing by exposing them to allegations of medical fraud and abuse.

The consequences of insufficient documentation, as terrifying as they are, can be avoided. To reduce claim denials and rejections while increasing reimbursements, hospitals must first understand the factors leading to revenue loss.

Leading Factors that Cut Revenue

The alarming number of denied reimbursement claims necessitates a detailed analysis of the flaws that lead to claims being denied or rejected:

1. Missed information: Because the coding process directly impacts a healthcare organization’s revenue, accuracy is critical. Coders must extract pertinent data from medical reports and assign precise diagnosis and treatment codes to each clinical identifier. However, the accuracy of the codes is dependent on the healthcare provider’s documentation. Errors and incomplete information in the documentation are a severe threat to hospitals, especially with physicians dealing with such a heavy workload. The coding problem is made worse by incomplete information.
2. Under- and over-coding: Under-coding is the practice of registering codes for treatments or diagnoses that are less expensive. As a result, hospitals suffer in the long run from lower reimbursement and claims that are frequently denied. In 2016, under-coded claims cost U.S. hospitals and healthcare institutions $1.2 million. On the other hand, over-coding or upcoding violations involve assigning more expensive diagnosis and treatment codes in exchange for higher billing values. Both actions are considered medical fraud, and they could result in hefty fines and legal issues.
3. Unbundling: Unbundling codes is another fraudulent practice. This type of false reporting involves the use of different codes for procedures that belong to the same code category. This, like over-coding, results in a higher billing value and is regarded as medical malpractice.

Billing errors, missing information, and inadequate patient coverage have all increased due to the increasing complexity of coding guidelines. It has impacted operations, data analysis, reporting, and IT systems that rely on diagnostic and procedural data. Employees who aren’t up to date on the latest changes are more likely to create incorrect documentation due to poor coding practices.

Incorrect coding is also caused by a lack of understanding of medical terminology in the patient’s discharge summary. As a result, a patient may be required to undergo more diagnostic or surgical procedures than are necessary.

It can be difficult to keep all these considerations in mind while optimizing the coding and documentation framework. However, with new technologies, the healthcare industry has been compelled to improve its coding and documentation framework. The introduction of AI-enabled clinical documentation improvement (CDI) software has been the most significant aspect of these advancements.

Your Secret Weapon to Revenue Generation

According to Black Book Market Research, 90% of hospitals boosted their revenue by $1.5 million after implementing CDI software.

Patient health information, clinical status, and EHR office visits represent data codes with the CDI tools. These codes track diseases, report quality, and manage administrative and clinical processes, among other things - resulting in significant revenue gains for hospitals.

Words of Wisdom

To improve operational efficiency in healthcare, accurate CDI is necessary. Increased documentation demands in the electronic health record (EHR) industry have resulted in more erroneous manual processing of low-quality redundant data. With physicians spending more time looking at screens rather than with patients, more than 40% of healthcare providers are experiencing burnout and depression.

With the help of AI- and clinical NLP-assisted CDI, hospitals can:

• Improve legacy CDI to avoid revenue loss.
• Evaluate opportunities for optimal reimbursement.
• Streamline the CDI process for increased efficiency.

Some ways strategically chosen AI tools bring new-age transformation in CDI include:

1. Clinical NLP: Analyzing physician documentation takes a lot of time. The time required increases if there is a discrepancy. Clinical NLP can automate documentation processes, reducing the need for medical note-reading staff and reducing the time it would take to code.
2. Machine learning: Machine learning can help medical experts stay current as the amount of medical literature has increased dramatically. This artificial intelligence (AI) technology aids in feedback and training, improving learning outcomes.
3. Computer-assisted physician documentation: This AI tool examines physician documentation in real-time to provide feedback and correct errors.

As these technologies evolve, so will their implementation in the healthcare domain. AGS Health’s CDI solutions improve data quality – resulting in better clinical data, compliance, and revenue. They assist hospitals in establishing consistent revenue generation by automatically identifying missing or incomplete diagnoses from any clinical documentation. In addition, it provides a unified service platform through which hospitals can gain critical insights from unstructured data, which can quickly and easily be organized into a structured format for use in clinical analysis.

By minimizing cost, time, error margins, and effort spent in processing and clinical coding, AGS helps hospitals receive quick, accurate healthcare data in near-real-time.