The clinical documentation improvement (CDI) market is set to reach USD 4.5 billion by 2023, at a compound annual growth rate (CAGR) of 7.9%. And for good reason. With 23 states embracing a value-based care model to improve their healthcare systems, the spotlight is firmly on clinical documentation.
Appropriate clinical documentation is the lifeblood of healthcare revenue streams. Missing or incomplete documentation of diagnosis or treatment could result in delayed, denied, or partially paid reimbursements from payers. A build-up of such cases not only makes it difficult for hospitals to sustain their emergency medical practice, but also causes financial, legal, and reputational damage through charges of medical fraud and abuse.
As terrifying as the consequences of inadequate documentation are, they can be avoided. In order to minimize claim denials and rejections, and maximize reimbursements, hospitals need to understand the factors that could lead to loss in revenue.
Leading Factors that Cut Down Revenue
The alarming number of rejected reimbursements calls for serious consideration of the loopholes that lead to denied or rejected claims:
- Missed information: Since the coding process directly impacts the revenue of healthcare organizations, accuracy is imperative. Coders need to extract relevant information from medical reports and assign accurate diagnosis and treatment codes for each clinical identifier. The quality of codes is, however, reliant on proper documentation by the healthcare provider. With physicians dealing with a tremendous workload, errors and incomplete information in the documentation is a serious threat for hospitals. Incomplete information further exacerbates the coding issue.
- Under- and over-coding: Under-coding involves registering codes for treatment or diagnosis that cost less. As a result, hospitals suffer in the long run from reduced, and often denied claims. In 2016, the cost of under-coded claims cost hospitals and healthcare institutions USD 1.2 million. Over-coding or upcoding violations, on the other hand, involve assigning costlier diagnosis and treatment codes for higher billing values. Both these practices are considered medical fraud and could result in hefty fines and legal complications.
- Unbundling: Another fraudulent practice is to unbundle code. This refers to false reporting that involves using separate codes for procedures that fall under one code category. Like over-coding, this leads to higher billing value and is considered medical abuse.
The transition to ICD-10, and the subsequent complexity, as a result, has further contributed to billing errors, missing information, and inadequate patient coverage. It has affected operations, data analysis, reporting, and IT systems that use diagnostic and procedural information. Employees who are not up to date with the changes are more prone to create erroneous documentation due to inaccurate coding practices.
A lack of understanding of medical terminology in the patient’s discharge summary also leads to inaccurate coding. As a result, a patient may have to undergo more than their required diagnostic or surgical procedures.
Keeping all these points in mind while trying to optimize the coding and documentation framework can be a difficult task. However, the advent of new innovative technologies has prompted the healthcare industry to improve their coding and documentation framework. The most significant aspect of these innovations has been the introduction of AI-enabled clinical documentation improvement (CDI) software.
Your Secret Weapon to Revenue Generation
According to Black Book Market Research, 90% of hospitals reportedly boosted their revenue by at least USD 1.5 million after implementing CDI software. The CDI tools help represent patient health information, clinical status, and EHR office visits as data codes.
These codes are used to report quality, process reimbursements, track diseases, and manage administrative and clinical processes. This results in revenue gains which are significant for hospitals.
Words of Wisdom
Accurately coded CDI is a necessity in boosting operational efficiency in healthcare. Increased documentation demands in the electronic health record (EHR) sector, has seen an increase in erroneous manual processing of poor-quality redundant information. With physicians spending more time looking at screens rather than with patients, more than 40% of healthcare givers are experiencing burnout and depression.
With the help of AI and clinical NLP-assisted CDI, hospitals can:
- Improve legacy CDI to minimize revenue loss.
- Identify opportunities for optimal reimbursement.
- Streamline the CDI process for increased efficiency.
Some of the ways that strategically chosen AI-tools are bringing in new-age transformation in CDI include:
- Clinical NLP: Much time is consumed in analyzing physician documentation. In case of a discrepancy, the time required is more. Clinical NLP can automate documentation processes, reducing the need for medical note-reading staff, and drastically bringing down the time required.
- Machine learning: With a dramatic increase in medical literature produced, machine learning can help medical experts stay up to date. This AI technology helps with feedback and training, enhancing learning outcomes.
- Computer-assisted physician documentation: This AI tool analyses physician documentation to provide real-time feedback and resolve inaccuracies.
As these technologies evolve, so will their implementation in the healthcare domain. ezDI’s CDI solution (ezCDI) solution allows for the transformation of clinical data into opportunities for quality and compliance and revenue improvement. By automatically identifying missing or incomplete diagnoses from any clinical documentation, the ezCDI software can help hospitals establish steady revenue generation. Alongside this, the ezNLP offers a cohesive service platform from which hospitals will gain crucial insights from unstructured data which can then be quickly and easily organized into a structured format for use in immediate clinical analysis. By reducing cost, time, error margins and effort spent in processing and clinical coding, ezNLP will enable hospitals to receive quick and accurate healthcare expertise in near-real-time.
To learn more about ezDI’s AI-based mid-revenue cycle management solutions visit www.ezDI.com and to see the live product demo of our Clinical Documentation, Coding, Compliance/Auditing, Quality Measures, Encoder, and Enterprise Analytics request a live demo.