The Healthcare industry is suffering huge losses each year due to errors in medical billings and misinterpretations of medical documents. Incorrect/missing documentation impacts patients’ Medicare/insurance coverage, appropriate care delivery, and the overall patient experience — all of these have a cascading effect on the revenues of a Healthcare business. Ineffective medical billings are considered one of the main reasons behind the growing medical debt in the US economy.
The US medical coding market is experiencing phenomenal growth and is forecasted to expand at a compound annual growth rate (CAGR) of 8.0% from 2022 to 2030.
– Grand View Research
Healthcare organizations face the constant challenge of ensuring accuracy and efficiency in their medical coding and billing processes. The latest ICD version consists of 70,000+ billing codes to search from. In addition to this, with a large volume of healthcare data (80% of which is unstructured data) flowing into the system, accurately converting detailed patient records into standardized medical codes like ICD-10 is a real struggle. Such difficulties make it challenging for healthcare businesses to bill patients and payers like Medicare and insurance companies. This is where the healthcare players are exploring AI-assisted medical coding software and solutions to improve the efficiency and accuracy of their medical billing and coding processes.
Bidding Adieu to Manual Processes
In a traditional medical coding setting, coders usually spend hours evaluating free-text clinical notes, discharge summaries, test reports, surgical records, and other such complex data stored in EHRs to translate it into the most appropriate ICD codes. Spelling errors, abbreviations, and other ambiguities in medical data further make the coding process difficult for them.
With healthcare emerging as an equally competitive market as any other industry today, there’s a need to evolve and embrace modern technologies to uplift traditional processes. By combining human and artificial intelligence, healthcare businesses can overcome the limitations of traditional processes to build a robust patient-centric business model. Research shows that healthcare organizations that use AI-driven medical coding solutions can reduce their coding process times by almost 80%.
Quality Matters!
As mentioned before, the quality of coding is crucial and inaccurate medical coding can be a disaster leading to a huge revenue drain in more ways than one –
- Increased Claims Denials/ Delays
- Impacted Revenues
- HIPAA Violations
- Legal Lawsuits and Federal Fines
- Compromised Patient Satisfaction
- Increase in Medical Debt
- Coder Fatigue and more…
In present times when the healthcare industry worldwide is undergoing a radical transformation to create value for all stakeholders of the healthcare system, particularly the patient, through a value-based care approach, inaccurate medical coding can mean a step backward.
In 2021, around 90% of the denied claims could be avoided. This part of the claim denials would have translated to about $235 billion as annual revenue for the American medical industry, according to a report by Healthcare Financial Management Association (HFMA).
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Decoding Medical Coding Challenges with Technology
An AI-assisted medical coding platform is trained on analyzing medical terminologies in EHR/EMR data and automatically maps this information to its corresponding ICD codes. The platform uses AI and ML to minimize/eliminate the common coding challenges of manual coding and improve billing accuracy.
Modern AI-powered coding platforms are cloud-enabled and therefore highly scalable and robust in terms of data security & compliance. Data can be accessed remotely at all times — a big plus for healthcare businesses in the post-pandemic world.
The flexibility of these automated platforms allows seamless integration with EHR/ EMR and other medical data repositories. With a host of handy features and smart tech tools, AI-Assisted medical coding software solutions are making their way into an increasing number of medical practices globally.
AI Use Case: Intelligent Z Code Extraction and Contextual Mapping
A Behavioral Wellness Center in Los Angeles was using SDOH-related Z Codes to document its patients’ conditions. Although Z Codes were meant to make it easier for behavioral health facilities to code and bill, it was becoming increasingly challenging – the insurance companies were turning down the claims very often. This was forcing patients to discontinue their treatment, translating into a significant revenue loss for the clinic.
In its current process, the clinic was going through the EHR documents manually to find relevant medical notes and decide on the most appropriate Z Codes. The unstructured EHR documents were in different formats, containing hand-written notes on patients’ social and behavioral history and family history of mental illnesses, etc. The manual process was leading to coding inaccuracies, which was one of the main reasons for the rising claim denials.
To address the situation, the clinic turned to an AI-assisted Medical Coding platform for coding accuracy and efficiency, and that really helped.
How did it work?
The AI-enabled medical coding solution was built on the context similarity model. It could process any new EHR document, and the outcome was a list of extracted phrases from the document along with mapped Z Codes and confidence scores.
- The platform identified the ICD/Z Codes automatically
- It also identified the key phrases in the EHR document against these ICD/Z Code descriptions
- Natural Language Processing (NLP) delivered contextual matches of the key phrases with the content
- The technology extracted portions/phrases from the EHR document that matched the key phrases along with the matching score. It supported multiple types of docs (.pdf,.doc, .TXT, and OUT).
- It created a top N recommended set of extracted phrases from the document that had the highest matching score
- And, displayed matching content with mapped ICD/Z Code in a UI
Looking for a solution to make your medical coding and billing process efficient?
Leveraging AI to Achieve the 3Cs of Medical Coding
Correctness, Consistency, and Compliance
1. Correctness
Misinterpretations of medical documents result in billion-dollar losses worldwide.
Let’s take a situation where a patient is suffering from a chronic disease with overlapping concerns. Applying the most accurate medical code can be a big challenge. Manual searching can take hours and at the end of it all, the applied code may not even be the right one.
AI can be a game changer here – reducing the room for any human error or knowledge gap. Medical coders can search both structured and unstructured medical data to find the most accurate code using intelligent algorithms in a jiffy.
2. Consistency
With its Natural Language Processing (NLP) capabilities, the engine learns over time to ensure interpretation consistency and coding precision in the future.
Task repetitions and process redundancies can be eliminated considerably, boosting overall productivity. A survey by 3M, a software and services company, has revealed that computer-assisted medical coding increases the productivity of healthcare organizations by 30%.
With laborious manual processes out of the way, AI-powered intelligent workflows relieve the coder of coding fatigue. They can instead focus on more meaningful tasks to improve efficiency, identify system gaps, and spot unusual trends to curb revenue leakage.
3. Compliance
AI-enabled automated medical coding platforms are programmed in line with the latest medical coding updates and guidelines to ensure full compliance with HIPAA and other healthcare regulations. The system can also flag potential compliance gaps and documentation errors.
Moving Forward with AI
In the past few years, the medical coding industry has come a long way from the redundancies of a legacy setting with new-age healthcare IT solutions. For healthcare businesses looking to scale and build a resilient care-delivery model, opting for a modern AI-assisted medical coding and billing platform can help them gain a competitive advantage.
At KANINI, we transform healthcare businesses through our digital engineering solutions and accelerators powered by AI/ML technologies. If you wish to know more about how we can help you solve your challenges in medical coding and other areas of healthcare, get in touch.
Author
Anand Subramaniam
Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.
