Artificial Intelligence and Machine Learning are not just buzzwords for modern-day innovation. It is what defines the future of the technology industry and practically every other industry, including healthcare. Almost nothing in the healthcare industry remains the same, be it the methodologies or business aspects. With AI automating the healthcare sector, the systemizing of medical coding has been crucial for healthcare progress. Outside the medical world, there aren’t people who would have any understanding of medical coding. With 72,000+ billable codes in the latest International Classification Diseases (ICD) version, the complicated system requires a careful watch of professionals for proper management.
With the medical billing outsourcing market reaching up to $16.9 billion by 2021, the challenge of coding accuracy remains. Centers for Medicare & Medicaid Services (CMS) reports that almost $36.21 billion errors resulted in FY2017 due to improper payment. The primary reason for erroneous reports is the lack of documentation for healthcare. The advancement of technology highlights the significance of coders while simplifying the process of medical coding. The process of medical coding involves accurate patient symptoms detection and the clinician’s ability to pinpoint the right treatment. However, accurate detection and treatment require detailed record-keeping. The information is used for hospital reviewing, planning and reimbursement.
Healthcare costing frauds cost up to $230 billion because of false claims and charges. With IBM’s Watson transforming healthcare and clinical needs through cloud analytics, AI is now revolutionizing medical coding and billing. Equipped with the ability to identify developments and patterns and evaluate errors, AI has reduced the chances of billing fraud and code duplication. Here’s how AI will change the dynamics of the medical sector:
1. Catering to code complexity
The World Health Organization has released the tenth edition of the International Classification of Diseases, which is the new standard for disease and virus detection and categorization. The latest upgrade comprises 3,800 procedure codes and approximately 14,000 diagnosis codes. However, the new upgrade is ICD-11 which will become effective in January 2022. With COVID19 accelerating the adoption of AI, enabling the professionals to choose better codes to help them streamline the claim process and cost reduction.
Medical coding is an ever-evolving yet core aspect of the healthcare sector. However, the codes and diagnosis standards have increased exponentially, reaching up to hundreds and thousands. The staggering increase has necessitated the incorporation of AI when it comes to accurate code assigning. Succeeded by the effective usage of the computer-based coding system, the AI-based coding systems will enhance code validation and identification. The real-time feedback will improve the coder’s efficiency while alleviating errors from the documentation.
2. Improved coding practice
Conventional medical billing and coding involve immense manual documentation and paperwork. The manual nature makes the process time-consuming with reduced turnaround time. Additionally, the nature of audits and cost of rectification are major setbacks of existing coding practice. Integration of agile process makes the medical coding and billing processes seamless. With the introduction of AI in the medical sector, many businesses and healthcare organizations are leveraging this technology for medical practice improvement. The AI-based coding practice helps contextualize the unstructured data while aligning the information from various sources and arranging it into the logical timeline.
The automated web-based system and ML-enabled coding procedure are the emerging additions of AI in the medical sector. These CAC (Computer Assisted Coding) systems provide the user with automated data extraction and identification from various documents while populating the systems with necessary details. The web-based systems ease the treatment and diagnosis processes for the physician while analyzing the documentation and pinpointing the relevant medical codes.
3. Workforce training
The medical coder is an essential part of the medical workforce, ensuring accuracy in the medical codes and billing. The AI-based systems learn from the medical coder interacting with the system as it studies the user behavior. Since the human-AI interaction is crucial for system optimization and accuracy, the coders and AI mechanism need to work in a harmonious relationship to ensure error reduction. In order to reduce the inaccuracy of coding patient charts, the coder requires the ability to interpret codes and ICD-10 and ICD-10-PCS guidance.
To ready the medical workforce for dealing with the increasing complexity of coding, the need for on-job training is critical. The medical professionals could sign for training courses offered by AAPC (American Academy of Professional Coders) and AHIMA (American Health Information Management Association). It is significant for the medical coders to stay current, up-to-date with trends, and knowledgeable about developing practices to grow professionally and in-pace with the industry growth. These artificially intelligent machines learn from the human users through behavior sampling, adding greater value to the medical profession.
4. Workflow Solutions: With a multitude of information contained in EMRs and the intersecting of artificial intelligence to compile that information, physicians, clinicians, nurses, patients, and others will be able to make more informed decisions about healthcare diagnosis, delivery, and personal wellness and disease management.
5. Population Health Management: Population health is a term that is used to identify individual patients and groups of patients who are most likely to require some kind of medical intervention to stay healthy. After identifying these patients, healthcare personnel can target them at the optimal time to achieve favorable outcomes.
The CDC views population health as an multi-sectoral, customizable approach that allows health departments to connect practice to policy for change to happen locally. This approach utilizes assistance across the board for public health, industry, academia, health care and local government entities.
6. Imaging, Diagnostics, and Disease Management: All predictions and studies show that AI will transform the diagnostic imaging industry, both in terms of enhanced productivity, increased diagnostic accuracy, more personalized treatment planning, and ultimately, improved clinical outcomes.
AI will play a key role in enabling radiology departments to cope with the ever-increasing volume of diagnostic imaging procedures, despite the chronic shortage of radiologists in many countries. Following the introduction of deep-learning technology and affordable cloud computing (graphics processing units) and storage, the pace of product development for AI-based medical image analysis tools is faster than ever before.
7. Drug Discovery & Development: Drug discovery and development are key aspects of healthcare. AI is being used in several areas of drug discovery and development. Through advancements in AI, it is now possible to automate drug design and compound selection. Researchers are using AI to select appropriate characteristics to design products that would reduce complexity in design, detect production and characterization issues, and discover new entities. Key companies in this space include Atomwise, Berg Health, BioXcel Therapeutics, Cloud Pharmaceuticals, Recursion Pharmaceuticals, and Sophia Genetics.
Even though Artificial Intelligence evolves continuously, the criticality of the technology in medical coding is quite apparent. Be it tracing the epidemiological trends or error detection; the technology is re-writing the typical perspectives of medical coding.