- It is always a challenging issue for healthcare practitioners to comply with the real-time changes in the medical coding guidelines. Moreover, translating a patient’s complex symptoms and clinician’s effort to address was quite challenging even in the simpler times. However, now hospitals and health insurance companies want very detailed information about the patient’s disease and how the treatment procedure was performed. They require complete information about clinical record-keeping for hospital operation review and planning. They use this information for hospital review, planning and perhaps most importantly for the financial reimbursement process.
- Medical coding a complex field:
- Impact Of AI (Artificial Intelligence) On Medical Coding:
- AI assisted medical coding practice:
- Consistent Training To Medical Coders:
ICD-10 is the current international standard of medical coding services introduced by WHO (world health organization). ICD-10 consists of over 14,000 medical codes for diagnosis. In May 2019, WHO members have formally adopted the next update in medical coding i.e. ICD-11. WHO member states that implementation of ICD-11 will begin as of January 2022, all across the world including the US. The new ICD-11 has over 55,000 diagnostic codes, four times greater than the number of diagnostic codes contained in the WHO’s ICD-10.
In fact, there are even substantially more codes than the numbers mentioned above, at least in the United States. An enhanced version of ICD-10 that is specific to the usage of healthcare services in the United States has almost 140,000 classification codes, about 70,000 for diagnosis, and another 70,000 codes for the classification of treatments. We can expect the enhanced version of ICD-11 that will only be specific to the usage in the United States, will have at least several times the number of medical codes. In the WHO version of ICD-11 given that the US version also includes treatment codes and has previously included a larger number of diagnostic codes as well.
No human being can remember all the codes for diseases and treatments, especially as the number of codes has increased over the recent years to tens of thousands. For decades, medical coders have to consult with “code books” to look up the right code for classifying a disease or treatment. Thumbing through a reference book of codes obviously can impact the efficiency of the overall negatively. It is not just all about finding the right code. With ICD-10 implementation and previous revisions of the medical coding classification scheme, there is often more than one way to code a diagnosis or treatment. Therefore, medical coders should decide the most accurate code option.
Over the past 20 years, the usage of computer-assisted medical coding systems has steadily increased across the healthcare continuum. This innovation has provided a medium to cope with the increasing complexity of medical coding diagnosis and treatments. More recent versions of computer-assisted coding systems have incorporated state-of-the-art machine learning methods.
Other aspects of artificial intelligence also contribute to the improvement of a system’s ability. Modern machine learning techniques help to analyze the clinical documentation_charts and notes_and determine the most appropriate code for a particular case. Some medical coders are now working hand-in-hand with AI-enhanced computer-assisted coding systems to identify and validate the correct codes.
Elcilene Moseley is a resident of Florida and she is an 11-year veteran medical coder. She has worked for an organization that owned multiple hospitals. However, now she works for a medical billing company that has a contract of medical coding in the same hospitals Moseley used to work for. She can manage to work from home and generally provides services during eight working hours of her office. She can do a certain number of patients’ charts per day. She specializes in outpatient therapies that often includes outpatient surgeries.
Moseley is also completely aware of the increased complexity of medical coding. She is a great advocate of the implementation of AI-enhanced medical coding systems. These systems could help her to review the maximum number of medical codes in less time than manual processing. As manually processing medical codes is highly prone to the human errors. It is quite difficult for a human being to remember every subtle detail such as right side, left side, fracture displaced or not. However, Artificial Intelligence can only go so far.
For instance, the AI enabled medical coding system may process the text in a chart document, note that the patient has congestive heart failure and select that disease as a code for diagnosis and reimbursement. Building that particular disease is in the patient’s history, not what he/she is being treated for now. This intelligent solution can amaze any human being with the 100% accuracy in medical codes.
When a medical coder opens a chart, on the left side of each page there are codes with the pointers. It also describes the source of that particular medical code in the chart report. Some medical coders don’t bother to read that patient chart thoroughly. But Moseley believes that it is important to read that chart from beginning to the end. However, it is quite a traditional methodology but it can help to ensure accuracy in the medical codes. Although, an AI enabled medical coding system can help you do things faster but it can make you a little lazy.
Some patient cases are relatively simple, while some are complicated to code. If it’s just an appendectomy for a healthy patient then any medical coder can check all the codes. It will just get through the whole procedure in five minutes. On the other hand, if there are multiple sections on a chart for even a simple surgery that includes the information of patient physical examination, anesthesiology, pathology, etc. then it can take too long to process that information manually. Obviously, those evaluation management codes are important to be processed accurately in order to prepare reimbursable claims.
It is the primary requirement of the healthcare practice to stay compliant with the ongoing changes in the medical coding. Medical coding is a complex and ever-changing field. Therefore, most of the healthcare centers hire experienced staff for medical coding and billing services.
AI has replaced use of entry-level hires and insufficiently trained staff. AI can make more straightforward and simpler coding decisions. Therefore, only expert medical coders are required to manage complex coding decisions and audits. Despite the basic certification, employees require too much on-the-job training for medical coding. AAPC (originally the American Academy of Professional Coders) and AHIMA (American Health Information Management Association), both have social media pages where their members discuss any issue regarding the medical coding field.
Artificial Intelligence provides great assistance to medical coders in this regard. Now, medical coders can complete their work efficiently with the help of smart AI solutions while also focusing on their training. In this way, medical coders can learn new skills and also stay up-to-date with the changing trends in the medical coding field.
Artificial Intelligence has boosted industrial development all across the world. It has also impacted the healthcare industry with profitable results. AI has provided a great flexibility to the medical coders as they can do their job at any hour of the day or night. Medical coders should also consider learning new skills to better understand the working of smart machines.
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