Coding
Some hospitals still use outdated legacy CAC software or manual processes for their medical coding, burdening their HIM professionals with unnecessary challenges in addition to their heavy workloads. Learn how the implementation of AI-assisted medical coding and auditing technology can resolve efficiency, accuracy, and team-based issues for medical coders.
Medical coders are worth more than their weight in gold. Yet, despite the necessary service they provide, medical coders often feel that their work is not valued and requires a significant burden on their time. Instead of being given the effective equipment and technology to help tackle their heavy workloads and identify high-value opportunities, they are often forced to use error-prone legacy CAC technology or manual processes that severely drag down their efficiency and accuracy.
Time is a valuable resource, especially in the healthcare industry. If there is technology available that can help workers and patients save more of it, it should be utilized wherever possible. Without it, HIM professionals are burdened with challenges that can be easily resolved with the newest technology. Coders face three such challenges when they are not equipped with AI-assisted software around efficiency, accuracy, and team workflows.
Efficiency is an area that can always be improved upon, and that is especially true for the medical coding and auditing industry. HIM employees working without AI assistance often report that it takes a long time to read through clinical charts. Additionally, medical coders have to consult several sources to code a chart. This amount is too much for them to do at a reasonable pace without the right tools. In fact, manual audit processes often leave coders with the capacity to review less than 10% of coded charts. This is incredibly inefficient in itself, especially when compared to the capabilities of an AI engine, which is able to review 100% of the coded data.
Because an AI engine is fully integrated into the hospital’s EMR, coders can review clinical documentation on a real-time basis when equipped with the Semantic Health Information Platform. Coders will have a direct link back to the clinical documentation for the most important factors affecting coding, like the most responsible diagnosis, family history, and other key indicators. After the charts are coded with the help of NLP technology’s automatic coding suggestions, the Semantic Health engine will then review all of the coded data and compare it with the clinical documentation to flag data quality opportunities that would have been more difficult to identify manually. Such errors include instances of undercoding, overcoding, or documentation deficiencies.
Another challenge HIM workers face is that some of the sources they need are paper based, which is less accessible and makes the coding process more difficult. The Semantic Platform is capable of offering AI suggestions for scans of paper-based documents and auditing workflow prioritization with a library of data quality checks, so digitization is not a requirement for seeing the benefits of AI-powered software.
That being said, it may be worth considering to fully digitize all of your hospital’s sources. Scanning digital documents is easier than scanning paper ones, and it is overall more efficient for all documents to be, first, digitized, then collected in one database for prime accessibility. When all HIM data sources are harmonized into a single source of truth, it helps coders uncover data quality opportunities impacting quality and funding. Without this, HIM professionals will constantly have to scramble through numerous sources for documents, slowing down their progress with this unnecessary complication. Therefore, incorporating an EHR would make it so that it can enable coding technology such as AI to offer coding suggestions. This will be more efficient in the long run, and it is also a time-saver for the coders who would not have to process paper-based sources in a manual approach.
When using legacy technology or manual processes, a large concern shared by coders revolves around the accuracy of coded charts. Manual processes will always have the possibility of human error, whether that be during coding or while reviewing the code. Similarly, legacy coding and auditing technology, such as CAC, cannot account for all errors while coding and reviewing. This contrasts AI and its cutting-edge NLP technology, which is not limited in efficiency and accuracy like CAC is due to its rules-based technology.
One instance of AI surpassing CAC’s accuracy limitations is how CAC’s coding suggestions do not account for clinical context. Not only do AI suggestions accomplish this, but they are also linked to the documentation to provide more accurate insight. Another area where CAC suffers in the accuracy department is its inability to offer a second pass to confirm data accuracy, whereas AI optimizes coded data for complete and accurate funding by auditing.
Finally, the lack of AI implementation raises several team-based challenges for HIM professionals. They are often expected and requested to work overtime often due to their heavy workloads and demanding deadlines. Many feel that their team is too small to keep up with coding demands, and with the reported “hiring challenges for… coding positions, especially in the wake of COVID-19,” it is clear that the coder shortages are a large factor behind what is taking its toll on these workers. These challenges can be boiled down to there being an overwhelming amount of work spread across too little people. With employee burnout and low team morale also being constant concerns, the solution is simple: the coder shortages need to be compensated by helpful technology that is able to lessen the workload for HIM professionals, boost workflow, increase efficiency and productivity, and make processes easier and more accessible for coders. AI-assisted medical coding and auditing technology is this solution.
When the very tools made to help people only limit them, it is difficult to make progress at a consistent rate. Manual processes and legacy CAC technology fail HIM professionals in that they bring them more challenges than solutions in this modern era of digital health. With these workers having expressed that they feel their work is undervalued, it is important to keep in mind that forcing them to do more with less is not sustainable or fair to them. The best way to help them is to listen to their concerns regarding outdated technology, and do something about it. AI-assisted medical coding and auditing technology is not just the solution to boosting efficiency, accuracy, and team tasks, it is also a way of letting your coders know that they are appreciated and heard.
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Semantic Health helps hospitals and health systems unlock the true value of their unstructured clinical data. Our intelligent medical coding and auditing platform uses artificial intelligence and deep learning to streamline medical coding & auditing concurrent with patient admission, improve documentation quality, optimize reimbursements, and enable real-time access to coded data for secondary analysis.
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