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Coding
The rise of EMRs and the introduction of ICD-10 led to computer-assisted coding use in the United States. Now, the US is starting to turn to AI. Canadian hospitals are just starting to see similar jumps in EHR adoption with new opportunities to evolve operational processes. Learn how Canadian hospitals can fast forward through a decade of HIM technology progress by leapfrogging to AI adoption, and the benefits they will see upon implementation in their own HIM departments.
It is not every day that a country can choose to fast forward through a decade of technological progress but Canadian hospitals have been given this very opportunity to learn from their U.S. neighbours. The American timeline for HIM technology adoption spans over the last 13 years, with the HITECH Act of 2009 opening the door to industry advancements. Presently, the U.S. is now moving towards AI-assisted medical coding and auditing in their hospitals. To leapfrog past this decade of progress that the Americans experienced, Canadian HIM leaders should learn about the journey to today’s AI technology so that you can weigh your options between manual, legacy software, and the unique AI platform Semantic Health has to offer.
With the Health Information Technology for Economic and Clinical Health (HITECH) Act calling for the meaningful use and adoption of EHRs in 2009, hospitals across the United States had a need to adopt EHR systems in order to continue working with government health plans. The provisions of the HITECH Act and the Federal Health IT Strategic Plan “led to widespread adoption and use of health IT, developing the infrastructure necessary to accomplish a central intent of the Affordable Care Act: affordability, access, and quality.” Where the HITECH act sparked the use of EHR systems in 2009, the introduction of ICD-10 in October 2015 did the same for CAC: the technology became more normalized in order to combat newfound capacity issues within mid-revenue cycle processes, like medical coding & auditing. While CAC software was helpful in keeping up with ICD-10 at the time, hospitals in the U.S. are now looking at using AI software to better aid their coders and apply predictive analytics across the organization. Now, Canada can fast forward a decade of progress by skipping the legacy software trialed by the U.S., and jumping to the AI innovation that they are now starting to embrace.
By 2015, he Office of the National Coordinator for Health Information Technology disclosed that “nearly all reported hospitals (96%) possessed a certified EHR technology… [and that] 84% of hospitals adopted at least a Basic EHR system.” These statistics represent a 9-fold increase since 2008.
The move from paper notes resulted in more clinical documentation, as it is faster to type or dictate than write, meaning that there was more content for the coders to review. An EHR was one of the first meaningful examples of HIM technology advancement and symbolizes a significant step forward in the development of the HIM department. EHRs are also important in that they enable coding technology like CAC and AI to offer coding suggestions because scanning digital documents is easier than scanning paper ones. Because of this, EHR systems marked the opening to future advancements, and served as a very important foundation for medical coding and auditing assistance.
With EHRs already incorporated in most American hospitals and clinicians having to become more detailed in their documentation, coders saw decreased rate of production due to being tasked with more to read and review. In addition, with the ICD-10 launch, there was a significant increase in the amount of potential codes that coders could look at. Coupled with higher pressure in getting claims to insurance companies quickly for payments to arrive faster, it is no wonder why the U.S. was eager to implement CAC to accelerate their preparations for the ICD-10 launch and keep productivity steady after the transition.
CAC was revolutionary for its time. The keyword-scanning technology succeeded in assisting coders and increasing efficiency at a time when hospitals were looking for any way to keep productivity levels up in the midst of the new codes post-ICD-10 transition. CAC solutions improved both coder productivity and coding accuracy, as defined by precision and recall. The improved results in productivity and accuracy for both the inpatient and outpatient settings are visible within days of the implementation.
There is no doubt that CAC has achieved a lot for the world of medical coding, but it is also not where the growth in technological assistance ends. As we delve into the era of AI exploration and innovation, we discover that the boundaries of the past, which were limited by legacy, manual processes, can be pushed further in ways and extents that were previously unheard of.
Today, American hospitals are starting to replace their CAC technology and invest in AI-assisted medical coding and auditing to scale efficiencies, improve data, and increase reimbursements. Many hospitals make the switch because AI and its cutting-edge NLP technology allow for several efficiency and accuracy upgrades that CAC, limited to its rules-based technology, is simply unable to reach. An example of this is how CAC’s coding suggestions do not account for clinical context, whereas AI suggestions accomplish this and are also linked to the documentation. Additionally, CAC does not offer a second pass to confirm data accuracy, whereas AI optimizes coded data for complete and accurate funding by auditing.
The differences between the two technology engines even expand to the amount of data each system is able to ingest: within audit, an AI engine is able to review 100% of the coded data, contrasting manual audit processes in which the providers are often able to review less than 10% of coded charts. Overall, AI has the range and capabilities to achieve maximized coder efficiency in areas that legacy CAC software is otherwise limited, and is thus the most modern and innovative technology in current day.
Contrasting the United States, Canada did not take a significant part in the increasing EHR and CAC adoption trend in the last decade, and is just starting now. However, that is not to say that the country is therefore destined to spend the next decade following the U.S.'s path through HIM technology adoption. In fact, Canada has the opportunity to fast-forward a decade of progress through the American timeline and jump right to the most current and intuitive technology: AI-assisted medical coding and auditing.
With the shortage of coders and auditors in the country, HIM technology adoption can support productivity and accuracy gains, as it will help departments to increase capacity. This will then prevent backlogging, provide less coding and auditing errors, and overall, speed up the process and increase efficiency at levels greater than even CAC software.
How will AI software accomplish this? With the Semantic Health Information Platform, hospitals are able to ingest clinical documentation on a real-time basis as it is fully integrated into the EMR. Then, using NLP technology, it automatically offers coding suggestions that coders can then review and accept. After charts are coded via this streamlined process, the Semantic Health engine will review all of the coded data and compare it with the clinical documentation to flag instances of undercoding, overcoding, or documentation deficiencies.
The past decade has seen the emergence of various HIM technologies in the U.S. The latest in the timeline are AI-operated systems, which open the floor to bigger possibilities and higher efficiency in the healthcare environment than any other technology in the industry thus far—there is no question about that. The question only lies in whether or not Canada will continue to trail behind in the timeline compared to their American neighbours, or if they will choose to blaze the trail ahead to the future of AI-assisted medical coding and auditing.
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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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