Coding
AI-assisted medical coding can bring providers a wide variety of benefits, but some hospitals have a more urgent need to adopt the technology than others. If you meet one or more of the following criteria, you should consider adopting an AI-assisted medical coding solution soon in order to avoid long-lasting and impactful issues with medical coding, compliance, and loss of valuable data.
Not sure what AI-assisted medical coding is yet? Read Next Generation Computer Assisted Coding: From Rules to Machine Learning
When your coded data is compared with other Canadian hospitals, how do you shape up? If you perform below average for any key performance indicators, this signals one of two things: (1) you need better access to data to make real strategic changes, or (2) you need to improve the accuracy and quality of your coded data so that your statistics are more reflective of the services provided. For example, if your HSMR score is higher than average, but many of the deaths were not preventable, more appropriate medical coding can significantly change your hospital’s ranking and, as a result, the funding that you receive. In a case like this, the ROI of AI-assisted medical coding software would be significant.
Hiring and retaining experienced coders is one of the biggest challenges that hospital leaders share with us. There is a greater demand for these professionals than there are coders. Adding to this challenge is that training coders to follow hospital-specific medical coding processes is incredibly time-consuming and complex. This can make the onboarding process long and tiresome, and when turnovers occur, more costly than you would expect. If your hospital is facing retention or onboarding issues, then it is important to future-proof against this operational risk. By leveraging technology to streamline the process of medical coding, your organization can decrease the initial learning curve, potentially to under two weeks, and enable coders to work on higher-value work moving forward, increasing employee retention.
From poor physician documentation that cannot be caught until months later to manually searching through thousand-page codebooks, medical coders face intense time pressures, growing backlogs, and are provided with legacy technology that reduces their efficiency. Medical coders deserve better. Medical coding has been a manual and labour-intensive process. With or without CAC, specialists are often required to comb through hundreds and sometimes thousands of pages of complex clinical documentation to identify relevant information, map key clinical events, and appropriately code diagnoses and treatments. This costs providers millions year after year and the complex and inefficient workflow design continues to put medical coders and auditors under significant time constraints and pressure.
The clinical documentation of hospitals with unique patient populations is different from that of other health providers. Physicians may use dissimilar terms, the medical coding rules vary, and consequently, different diagnosis and procedure codes are relevant for their individual contexts. Since CAC is a generic tool, built to search for keywords, it cannot be tailored to meet the needs of complex and unique populations. By contrast, AI-assisted medical coding can be quickly tuned to these unique patient populations because it uses deep learning and NLP to accurately digest clinical information and suggest medical codes based on the context of the documentation. This drives better medical coding outcomes across care settings and offers the full benefits of assisted coding to every type of provider. For example, Semantic Health recently implemented its AI-assisted medical coding and auditing platform at a large children’s hospital in Canada.
Read Semantic Health Announces Multi-Year Contract with SickKids to learn more.
This paediatrics context required a tailor-made solution that was tuned to how documentation, coding, and auditing was created and performed at that hospital. To do this successfully, the algorithm was trained on paediatrics data and the model was tuned to account for documentation nuances and medical coding directives in predictions to make them more accurate and relevant.
If current medical coding processes do not support collaboration between clinical documentation improvement (CDI), medical coding, and physician teams, then efficiency is missing. High-quality clinical documentation is at the heart of provider operations, medical coding, reimbursement, and reporting. However, valuable CDI specialists’ work demands access to high-quality, accurate, and timely data. To enable this, medical coding must be performed rapidly, so that CDI teams can analyze the results, and implement effective programs with physicians. When physicians’, CDIs’, and coders’ workflows allow for collaboration, these innovations become possible. If your hospital, like many others, has coders facing weeks or months of backlogged charts, then your CDI programs are suffering and so is your hospital. Using AI-assisted medical coding software to implement concurrent coding will streamline the workflow of your teams and improve the impact of CDI initiatives.
The healthcare industry is often criticized for being slow to innovate. This is not a result of providers being unwilling to prioritize patient satisfaction, but, in part, not having access to timely, accurate, and high-quality data. With medical coding teams at many hospitals facing months-long backlogs of charts, data access is slow. Due to this delay, documentation issues are late to be caught and, consequently, physician inquiries are unlikely to turn up valuable information. As a result, the data generated by coding is not particularly valuable for AI initiatives.
Due to the complex and problematic nature of manual and CAC coding processes, medical coding data is slow and error-prone, which can limit leaders’ ability to see how their teams are performing. This lack of visibility on health system performance has had long-term consequences for the standards of care, medical coding, and decision-making quality. By accelerating data use and optimizing for quality, AI-assisted medical coding systems create an enriched data layer that can power various operational and research initiatives, like population health management, clinical research, and wastage in revenue cycle management. Real-time access to coded data can provide insight into current, predicted, and historical activities, helping providers to understand their risk and quality score data. This data will enable providers to improve efficiency and care coordination. With better insights, providers can make informed decisions to maximize their impact and minimize their costs. If you are not able to perform accurate predictive analytics or your research and operations are limited by a lack of access to data, AI-assisted medical coding will significantly enhance your current processes.
Want to learn more about what AI-assisted medical coding is, how it compares to computer-assisted coding (CAC), and if it will provide measurable benefits to your hospital? Read our white paper, AI-Assisted Medical Coding for Canadian Hospitals.
By implementing an AI-assisted medical coding platform to address key challenges, healthcare providers can improve operational efficiency, increase reimbursement and data quality, future proof workflows, and lay a foundation for future AI initiatives.
If you are interested in learning more about how AI-assisted medical coding can improve efficiency and accuracy, our team of experts would be happy to discuss the unique needs of your health system. Reach out to contact@semantichealth.ai to learn more.
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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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