The Future of Medical Coding and Auditing in Canada

Semantic Health focuses on making health data more actionable. We leverage the power of artificial intelligence (AI) and machine learning (ML) based algorithms to improve data quality. Our simple deployment process and state-of-the-art coding and auditing software, the Semantic Health Information Platform, assists our clients with a seamless experience to improve data quality and achieve better coding efficiency.

The medical coding and auditing industry requires innovative solutions that improve the overall accuracy and efficiency of operations. However, the majority of solutions available in the market today are based on legacy, keyword-matching technology. In Canada, computer-assisted coding (CAC) tools have traditionally been unable to suggest medical codes and are limited to highlighting clinical keywords. Medical auditing practices still remain largely manual, with auditors manually comparing current procedures against a defined (desired) standard and relying on random sample audits.

Consequently, this key part of hospital operations, which aims to safeguard clear analytics that can help us measure health system performance and ensure a high quality of clinical care for patients, is time-consuming and error-prone. Change is required if hospitals hope to keep up with increasing demands and plan for a data-driven future.

AI-powered coding and audit solutions can provide predictions based on available patient data, allowing coders to focus on more complicated scenarios and move past low-hanging fruit. Using a dynamic machine learning method, new AI-assisted coding tools can quickly and effectively learn from years of coded data to address deficiencies associated with manual and error-prone coding processes by accelerating coders’ review of charts with accurate code predictions based on the clinical context.

How is AI-assisted coding more efficient than computer-assisted coding (CAC)?

CAC is software used for extracting and transcribing clinical notes into diagnosis and procedure codes for coding and billing purposes. Many hospitals have implemented this tool in hopes of improving the coder's efficiency and accuracy.

Traditional CAC, however, has its limitations, such as difficulty processing longer and more complex inpatient visits. It also leaves a slow and fragmented coding process. Often, CAC is simply one tool out of many that a coder is required to reference during their work. Rather than help coders work faster, it often adds work by highlighting unimportant information, leading to reduced operational efficiency.

Advancements and groundbreaking improvements in natural language processing (NLP), coupled with AI and deep learning, have enabled Semantic Health to develop a revolutionary unified medical coding and auditing software. The software helps to identify the lack of documentation to support assigned codes. AI-assisted coding can proactively predict acceptable diagnosis and procedure codes (including ICD-10-CA, CCI 2018 & 2021, ICD-10-CM, and ICD-10-PCS) while creating an evidence trail for such codes inside the medical record. Coders can review the code suggestions with reference to the exact locations in the clinical documentation that are supporting evidence for such codes. This means that AI-assisted coding can operate as an efficient, first-line medical coder by making intelligent and appropriate code predictions when the patient is admitted and documentation is created.

How can AI-assisted auditing make a difference in your health system?

Today, most hospitals have an audit process around coding that is largely manual. It is typically a structured program that objectively monitors and assesses all practitioners' clinical performance, identifies potential possibilities for improvement, and delivers queries for  implementing and maintaining  improvements. Audit programs, however, have difficulty scaling to include all data running through an organization and tend to focus only on a small, random subset of an organization’s data.

Coding and documentation errors may be rectified proactively by deploying an AI medical auditing platform to overcome manual coding failures, saving time and money and enabling the top actionable data use cases. An AI-powered solution can help your health system in facilitating the following:

  1. Prioritizing the most important coding and documentation deficiencies for auditor review, removing the need for random sample audits.
  2. Identifying the exact potential coding deficiencies with evidence trails back to the documentation.
  3. Optimizing data quality for billing by ensuring documentation is not under-coded and complete case complexity is covered.
  4. Identifying opportunities in clinical documentation to identify documentation improvements and increased data quality

What does the Semantic Health Information Platform do?

The Semantic Health Information Platform analyzes clinical notes as they are created to generate longitudinal patient narratives, identify data quality deficiencies, and assign medical codes based on clinical evidence. It also audits coded charts in real-time to determine whether the clinical documentation supports assigned codes. All insights are then presented to coders and auditors in an intuitive user interface to increase efficiency and accuracy, spin up clinical documentation improvement (CDI) programs from scratch, and build an actionable data layer.

From the coding perspective, the platform provides coders with an immediate idea of the most applicable medical codes within a chart, accelerating their process. On the audit perspective, the platform then flags missed or unspecified codes and directs the auditor's review towards the exact documentation that requires further review. The insights can be used to map out data-driven, high-quality, and relevant clinical documentation improvement programs to address data quality.

At its core, the Semantic Health Information Platform  seeks to make unstructured health data more actionable and easily navigable, which helps HIM professionals focus on high-value data opportunities and empowers CDI programs. The platform has a low-touch deployment process and can be seamlessly and securely deployed on-premise, through remote and touchless means, or our PIPEDA-compliant cloud environment. The platform is EMR agnostic and can be integrated with all EMR systems. This helps in making the platform compatible with all kinds of data environments. The platform can also ingest custom data feeds and sources and unify these data sources into a single source of truth for coding and auditing. The Semantic Health Information Platform’s real strength lies in its ability to make the coder’s job more efficient and the auditing process faster and more streamlined.

Results drive the Semantic Health Information Platform. At leading hospitals, the Semantic Health Information Platform has:

  • Made the audit process 3x faster
  • Increased the audit yield, measured in increased HIG/CMG+ weighted cases, by up to 50%
  • Improved coding efficiency by 25%
  • Uncovered millions in missed reimbursements
  • Created an actionable data layer to enable further analysis and research of unstructured data

In addition, the Semantic Health platform has addressed the most common coding issues that are not caught in coding audits.

Do you want to know more about the working of AI-assisted medical coding and auditing and how it can improve the accuracy and efficiency of your health system?

Book a demo with us today.

About Semantic Health

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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Learn what AI-assisted medical coding is, how it compares to computer-assisted coding (CAC), and if it will provide measurable benefits to your hospital.

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