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Automated Medical Coding

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Delivering Transparency and Accuracy to Medical Coding Using AI/ML

Drawing from his extensive years of experience in the industry, aiHealth’s chief technology officer, Dave Wesley, talks about the launch of aiH.Automate™ and how it fits into today’s market. He focuses on the importance of transparency in creating an AI-ML platform, as well as the interaction between human and machine.


Q: Over the past 3 decades you and your teams have successfully delivered a myriad of software solutions ranging from complex cloud-based enterprise software solutions to mobile applications enabling and supporting healthcare and other business domains in the USA. As an experienced technologist and entrepreneur, what excites you about the launch and team at aiHealth?

A: aiHealth’s business and clinical value-proposition is very straightforward and exciting. We are driven to improve process and economics for revenue cycle management (RCM) and provider organizations with clear benefits. Our platform provides coders, clinicians, IT, and support staff with a streamlined and holistic workflow. We believe there is a direct correlation between efficiency and workflow enhancements positively impacting financial and clinical outcomes.

The technology development, especially related to deploying “real” AI-ML, is extremely challenging, and therefore invigorating, for me and our seasoned healthcare software development team. aiHealth’s early strategic development partners are experiencing positive results and it reinforces the “why” behind our technology design.

Q: There is a lot of buzz about the applicability and efficiency gained when deploying Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA) in the healthcare space. What differentiates aiHealth’s Digitally Enabled Coding platform (DEC) from its competitors?

A: aiH.Automate™ is powered by a unique combination of NLP, machine reasoning, AI/ML, and expert rules. Today, provider organizations are using Computer Assisted Coding (CAC) which still requires 100% human review and re-work. With the combination of labor shortages and increase in denials many organizations will be unable to scale without AI/ML in place to address menial tasks.

"aiHealth’s autonomous coding engine delivers precision and transparency by diving deep into high-value medical specialties and extracting clinical insights required to present the most accurate code (CPT or E&M) for billing."

Our ability to process data coming from disparate sources including scanned documents has become a workflow-enhancing software platform, which has demonstrated benefits beyond autonomous coding.

With 3 decades of experience in healthcare software development and a team of AI-ML subject matter experts, aiHealth takes great pride in combining our technical prowess with integration, operational, and clinical workflow capabilities.

Q: How do you ensure aiHealth’s platform is transparent and compliant when generating autonomous CPT and E&M codes?

A: aiH.Automate™ facilitates transparency in the autonomous coding process by visually representing and reporting on confidence metrics at an aggregate note/encounter level. We pair an expert coder with the autonomous coding engine to ensure accuracy and compliant recommendations. Our autonomous coding approach includes a feedback loop to continuously learn and improve. Deploying AI/ML is a continuous learning and improvement exercise. We need to ensure continued performance in compliance, accuracy and transparency for our clients.

Q: How do you ensure the successful interaction between AI/ML and its human counterparts?

A: Our customer partners collaborate with the aiHealth team in an iterative user experience design and software development process. Our development team has experience building care management and patient engagement solutions, where optimized user experience and user interface are keys to success. We apply that experience forward to aiHealth.

"While a lot of the aiHealth solution is happening on the backend (i.e. autonomous coding, etc.), we are committed to providing an efficient and pleasurable workflow for monitoring the coding process and handling auto-coding exceptions."

In fact, early customer development partners are using our platform to support pure manual coding workflows because of the streamlined user experience and enhanced efficiency.

Q: What advice would you give fellow technologists and operators when evaluating where and how to deploy AI/ML within their revenue cycle processes?

A: Stay focused and don’t try to boil the ocean. Find clear problems to solve and prioritize the highest value drivers. Identify the ideal early partners and strive to adapt to current customer partner data flows and workflows. Most importantly, deploying AI/ML to any process isn’t easy and requires setting realistic expectations and goals.


About Dave Wesley:

Dave began his 30+ year information technology career as a software developer with Accenture and since has worked with Fortune 1000 and small start-up companies to design, develop, and implement progressive software solutions. He has also founded and led several healthcare information systems businesses, including Velocity Healthcare Informatics, the first commercial patient-centered outcomes software company in the United States, which was acquired.


Automated Medical Coding

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©2023 aiHealth   Privacy Policy | Cookie Policy

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©2023 aiHealth. All rights reserved.

©2023 aiHealth. All rights reserved

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