Michael Laidlaw, Healthcare Revenue Cycle Consultant to SmartSource, LLC

Dinesh Gambler, PhD, President, First Outcomes.

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Every day someone is touting "something new" with aftificial intelligence and how it is either going to save or destroy the world. ln the meantime, we believe it would be helpful to understand a little bit more about what they are talking about and how these applications apply to healthcare revenue cycle activities.

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Al are "intelligent" program applications that can work independently or in concert with other programs to produce desired results.

 

The four basic "intelligent" applications are:

 

Speech Al
Al Vision (OCR)

Robotics Process Automation (RPA)

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Before our apocalyptic demise, if you believe it or not, we will define each application, what it does and how it may be used in the revenue cycle. There are a number of companies out there that have developed some good and some not so good Al applications.

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We believe having the knowledge of the applications and how they work within the revenue cycle will be helpful when you are making decisions that impact your department and your healthcare provider.

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As you read through some of the examples, they may sound repetitive in that they are used multiple times; however, you will note that they may use a different AI application. For example, where you may use Speech Al for

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Patient intake and registration you may also use AI Vision during the registration to translate paper documents to data that can be uploaded to your EHR.

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There are multiple examples so it would be beneficial to have the right Al "tools" enhance all of your revenue cycle activities.

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So let us begin:

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Speech Al, also known as Speech Artificial Intelligence, refers to technologies that utilize artificial intelligence to enable machines to understand, generate, and interact using human language. lt involves two primary fields of study: Natural Language Processing (NLP) and Automatic Speech Recognition (ASR).

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Natural Language Processing (NLP): This branch of Al is focused on the interaction between computers and human language. lt involves understanding, interpreting, generating, and manipulating human language in a way that is both meaningful and contextually appropriate. This is what allows Al to understand commands, questions, or even write and generatehuman-!ike text.

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Automatic Speech Recognition (ASR)' This involves the conversion of spoken language into text. This technology is often used in applications such as voice assistants, transcription services, and any other application where spoken language needs to be understood by a computer.

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These technologies, when combined, enable a variety of applications, including but not limited to virtual assistants (!ike Siri, Alexa, and Google Assistant), speechtotext transcription services, automated customer service systems, and many more. ln all of these, the aim is for the Al to understand and respond to human speech'as naturally and accurately as possible.

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Here are examples of Speech Al in the healthcare revenue cycle

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Patient lntake and Registration: Al can be used to streamline the process of collecting information from new patients or verifying information from existing ones. Speech recognition technology can transcribe spoken data into digital forms, which can reduce administrative workloads and minimize

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errors. This typically takes the form of an incoming telephone call from the patient interacting with a digital employee who can assist with the registration.

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Insurance Verification: Speech Al can interact with patients to gather their insurance information. The AI can understand complex insurance jargon and ask the necessary questions to clarify coverage details, thus ensuring the hospital or clinic has all the necessary information to bill correctly. This most often happens over the phone, outgoing call, when an eligibility check fails.