Dumb Reasons AI Strategies Fail

Artificial Intelligence (AI) is expected to be a game changer for healthcare revenue cycle management, especially as providers grapple with the impact of COVID-19. The right AI technologies can drive revenue by reducing human touches and increasing efficiencies. Unfortunately, many companies attempting to adopt AI fail.

Why AI Projects Often Fail

Becker’s Healthcare^[i] sums up the findings published in a June 8 Harvard Business Review report^[ii], noting that the missing component of many AI projects is AI operations, or AIOps:

“AIOps is the process of "building, integrating, testing, releasing, deploying and managing the system to turn the results from AI models into desired insights of the end-users," according to the report.

Businesses often spend time and resources working on the AI models themselves, but often fail to consider how to make the tech work with the systems they already have. AIOps relies on the necessary hardware, software and team members to integrate the new AI into the company's existing processes and systems.

Achieving a successful AI integration begins with a well-designed production environment, which requires dependability. The AIOps team must avoid data slowdowns by putting proper processing and storage systems in place that can handle latency issues.

The production environment must also be flexible enough to support quick system reconfiguration and data synchronization without compromising running efficiency. The architecture should be flexible and separated into manageable portions that can be added or replaced if an issue arises.

Scalability and extendibility are also critical in a successful production environment, because when integrating a new AI solution, the existing infrastructure must adapt. The team's ability to "adjust, tinker and test the existing system with the new proposed system" is important for business to continue while implementing upgraded AI models.”

Customized Solutions

Meduit leads the healthcare RCM industry with customized AI solutions that integrate with your organization’s systems and are easily adapted to your needs. These solutions are scalable, flexible and extendable. The MeduitAI solution suite includes:

• MedAutoCode – autonomous clinical coding that eliminates manual processes, increases accuracy and lowers cost
• Voice Analytics – that analyzes collections calls to hospitals and health system patients to drive better patient experiences
• Automated Pre-Authorization – that automatically submits an authorization when triggered by a new order or schedule entry
• Automated Claims Follow-up – that goes beyond a simple claim status to determine and execute the next action needed to get the claim resolved
• Prediction Tools
• A patient’s propensity to pay
• The probability of if and when payer will pay claim
• Patients who are most likely to qualify for charity programs

By leveraging true machine learning and reducing human touches, MeduitAI solutions increase quality and accuracy while lowering cost and accelerating cash. Learn more about how MeduitAI can help take your organization’s revenue cycle management to the next level with proven solutions that are designed to fit your systems. 

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[i] Becker’s Healthcare. https://www.beckershospitalreview.com/artificial-intelligence/why-ai-projects-fail-5-things-to-know.html?origin=BHRE&utm_source=BHRE&utm_medium=email&utm_source=BHRE&utm_medium=email&oly_enc_id=5889F7201556H1J. Accessed June 11, 2020.
[ii] Harvard Business Review. https://hbr.org/2020/06/the-dumb-reason-your-ai-project-will-fail?ab=hero-subleft-3. Accessed June 11, 2020.