I know that technology can help us become more productive, however we need to make sure we apply the right technology to help, considering that OCR-based processing has not worked as expected. We are looking forward to having Machine Learning help us improve processing physician’s authorizations and use that learning to improve other processes.EVP and Leader, Managed Healthcare provider

Introduction

The client is a leading managed healthcare provider, issuing Medicaid, Medicare Advantage and Medicare Prescription Drug plans to over 3 million Americans. The client engaged Pluto7 to help identify practical applications of Machine Learning (ML) to solve key business challenges for its products, offerings and services.

Why We Chose Pluto7

This client looked to Google Cloud and Pluto7 to help them identify the right use cases for driving transformational change. Pluto7, using their deep Machine Learning expertise, ran workshops with the customer to establish the root of their needs. The client chose to focus on routing classification to achieve the transformation they needed: expedited vs. routine authorization vs. retro identification.

Solution

This managed healthcare provider has experienced rapid growth in recent years, with their products and services used by over 3 million members. This growth meant that certain manual processes, such as physician’s authorizations processing, were slowing things down. They needed to be reassessed from the perspective of achieving breakthrough productivity.

The client received faxed documents which were either printed or handwritten. The fax contained various forms, some relevant and some non- relevant. These could include Authorization Requests, Expedited Authorization Requests, Retro, Ad Hoc Ads, or more. From the fax documents, the client needed to identify relevant information, then classify whether to expedite the document using ML or AI.

The client’s current OCR based system unfortunately failed as it was unable to process whether the authorization notes from physicians were expedited or not. This is because the notes were often handwritten.By using Machine Learning, human decision making was reduced. ML and AI was able to determine the content of the faxed documents autonomously, being able to more accurately identify the contents of handwritten notes. This in turn helped drive faster processes and reduce lead time to action on the content of the fax. Ultimately, the ability to classify expedites became faster.

“Once we understood the power of Machine Learning and its ability to detect images and also process text we felt comfortable that the solution will work ”– Claim Handling Specialist, Managed Healthcare Provider

With these objectives in mind, this managed healthcare provider partnered with Pluto7 to transform their physician’s authorization processing. Faxed document data, fax volume, and the labor hours involved were analyzed to finalize the solution.

Results

“Google Cloud Platform’s immense processing power and ability to leverage easy to morph pre-built vision and natural language processing models along with Tensorflow to model custom prediction was the key enabler in developing a productivity platform for the managed healthcare provider.” 
– Salil Amonkar, Global Head ML/AI Practice and Delivery, Pluto7

Working with Pluto7, this client was able to take their innovative mindset and apply it to their business, improving internal productivity and changing their members’ experience. By leveraging Google Cloud Platform and its components (Cloud Storage, AutoML Vision, NLP, TensorFlow, Data Studio and Machine Learning Engine) they were able to drive innovative process improvement, seeing tangible business benefits.

Industry High-tech| Healthcare

Solutions Demand-ml| Preventive-maintenance-ml| Marketing-ml| Healthcare-ml

Challenges

  • Currently, most of the client’s decisions are made by people and based on the information they gather and assess. This includes specific care provided to customers, claims management, monitoring the patient’s lifestyle, determining their risk levels and also the physician’s authorization processing.
  • Although the client was using Optical Character Recognition (OCR) software, the physician’s authorization processing was manual, requiring high levels of human intervention. This caused delays in handling priority authorization requests, including patients needing emergency or urgent care.

Results

  • Using the Machine Learning model to drive the processing of emergency authorizations reduced the delays significantly, with no hourly delays recorded.
  • The team was able to scale and process more physician’s authorizations with reduced headcount.

Products Used

  • Google Cloud Platform
  • TensorFlow,
  • Google Cloud AutoML Vision
  • Google Cloud Storage
  • Google Cloud Dataflow
  • Google Machine Learning

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