Introduction

The client is a leading Healthcare Analytics Company exclusively imparting data analytics and business procedural enhancement services aiming to improvise the patients’ medical conditions and lifestyles. They provide a platform that is EMR agnostic, real-time with improved data integration capabilities using avant-garde technologies like Data Visualization, Predictive Modeling tools to support healthcare strategies. The client has a huge number of patients’ information which is growing at a healthy rate. To curb the essential need for content management they are looking forward to adopting new technologies and leveraging their business intelligence.

Why did they choose Pluto7?

Entering manually to update the database with the details about every patient was a hugely time-consuming task. They discovered that even to review a piece of the single necessary information was strenuous. This manual procedure was barricading the growth and hampering the efficiency of the organization. Additionally, it required a lot of effort and extra working staff. Pluto7 is a tech-enabled organization enthralled with Artificial Intelligence, Machine Learning solutions for Supply Chain, Retail, Manufacturing, Automotive, Hi-tech, and Healthcare domains accompanied by GCP based business solutions for reshaping businesses.

Solution

Maintaining an entire database is not a trouble-free task, keeping in mind that no essential information is missed out. As a result, the Pluto7 team collaborated with Google to enhance the content management strategy followed by the client so far reviewing the manual tool automation and predictive analytics around the operational workflow. The objective of the team was 

  • To develop a solution for one structure Facesheet format by processing documents and automate the process of entity extraction by migrating the on-prem data to the GCP. 
  • To build Machine Learning models for extracting text from the pre-specified category of documents.
  • Employing the best, high-performance infrastructure, security, and pre-built solutions by AI as offered by the Google Cloud Platform.

Pluto7’s team recognized the loopholes faced by the client while maintaining their data and helped them migrate to Google Cloud Platform to further leverage their organization towards a well-structured content management strategy. We used Google Cloud Vision API to identify the document context and extract the raw data from the same. An AutoML NLP extraction model was used to extract the key identities. Saving the key extraction loop, the team also focused on measuring the accuracy and performance of the prepared model by developing an output Excel file that recognized keywords for Facesheet Formats.

Results

The approach used by the Pluto7’s team in collaboration with Google led us to achieve around 90% accuracy assisting the client in designing a dynamic data platform using an AutoML model. A cycle of procedure was followed beginning with the input and migration of the on-prem data to the Google Cloud Platform converting the data into text followed by automatic extraction of entities using the AutoML NLP model.

Industry Healthcare

Challenges

  • Established as a premier institute, the client had to manage bulk data of their patients.
  • This involved the workforce to manually feed in the data of the patients into the organization’s database leading to two major difficulties. One, this required extra staff, two the time management of the organization was hampered.
  • Helping the organization to migrate to Google Cloud Platform and making them familiar with how it works was done to ensure they minimize their time consumption over these manual tasks.

Results

  • The accuracy obtained from the AutoML NLP model for extracting entities was nearly 90%.
  • A fully configured application with Vision API based solution for raw extraction of data was developed.
  • A fully configured application with the NLP model to extract key identities was deployed in GCP.

Products Used

  • Vision API
  • Cloud Functions
  • Cloud Storage
  • Auto ML Natural Language.

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