Analyzing Patient Sentiment with Peerlogic (full video) | C2C Community

Analyzing Patient Sentiment with Peerlogic (full video)

Categories: AI and Machine Learning API Management Industry Solutions Google Cloud Partners Healthcare and Life Sciences Session Recording
Analyzing Patient Sentiment with Peerlogic (full video)

This C2C Deep Dive was led by Sindhu Adini (@Sindhu Adini) , director of Google Cloud for HLS at SpringML, a C2C foundational platinum partner. Joining Sindhu from the Peerlogic team were CEO Ryan Miller (@ramill401) and Alex Maskovyak, engineering and product development executive.

Peerlogic is an innovative provider of cloud communications, building better conversations and high production through the power of AI. Their products allow individuals to work more productively, teams to collaborate more freely, and organizations to better understand their data.

The full recording from this session includes:

  • (2:05) Speaker introductions
  • (4:20) SpringML’s specializations and industry reach
  • (6:50) An introduction to Peerlogic and how they are empowering dental practices with improved communications between staff and patients
  • (12:05) Analyzing patient sentiment with AI and ML
    • Adopting call center best practices and front desk assistance
    • Identifying revenue leakage
    • Benchmarking and understanding conversion opportunities
  • (17:10) Overview of Peerlogic’s application
  • (21:15) Google Cloud services and components used
    • Choosing Google Cloud
    • Spectrum of AI in the Google Cloud ecosystem
    • Google Cloud Vertex AI for pre-trained APIs and end-to-end integration for data and AI
  • (31:25) Architectural overview of the solution and model
    • Data pipeline to ingest audio scripts and Google Cloud Speech-to-Text
    • Enhanced augmentation of the solution using custom ML algorithms
    • FireStore to authenticate AppEngine access only to Service Accounts
  • (38:50) Key considerations for Machine Learning
    • Identifying the business problem that needs to be solved
    • How predictions are made
    • Supervised learning
  • (44:55) Custom patient call analysis model

Watch the full recording below:


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