Large call volumes into customer service call centers can lead to frustrated customers, delayed responses, and overburdened staff, particularly when a large number of queries could have been resolved with simple yes or no answers, or formulaic responses. In this session hear from the National Health Service (NHS) Business Services Authority, the support body for multiple NHS organizations in England, how it is using machine learning to manage the large volumes of calls coming to their call center (five million calls each year). Learn how these services have been used to reduce call response times, increase staff morale, and maximize staff utilization for value-add activities. See how to develop and implement Amazon Connect, Amazon Lex, and Amazon Polly to automate call centers, reduce labor costs, and provide a consistent experience for customers.

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Optimizing Healthcare Call Centers with Natural
Language Understanding
H L C 3 0 5
Corina Motoi
Solutions Architect
AWS UK Public Sector
Angus McAllister
Solutions Architect Lead
AWS UK Public Sector
Chris Suter
Lead Cloud Architect
NHS Business Services
Authority UK

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS re:Invent this chalk talk
Large call volumes into customer service call centers can lead to frustrated customers, delayed
responses, and overburdened staff, particularly when a large number of queries could have been
resolved with simple yes or no answers, or formulaic responses
In this session, hear from the National Health Service (NHS) Business Services Authority, the support
body for multiple NHS organizations in England, and learn how it is using machine learning to
manage the large volumes of calls coming to their call center (5 million calls each year)
Learn how these services have been used to reduce call response times, increase staff morale, and
maximize staff utilization for value-add activities
See how to develop and implement Amazon Connect, Amazon Lex, and Amazon Polly to automate
call centers, reduce labor costs, and provide a consistent experience for customers

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
Intro
Quick win—voice chatbot for your call centers
NHSBSA case study
Optimizing healthcare call centers with more natural
language understanding
Wrap-up

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Related breakouts
Wednesday, Nov 28
AIM202-L - Leadership Session: Machine Learning
1:45 PM - 2:45 PM | Venetian
Wednesday, Nov 28
AIM304 - Transform the Modern Contact Center Using Machine Learning and
Analytics
4:45 PM - 5:45 PM | Venetian
Wednesday, Nov 28
LFS303 - AI/ML in Life Sciences: Predictive Modeling with Amazon SageMaker
7:00 PM - 9:15 PM | Aria West, Level 3, Ironwood 1

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Let’s get to know each other (1)
How many of you are providing a
call center solution for your
company?

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Let’s get to know each other (2)
How many of you are working for
a healthcare organization?

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Let’s get to know each other (3)
How many of you are familiar
with AWS solutions for AI/ML?

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How to build it
Amazon Connect is a self-service, cloud-based contact center,
based on the same contact center technology used by Amazon
customer service associates around the world to power
millions of customer conversations
Amazon Lex is a service for building conversational interfaces
into any application using voice and text
Amazon Polly is a service that turns text into lifelike speech,
allowing you to create applications that talk, and build entirely
new categories of speech-enabled products

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Call flow example
Id to where
“I’d like to know where
my health card is”
Automatic speech
recognition
Health card location
Natural language
understanding
Intent/Slot model
Utterances
Health card chatbot
Intent Request
information
Item slot Health card
location
Would you like to find out
where your health card is?
like know
my Health card is
Request information

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Salesforce
Zendesk CRM
Call
recordings
Soft phone
Desk phone
Amazon Connect contact control panel

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So … what is the quick win for your
business?

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How we use AWS Connect
Set objective: test whether an automated service can reduce call center
load by providing simple answers to information advice and guidance
Prototype: iterated service to create a series of “utterances” that
would be recognized and “intents” that it would understood
Public trial: service went live for the EHIC line
Increase scope of public trial: service provided 24/7 to
the public
Conclude POC: demonstrated service improvements

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Benefits observed
• Over 75% of calls handled
within six minutes
• Contact center saw a 26%
reduction in traffic to agents
• Amazon Connect answered 3.8
average voice requests per call

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Results after the Amazon Connect POC
4,316 calls resolved by
Amazon Lex (42.34%)
663 calls dropped by
Amazon Connect (6.5%)
5,215 calls transferred to
agent (51.16%)
10,914 calls
received

19. “All this benefit was delivered
in two weeks …”

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more

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The AWS AI/ML stack
Application
services
Amazon
Rekognition
Amazon
Rekognition
video
Amazon Polly
Transcribe
Translate
Comprehend
Amazon Lex
Amazon SageMaker Amazon Mechanical Turk
Platform
services
Frameworks KERAS

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sentiment -
NLU

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How to build it
Amazon Transcribe is an automatic speech recognition (ASR)
service. Using the Amazon Transcribe API with its speech-to-
text capability, you can analyze audio files stored in Amazon
Simple Storage Service (Amazon S3) and have the service
return a text file of the transcribed speech.
Amazon Comprehend is a natural language processing (NLP)
service that uses machine learning to find insights and
relationships in text.

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Salesforce
Zendesk CRM
Call
recordings
Transcripts
{
“SentimentScore": {
"Mixed": 0.030585512690246105,
"Positive": 0.94992071056365967,
"Neutral": 0.0141543131828308,
"Negative": 0.00893945890665054
},
"Sentiment": "POSITIVE",
"LanguageCode": "en"
}

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Salesforce
Zendesk CRM
Call
recordings
Transcripts Enriched
data

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neural machine translation service

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How to build it
Amazon Translate is a neural machine translation service that
delivers fast, high-quality, and affordable language translation.
The service can be used via an API, enabling real-time
translation of text from the source language to the target
language.
Amazon Translate supports translation between English and
any of the following languages: Arabic, Chinese (simplified),
Chinese (traditional), Czech, French, German, Italian, Japanese,
Portuguese, Russian, Spanish, and Turkish.
Six additional languages will be supported in the coming
months: Danish, Dutch, Finnish, Hebrew, Polish, and Swedish.

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Salesforce
Zandesk CRM
Call
recordings
Transcripts Enriched
data

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Salesforce
Zendesk CRM
Call
recordings
Transcripts Enriched
data

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Amazon Connect

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Salesforce
Zendesk CRM
Call
recordings
Transcripts Enriched
data
Backend
systems

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What about the performance of
your call center?

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Salesforce
Zendesk CRM
Call
recordings
Transcripts Enriched
data
Backend
systems

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Salesforce
Zendesk CRM
Call
recordings

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Wrap-up—What we have used

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Wrap-up—What we have learned?
Automatic speech recognition/natural language understanding
(Amazon Lex & Amazon Polly)
Natural language understanding (Amazon Comprehend)
Neural Machine Translation (Amazon Translate & Amazon Lex &
Amazon Polly)

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Demo—Amazon Connect/Amazon Lex setup
Step 1: Basic Amazon Connect setup
• Create Amazon Connect instance
• Claim phone number
• Build a contact flow
• Make a test call
Step 2: Basic Amazon Lex integration
• Create sample Amazon Lex chatbot
• Manage Amazon Lex/Amazon Connect permissions
• Integrate bot with contact flow
• Make a test call
Step 3: Customize your Amazon Lex bot
• Build slots
• Understand intents/utterances

40. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Corina Motoi
cmotoi@amazon.co.uk

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.