AWS Healthcare United Kingdom

NHS Midlands and Lancashire

Customer Service Automation

NHS ML helps to support general practitioners (GPs), hospital trusts, and other healthcare providers with services for patient communication and engagement.

67% of patients served with automated calls
9-14% reduction in patient waiting lists, depending on specialty
In partnership, NHS ML and Digital Space have developed an intelligent, automated, and cost-effective solution for managing patient waiting lists using Connect Customer, which...
NHS Midlands and Lancashire Launches Activate, Incorporating a Scalable, Cost-Effective Patient Contact Center Solution with Digital Space on AWS, Reducing Patient Waiting Lists...

Reported impact

Business outcome

In partnership, NHS ML and Digital Space have developed an intelligent, automated, and cost-effective solution for managing patient waiting lists using Connect Customer, which resulted in patient waiting lists being reduced by 9–14%, depending on specialty.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

NHS Midlands and Lancashire (NHS ML) supports nine Integrated Care Boards (ICBs) across England from its bases in the Midlands and North West, providing the high-quality transformational and back-office support they need. Using traditional methods for all patient contact was time consuming, ineffective, and expensive. The NHS ML Referral Management Centre chose AWS Partner Digital Space to migrate its patient contact center to Amazon Web Services (AWS) and deployed an automated multi-channel approach to patient communication and engagement. The innovative digital transformation of its patient waiting list management reduced waiting lists 9–14%, depending on specialty. NHS ML asked Digital Space to help it consolidate the services into a single cloud-based solution and develop its chatbots to automate a large proportion of telephone calls.

Data flow

The process can be repeated as many times as required.

Solution architecture

2 components · 1 layer
  1. Serving
    • Amazon Connect Handles omnichannel interactions for the customer and agent support workflows.
    • Amazon Lex Powers conversational self-service used by the customer and agent support workflows.

Architecture clues

  • If a patient informs the chatbot that they want to come off the list, the system diverts the chatbot call to an NHS ML Referral Management Centre call center operator in near real time and supplies...
  • If after a set period the patient has not completed the web survey, they will receive a chatbot call.
  • The chatbot calls the patients by name, quoting their specialty listing on the waiting list, and asks them a series of clinically approved questions to assess their current needs.
  • This supports patients who do not want to engage with the chatbot, along with those whose details could not be easily comprehended by the system.
  • Vulnerable patients are also excluded from chatbot calls.

Evidence from the source

"We cannot do everything for ourselves and recognize the effectiveness of using an expert partner like Digital Space to use AWS technology," says John Uttley, digital innovation director at NHS ML.
Digital Space worked with NHS ML to commission an Amazon Connect Customer telephony solution and to create chatbots with Amazon Lex that would implement clinically validated scripts to ask a series of questions and collect information from...
NHS ML saw an opportunity to transform the waiting list validation process and associated patient contacts by automating the patient contact center, introducing innovative solutions such as automation to improve the service for both the...
The NHS ML Referral Management Centre chose AWS Partner Digital Space to migrate its patient contact center to Amazon Web Services (AWS) and deployed an automated multi-channel approach to patient communication and engagement.
The NHS ML team chose to use AWS, supported by Digital Space.
The innovative digital transformation of its patient waiting list management reduced waiting lists 9–14%, depending on specialty.
Transparency Evidence & methodology 100/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    NHS Midlands and Lancashire customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/nhs-midlands-lancashire-digital-space/
    Source type
    Vendor-published customer story
    Published
    Feb 14, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.