Normalized claim
Patient waiting lists reduced: 9-14% decrease
reduced waiting lists 9–14%, depending on specialty
NHS Midlands and Lancashire cuts patient waiting lists with Amazon Connect Customer + Amazon Lex chatbot patient contact center
Use case typePatient engagementUpdated Jul 8, 2026
NHS Midlands and Lancashire (NHS ML) migrated its patient contact center to AWS with Digital Space to automate patient communications and waiting-list validation. The platform uses Amazon Connect Customer and Amazon Lex to send SMS links, run secure web portal surveys and place chatbot calls using clinically validated scripts, with escalation to human operators when patients need to come off the list.
- Organization
- NHS Midlands and Lancashire
- Industry
- Healthcare
- Location
- United Kingdom
- Published
- July 2026
Reported outcomes
9-14%
patient waiting lists reducedOther quantified impact
80%patient contact rate2,000,000 patientspatients contacted250,000 patientspatients contacted via chatbot67%patients served with automated calls
Strategic outcomes
Cost efficiencyReduced contact costs and staff workloadOther strategic outcomeImproved patient autonomy and reassuranceCost efficiencyPrioritized waiting lists more efficientlyScale & capacityEnabled scalable, reliable multi-channel outreach
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Patient contact rate: 80%
achieved an 80 percent patient contact rate
Normalized claim
Patients contacted: 2,000,000 patients
successfully contacting over 2 million patients
Normalized claim
Patients contacted via chatbot: 250,000 patients
250,000+ patients were contacted using a chatbot
Normalized claim
Patients served with automated calls: 67%
67 percent of patients are served with automated calls made by the chatbots
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- NHS Midlands and Lancashire
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1Patient engagement
- 2Customer communication automation
- 3Contact center modernization
- Traditional postal and telephony outreach for waiting-list validation was time-consuming, expensive and ineffective.
- NHS ML needed a more efficient automated multi-channel approach to reduce waiting-list backlog and improve patient experience.
- Digital Space commissioned Amazon Connect Customer telephony and Amazon Lex chatbots for NHS ML.
- Patients receive an SMS text with a link to a secure web portal survey; if they do not complete it, they receive a chatbot call that asks clinically approved questions. If a patient wants to come off the list, the call diverts to a call-center operator in near real time with the answers already on screen.
Technologies
- Patient waiting lists were reduced by 9–14% depending on specialty.
- The solution achieved an 80% patient contact rate and successfully contacted over 2 million patients.
- More than 250,000 patients were contacted using chatbot calls, and 67% of patients are served with automated calls.
- The approach reduced costs, staff workload and the volume of agent-handled calls.
Architecture
NHS ML migrated its patient contact center to AWS. The workflow combines Amazon Connect Customer telephony, Amazon Lex chatbots, SMS outreach and a secure web portal survey. Patients who do not complete the survey receive chatbot calls using clinically validated scripts; if they request removal from the waiting list, the process transfers to an NHS ML call-center operator with the captured answers shown on screen. Specialist-specific scripts and repeated validation cycles support the ongoing waiting-list management process.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jul 8, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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