DrDoctor
2024 - 2025
Appointment Confirmations
AI-driven reminders that reduce no-shows by predicting which patients are most likely to miss their appointments.
Executive Summary
Situation: To reduce NHS Trust churn and protect DrDoctor’s ARR, we focused on lowering Did Not Attend (DNA) rates, each costing the NHS ~£120, accumulating to £1.2 billion annually.
Task: The scheduling and ai pods needed to find the most cost-efficient way to help admin staff in Trusts reduce DNA rates.
Action: From Q1 2024 to Q3 2025, I led discovery, user research, and iterative testing for Appointment Confirmations: AI-driven tiered interventions that automatically ask high DNA risk patients to confirm, reschedule, or cancel at scale.
Result: The intervention reduced DNA rates by 35% across 8 Trusts, significantly easing admin workload and delivering measurable ROI to clients.
Metrics
35%
Trusts’ reduction in DNA rates.
100%
increase in Appointment Engagements from patients by introducing tiered interventions - from 30k to 60k appointment engagements.
3 million
appointments predicted with DNA Predictor.
Discovery
To see a significant reduction in did not attend (DNA) rates, confirmation callers need to scale their outreach across multiple clinics.
However, they currently lack the
resources to do so effectively.
Goal
Increase patient engagement with appointment confirmations, encouraging more patients to confirm, change, or cancel their appointments - in order to reduce DNAs and minimise slot wastage.
Identify. Intervene. Optimise.
Our results show that scaling this tiered intervention approach, asking high risk patients to confirm, change or cancel - escalating with each attempt to reach non-responsive patients - effectively reduces DNAs and eases the administrative burden.
Clinic Config
Staff can when the interventions are triggered days notice from their appointment within DrDoctor’s Staff Portal Clinic Config.
8
Trusts fully adopted Appointment Confirmations.
5,400
clinics live with Smart SMS.
First Interventions
Optimised Smart SMS & Patient Portal
DrDoctor’s Ai model helps Trusts reduce DNA rates and SMS costs by only targeting patients with a high DNA risk - asking them to Confirm, Change or Cancel.
Patients can also manage their appointments on the web-based app Patient Portal.
3500%
patient engagement with SMS Reminders after implementing ‘Confirm’ prompt. From 1% to 35%.
143k
Smart SMS sent - March 2024 and June 2024.
151k
Patients engaged with their appointments via Patient Portal - May 2024 to Feb 2025.
Second Intervention
End to end
Automated Calls
If high DNA risk patients do not respond to the first set of interventions, an automated call is triggered .
After the patient verifies, they are presented with 3 appointment engagement options to choose from - asking to confirm, change or cancel.
65%
patient engagement with automated calls.
20%
further reduction in DNAs rates when targeting high & medium DNA risk patients.
Appointment Confirmations Worklist
End to end
Smart worklist
Confirmation callers can triage which high DNA risk patients have engaged with the interventions, and escalate their efforts to make further contact with non-responding recipiants.
140%
reduction in manual call hours captured 14,000 hours captured prior to Appointment Confirmations.
100%
confirmations callers unanimously agree that worklist is intuitive.
Metrics
Challenges, Risks and Constraints
I took ownership due to no PM. Thrice.
After three PMs left the Appointment Confirmations workstream, I stepped up to lead. I ensured clarity across pods and stakeholders by communicating progress and aligning priorities. For example, I created a Product Requirements Document (PRD) that defined scope and responsibilities—helping prevent misalignment and keep momentum.
NHSE being scrapped dramatically stagnated the project.
In 2025, DrDoctor joined an NHSE-funded pilot to showcase the impact of Appointment Confirmations. Progress slowed significantly after Labour announced plans to dismantle NHSE, creating uncertainty and deprioritising a national rollout.
DrDoctor
Year
2024 - 2025
Tackling the NHS’s £1.2 Billion no-show problem with AI-powered reminders.
Appointment Confirmations
AI-driven reminders that reduce no-shows by predicting which patients are most likely to miss their appointments.
Executive Summary
Situation: To reduce NHS Trust churn and protect DrDoctor’s ARR, we focused on lowering Did Not Attend (DNA) rates, each costing the NHS ~£120, accumulating to £1.2 billion annually.
Task: The scheduling and ai pods needed to find the most cost-efficient way to help admin staff in Trusts reduce DNA rates.
Action: From Q1 2024 to Q3 2025, I led discovery, user research, and iterative testing for Appointment Confirmations: AI-driven tiered interventions that automatically ask high DNA risk patients to confirm, reschedule, or cancel at scale.
Result: The intervention reduced DNA rates by 35% across 8 Trusts, significantly easing admin workload and delivering measurable ROI to clients.
3 million
appointments predicted with DNA Predictor.
100%
increase in Appointment Engagements from patients by introducing tiered interventions - from 30k to 60k.
Metrics
35%
Trusts’ reduction in DNA rates.
Team
Scheduling Pod
Senior Product Manager
2 Lead Developers
3 Developers
Engineering Manager
Senior Data Analyst
Designer (Me)
Ai Pod
Product Manager
3 Senior Machine Learning Engineers
Front end Developer
Role
Responsibilities
UX/UI Designer
User Researcher
User Testing
Strategist
Tools
Figma
Miro
Jira
Dovetail
Notion
Microsoft Fabric
Planning
Q1 2024 the scheduling team planned to maximise DrDoctor’s impact in hospitals by reducing did not attend (DNA) rates. However, we had large knowledge gaps which put us at medium-risk.
I facilitated a workshop with pod leadership to map knowledge gaps, assess risks, and align with execs on the need for discovery. I shaped key questions and, with the PM, defined our approach: interviews, pathway mapping, and onsite visits to build confidence and reduce risk.
Discovery
To see a significant reduction in did not attend (DNA) rates, confirmation callers need to scale their outreach across multiple clinics.
However, they currently lack the
resources to do so effectively.
Goal
Increase patient engagement with appointment confirmations, encouraging more patients to confirm, change, or cancel their appointments - in order to reduce DNAs and minimise slot wastage.
Challenges, Risks and Constraints
I disagreed with PM’s original solution - used data and insights to prove my proposed solution
Challenged a scope-led solution by a seasoned PM, using research and opportunity mapping to deliver a tiered intervention that was more effective, user-friendly, and cost-efficient.
PM proposed sending 4 SMS reminders, I recommended a tiered intervention approach instead, one that escalates for non-respondents, offering a more effective, cost-efficient, and user-friendly solution.
Identify. Intervene. Optimise.
Our results show that scaling this tiered intervention approach, asking high risk patients to confirm, change or cancel - escalating with each attempt to reach non-responsive patients - effectively reduces DNAs and eases the administrative burden.
Click the image for a closer look
Clinic Config
Staff can set how many days before the appointment interventions are triggered, via Clinic Config in DrDoctor’s Staff Portal.
9-Year Tech Debt – Implementation challenges arose due to longstanding tech debt within Clinic Config. To help staff understand the purpose of tiered interventions, SMS reminders and automated calls were strategically arranged in a hierarchy that clearly demonstrated how the tiers functioned.
AI-SMS Reminders – A linguistic analysis revealed that the original definition of this tool was confusing, which led to it being underused. By rewriting the definitions to better reflect the tool’s actual functions, adoption increased across multiple clinics.
A Vision – Instead of relying on hierarchy, dropdowns, and error states, I initially designed a visual slider to show exactly when each intervention would be triggered in relation to the appointment date.
8
Trusts fully adopted Appointment Confirmations.
5,400
clinics live with Smart SMS.
First Interventions
Optimised Smart SMS & Patient Portal
Trigger an automated ai-driven SMS reminder that prompts patients to engage with confirm, change or cancel.
Value – Initially, patients could only ‘Cancel’ or ‘Change’ their appointments, leading clinics to question the value of SMS reminders. After introducing a ‘Confirm’ prompt, Trusts quickly adopted SMS reminders as a way to streamline responses and manage non-responders more effectively.
Influential Hierarchy – Staff were primarily interested in knowing whether patients intended to attend. To support this, I updated the hierarchy in both SMS and the Patient Portal, making ‘Confirm’ the primary call-to-action.
Missed Experiments – I emphasised the importance of experimenting with different prompts to compare error rates and engagement levels, identifying opportunities for optimisation that were previously overlooked.
3500%
patient engagement with SMS Reminders after implementing ‘Confirm’ prompt - from 1% to 35%.
143k
Smart SMS sent - March 2024 and June 2024.
151k
patients engaged with their appointments via Patient Portal - May 2024 to Feb 2025.
Second Intervention
End to end Automated Calls
Trigger an automated call to the high DNA risk patients who have not engaged with the first interventions - reminding patients of their appointments and asking to confirm, change or cancel.
Product-Market Fit – I designed Appointment Confirmations to align with our new SaaS model, introducing both a core and a smart version of the product. However, the product lacked true product-market fit without the inclusion of automated calls.
Successful Trials – I supported the AI team with automated call trials aimed at understanding whether patients would engage with the calls and if confirmations would lead to improved attendance. I designed the conversion flow that guided how patients interacted with the automated calls.
Designing the First Iteration – I led the end-to-end design of the initial automated calls experience, using MoSCoW prioritisation and Elephant Carpaccio to guide iterative development. Designs were delivered and refined through a sequence of focused, incremental efforts.
65%
patient engagement with automated calls
20%
DNA reduction with automated calls alone.
Here’s a closer look.
End to end
Then, we help to optimise with Appointment Confirmations worklist
Confirmation callers can improve booking efficiency by triaging patients who haven’t responded to automated interventions.
Technical Constraints – Developers proposed showing all confirmation statuses on the worklist due to limitations in how data rendered. I pushed for progressive disclosure to minimise cognitive load and surface only relevant information when needed.
Metrics and Insights
Standard Operating Procedure (SOP) – During an NHSE-funded pilot, staff responded positively to the Appointment Confirmations worklist.
However, they encountered challenges when developing their SOP, as they were unsure about the appropriate timing for triggering each automation and when to manually contact patients who had not responded, particularly in relation to how many days’ notice before the appointment this should occur.
Continuous Discovery – I conducted qualitative research via interviews and SUS questionnaires to validate whether the current Appointment Confirmations design helps staff identify which patients require manual follow-up.
Product Analytics Recordings – During the release of Appointment Confirmations, I regularly reviewed session recordings via PostHog to analyse user behaviour. In a typical 3-hour session, staff engaged with Appointment Confirmations for only around 5 minutes.
They spent the rest of the time navigating through other products to gain a holistic view of patient engagement, helping them assess the likelihood of a slot going to waste.
Challenges, Risks and Constraints
I took ownership due to no PM. Thrice.
After three PMs left the Appointment Confirmations workstream, I stepped up to lead. I ensured clarity across pods and stakeholders by communicating progress and aligning priorities. For example, I created a Product Requirements Document (PRD) that defined scope and responsibilities, helping prevent misalignment and keep momentum.
NHSE being scrapped dramatically stagnated the project.
In 2025, DrDoctor joined an NHSE-funded pilot to showcase the impact of Appointment Confirmations. Progress slowed significantly after Labour announced plans to dismantle NHSE, creating uncertainty and deprioritising a national rollout.