Waiting Lists and Waiting Times
Discrete Event Simulation to model elective surgery pathways
The project created a Discrete Event Simulation to model elective surgery pathways. This tool optimises surgical pathways and provides a ready-made format for creating interactive webapps, benefiting future HSMA participants and other interested users.
Forecasting Demand – Investigating approaches to forecast clock starts
The project aimed to explore and evaluate different forecasting methods for NHS England's clock starts, moving beyond Excel-based scenario modelling. The team tested various models and suggested translating the Excel model into Python and obtaining more data to improve demand forecasting and manage wait lists.
• NHS North of England CSU (NECS)
A Discrete Event Simulation Model to reduce Rheumatology waiting times in Dorset
The project aimed to reduce the Rheumatology waiting list and times in Dorset using Discrete Event Simulation (DES). The team built a DES model and a Streamlit app to simulate capacity changes.
• BCP Council
DESmond: Discrete Event Simulation and Artificially Intelligent Forecasting: Modelling the 62 Day Prostate Cancer Pathway
We're missing the 62-day prostate cancer treatment target due to resource allocation issues. The project aims to develop a Discrete Event Simulation model to predict demand and identify bottlenecks using live data. This model will help reallocate resources proactively, ensuring targets are met. The outcome includes an interactive web app and dashboard for key metrics.
Discrete Event Simulation to model othopaedic theatre capacity optimisation
The project aims to use Discrete Event Simulation to optimize theatre capacity for elective and major trauma orthopaedic procedures at UHCW. Increasing emergency referrals are impacting elective lists and finances. The model will explore adding theatre slots and increasing capacity to improve patient flow and reduce waiting lists.
Discrete Event Simulation modelling of childrens ADHD diagnosis and treatment
The project uses Discrete Event Simulation to model the children's ADHD diagnostic and treatment pathway, aiming to reduce waiting times and lists. It proposes a new pathway with preliminary diagnostic testing to ensure accurate ADHD assessments. The model will evaluate the impact of these changes and may extend to include 1:1 and group session appointments.
Referral to treatment waiting times for Neurosurgical patients
The project models the neurosurgical patient pathway to predict waiting list changes and treatment wait times. It aims to add user interaction to explore how capacity adjustments affect waiting times and track patients waiting over 52 weeks each month.
Modelling delays in breast, head and neck cancer pathways
The project uses DES to model post-diagnosis pathways for breast and head and neck cancer. It aims to identify delays and treatment variations across England, focusing on two key steps - time from neoadjuvant SACT to surgery or radiotherapy, and time from surgery to first adjuvant treatment.
Modelling secondary care psychological therapy flow
The project aims to improve patient flow for severe mental health treatment by assessing and projecting service user pathways under various conditions. It will provide statistical reasoning for future changes to enhance patient experience. Outputs include time in system, queue lengths, resource utilisation rates, and probabilities of exceeding thresholds for queue length, queue time, and resource utilisation.
Using Data Science techniques to address delays in the pathway for Neurodiversity Diagnoses in Children and Young People in Devon
In Devon, around 6,000 children have waited over 52 weeks for a neurodiversity diagnosis, crucial for their education and independence. This project aims to reduce the waiting list and streamline the process using DES modelling, NLP, machine learning, and geographic modelling to identify inequalities and optimise resource allocation.
Using machine learning to identify factors that increase number of appointments per pathway
The new EPP data set offers the most comprehensive view of elective pathway activity. Currently, the reasons for varying appointment numbers are unclear, but one theory is that longer waits lead to sicker patients and more appointments. This project plans to use Machine Learning to investigate this further.
Geographic modelling of resource utlitisation at Devon Air Ambulance
A geographical modelling project on the utilisation of resources at Devon Air Ambulance. Modelling current demand and conducting what if scenarios to optimise service…
Automating and scaling waiting list equity analysis at a trust level
This project uses a streamlit app to automates/scales waiting list equity analysis at a trust level and (optionally) service level.
Reducing Backlogs
Modelling the potential impact of having a Clinical Decision Unit at Royal Devon and Exeter hospital
A Clinical Decision Unit (CDU) at the Royal Devon & Exeter NHS Foundation Trust (RD&E) would manage ED patients needing up to 12 hours of care. Analysis showed that a 24/7 CDU with 4 cubicles could handle 2% of attendances, improving the 4-hour standard by 1.7%.
Reducing waiting times for spinal patients using simulation modelling
Routine reporting revealed increasing wait times for spinal patients, with elective patients waiting too long for treatment decisions due to factors like more appointments, diagnostic tests, and a complex pathway. A discrete event simulation of the spinal pathway looked at referral routes and wait times, showing variation in patient queues per consultant for further investigation.
• Somerset NHS Foundation Trust
Testing interventions to reduce the Continuing Healthcare Assessment backlog using System Dynamics
A System Dynamics model was developed to show the rated of system flow and interdependencies of components in the system. The model aids Clinical Commissioning Groups planning and decision-making, supporting future demand management.
Use of Discrete Event Simulation to Tackle Long Waits and a Growing Backlog for Children Requiring Neuro Development Assessment (Autism and ADHD)
The project aimed to identify bottlenecks in the Autism and ADHD assessment pathway for children and determine changes needed to manage demand and avoid backlogs. The main issue was clinical staffing capacity. Could adding one clinician could maintain a steady state and prevent backlog growth?
The role of Patient Initiated Follow-up (PIFU) and ‘Digital Outpatients’ in Supporting the Elective Recovery - Can We Better Size Potential for Clearing the Backlog?
The project aimed to explore the role of Patient Initiated Follow-up (PIFU) in addressing backlogs by mapping rheumatology outpatient pathways using discrete event simulation. The team modelled resource use and redeployment, focusing on PIFU's impact on referral-to-treatment waiting lists.
Patient Flow
Modelling A & E flows to inform redesign at North Devon District Hospital
The project aimed to enhance A&E performance by optimising staffing and bed requirements, focusing on the 4-hour target. Various staffing scenarios were modelled, revealing near-optimal staffing with minor adjustments. Success was achieved through collaborative efforts, meeting the 4-hour target in Q1-Q3 despite increased attendances. The project promoted system-wide improvements and cultural change within NDHT and Devon STP.
Using DES to Improve Flow through an Acute Medicine Assessment Pathway
Nottingham University Hospitals' Acute Medicine Assessment areas face high occupancy, causing delays in the Emergency Department. A Discrete Event Simulation showed that if patients left within 16 hours, ED wait times over 6 hours would reduce by 90%. This insight will help quantify additional ward beds needed to improve patient flow and reduce harm.
• Sandwell and West Birmingham NHS Trust
Modelling the effect of complex discharge delays on acute performance
Hospitals face increasing A&E wait times, ambulance delays, and growing waiting lists, partly due to inefficient patient discharges. This project modelled patient flow in and out of acute hospitals, focusing on discharge delays.
Discrete Event Simulation modelling of Non-elective flow
Poor patient flow in Emergency Departments leads to long admission waits and poorer patient outcomes. Strategies include increasing beds and reducing discharge delays. This project uses Discrete Event Simulation to explore bed numbers, length of stay, and Same Day Emergency Care impacts on ED waits, aiming to optimise patient flow.
Discrete Event Simulation to model othopaedic theatre capacity optimisation
The project aims to use Discrete Event Simulation to optimize theatre capacity for elective and major trauma orthopaedic procedures at UHCW. Increasing emergency referrals are impacting elective lists and finances. The model will explore adding theatre slots and increasing capacity to improve patient flow and reduce waiting lists.
Discrete Event Simulation model to support the flow of patients through Community Diagnostic Centres
Community Diagnostic Centres (CDCs) aim to improve patient flow and reduce diagnostic test waits. This project will create a DES model to identify bottlenecks and model scenarios to optimise investments.
Addressing Inequalities
Developing a Service Planning Decision Support Tool to Tackle Inequalities and Minimise Carbon Output
The project explored the feasibility of considering inequalities and carbon emissions in new clinic locations. Achievements include automating a Health Equity Assessment, running logistic regression models to predict appointment no-shows, and estimating patient travel carbon emissions.
• Torbay Council
• West Sussex County Council
Using Machine Learning to estimate inequities in access to hospital procedures
The project aimed to determine disparities in accessing planned hospital appointments by calculating true admission rates for high deprivation areas. They found that minimal public data and simple models were insufficient. Future work includes creating synthetic data and exploring additional features like emergency admissions.
Creating a tool to automatically generate health equity audits for Community Diagnostic Centres
The project aimed to create a tool to perform a health equity audit for Community Diagnostic Centres (CDCs) in England. The tool will identify healthcare inequalities, suggests data improvements, and supports local CDCs in understanding their impact. Developed using Python and Streamlit, it allows for easier comparison and sharing of learning across regions, with potential applications beyond CDCs.
• Reading Borough Council
Developing a tool to assess inequalities and demographic coverage of service locations
The project aimed to empower NHS service providers with a tool to understand their patient population and direct service expansion to underserved groups. It combines geospatial analysis, demographic data, and travel calculations.
• Health Innovation Wessex
• Nottingham University Hospitals NHS Trust
Mapping health inequalities, depreviation, ethnicities and crime across the UK
The aim of the project is to use ONS data around ethnicities of population, health index England, Deprivation data from QOF, and overlay the same with crime statistics at a…
Identifying potential concurrent treatment areas and services that would better support patients with multiple, complex referral to treatment (RTT) pathways.
The aim of this project is to use machine learning approaches to support analysis of patients with multiple concurrent RTT pathways, focusing particularly on healthcare…
Automating and scaling waiting list equity analysis at a trust level
This project uses a streamlit app to automates/scales waiting list equity analysis at a trust level and (optionally) service level.
eFIT: Extra funding allocation - inequality tool
The project addresses the lack of national guidance for allocating extra primary care funding by ICBs. It proposes using an equation based on deprivation scores and local needs. A Streamlit web-app tool will help ICBs allocate funds more equitably, considering various indicators and demographics, ensuring a fair distribution and reducing inequalities.
Patient Non-attendance
Developing a Service Planning Decision Support Tool to Tackle Inequalities and Minimise Carbon Output
The project explored the feasibility of considering inequalities and carbon emissions in new clinic locations. Achievements include automating a Health Equity Assessment, running logistic regression models to predict appointment no-shows, and estimating patient travel carbon emissions.
• Torbay Council
• West Sussex County Council
Machine learning to identify possible outpatient DNAs
This project will train a machine learning model to predict outpatient DNAs (did not attend) and prioritise support, such as extra reminders and calls. It may also use discrete event simulation to show the impact on clinic utilisation and waiting times if DNA rates are reduced.
Forecasting blood donation session capacity
Blood donation sessions face issues with cancellations, non-attendance, and medical rejections, leading to missed donations. This project aims to develop a Machine Learning tool to predict actual attendance and assess additional capacity. It will also build a web app for users to review current session capacity and manage bookings effectively.
Proactive Patient Attendance Prediction: Enhancing Healthcare Efficiency through Attendance Forecasting
At Barts Health NHS Trust, 12% of outpatient appointments are missed monthly, wasting over 10,000 hours of clinical resources. Missed appointments can lead to extended waiting lists and patient deterioration. This project aims to develop a machine learning model to forecast non-attendance, a patient contact capture tool, and integrate the model into enterprise reports.