Here you can find details of a range of current and previously-completed HSMA projects, split by the key areas the project addresses.
Click on the preview for each project to read the full details. For many completed projects, you can watch a short talk from the HSMAs who undertook the project on their plans, successes and challenges.
Mental Health
Neurodiversity
Use of Discrete Event Simulation to Tackle Long Waits and a Growing Backlog for Children Requiring Neuro Development Assessment (Autism and ADHD)
Complete
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?
• Oxford Health NHS Foundation Trust
Discrete Event Simulation modelling of childrens ADHD diagnosis and treatment
Active
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.
• Lancashire and South Cumbria NHS Foundation Trust
Using Data Science techniques to address delays in the pathway for Neurodiversity Diagnoses in Children and Young People in Devon
Active
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.
• Devon Partnership NHS Trust
No matching items
Mental Health Inpatients
Predicting Violent Incidents on Mental Health Inpatient Units
Complete
This project aimed to see if violent incidents could be reduced on hospital wards by predicting and preventing them, using machine learning. A dashboard would highlight high-risk wards to senior staff for intervention.
• Birmingham & Solihull Mental Health NHS Foundation Trust
No matching items
NHS Talking Therapies (Formerly IAPT)
Discrete Event Simulation of Cognitive Behavioural Therapy Pathway in an IAPT Service
Complete
The project used Discrete Event Simulation to model the wait list for High Intensity Cognitive Behavioural Therapy. It found that changing an evening group to a face-to-face clinic could reduce wait times, especially for face-to-face or evening 1-1 appointments.
• Dorset HealthCare University NHS Foundation Trust
• NHS Dorset ICB
Modelling secondary care psychological therapy flow
Active
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.
• Solent NHS Trust
Modelling the Talking Therapies clinical pathway using a Discrete Event Simulation
Active
The NHS Talking Therapies programme, supports NICE guidelines for treating anxiety and depression. The aim is to develop a Discrete Event Simulation to model patient flow through the new clinical pathway. This project will identify potential waiting list build-ups due to increased referrals into Talking Therapies.
• Lancashire & South Cumbria NHS Foundation Trust
No matching items
Other Mental Health Projects
Modelling resources needed to reduce out of area placements in the Mental Health Acute Care pathway in Devon, leading to the construction of a new mental health ward
Complete
Devon Partnership NHS Trust explored changes to its Urgent Care Pathway to reduce system pressure and avoid out-of-county care. Process mapping and a simulation model identified a need for additional beds. Key findings included high occupancy rates, the necessity to reduce demand and lengths of stay, and the importance of increasing bed numbers to enhance patient care and workforce efficiency.
• Devon Partnership NHS Trust
No matching items
Diseases
Diabetes
Predicting Gestational Diabetes and other maternity-related conditions using machine learning
Active
The project aims to use machine learning to predict gestational diabetes and other maternity-related conditions early in pregnancy, enabling timely interventions and personalised care. It will develop a predictive model, an interactive web app for clinicians to input patient data and receive predictions, and reports on the model's accuracy and effectiveness.
• Epsom and St Helier University Hospitals NHS Trust
No matching items
Cancer
Use Of Discrete Event Simulation (DES) to reduce delays in Cancer Diagnosis & Treatment
Complete
The project developed a Discrete Event Simulation model for the Colorectal Cancer pathway identify key delays and solutions. Using three years of anonymised patient records, the model highlighted diagnostic delays during investigative stages.
• Gloucestershire Hospitals NHS Foundation Trust
• University Hospitals of Morecambe Bay NHS Foundation Trust
• Dartford and Gravesham NHS Trust
DESmond: Discrete Event Simulation and Artificially Intelligent Forecasting: Modelling the 62 Day Prostate Cancer Pathway
Active
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.
• Great Western Hospitals NHS Foundation Trust
Optimising the location of Breast Cancer diagnostic services across Devon
Active
Over 6000 patients are referred annually for fast-track breast symptom diagnosis at RD&E and NDDH. Increasing referrals and limited infrastructure necessitate building or extending diagnostic units. The project aims to determine optimal locations for additional services to minimize patient travel time and reduce costs, using data science to map demand and calculate travel distances and benefits.
• Royal Devon University Healthcare NHS Foundation Trust
Modelling delays in breast, head and neck cancer pathways
Active
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.
• NHS England
Forecasting demand in RDUH breast care services and the impact of urban development
Active
Between 2011 and 2021, the population served by Royal Devon & Exeter Hospital grew by 13%, doubling the national average, leading to long waiting lists. This project aims to develop a forecasting tool for breast services to predict referrals and service demand, inform workforce planning, and justify infrastructure expansion. It will also predict geographical demand changes for optimal service locations.
• Royal Devon University Healthcare NHS Foundation Trust
Evaluating the use of machine learning calssifiers for the identification of the determinants of stage at diagnosis of prostate cancers using registry data
Active
This project uses various Machine Learning methods to classify prostate cancer diagnosis stages using registry fields. The focus is on methods comparison in prostate cancer diagnosis, driven by a recent increase in diagnoses.
• National Disease Registration Service (NDRS)
RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters
Active
This project is an app that reads in patient letters ahead of surgery, using Natural Language Processing techniques to extract key information from the text, and then feeds all of that into a Machine Learning model which then predicts how long the Robotic-assisted laparoscopic prostatectomy (RALP) surgery is going to take.
• Great Western Hospitals NHS Foundation Trust
No matching items
COVID-19
Developing a generic vaccination service model for the COVID-19 pandemic and beyond
Complete
In winter 2020, a mass COVID-19 vaccination program required GP surgeries to deliver vaccines at an unprecedented scale. A Discrete Event Simulation model was the vaccination pathway in North Devon, predicting vaccination rates and identifying potential issues. The model was refined for safe delivery and made available for future use, aiding efficient vaccination efforts globally.
• Torridge Primary Care Network
South East Regional Covid 19 Vaccination Demand & Capacity Modelling
Complete
The project aimed to create an easy-to-use model for predicting Covid vaccination demand and capacity. Despite data access barriers, the team made progress by using past vaccination data. The tool will help track vaccinations, identify problem areas, and save time and money.
• NHS South, Central and West CSU
• Surrey and Borders Partnership NHS Foundation Trust
Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake
Active
There is a decreasing uptake of COVID and flu vaccinations among healthcare workers. Identifying patterns by staff uptake, gender, ethnicity, deprivation, and other factors can help. Using geographic and boosted tree modelling, along with regression analysis, can capture these patterns and provide useful data for stakeholders to address the issue.
• NHS England
No matching items
Public Health
Smoking and Weight Management
Modelling the benefit of MECC (Making Every Contact Count) Training using agent based simulation
Active
Making Every Contact Count (MECC) is an e-learning program for health and social care staff to promote healthy lifestyles. This project uses Agent Based Simulation to model MECC's impact on behaviors like smoking, drinking, and exercise.
• NHS North of England CSU
• Surrey and Sussex Healthcare NHS Trust
• Somerset NHS Foundation Trust
Predictive modelling for smoking cessation success
Active
Smoking cessation remains challenging despite public health efforts. This project aims to develop a predictive model to identify individuals likely to quit smoking based on demographics and behaviours. It will uncover key predictors and effective pathways using machine learning algorithms like logistic regression, decision trees, random forests, and neural networks, evaluating each for accuracy and interpretability.
• Dorset Council
No matching items
Vaccination
Developing a generic vaccination service model for the COVID-19 pandemic and beyond
Complete
In winter 2020, a mass COVID-19 vaccination program required GP surgeries to deliver vaccines at an unprecedented scale. A Discrete Event Simulation model was the vaccination pathway in North Devon, predicting vaccination rates and identifying potential issues. The model was refined for safe delivery and made available for future use, aiding efficient vaccination efforts globally.
• Torridge Primary Care Network
South East Regional Covid 19 Vaccination Demand & Capacity Modelling
Complete
The project aimed to create an easy-to-use model for predicting Covid vaccination demand and capacity. Despite data access barriers, the team made progress by using past vaccination data. The tool will help track vaccinations, identify problem areas, and save time and money.
• NHS South, Central and West CSU
• Surrey and Borders Partnership NHS Foundation Trust
Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake
Active
There is a decreasing uptake of COVID and flu vaccinations among healthcare workers. Identifying patterns by staff uptake, gender, ethnicity, deprivation, and other factors can help. Using geographic and boosted tree modelling, along with regression analysis, can capture these patterns and provide useful data for stakeholders to address the issue.
• NHS England
No matching items
Other Public Health
Understanding Excess Mortality in Dorset
Complete
The project aimed to improve access and service experience through Dorset ICS's Health Inequalities programme. It focused on defining and measuring excess mortality, identifying unexpected mortality trends, and understanding driving factors.
• Public Health Dorset
Identifying which patients are most at risk for an outcome across integrated neighbourhood teams
Active
Population health management uses segmentation to categorise people by health status and needs. However, generic segments may not identify high-risk groups effectively. This project aims to create a tool to identify at-risk patient groups across different geographies, focusing on outcomes like emergency admissions, vaccination rates, and screening uptake.
• North West London ICB
No matching items
Medical Specialties
Cardiac
Modelling the impact of regionalising cardiac arrest centres, leading to work to standardise best practices
Complete
Survival to hospital discharge after out-of-hospital cardiac arrest (OHCA) is around 7-8%. Analysis of the SWASFT Cardiac Arrest Registry showed survival rate variations due to different hospital services. A regional care system could improve survival rates. A collaborative project between ambulance services and hospitals aims to standardise care, addressing gaps and enhancing outcomes through shared data and best practices.
• South Western Ambulance Service NHS Trust (SWAST)
Reducing Travel Times to Treatment for Cardiac Patients in the South East of England
Complete
The project analysed travel times to understand the impact of flow of activity into London on patient travel. It identified a gap in cardiac surgery access for Kent & Medway patients, suggesting new sites could help. A Streamlit app visualised these impacts.
• NHS England Specialised Commissioning – South East Region
• NHS England
• The Strategy Unit
Using Machine Learning to identify factors that predict cardiac mortality
Active
This project will use machine learning and explainable AI techniques to try to identify the factors that predict cardiac mortality.
• NHS Arden & GEM CSU
No matching items
Colorectal
Use Of Discrete Event Simulation (DES) to reduce delays in Cancer Diagnosis & Treatment
Complete
The project developed a Discrete Event Simulation model for the Colorectal Cancer pathway identify key delays and solutions. Using three years of anonymised patient records, the model highlighted diagnostic delays during investigative stages.
• Gloucestershire Hospitals NHS Foundation Trust
• University Hospitals of Morecambe Bay NHS Foundation Trust
• Dartford and Gravesham NHS Trust
No matching items
Maternity
Predicting Gestational Diabetes and other maternity-related conditions using machine learning
Active
The project aims to use machine learning to predict gestational diabetes and other maternity-related conditions early in pregnancy, enabling timely interventions and personalised care. It will develop a predictive model, an interactive web app for clinicians to input patient data and receive predictions, and reports on the model's accuracy and effectiveness.
• Epsom and St Helier University Hospitals NHS Trust
No matching items
Neonatal
Modelling the location of neonatal critical care units in North West England
Complete
The project explored the optimal location for 22 neonatal care sites in North West England, using an algorithm that balanced travel time, distance, NICU care episodes, and admission numbers. A discrete event simulation model and dashboard were created to explore various scenarios, aiding stakeholder decision-making.
• North West Neonatal operational Delivery Network (NWNODN)
No matching items
Neurology
Referral to treatment waiting times for Neurosurgical patients
Active
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.
• The Walton Centre NHS Foundation Trust
No matching items
Orthopaedics
Modelling strategies to reduce the elective backlog in hip surgery
Complete
This project used Discrete Event Simulation to model the hip surgery patient pathway in Exeter, assessing strategies to reduce the surgery backlog. It was a collaborative effort between the CCG and the acute provider, contributing to long-term planning and decision-making.
• Royal Devon University Healthcare NHS Foundation Trust
• NHS Devon ICB
No matching items
Physiotherapy
Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon
Active
The project explores demand and access to Musculoskeletal Physiotherapy services in Exeter, Mid, and East Devon. It uses Geographic Modelling to show variations in demand and access times, using DES to optimize resource placement. A Streamlit App will be developed for ongoing service development and review.
• Royal Devon University Healthcare NHS Foundation Trust
No matching items
Renal
Predicting the future demand for Renal replacement therapy
Active
Kidney disease is projected to be the fifth leading cause of premature deaths globally by 2040. Rising demand for dialysis and transplants exceeds capacity in England. The project aims to develop a model using forecasting techniques to predict future demand and address it by increasing home dialysis or introducing a new dialysis centre.
• NHS England
DES model of a North West Renal Dialysis Unit to evaluate the impact of the growing need for in-centre Chronic Kidney Disease dialysis provision
Active
Rising dialysis demand for Chronic Kidney Disease patients is straining regional centres. The North West Kidney Network aims to demonstrate this pressure and explore adjusting staffed capacity and training ratios. The project will create a DES model of a renal unit to evaluate impact, justify investment, and optimise resources.
• NHS North of England CSU (NECS)
No matching items
Respiratory
Using Machine Learning to Predict Hospital Admissions and Length of Stay for Respiratory Conditions
Complete
The project aimed to predict respiratory condition admissions and length of stay using patient history and demographics. Early-stage modelling showed promising preliminary findings. Further development will include more patient history to improve predictions and extend the model's capabilities.
• NHS Midlands and Lancashire CSU
No matching items
Rheumatology
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?
Complete
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.
• NHS England
A Discrete Event Simulation Model to reduce Rheumatology waiting times in Dorset
Complete
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.
• Dorset Intelligence and Insight Service (NHS Dorset)
• BCP Council
No matching items
Urology
Evaluating the use of machine learning calssifiers for the identification of the determinants of stage at diagnosis of prostate cancers using registry data
Active
This project uses various Machine Learning methods to classify prostate cancer diagnosis stages using registry fields. The focus is on methods comparison in prostate cancer diagnosis, driven by a recent increase in diagnoses.
• National Disease Registration Service (NDRS)
RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters
Active
This project is an app that reads in patient letters ahead of surgery, using Natural Language Processing techniques to extract key information from the text, and then feeds all of that into a Machine Learning model which then predicts how long the Robotic-assisted laparoscopic prostatectomy (RALP) surgery is going to take.
• Great Western Hospitals NHS Foundation Trust
No matching items
Patient Groups
Children (Paediatrics)
Simulation modelling to test proposed models of pediatric critical care in South West England
Complete
This project used Discrete Event Simulation and Geographic Modelling to identify optimal locations for Level 2 Paediatric Critical Care Units in South West England, highlighting the need for units outside Bristol. Proposed locations were identified to better serve the population.
• NHS England
Use of Discrete Event Simulation to Tackle Long Waits and a Growing Backlog for Children Requiring Neuro Development Assessment (Autism and ADHD)
Complete
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?
• Oxford Health NHS Foundation Trust
Forecasting Demand and Length of Stay in the Emergency Department
Complete
The project aimed to use forecasting and machine learning to predict Emergency Department arrivals and length of stay. A web-based app was designed to explore trends and suggest improvements. The app forecasts attendances and stay durations, helping plan workforce needs.
• Sheffield Children's NHS Foundation Trust
Discrete Event Simulation modelling of childrens ADHD diagnosis and treatment
Active
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.
• Lancashire and South Cumbria NHS Foundation Trust
Using Data Science techniques to address delays in the pathway for Neurodiversity Diagnoses in Children and Young People in Devon
Active
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.
• Devon Partnership NHS Trust
No matching items
Older Adults
Using machine learning models to predict future frailty
Active
The project uses machine learning to estimate future frailty in the Wakefield District and identify key predictive features. It aims to plan resource allocation based on evidence, using two years of linked data. The project will also explore predicting other long-term conditions and produce reports and a user interface for stakeholders.
• NHS West Yorkshire ICB
Predicting the risk of injurious falls in older people with atrial fibrillation
Active
Atrial fibrillation (AF) increases stroke risk, and anticoagulation reduces this risk but can cause bleeding. Despite guidelines, many clinicians avoid prescribing anticoagulants to those at risk of falls. This project explores using machine learning to predict injurious falls in older AF patients and aims to develop a tool to personalize anticoagulant treatment based on falls risk.
• University Hospitals Plymouth NHS Trust
Developing a web app to recommend appropriate technology enabled care
Active
Technology Enabled Care (TEC) supports independence and health and social care. This project aims to develop a web app providing comprehensive, user-friendly guidance on TEC equipment. It will serve as a one-stop platform for health professionals, carers, and individuals, enhancing personal safety, independence, and reducing the burden on health and social care systems.
• Torbay and South Devon NHS Foundation Trust
No matching items
Women’s health
Optimising the location of Breast Cancer diagnostic services across Devon
Active
Over 6000 patients are referred annually for fast-track breast symptom diagnosis at RD&E and NDDH. Increasing referrals and limited infrastructure necessitate building or extending diagnostic units. The project aims to determine optimal locations for additional services to minimize patient travel time and reduce costs, using data science to map demand and calculate travel distances and benefits.
• Royal Devon University Healthcare NHS Foundation Trust
Predicting Gestational Diabetes and other maternity-related conditions using machine learning
Active
The project aims to use machine learning to predict gestational diabetes and other maternity-related conditions early in pregnancy, enabling timely interventions and personalised care. It will develop a predictive model, an interactive web app for clinicians to input patient data and receive predictions, and reports on the model's accuracy and effectiveness.
• Epsom and St Helier University Hospitals NHS Trust
Modelling delays in breast, head and neck cancer pathways
Active
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.
This project aimed to work out which factors are the biggest drivers of staff turnover using regression modelling on staff workforce figures as well as other local factors such as employment. This was turned into a dashboard for internal use.
• NHS England
• Yorkshire Ambulance Service NHS Trust
Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake
Active
There is a decreasing uptake of COVID and flu vaccinations among healthcare workers. Identifying patterns by staff uptake, gender, ethnicity, deprivation, and other factors can help. Using geographic and boosted tree modelling, along with regression analysis, can capture these patterns and provide useful data for stakeholders to address the issue.
• NHS England
Forecasting the supply of medical doctors
Active
The project aims to forecast the regional demand for medical doctors, considering factors like trainees and population. It will plot a graph showing the current supply versus demand. Additionally, it will simulate outcomes under different scenarios, such as changes in trainee numbers or population health, to better understand and address the gap.
• The Royal College of Pathologists
No matching items
Inequalities
Developing a Service Planning Decision Support Tool to Tackle Inequalities and Minimise Carbon Output
Complete
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.
• Derbyshire Community Health Services NHS Foundation Trust
• Torbay Council
• West Sussex County Council
Using Machine Learning to estimate inequities in access to hospital procedures
Complete
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.
• Norfolk County Council
Creating a tool to automatically generate health equity audits for Community Diagnostic Centres
Complete
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.
• UCL Partners Health Innovation
• Reading Borough Council
Developing a tool to assess inequalities and demographic coverage of service locations
Complete
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.
• NHS Arden & GEM CSU
• Health Innovation Wessex
• Nottingham University Hospitals NHS Trust
Mapping health inequalities, depreviation, ethnicities and crime across the UK
Active
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…
• NIHR Clinical Research Network
Identifying potential concurrent treatment areas and services that would better support patients with multiple, complex referral to treatment (RTT) pathways.
Active
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…
• NHS England
Automating and scaling waiting list equity analysis at a trust level
Active
This project uses a streamlit app to automates/scales waiting list equity analysis at a trust level and (optionally) service level.
• Derbyshire Community Health Services NHS Foundation Trust
eFIT: Extra funding allocation - inequality tool
Active
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.
• NHS England
No matching items
Other Care Areas
Tip
For a more granualar breakdown by service types, head to the Projects by Area page by clicking here
Primary Care (GPs)
Exploring the use of Machine Learning and Natural Language Processing to teach a machine to predict whether a patient is likely to be imminently admitted to hospital based on GP data and clues in GP notes
Complete
This project used Natural Language Processing to automate key information extraction from patient notes, aiming to predict imminent hospital admissions. Findings included indicators like increased note frequency, verbosity, and specific words.
• Torridge Primary Care Network
• Peninsula Dental Social Enterprise
• Royal Devon University Healthcare NHS Foundation Trust
Meeting the demand of 111 for primary care services
Complete
The project modelled NHS 111 calls triaged to primary care. Simulations showed timely primary care contact could reduce 999 calls and ED attendances but would require nearly doubling primary care services.
• Yorkshire Ambulance Service NHS Trust
• NHS Devon ICB
• University of Plymouth
Modelling the impact of 111 and GP access on Emergency Departments
Active
This project will use Discrete Event Simulation to model patients’ navigation of GP vs 111 services and assess the knock-on effect for Emergency Departments.
• NHS England
Developing a primary care load management tool
Active
A GP practice with 35,000 patients has peak call wait times of 60 minutes. Adding more staff improved this, but staff are idle during lulls. This project will model the telephone system and call handlers' workload to optimise resourcing and efficiency. A web app will be developed to test different scenarios.
• The Park Medical Practice
No matching items
Emergency Access (111, Ambulance Services)
Improving ambulance response times for life-threatening emergencies using simulation modelling
Complete
Category 1 ambulance calls are life-threatening, making up 8% of calls in the South West in 2018. The project aimed to reduce response times by reserving resources for these calls. A discrete event simulation model was created to determine the optimal resource allocation, balancing the needs of high and low acuity patients.
• South Western Ambulance Service NHS Trust (SWAST)
Can ambulance dispatch codes be used to determine when an ambulance is really needed?
Complete
This project aimed to rank dispatch codes by clinical acuity using Machine Learning, incorporating PPI group insights on medical history and co-morbidities. Phase 1 used call-taker info and Logistic Regression to optimise accuracy, leading to phase 2 algorithm generation. The active risk stratification model increased HART team utilisation, leading to a trial of Enhanced Hear and Treat.
• South Western Ambulance Service NHS Trust (SWAST)
Improving ambulance care via fast feedback from Quality Care Indicators
Active
The project aims to improve ACQI data quality and provide rapid feedback using a tool to analyse free text fields, capture sentiment, categorize incidents, and assess treatment appropriateness. This tool could be shared with other Ambulance Trusts and serve as a backup for to manually review data. It will also predict rule changes and their impact on scores.
• South Central Ambulance Service NHS Foundation Trust (SCAS)
Redrawing North West Ambulance dispatch Boundaries
Active
North West Ambulance Service (NWAS) has three dispatch suites in Manchester, Preston, and Liverpool, with dispatch areas mainly based on postcodes. Changes in the landscape and increased emergency demand have led to unequal resource distribution, causing delays. This project aims to generate evidence to redraw boundaries to ensure equitable resource allocation across the three areas.
• North West Ambulance Service NHS Trust
Geographic modelling of resource utlitisation at Devon Air Ambulance
Active
A geographical modelling project on the utilisation of resources at Devon Air Ambulance. Modelling current demand and conducting what if scenarios to optimise service…
• Devon Air Ambulance
No matching items
Emergency Departments
Modelling A & E flows to inform redesign at North Devon District Hospital
Complete
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.
• Royal Devon University Healthcare NHS Foundation Trust
The Effect of Booked Appointments on Waiting Times at Urgent Treatment Centres
Complete
The project aimed to determine if booked appointments reduce waiting times in Urgent Treatment Centres. Using a Discrete Event Simulation model, it showed that higher percentages of booked appointments led to decreased waiting times.
• NHS England
Using DES to Improve Flow through an Acute Medicine Assessment Pathway
Complete
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.
• Nottingham University Hospitals NHS Trust
• Sandwell and West Birmingham NHS Trust
Discrete Event Simulation to Improve Flow and Performance in the Urgent Treatment Centre
Complete
The project aimed to improve Urgent Treatment Care by developing a model to help the Emergency Department allocate staff and rooms efficiently. The model tested alternative pathways and identified the need for additional rooms and better-aligned staffing rotas. These changes aimed to reduce bottlenecks and meet the target for patient flow.
• University College London Hospitals NHS Foundation Trust
Forecasting Demand and Length of Stay in the Emergency Department
Complete
The project aimed to use forecasting and machine learning to predict Emergency Department arrivals and length of stay. A web-based app was designed to explore trends and suggest improvements. The app forecasts attendances and stay durations, helping plan workforce needs.
• Sheffield Children's NHS Foundation Trust
Meeting the demand of 111 for primary care services
Complete
The project modelled NHS 111 calls triaged to primary care. Simulations showed timely primary care contact could reduce 999 calls and ED attendances but would require nearly doubling primary care services.
• Yorkshire Ambulance Service NHS Trust
• NHS Devon ICB
• University of Plymouth
Using Discrete Event Simulation to model the bottlenecks in the Acute Medical Unit pathway
Complete
In this project, a computer simulation of the acute medical pathway in a Devon trust was created, along with an interactive tool allowing parameters such as the staffing levels to be changed. This allowed staff to explore the optimum levels of resourcing, enabling risk-free testing of staffing and resource changes before committing to these changes in the real-world.
• Royal Devon University Healthcare NHS Foundation Trust
Network Analysis of diagnostic procedures in A&E setting
Complete
The project aimed to analyse the relationship between different diagnostic procedures in A&E using NHS ECDS data. A web tool was developed to provide insights into diagnostic procedure usage across the country. The tool will allow users to explore graphic visualisations and accompanying analytics.
• NHS England
Discrete Event Simulation modelling of Non-elective flow
Active
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.
• Countess of Chester Hospital NHS Foundation Trust
Modelling the impact of 111 and GP access on Emergency Departments
Active
This project will use Discrete Event Simulation to model patients’ navigation of GP vs 111 services and assess the knock-on effect for Emergency Departments.
• NHS England
Forecasting modelling for A&E attendance
Active
The project aims to forecast A&E attendances 6 weeks in advance, considering seasonal patterns. This helps manage limited resources, plan staffing levels, and assess the need for escalation during abnormal events. Using Time Series Forecasting and a Repeatable Analytical Pipeline, the project addresses the unpredictability of A&E demand influenced by various factors.
• Liverpool University Hospitals NHS Foundation Trust
Modelling Same Day Emergency Care (SDEC) Pathways
Active
This project uses Discrete Event Simulation to model alternative pathways, like Same Day Emergency Care (SDEC), for patients to avoid ED attendance. It aims to understand pathway availability for SWAST, impact analysis, and implementation of new SDEC pathways. Initially piloted in Devon, it includes clinical variables and pathway criteria, with plans to expand to other areas SWAST covers.
• South Western Ambulance Service NHS Trust (SWAST)
Optimising Same Day Emergency Care (SDEC) Resourcing
Active
This project will model Same Day Emergency Care (SDEC) and Emergency Department (ED) pathways at University Hospitals Bristol and Weston NHS Foundation Trust.
• University Hospitals Bristol and Weston NHS Foundation Trust
No matching items
Inpatients
Simulation modelling to test proposed models of pediatric critical care in South West England
Complete
This project used Discrete Event Simulation and Geographic Modelling to identify optimal locations for Level 2 Paediatric Critical Care Units in South West England, highlighting the need for units outside Bristol. Proposed locations were identified to better serve the population.
• NHS England
Using DES to Improve Flow through an Acute Medicine Assessment Pathway
Complete
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.
• Nottingham University Hospitals NHS Trust
• Sandwell and West Birmingham NHS Trust
Forecasting Demand and Length of Stay in the Emergency Department
Complete
The project aimed to use forecasting and machine learning to predict Emergency Department arrivals and length of stay. A web-based app was designed to explore trends and suggest improvements. The app forecasts attendances and stay durations, helping plan workforce needs.
• Sheffield Children's NHS Foundation Trust
Understanding drivers of increased length of stay
Active
NCL has seen a rise in long Length of Stay (LoS) over the past 5 years, causing system strain. This project aims to develop a causal model to identify factors affecting LoS and estimate the impact of interventions. Key aims include building qualitative and quantitative models of LoS and modelling changes in response to interventions.
• NHS North Central London ICB
Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds
Active
The project aims to develop a tool for time series forecasting of bed occupancy using historical data, incorporating seasonality and growth. A web-based app will simulate acute bed models, including variables like closed beds and additional capacity. Using machine learning and discrete event simulation, the tool will aid decision-making and provide reliable daily forecasts.
• Royal United Hospitals Bath NHS Foundation Trust
SIMPACT: Simulation of Medical Patient Admission and Care Throughput
Active
Project details coming soon.
• Manchester University NHS Foundation Trust
No matching items
Non-NHS Project Areas
Police
What are they saying about us? An AI tool to determine the sentiment of tweets to police forces across the country, and what people are talking about
Complete
This project used AI-based Natural Language Processing to develop a dashboard predicting tweet sentiment and identifying discussion topics for police forces. It transformed Avon and Somerset Constabulary's social media can respond to public concerns.
• Avon & Somerset Constabulary
Generating a richer understanding of relationships in crime data in order to identify opportunities to safeguard individuals and families
Complete
This project used Network Analysis to identify social relationships and enable proactive police interventions for those at risk of offending due to their links with offenders. A proof of concept showed it could identify "at risk" individuals in hours instead of months. Applied in Devon, it highlighted significant benefits for future policing, including child protection and resource savings.
• Devon & Cornwall Police
Spatial Modelling of Violent Crime to Support Strategic Analysis
Complete
The project aimed to use crime data and spatial analysis for intelligence reporting. Focusing on violent crime, the team used QGIS and Python libraries to analyse geographic offence data. The model identified hotspots, coldspots, and outliers, providing statistical confidence for intelligence reports.
• Counter Terrorism Policing
• Devon & Cornwall Police
• Devon County Council
Using Natural Language Processing to detect drug related content within free text
Complete
The project aimed to detect drug-related content in datasets using Natural Language Processing to automate text classification. Initial findings were promising, though tested only on dummy data. If successful, the model could be adapted for other crime types.
Using Agent Based Simulation to understand criminal adoption of new technologies
Active
This project tests agent-based modelling, using consumer behaviour theory, criminology, and domain knowledge. The project aims to create a general methodology for digital behaviour models, applicable to specific cases, and develop methods to present findings static and interactively.
• National Crime Agency
Mapping health inequalities, depreviation, ethnicities and crime across the UK
Active
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…
• NIHR Clinical Research Network
No matching items
Council
Identifying unpaid carers in Norfolk
Active
Unpaid carers face mental health risks, loneliness, and isolation. The project will identify carer locations using geographic modelling and predict high-need areas with regression models, focusing on older populations and unpaid carers.
• Norfolk County Council
Predictive modelling for smoking cessation success
Active
Smoking cessation remains challenging despite public health efforts. This project aims to develop a predictive model to identify individuals likely to quit smoking based on demographics and behaviours. It will uncover key predictors and effective pathways using machine learning algorithms like logistic regression, decision trees, random forests, and neural networks, evaluating each for accuracy and interpretability.
• Dorset Council
No matching items
Modelling strategies to reduce the elective backlog in hip surgery
Complete
This project used Discrete Event Simulation to model the hip surgery patient pathway in Exeter, assessing strategies to reduce the surgery backlog. It was a collaborative effort between the CCG and the acute provider, contributing to long-term planning and decision-making.
• Royal Devon University Healthcare NHS Foundation Trust
• NHS Devon ICB
Discrete Event Simulation to model elective surgery pathways
Complete
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.
• Guy’s and St Thomas’ NHS Foundation Trust
No matching items
Exploring the use of Machine Learning and Natural Language Processing to teach a machine to predict whether a patient is likely to be imminently admitted to hospital based on GP data and clues in GP notes
Complete
This project used Natural Language Processing to automate key information extraction from patient notes, aiming to predict imminent hospital admissions. Findings included indicators like increased note frequency, verbosity, and specific words.
• Torridge Primary Care Network
• Peninsula Dental Social Enterprise
• Royal Devon University Healthcare NHS Foundation Trust
Meeting the demand of 111 for primary care services
Complete
The project modelled NHS 111 calls triaged to primary care. Simulations showed timely primary care contact could reduce 999 calls and ED attendances but would require nearly doubling primary care services.
• Yorkshire Ambulance Service NHS Trust
• NHS Devon ICB
• University of Plymouth
Modelling the impact of 111 and GP access on Emergency Departments
Active
This project will use Discrete Event Simulation to model patients’ navigation of GP vs 111 services and assess the knock-on effect for Emergency Departments.
• NHS England
Modelling 111 option 2 call centre
Active
The project aims to improve two struggling 111 call centres for mental health patients in Norfolk and Suffolk. It will develop a DES model to compare staffing approaches and determine the resources needed for safe service. Additionally, a tool will be created to ensure safe rosters by inputting current rosters and forecasting call levels.
• Norfolk and Suffolk NHS Foundation Trust
111 Downstream forecasting
Active
This project aims to develop a model predicting the place, time, and volume of downstream activity from 111 calls, deployed as a secure API. It integrates with an existing UEC webapp, using weekly patient-level 111 call data and near-live feeds.
• NHS North of England CSU
Population segmentation of GP-registered population in Dorset
Active
This project aims to develop a machine learning-based population segmentation model for the GP-registered Dorset population, using multiple characteristics including healthcare utilisation. The goal is to identify segments based on care needs to inform service design, enhancing patient understanding and improving resource allocation.
• Dorset Council
Developing a primary care load management tool
Active
A GP practice with 35,000 patients has peak call wait times of 60 minutes. Adding more staff improved this, but staff are idle during lulls. This project will model the telephone system and call handlers' workload to optimise resourcing and efficiency. A web app will be developed to test different scenarios.
