Here you can find details of a range of previously-completed HSMA projects.
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.
Completed Projects
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.
Modelling the resources needed to run an acute frailty unit in Cornwall leading to the trust changing their plans
A discrete event simulation model to identify number of beds to improve care for frail older patients in the first 72 hours of emergency care. A model identified optimal bed numbers, showing an 18-bed unit would meet the 4-hour ED standard for 94% of patients, enhancing outcomes and reducing admissions.
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
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.
Modelling the impact of various strategies to improve weekend discharge rates at Derriford Hospital
Ensuring continuous inpatient bed availability is crucial for emergency and non-elective care. Discharge rates vary. Process mapping and Discrete Event Simulation (DES) identified bottlenecks and improved discharge pathways. Changes led to increased weekend discharges and better communication, positively impacting the discharge process, especially for complex patients.
Modelling the impact of regionalising cardiac arrest centres, leading to work to standardise best practices
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.
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%.
Using modelling to understand the delays in mental health services in Devon
This project aimed to support Crisis Cafes, offering an alternative service for those in a mental health crisis. Using modelling and data science, it will look at activity diversion, capacity needs, demand, and resource allocation. Seven models for Barnstaple, Exeter, and Torquay were created, demonstrating system demand and capacity, aiding resource planning and financial decisions.
RCHT Eldercare workforce mapping to identify needs and alternatives to cover the frailty pathway
After hospital admission, 12% of people over 70 experience reduced daily living abilities. Those with deteriorated balance and mobility in the first 48 hours had a 17.1% relative risk of death within 14 days. This project aimed to determine frailty workforce placement to impact more patients, reduce length of stay, and improve care quality. The model simulated patient pathways involving frailty nurses and doctors.
Artificial Ian – can a machine learning tool learn when it is necessary to cancel surgery at Derriford Hospital? And can we use geographic modelling methods to improve outpatient clinic utlilisation in the community?
This project aimed to use AI approaches to improve surgical cancellation decisions, addressing poor patient experiences. The model struggled due to insufficient data. A second project used geospatial visualisation (QGIS) to assess community outpatient clinic demand, finding increasing waiting lists and poor clinic utilisation, leading to improved patient booking into Peripheral Sites.
Reducing the delays to glaucoma treatment at Torbay Hospital
This project assessed if the Glaucoma pathway increased treatment delays and blindness risk. The model showed reducing visual field check times had little impact on waiting times, but adding a second Photo & Scan machine nearly eliminated queues for this part of the pathway.
Using modelling to optimise catheter lab efficiency and predict stroke bed demand at Royal Cornwall Hospital
This project supported a business case for reconfiguring Acute Stroke Unit beds using a Discrete Event Simulation model. The model assessed patient cohorts, pathways, and bed requirements. It also aimed to improve Cardiac Catheter Labs' efficiency, addressing a 13% capacity loss and reducing patient delays.
Improving ambulance response times for life-threatening emergencies using simulation modelling
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.
Can ambulance dispatch codes be used to determine when an ambulance is really needed?
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.
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.
Developing a model to understand delays to discharge at Royal Devon and Exeter Hospital
The hospital faces increasing bed pressures, with over 100 medically fit patients often waiting for discharge due to social care and rehabilitation delays. This project aimed to create a Discrete Event Simulation model to estimate patient length of stay based on demographics, comorbidities, and social care needs, experimenting with scenarios like reducing placement times and accelerating discharge for frail patients.
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
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.
Generating a richer understanding of relationships in crime data in order to identify opportunities to safeguard individuals and families
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.
Modelling strategies to reduce the elective backlog in hip surgery
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.
• NHS Devon ICB
Developing a generic vaccination service model for the COVID-19 pandemic and beyond
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.
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
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.
• Peninsula Dental Social Enterprise
• Royal Devon University Healthcare NHS Foundation Trust
Simulation modelling to test proposed models of pediatric critical care in South West England
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.
The Effect of Booked Appointments on Waiting Times at Urgent Treatment Centres
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.
South East Regional Covid 19 Vaccination Demand & Capacity Modelling
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.
• Surrey and Borders Partnership NHS Foundation Trust
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?
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
Discrete Event Simulation of Cognitive Behavioural Therapy Pathway in an IAPT Service
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.
• NHS Dorset ICB
Use Of Discrete Event Simulation (DES) to reduce delays in Cancer Diagnosis & Treatment
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.
• University Hospitals of Morecambe Bay NHS Foundation Trust
• Dartford and Gravesham NHS Trust
Discrete Event Simulation to Improve Flow and Performance in the Urgent Treatment Centre
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.
Forecasting Demand and Length of Stay in the Emergency Department
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.
Meeting the demand of 111 for primary care services
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.
• NHS Devon ICB
• University of Plymouth
Using Machine Learning to Predict Hospital Admissions and Length of Stay for Respiratory Conditions
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.
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.
Predicting Non-Elective Admissions
The project aimed to develop a predictive model to identify patients at high risk of admission and provide explanatory feedback. It combined structured and unstructured data models. The model helps predict non-elective admissions, enabling preventative care and better health outcomes, despite data limitations.
• NHS North East London CSU
Predicting Violent Incidents on Mental Health Inpatient Units
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.
Reducing Travel Times to Treatment for Cardiac Patients in the South East of England
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
• The Strategy Unit
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
Spatial Modelling of Violent Crime to Support Strategic Analysis
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.
• Devon & Cornwall Police
• Devon County Council
Using Natural Language Processing to detect drug related content within free text
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 Discrete Event Simulation to model the bottlenecks in the Acute Medical Unit pathway
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.
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 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.
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.
Modelling the location of neonatal critical care units in North West England
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.
Network Analysis of diagnostic procedures in A&E setting
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.
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
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)
Understanding Excess Mortality in Dorset
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.
Investigating factors impacting NHS workforce retention
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.
• Yorkshire Ambulance Service NHS Trust
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
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