Here you can find details of a range of current and previously-completed HSMA projects, split by where care takes place.
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.
Ambulance
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
Community
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
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
No matching items
Telephone-based Services (including NHS 111)
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
No matching items
Community Mental Health
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
No matching items
Community Diagnostic Centres (CDCs)
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
Discrete Event Simulation model to support the flow of patients through Community Diagnostic Centres
Active
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.
• NHS England
No matching items
Digital Outpatients
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
No matching items
Hospitals
Acute Medical Unit (AMU)
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
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
No matching items
Inpatients
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
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
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
Predicting Non-Elective Admissions
Complete
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.
• Royal Devon University Healthcare NHS Foundation Trust
• NHS North East London CSU
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
Intensive Care Units & Intensive Therapy Units (ICU & ITU)
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
No matching items
Outpatients
Machine learning to identify possible outpatient DNAs
Active
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.
• Chesterfield Royal Hospital NHS Foundation Trust
Proactive Patient Attendance Prediction: Enhancing Healthcare Efficiency through Attendance Forecasting
Active
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.
• Barts Health NHS Trust
No matching items
Same-Day Emergency Care (SDEC)
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
No matching items
Urgent Treatment Centres (UTCs)
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
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
No matching items
Supporting Services
NHS Blood & Transplant
Forecasting blood donation session capacity
Active
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.
• NHS Blood and Transplant
No matching items
Pharmacy
Pharmacy Clinical prioritisation tool
Active
The project aims to enhance the Clinical Prioritisation Tool in the pharmacy department using machine learning and a feedback loop system. This will streamline the identification of high-risk patients and improve scoring accuracy based on feedback from pharmacists and pharmacy technicians, addressing workforce challenges and ensuring better patient interaction.
