Here you can find details of a range of current and previously-completed HSMA projects, split by the key methods used in the project.
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.
Discrete Event Simulation
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 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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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
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.
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.
Modelling eye injection pathways
The project aims to develop a flexible simulation model to optimise anti-VEGF treatment strategies in ophthalmology. It will use dual modelling frameworks, a modular design, and an interactive dashboard. The model will analyse clinical effectiveness, costs, and resource requirements, adapting to new treatments. The objective is to provide a tool to improve patient outcomes and optimize resource use and costs.
Modelling the impact of 111 and GP access on Emergency Departments
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.
Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds
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.
Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon
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.
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 111 option 2 call centre
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.
Intelligent Pathway Management
The project uses data science to quantify the impact of deviations from the "Best Case Scenario" in patient pathways. It involves process mining, an unsupervised learning model to determine relationships between deviations and patient risk, validation, to create Streamlit app reporting suite. The suite presents statistics and risk stratification, with simulations to identify risk profile changes as pathways change.
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.
Modelling of theatre recovery flow requirements
Staffing levels in NHS recovery are unclear, with minimal literature on requirements. Factors like operation type, patient variables, and anaesthetic risk affect recovery time, causing queues. Current systems log patient flow but lack integration. The project aims to predict recovery stay, determine staffing needs, and identify key factors, improving workload planning for recovery staff.
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.
111 Downstream forecasting
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.
Forecasting demand in RDUH breast care services and the impact of urban development
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.
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.
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.
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
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.
Modelling Same Day Emergency Care (SDEC) Pathways
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.
Optimising Same Day Emergency Care (SDEC) Resourcing
This project will model Same Day Emergency Care (SDEC) and Emergency Department (ED) pathways at University Hospitals Bristol and Weston NHS Foundation Trust.
Developing a primary care load management tool
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.
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…
SIMPACT: Simulation of Medical Patient Admission and Care Throughput
Project details coming soon.
Modelling resourcing scenarios to meet increases in demand on a Devon eye clinic
The project aims to develop a DES model for an eye clinic at Royal Devon University Healthcare NHS Foundation Trust. It will use "what if?" analysis to explore the impact of increasing staff and room resources, and AI support in the POD clinic, on meeting a 10% annual demand increase, reducing waiting times, and improving service efficiency.
Machine learning
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 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
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
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.
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.
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.
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.
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)
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
Using Machine Learning and Explainable AI to predict and understand mental health appointment DNAs to reduce inequalities
This project uses machine learning to explore correlations whether there is a correlation between missed mental health appointments and factors like deprivation, age, ethnicity, and travel distance. Reducing non-attendance can significantly impact waiting lists.
Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake
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.
Improving ambulance care via fast feedback from Quality Care Indicators
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.
Using machine learning models to predict future frailty
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.
Understanding drivers of increased length of stay
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.
Using classification modelling technques to investigate changes in Healthcare Resources Group (HRG) coding over time
The project will use classification modelling and explainable AI to identify changes in complexity and comorbidity categorisation over time, ensuring accurate cost pressure insights.
Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds
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.
Predicting the risk of injurious falls in older people with atrial fibrillation
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.
Predicting Gestational Diabetes and other maternity-related conditions using machine learning
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.
Pharmacy Clinical prioritisation tool
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.
Modelling of theatre recovery flow requirements
Staffing levels in NHS recovery are unclear, with minimal literature on requirements. Factors like operation type, patient variables, and anaesthetic risk affect recovery time, causing queues. Current systems log patient flow but lack integration. The project aims to predict recovery stay, determine staffing needs, and identify key factors, improving workload planning for recovery staff.
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.
Exploring factors that impact Opiate Cessation using explainable machine learning
Many patients on opiates for acute pain continue long-term, causing adverse effects and dependence. Efforts are underway to reduce long-term opiate use. This project aims to develop a machine learning model to predict successful opiate cessation and explore factors influencing cessation success.
111 Downstream forecasting
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.
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.
Evaluating the use of machine learning calssifiers for the identification of the determinants of stage at diagnosis of prostate cancers using registry data
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.
Identifying which patients are most at risk for an outcome across integrated neighbourhood teams
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.
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.
Predictive modelling for smoking cessation success
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.
Population segmentation of GP-registered population in Dorset
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.
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.
RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters
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.
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…
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.
Analysis and prediction of seasonality in pathologies requiring ITU admission
This project aims to model patterns in intensive care admissions, such as seasonal variations, using existing data. If patterns are found, Machine Learning will predict patient numbers and time periods. The project will also identify patients needing more physiotherapy, informing staffing, leave, and training.
Building a Machine Learning tool to predict Did Not Attend (DNA) events
This project is developing a machine learning-based tool that looks at the likelihood outpatient Did Not Attend (DNA) incidences across different services/demographics.
Automating injury coding using language models
Traumatic injuries are common in emergency care and a leading cause of death and disability in working-age individuals. Patients undergo clinical assessments, blood tests, and CT imaging upon arrival. Major trauma centres (MTCs) are funded based on injury severity, requiring accurate coding. The HSMA project aims to train a language model to generate injury codes from free-text radiology reports.
Using Machine Learning to predict whether a patient might develop a SSI (Surgical Site Infection)
This project is to develop a simple classification model to predict whether a patient will develop a SSI (Surgical Site Infection).
Natural Language Processing
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.
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
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
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.
Improving ambulance care via fast feedback from Quality Care Indicators
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.
Clinical coding automation using Natural Language Processing
The project aims to use Natural Language Processing (NLP) to automate the prediction of ICD-10 or OPCS-4 codes from doctor/patient notes, currently done manually. Initially focusing on 3-character ICD-10 chapters, it will eventually predict full 4-character codes. Collaborating with a provider, the project will streamline coding and improve accuracy.
Applying Natural Language Processing to automate the extraction and classificiation of congenital anomaly diagnoses from free text and genetic data
This project aims to use Natural Language Processing to automate and standardise extraction and classification of Congenital anomaly diagnoses under ICD10 code Q87.8 which are manually classified from free text, risking errors and inefficiency. To validate diagnoses with genetic data by defining a data linkage method.
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.
RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters
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.
Automating injury coding using language models
Traumatic injuries are common in emergency care and a leading cause of death and disability in working-age individuals. Patients undergo clinical assessments, blood tests, and CT imaging upon arrival. Major trauma centres (MTCs) are funded based on injury severity, requiring accurate coding. The HSMA project aims to train a language model to generate injury codes from free-text radiology reports.
Mapping and Location Optimisation
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.
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
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
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.
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
Using Machine Learning and Explainable AI to predict and understand mental health appointment DNAs to reduce inequalities
This project uses machine learning to explore correlations whether there is a correlation between missed mental health appointments and factors like deprivation, age, ethnicity, and travel distance. Reducing non-attendance can significantly impact waiting lists.
Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon
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.
Optimising the location of Breast Cancer diagnostic services across Devon
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.
Identifying unpaid carers in Norfolk
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.
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…
Geographical mapping in specialist palliative and end of life care
This project aims to use geographic mapping with national health and census data to assess if we are caring for a fair represent the population. It will map some characteristics to include cancer/non-cancer status and protected characteristics like gender, sexual orientation, religion, and ethnicity. This understanding can support funding for specific areas and target referrals from underrepresented groups.
Forecasting
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
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.
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.
Forecasting modelling for A&E attendance
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.
Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds
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.
Forecasting the supply of medical doctors
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.
Modelling 111 option 2 call centre
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.
Predicting the future demand for Renal replacement therapy
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.
111 Downstream forecasting
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.
Forecasting demand in RDUH breast care services and the impact of urban development
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.
Forecasting NHS planning and performance metrics
This project aims to create a robust forecasting approach for NHS Planning metrics, improving system planning and operational management, and ensuring consistent adoption across the system to aid decision-making.
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.
Analysis and prediction of seasonality in pathologies requiring ITU admission
This project aims to model patterns in intensive care admissions, such as seasonal variations, using existing data. If patterns are found, Machine Learning will predict patient numbers and time periods. The project will also identify patients needing more physiotherapy, informing staffing, leave, and training.
Analysis and forecasting of referrals into hospital
This project will use Python and forecasting approaches to analyse growth in referrals into the hospital.
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…
Reporting Automation Projects
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
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 modelling for A&E attendance
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.
Modelling 111 option 2 call centre
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.
Analysis and prediction of seasonality in pathologies requiring ITU admission
This project aims to model patterns in intensive care admissions, such as seasonal variations, using existing data. If patterns are found, Machine Learning will predict patient numbers and time periods. The project will also identify patients needing more physiotherapy, informing staffing, leave, and training.
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.
Network Analysis
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.
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
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.
Agent-Based Simulation (ABS)
Modelling eye injection pathways
The project aims to develop a flexible simulation model to optimise anti-VEGF treatment strategies in ophthalmology. It will use dual modelling frameworks, a modular design, and an interactive dashboard. The model will analyse clinical effectiveness, costs, and resource requirements, adapting to new treatments. The objective is to provide a tool to improve patient outcomes and optimize resource use and costs.
Modelling of theatre recovery flow requirements
Staffing levels in NHS recovery are unclear, with minimal literature on requirements. Factors like operation type, patient variables, and anaesthetic risk affect recovery time, causing queues. Current systems log patient flow but lack integration. The project aims to predict recovery stay, determine staffing needs, and identify key factors, improving workload planning for recovery staff.
Using Agent Based Simulation to understand criminal adoption of new technologies
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.
Agent Based Simulation modelling influences on access to hospice care
Not everyone eligible for hospice care is offered or accepts it, healthcare professionals' reluctance to refer early and patients' or carers' lack of awareness or willingness to discuss options. This project uses Agent Based Simulation to explore motivations for accessing hospice care, aiming to improve early access and outcomes for patients and carers.
System Dynamics
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.
Understanding drivers of increased length of stay
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.
Projects with a Streamlit Interface
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.
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
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.
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.
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.
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
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.
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
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.
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.
Modelling eye injection pathways
The project aims to develop a flexible simulation model to optimise anti-VEGF treatment strategies in ophthalmology. It will use dual modelling frameworks, a modular design, and an interactive dashboard. The model will analyse clinical effectiveness, costs, and resource requirements, adapting to new treatments. The objective is to provide a tool to improve patient outcomes and optimize resource use and costs.
Understanding drivers of increased length of stay
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.
Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds
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.
Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon
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.
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 111 option 2 call centre
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.
Intelligent Pathway Management
The project uses data science to quantify the impact of deviations from the "Best Case Scenario" in patient pathways. It involves process mining, an unsupervised learning model to determine relationships between deviations and patient risk, validation, to create Streamlit app reporting suite. The suite presents statistics and risk stratification, with simulations to identify risk profile changes as pathways change.
Predicting Gestational Diabetes and other maternity-related conditions using machine learning
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.
Developing a streamlit app for creating Theographs of patient journeys
The project aims to create an open-source application for generating interactive theograph visuals to understand patient/client journeys. It will be a generic tool requiring minimal data fields, usable in various healthcare or social care settings.
Pharmacy Clinical prioritisation tool
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.
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.
Developing a web app to recommend appropriate technology enabled care
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.
Forecasting demand in RDUH breast care services and the impact of urban development
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.
Applying Natural Language Processing to automate the extraction and classificiation of congenital anomaly diagnoses from free text and genetic data
This project aims to use Natural Language Processing to automate and standardise extraction and classification of Congenital anomaly diagnoses under ICD10 code Q87.8 which are manually classified from free text, risking errors and inefficiency. To validate diagnoses with genetic data by defining a data linkage method.
Modelling the benefit of MECC (Making Every Contact Count) Training using agent based simulation
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.
• Surrey and Sussex Healthcare NHS Trust
• Somerset NHS Foundation Trust
Identifying which patients are most at risk for an outcome across integrated neighbourhood teams
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.
RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters
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.
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…
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.
Analysis and prediction of seasonality in pathologies requiring ITU admission
This project aims to model patterns in intensive care admissions, such as seasonal variations, using existing data. If patterns are found, Machine Learning will predict patient numbers and time periods. The project will also identify patients needing more physiotherapy, informing staffing, leave, and training.
DES Modelling of The Hyperacute / Acute Stroke Pathway - Patient and Economic Outcomes
Stroke prevalence in the UK is forecasted to increase by 40-60% from 2021 to 2030, straining hospitals and society. This project aims to develop a discrete simulation model to optimise the Hyperacute/Acute stroke pathway, improving patient outcomes, reducing costs, and enhancing economic benefits. It will analyse variables like staffing and operating hours
Building a Machine Learning tool to predict Did Not Attend (DNA) events
This project is developing a machine learning-based tool that looks at the likelihood outpatient Did Not Attend (DNA) incidences across different services/demographics.
Evaluating the Impact of Community Diagnostic Centres on Health Inequalities and Patient Access to Diagnostic Services
Community Diagnostic Centres (CDCs) aim to expand diagnostic capacity and improve access, but their equitable distribution is unclear. This project will assess CDCs' impact on patient access and health inequalities by analysing socio-demographic, geographic, and utilisation data.
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.