Upload remaining HSMA 6 projects (6044-6053)

previous_projects/hsma_6/H6_6044_Population_segmentation_GP_registered_Dorset/index.qmd

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+---
+title: "Population segmentation of GP-registered population in Dorset"
+techniques:
+  - Machine Learning
+  - Unsupervised Learning
+areas:
+  - Primary Care (GPs)
+categories:
+  - Machine Learning
+  - Unsupervised Learning
+  - Primary Care (GPs)
+author:
+  - name: Rhianna O'Connor
+    affiliation: Dorset Council
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+The aim of this project is to create a machine learning-based population segmentation model for the GP-registered Dorset population using multiple characteristics including primary and secondary healthcare utilisation.
+
+We want to identify population segments based on care needs to inform service design.
+
+This has the potential to provide enhanced patient understanding leading to improved resource allocation.

previous_projects/hsma_6/H6_6045_Forecasting_NHS_Planning_Performance_metrics/index.qmd

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+---
+title: "Forecasting NHS planning and performance metrics"
+techniques:
+  - Forecasting
+areas:
+  - NHS
+categories:
+  - Forecasting
+  - NHS
+author:
+  - name: Sam Wheeler
+    affiliation: Bath and North East Somerset, Swindon and Wiltshire ICB
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+The ICB leads on development of Operational Planning submissions to NHS England on an annual basis. This involves development trajectories against various activity and performance metrics, usually covering the next 12 months (e.g. ICB-level A&E attendances).  Analysts are often asked to forecast these measures forward based on historic information. The system’s approach to this is often very basic and crude and, partly because of this, organisations often use their own, different approaches which are often open to manipulation.
+
+The aim of this project is to develop an approach to forecasting the main NHS Planning performance and activity metrics, which is more robust than existing, crude techniques and can form the basis of System Planning. To develop an approach that, given it’s more robust, will be consistently adopting across our system to aid decision makers with both planning but also operational management at system level.

previous_projects/hsma_6/H6_6046_Proactive_Patient_attendance_prediction/index.qmd

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+---
+title: "Proactive Patient Attendance Prediction: Enhancing Healthcare Efficiency through Attendance Forecasting"
+techniques:
+  - Machine Learning
+areas:
+  - Non-attendance Prediction
+  - Outpatients
+categories:
+  - Machine Learning
+  - Non-attendance Prediction
+  - Outpatients
+author:
+  - name: Peter Andrews
+    affiliation: Barts Health NHS Trust
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+Every month ~ 12% of outpatient appointments are not attended at Barts Health NHS Trust. This equates to over 10 thousand hours of clinical input, space and equipment that is not being used optimally to progress patients’ treatments or manage ongoing care.
+
+If a patient does not attend, they could be placed back on a waiting list for an extended period which increases the risk of further deterioration or more severe progression of their condition that may result in them needing emergency care. The deterioration of the patient could have long term impacts on their future health.
+
+This project will develop :
+
+- Machine learning model to forecast non-attendance
+- Patient contact capture tool
+- Integration of machine learning model into enterprise level reports

previous_projects/hsma_6/H6_6047_RALPulator/index.qmd

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+---
+title: "RALPulator : Predicting Robotic-assisted laparoscopic prostatectomy (RALP) operative times from patient letters"
+techniques:
+  - Machine Learning
+  - Natural Language Processing (NLP)
+  - Streamlit
+areas:
+  - Urology
+  - Cancer
+  - Surgical
+categories:
+  - Machine Learning
+  - Natural Language Processing (NLP)
+  - Streamlit
+  - Urology
+  - Cancer
+  - Surgical
+author:
+  - name: Jake Wilson
+    affiliation: Great Western Hospital
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url: https://ralpulator.streamlit.app/
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+This project is building an app that reads in patient letters ahead of surgery, uses 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.
+
+A first version of the app has been developed and deployed, and is available here: <https://ralpulator.streamlit.app/>
+
+Jake is presenting the work to the National Urological Conference in November 2024, and has been invited to present in Perth, Australia in March 2025.

previous_projects/hsma_6/H6_6048_Concurrent_treatment_areas_RTT_pathway/index.qmd

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+---
+title: "Identifying potential concurrent treatment areas and services that would better support patients with multiple, complex referral to treatment (RTT) pathways."
+techniques:
+  - Machine Learning
+  - Streamlit
+areas:
+  - Inequalities
+  - Patient Pathways
+categories:
+  - Machine Learning
+  - Streamlit
+  - Inequalities
+  - Patient Pathways
+author:
+  - name: Amaia Imaz Blanco
+    affiliation: NHS England
+  - name: Sean Aller
+    affiliation: NHS England
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+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 inequalities.
+
+The intention is to build a model that could identify/predict/suggest colocated services as well as points at which patients start having concurrent pathways.

previous_projects/hsma_6/H6_6049_ML_appointments_pathway/index.qmd

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+---
+title: "Using machine learning to identify factors that increase number of appointments per pathway"
+techniques:
+  - Explainable AI
+  - Patient Pathways
+  - Casual Analysis
+areas:
+  - Patient Pathways
+  - EPP Dataset
+  - Waiting Times
+categories:
+  - Machine Learning
+  - Explainable AI
+  - Patient Pathways
+  - Waiting Times
+  - Casual Analysis
+author:
+  - name: Sarah Mole
+    affiliation: NHS England
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+The new EPP data set combines data to create the most complete version of elective pathway activity we have ever had.  At the moment we don’t know why some people/pathways have more appointments than others, but one theory is that long waiting times result in sicker patients which results in more total appointments.
+
+This project plans to use Machine Learning methods to try to unpick this.

previous_projects/hsma_6/H6_6050_DES_pathway_community_diagnostic_centres/index.qmd

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+---
+title: "Discrete Event Simulation model to support the flow of patients through Community Diagnostic Centres"
+techniques:
+  - Discrete Event Simulation (DES)
+  - Streamlit
+  - Forecasting
+areas:
+  - Community Diagnostic Centres (CDCs)
+  - Demographics
+  - Patient Flow
+categories:
+  - Discrete Event Simulation (DES)
+  - Streamlit
+  - Community Diagnostic Centres (CDCs)
+  - Forecasting
+  - Demographics
+  - Patient Flow
+author:
+  - name: Martin Bloyce
+    affiliation: NHS England
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+Community Diagnostic Centres (CDCs) are a relatively new implementation in the NHS, with the intention of improving patient flow and reducing waits for diagnostic tests, by ring-fencing the diagnostic process.
+
+These have been relatively successful so far, with the actual diagnostic part of the process being sped up significantly through CDCs.  However, providers need to make bids to implement CDCs and prove value.  As the NHS is in a period of significant financial pressure, it is possible these decisions could come under further scrutiny.
+
+The aim of this project is to create a DES model which identifies the bottlenecks in the process and allows the user to model different scenarios with the intention of seeing which investments may have the largest impact on improving flow.
+
+There are also further parts of the process outside of the actual CDC setting: the referral to diagnosis time, and diagnosis to result time.  We aim to include this in the analysis to accurately reflect the whole of the patient’s experience of the process.
+
+A stretch goal for the project is to build in future demand requirements based on increasing and changing population in the region.

previous_projects/hsma_6/H6_6051_ABS_influences_access_hospice_care/index.qmd

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+---
+title: "Agent Based Simulation modelling influences on access to hospice care"
+techniques:
+  -
+areas:
+  -
+categories:
+  - Agent based simulation
+author:
+  - name: Helen Cameron
+    affiliation: Ashgate Hospice
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+Not everyone eligible for hospice care is offered, or accepts it.  There is research relating to all potential agents in the decision to access hospice care where healthcare professionals can be disinclined to refer until late in a patient's presentation and patients (& carers) are not wanting to discuss, or not being aware of, their options.  The system is influenced by multiple beliefs/assumptions about what hospice care is, and when referral might be appropriate, although earlier access to services provides better outcomes in the patient's care as well as the experiences of patients and carers.
+
+The aim of this project is to use Agent Based Simulation approaches to explore referrers, patients (& possibly carers) motivations about accessing hospice care.  Any findings from this project would be relevant to any hospice providers.

previous_projects/hsma_6/H6_6052_GM_specialist_palliative_end_of_life_care/index.qmd

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+---
+title: "Geographical mapping in specialist palliative and end of life care"
+techniques:
+  - Mapping
+  - Geographic Modelling
+areas:
+  - End-of-life Care & Hospices
+  - Demographics
+categories:
+  - Geographic Modelling
+  - Mapping
+  - Demographics
+author:
+  - name: Helen Cameron
+    affiliation: Ashgate Hospice
+image: "../placeholder.png"
+title-block-banner: ../../banner.png
+status: Active
+pub-info:
+  abstract:
+  links:
+  - name: Video
+    url:
+    icon: fa-brands fa-youtube
+  - name: Code
+    url:
+    icon: fa-brands fa-github
+  - name: Website
+    url:
+    icon: fa-solid fa-globe
+  - name: Paper
+    url:
+    icon: fa-solid fa-file-contract
+---
+
+Using national health and census data, the aim of this project is to use geographic mapping to show if we are caring for a fair representation of our population. The two populations to be mapped would be based on the same characteristics to include cancer/non-cancer status, protected characteristics such as gender, sexual orientation, religion, ethnicity.
+
+This would help us to understand our population and could be used to support funding for specific geographical areas or targeting referrals from underrepresented groups.