Upload additional projects; tag normalisation

previous_projects/hsma_6/H6_6014_Modelling_musculoskeletal_physiotherapy_services/index.qmd

@@ -1,13 +1,16 @@
 ---
 title: "Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon"
 techniques:
-  -
+  - Mapping
+  - Discrete Event Simulation (DES)
+  - Streamlit
 areas:
-  -
+  - Physiotherapy
 categories:
-  - Geographic modelling
-  - Discrete Event Simulation
+  - Mapping
+  - Discrete Event Simulation (DES)
   - Streamlit
+  - Physiotherapy
 author:
   - name: Martin Greenslade
     affiliation: Royal Devon University Healthcare NHS Foundation Trust

previous_projects/hsma_6/H6_6015_Forecasting_the_supply_of_medical_doctors/index.qmd

@@ -1,11 +1,12 @@
 ---
 title: "Forecasting the supply of medical doctors"
 techniques:
-  -
+  - Forecasting
 areas:
-  -
+  - Workforce
 categories:
   - Forecasting
+  - Workforce
 author:
   - name: Sara Fundu
     affiliation: The Royal College of Pathologists
@@ -34,4 +35,5 @@ The problem: Not enough medical doctors by region and we need to establish the g
 The Aim: Forecast medical doctors required by region, taking into account relevant factors for example, trainees coming in, population etc.
 
 The Output: To plot a graph to show our current supply and what we demand.
+
 Bonus would be to be able to simulate outcome of demand, if input is amended to reflect different scenarios i.e. number of trainees decreases or population ill health increases etc

previous_projects/hsma_6/H6_6016_Optimising_the_location_of_breast_cancer_diagnostic_services/index.qmd

@@ -1,11 +1,16 @@
 ---
-title: "Optimising the location of Breast Cancer diagnostic services within RDUH"
+title: "Optimising the location of Breast Cancer diagnostic services across Devon"
 techniques:
-  -
+  - Geographic Modelling
+  - Travel Times
 areas:
-  -
+  - Cancer
+  - Women's Health
 categories:
-  - Geographic modelling
+  - Geographic Modelling
+  - Travel Times
+  - Cancer
+  - Women's Health
 author:
   - name: Gill Baker
     affiliation: Royal Devon University Healthcare NHS Foundation Trust
@@ -29,4 +34,12 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
+Over 6000 patients a year are referred to RD&E and NDDH for fast-track breast symptom diagnosis. The fast-track clinic requires a clinician, a mammography team and a consultant radiologist/radiographer plus support staff. The imaging needs to take place in specialised rooms. As population and awareness of breast cancer increases there are increasing referrals and the current infrastructure at both RD&E and NDDH will not be sufficient to meet future demand. It is therefore necessary to build a new breast diagnostic unit or extend an existing one. It is likely to be prohibitively expensive to build at both NDDH and RD&E. At present specific GP surgeries refer patients to either NDDH or RD&E. However, the two hospitals are now managed as part of a single NHS Trust called RDUH. Each hospital requires breast diagnosis infrastructure on site as it is used for inpatient and emergency admissions as well as for elective diagnosis services so we need at least two breast diagnosis centres but data science may be used to help determine whether there is a value in building a third centre and/or diverting referrals from specific GPs to one of the existing units.
 
+The aim of this project is to determine where additional breast diagnostic services should be placed to minimise overall travel time for patients whilst reducing infrastructure and running costs.
+
+Objectives include the following:
+
+- To map breast referral demand by GP and calculate the weighted average distance and/or travel time from each GP to RD&E, NDDH and a potential central location.
+- To calculate the percentage of patients who would benefit from a central location.
+- To calculate the shortest distance/travel time from each GP to the three locations and determine the reduction or increase in mileage if patients were diverted from their current default hospital to one of the other two centres.

previous_projects/hsma_6/H6_6017_Referral_to treatment_waiting_times_for_Neurosurgical_patients/index.qmd

@@ -1,11 +1,20 @@
 ---
 title: "Referral to treatment waiting times for Neurosurgical patients"
 techniques:
-  -
+  - Discrete Event Simulation (DES)
+  - Streamlit
 areas:
-  -
+  - Surgical
+  - Neurology
+  - Waiting Lists
+  - Waiting Times
 categories:
-  - Discrete Event Simulation
+  - Discrete Event Simulation (DES)
+  - Streamlit
+  - Surgical
+  - Neurology
+  - Waiting Lists
+  - Waiting Times
 author:
   - name: Andrew Sharrock
     affiliation: The Walton Centre NHS Foundation Trust
@@ -29,6 +38,9 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
-This project will aim to model the pathway for Neurosurgical patients, and to explore, based on current waiting times, will the waiting list grow or reduce, and how long will patients wait for treatment.
+This project will aim to model the pathway for Neurosurgical patients, and to explore, based on current waiting times,
+
+- will the waiting list grow or reduce
+- how long patients may wait for treatment
 
 The aim is to add user interaction, to see how an increase/reduction in capacity affects waiting times. Also to be able to see how many patients are waiting over 52 weeks at each month end.

previous_projects/hsma_6/H6_6018_predicting_risk_of_injurious_falls/index.qmd

@@ -1,12 +1,14 @@
 ---
 title: "Predicting the risk of injurious falls in older people with atrial fibrillation"
 techniques:
-  -
+  - Machine Learning
+  - Explainable AI
 areas:
-  -
+  - Older Adults
 categories:
-  - Machine learning
+  - Machine Learning
   - Explainable AI
+  - Older Adults
 author:
   - name: Anneka Mitchell
     affiliation: University Hospitals Plymouth NHS Trust / University of Exeter
@@ -31,6 +33,7 @@ pub-info:
 ---
 
 Atrial fibrillation (AF) is a common cardiac arrhythmia which increases the risk of stroke.
+
 Anticoagulation is very effective in reducing stroke risk but can increase the risk of bleeding, a much feared consequence of anticoagulation is bleeding on the brain. National and international guidance states that anticoagulation should not be withheld because of falls as the benefits still outweigh the risks but many clinicians choose not to prescribe these medications to people who fall or those at risk of falls because they don’t believe the evidence supports this recommendation.
 
 The initial stage aims of this project is to explore if machine learning techniques can be used to develop a model that can predict injurious falls in older people with AF and determine what features are important.

previous_projects/hsma_6/H6_6019_clinical_coding_automation/index.qmd

@@ -1,11 +1,12 @@
 ---
 title: "Clinical coding automation using Natural Language Processing"
 techniques:
-  -
+  - Natural Language Processing (NLP)
 areas:
-  -
+  - Clinical Coding
 categories:
-  - Natural Lanaguage Processing
+  - Natural Language Processing (NLP)
+  - Clinical Coding
 author:
   - name: Sid Kumar
     affiliation: South West London ICB

previous_projects/hsma_6/H6_6020_modelling_111_option_2_call_centre/index.qmd

@@ -1,14 +1,22 @@
 ---
 title: "Modelling 111 option 2 call centre"
 techniques:
-  -
+  - Discrete Event Simulation (DES)
+  - Forecasting
+  - Streamlit
+  - Quarto
 areas:
-  -
+  - 111 Service
+  - Telephone-based Services
+  - Staffing Level Optimisation
 categories:
-  - Discrete Event Simulation
+  - Discrete Event Simulation (DES)
   - Forecasting
   - Streamlit
   - Quarto
+  - 111 Service
+  - Telephone-based Services
+  - Staffing Level Optimisation
 author:
   - name: Richard Hall
     affiliation: Norfolk and Suffolk NHS Foundation Trust

previous_projects/hsma_6/H6_6021_predicting_future_demand_renal_replacement_therapy/index.qmd

@@ -1,11 +1,14 @@
 ---
 title: "Predicting the future demand for Renal replacement therapy"
 techniques:
-  -
+  - Forecasting
 areas:
-  -
+  - Renal
+  - Dialysis
 categories:
   - Forecasting
+  - Renal
+  - Dialysis
 author:
   - name: Sandhya Radha Krishnakumar
     affiliation: NHS England
@@ -29,4 +32,8 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
+The problem: Kidney disease is projected to be the fifth leading cause of premature deaths globally by 2040. With people living longer and having more comorbidities, the demand for dialysis and transplants is rising and exceeding capacity. In England, the system is at full capacity for in-centre dialysis, requiring immediate action and a long-term strategy to optimise care.
+
+The result: Incorporate forecasting techniques to predict the future demand based on population prediction (data available in ONF and prevalence data available in QOF).
 
+The aim: To develop a model to help tackle this demand - either increasing home dialysis or introducing a new dialysis centre

previous_projects/hsma_6/H6_6022_Intelligent_pathway_management/index.qmd

@@ -1,13 +1,16 @@
 ---
 title: "Intelligent Pathway Management"
 techniques:
-  -
+  - Process Mining
+  - Unsupervised learning
+  - Discrete Event Simulation (DES)
+  - Streamlit
 areas:
-  -
+  - Patient Pathways
 categories:
   - Process Mining
   - Unsupervised learning
-  - Discrete Event Simulation
+  - Discrete Event Simulation (DES)
   - Streamlit
 author:
   - name: Portia Eze
@@ -32,4 +35,10 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
+The aim of the project is to use data science techniques to quantify the impact of deviations from the "Best Case Scenario" in a patient pathway, this could be diagnostics, surgical or pre-operative assessment.
 
+1. Process Mining – analysis is done into the number of deviations from a best-case scenario patient pathway.
+2. An unsupervised learning model is used to determine the relationship between these deviations, and the level of patient risk.
+3. The relationships identified in step 2 are validated.
+4. The End product will be a live reporting suite in Streamlit, presenting statistics in relation to the newly identified measures, plus risk stratification of the patient population.
+5. Simulation to identify whether risk profile changes as the pathway changes

previous_projects/hsma_6/H6_6023_predicting_gestational_diabetes_using_ML/index.qmd

@@ -1,12 +1,18 @@
 ---
-title: "Predicting Gestational Diabeted and other maternity-related conditions using machine learning"
+title: "Predicting Gestational Diabetes and other maternity-related conditions using machine learning"
 techniques:
-  -
+  - Machine Learning
+  - Streamlit
 areas:
-  -
+  - Diabetes
+  - Maternity
+  - Women's Health
 categories:
   - Machine Learning
   - Streamlit
+  - Diabetes
+  - Maternity
+  - Women's Health
 author:
   - name: Rochelle Francis-Reid
     affiliation: Epsom and St Helier University Hospitals NHS Trust
@@ -30,4 +36,12 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
+Maternity-related conditions, such as gestational diabetes, can have significant impacts on the health of both the mother and baby if not identified early. Current approaches often detect these conditions after symptoms appear, potentially delaying important interventions. This project aims to use machine learning to predict the likelihood of developing gestational diabetes and other related conditions early in pregnancy, allowing for timely interventions and personalised care.
+
+The aim of this project is to develop a machine learning model that predicts the likelihood of gestational diabetes and other maternity-related conditions based on patient data. The model will serve as a decision support tool for clinicians to provide proactive and personalised care, improving health outcomes for mothers and babies.
+
+The project will generate :
 
+- A machine learning predictive model.
+- An interactive web app or interface for clinicians to input patient data and receive predictions.
+- Reports outlining the model's predictions, accuracy, and effectiveness.

previous_projects/hsma_6/H6_6024_Streamlit_app_for_theographs_of_patient_journeys/index.qmd

@@ -0,0 +1,39 @@
+---
+title: "Developing a streamlit app for creating Theographs of patient journeys"
+techniques:
+  - Data Visualisation
+areas:
+  - Patient Pathways
+categories:
+  - Streamlit
+  - Theographs
+  - Data Visualisation
+  - Patient Pathways
+author:
+  - name: Suprasad Gavhane
+    affiliation: North of England Commissioning Support (NECS)
+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: https://github.com/sp-necs/TheoGraph
+    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 an open source application to generate interactive theograph visuals to understand a patients'/clients' journey through a system.
+
+The goal is to make a generic application requiring the minimum of data fields so it can be used in different healthcare or social care settings (working with dynamic 'Event_Type' values).
+
+More information about theographs can be found [here](https://www.nuffieldtrust.org.uk/resource/a-10-year-story-visualising-patient-journeys).

previous_projects/hsma_6/H6_6025_Modelling_delays_in_breast_head_neck_cancer_pathways/index.qmd

@@ -1,11 +1,16 @@
 ---
 title: "Modelling delays in breast, head and neck cancer pathways"
 techniques:
-  -
+  - Discrete Event Simulation (DES)
 areas:
-  -
+  - Cancer
+  - Women's Health
+  - Waiting Times
 categories:
-  - Discrete Event Simulation
+  - Discrete Event Simulation (DES)
+  - Cancer
+  - Women's Health
+  - Waiting Times
 author:
   - name: Michael Baser
     affiliation: NHS England
@@ -29,4 +34,9 @@ pub-info:
     icon: fa-solid fa-file-contract
 ---
 
+Breast cancer and head and neck cancer are two common cancer sites, where the main clinical pathway is surgery, followed by adjuvant radiotherapy or chemotherapy. Clinical feedback has reported large waits after surgery and often before the point of referral to e.g. the radiotherapy department.
 
+Our project is using Discrete Event Simulation to model the post-diagnosis pathway for breast and head and neck cancer. The model will be used to identify mid-pathway delays and treatment variation across England with the aim to report on two clinically important steps in the clinical pathway:
+
+1. time from neoadjuvant SACT to surgery or radiotherapy (RT)
+2. time from surgery to first adjuvant treatment (either RT or SACT)