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Operational Research, Data Science and Programming – HSMA +HSMA - Module 1: Introduction to Operational Research, Data Science and Programming - + + + + @@ -68,6 +70,35 @@ "search-label": "Search" } } + + + + @@ -120,7 +180,7 @@ - -
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3A: Introduction to Geospatial Problems in Health & Geographic Visualisation using QGIS - Part 1

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3B: QGIS - Part 2 and Creating Static Maps in Python with geopandas and matplotlib

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3C: Interactive Maps in Python with folium, visualising travel time data in Python, and obtaining travel time data with routingpy

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3D: Location Allocation Problems

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4A: Introduction to AI and Machine Learning

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4B: Logistic Regression - Who Would Survive the Titanic?

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4C: Ethics in AI

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4D: Decision Trees and Random Forests

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4E: Boosted Trees for Classification, and Tackling Regression (Numeric Prediction) Problems with Machine Learning

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4F: Neural Networks

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4G: Explainable AI

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4H: Reinforcement Learning

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4I: Synthetic Data using SMOTE

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4J: Optimising Machine Learning Models

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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+ + + + + \ No newline at end of file diff --git a/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8a_reproducibility_quarto_xlsxwriter.html b/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8a_reproducibility_quarto_xlsxwriter.html new file mode 100644 index 00000000..c8cc239c --- /dev/null +++ b/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8a_reproducibility_quarto_xlsxwriter.html @@ -0,0 +1,637 @@ + + + + + + + + + +HSMA + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8b_forecasting.html b/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8b_forecasting.html new file mode 100644 index 00000000..c8cc239c --- /dev/null +++ b/docs/hsma_content/modules/current_module_details/module_8_modern_analytics/8b_forecasting.html @@ -0,0 +1,637 @@ + + + + + + + + + +HSMA + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/hsma_content/modules/modules.html b/docs/hsma_content/modules/modules.html index 4343942c..b77c916c 100644 --- a/docs/hsma_content/modules/modules.html +++ b/docs/hsma_content/modules/modules.html @@ -2,12 +2,12 @@ - + -HSMA Modules – HSMA +HSMA - HSMA Modules - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Using Machine Learning and Explainable AI to predict and understand mental health appointment DNAs to reduce inequalities

+ + + + +
+
Machine Learning
+
Geographic Modelling
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+ +
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Author
+
Affiliation
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+

Heath McDonald

+
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+

+ Lancashire and South Cumbria NHS Foundation Trust +

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As a provider of Mental Health Services for SMI, Lancashire & South Cumbria NHS Foundation Trust is committed to helping to deliver the aims of Core20Plus5. Core20PLUS5 is a national NHS England approach to inform action to reduce healthcare inequalities at both national and system level.

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The aim of this HSMA project is to use machine learning to help understand whether there is a correlation between failed attendance of mental health outpatient appointments and deprivation, or whether this could be being influenced by other factors (features) such as age, ethnicity, the distance travelled to get to those appointments etc.

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With constantly growing waiting lists. anything we can do to reduce non-attendance can have a significant impact on these.

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The project idea is to develop a Machine learning model to investigate whether there is a relationship between travel distance and method and appointment attendance. The project will also use explainable AI to help users understand what the results mean.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6001_DES_modelling_non_elective_flow/index.html b/docs/previous_projects/hsma_6/H6_6001_DES_modelling_non_elective_flow/index.html new file mode 100644 index 00000000..82acef89 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6001_DES_modelling_non_elective_flow/index.html @@ -0,0 +1,694 @@ + + + + + + + + + + +HSMA - Discrete Event Simulation modelling of non elective flow + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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+

Discrete Event Simulation modelling of non elective flow

+ + + + +
+
Discrete Event Simulation
+
streamlit
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+ +
+ +
+
Author
+
Affiliation
+ +
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Helena Robinson

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+ Countess of Chester Hospital NHS FOundation Trust +

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Poor patient flow is leading to (very) long waits for admission in Emergency Departments. This means there is poor performance against all the key ED wait metrics for the hospital and more importantly, there is evidence that long waits for admission in ED are associated with poorer outcomes for patients.

+

The two main strategies employed to tackle this problem is increasing the number of beds (by creation of escalation beds) and trying to decrease discharge delays (reducing length of stay). Additionally, with Same Day Emergency Care (SDEC) facility it is unclear how the number of people admitted through this facility impacts the waits of those in ED.

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Given x beds, how far does admitted length of stay have to reduce to meet particular waiting time targets for those queuing in ED? (Evidence based target) If we open 15 beds but keep admitted length of stay the same, what is the impact on ED waiting times and the various targets? (Evidence for a particular management strategy) What is the optimum number of people to stream from ED to SDEC to minimise ED waits? (Evidence for a particular management strategy)

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This project plans to create Discrete Event Simulation model(s), using HSMA training, to provide evidence for the questions above. Also hoping to create a friendly user interface that stakeholders can try out scenarios and help understand how the model works.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6002_GM_healthcare_worker_vaccination_uptake/index.html b/docs/previous_projects/hsma_6/H6_6002_GM_healthcare_worker_vaccination_uptake/index.html new file mode 100644 index 00000000..c815c6cc --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6002_GM_healthcare_worker_vaccination_uptake/index.html @@ -0,0 +1,692 @@ + + + + + + + + + + +HSMA - Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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Geographic and Boosted Tree Modelling of Healthcare worker vaccination uptake

+ + + + +
+
Geographic Modelling
+
Machine Learning
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+ +
+ +
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Author
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Affiliation
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+

Yasmin Ibrahim

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+

+ NHS England +

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There is low uptake of Healthcare Workers for COVID and Flu, which is in many Trusts decreasing with each season.

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Trusts and regions, as well as NHS England policy teams would benefit from identifying patterns among healthcare workers: staff uptake, gender, ethnicity, deprivation, frontline status, age group, eligibility (in another cohort other than healthcare worker status).

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Using a mixed-method approach. Geographic modelling of healthcare worker uptake by Trust, ICB and region, and boosted tree modelling of the data to capture patterns in the data and reduces bias and variance. Regression modelling would also be useful to produce data tables to present to stakeholders (e.g. odds ratios for logistic regression).

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6003_DESmond_62_day_prostate_cancer_pathway/index.html b/docs/previous_projects/hsma_6/H6_6003_DESmond_62_day_prostate_cancer_pathway/index.html new file mode 100644 index 00000000..4699261d --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6003_DESmond_62_day_prostate_cancer_pathway/index.html @@ -0,0 +1,645 @@ + + + + + + + + + +HSMA - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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At present we’re missing the 62 day target from referral to treatment of prostate cancer. The pathway involves numerous activities such as triaging, imaging, biopsies as well as multiple decisions along the way. The smooth running of the pathway relies upon having the necessary resources allocated each of these events e.g. biopsy clinics, access to imaging etc.

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The system is reactive- it can take months before it’s apparent the targets have not been met, and even longer to reallocate resources appropriately.

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We want to develop a system which can anticipate the demand for resources, dependant on the number of referrals and resources allocated. The aim is to create a Discrete Event Simulation model to model the current pathway. To feed live data into the model in order to predict where bottlenecks occur and if the pathway will breach the 62 day target. Make use of forecasting methods to predicts what changes are needed in resource allocation in order to ensure the 62 day target is met.

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The project outcome is a simulation model with an interactive, web app based interface and a dashboard which illustrates key metrics of the 2 week wait pathway

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6004_DES_orthopaedic_theatre_capacity_optimisation/index.html b/docs/previous_projects/hsma_6/H6_6004_DES_orthopaedic_theatre_capacity_optimisation/index.html new file mode 100644 index 00000000..3eb68301 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6004_DES_orthopaedic_theatre_capacity_optimisation/index.html @@ -0,0 +1,693 @@ + + + + + + + + + + +HSMA - Discrete Event Simulation to model othopaedic theatre capacity optimisation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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Discrete Event Simulation to model othopaedic theatre capacity optimisation

+ + + + +
+
Discrete Event Simulation
+
Forecasting
+
Streamlit
+
+
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Author
+
Affiliation
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+

Rebecca Marshall

+
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+

+ University Hospitals Coventry and Warwickshire Trust +

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The aim of this project is to use Discrete Event Simulation to optimise theatre capacity for elective procedures and major trauma arrivals in Orthopaedics.

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Orthopaedic Trauma – Emergency Referrals (Major Trauma) increasing over the last 4-5 months; UHCW is a Major Trauma Centre so receive patients from further afield as well as local patients. Also, a Tertiary referral centre for specialised surgeries. This is impacting on elective lists as not enough capacity to do alongside emergency cases. Having a financial impact on the Trust due to money Orthopaedics brings in but activity is unable to happen due to emergency cases, waiting lists increasing. Harder to get patients back from elsewhere due to lack of resource/capacity.

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Using a Discrete Event Simulation model showing the major trauma and elective surgery pathways in Orthopaedics and experiment with adding extra theatre slots and/or increasing capacity elsewhere in the pathway to improve flow and the waiting list.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6005_Improving_ambulance_care/index.html b/docs/previous_projects/hsma_6/H6_6005_Improving_ambulance_care/index.html new file mode 100644 index 00000000..e3ffa506 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6005_Improving_ambulance_care/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Improving ambulance care via fast feedback from Quality Care Indicators + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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Improving ambulance care via fast feedback from Quality Care Indicators

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+
Machine Learning
+
NLP
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+
+ +
+ +
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Author
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Affiliation
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+

Phil King

+
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+

+ South Central Ambulance Service +

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Our aim is to improve Ambulance Clinical Quality Indicators (ACQI) data quality and rapidly provide feedback from all ACQI records. There is potential for this tool to be shared with other Ambulance Trusts using the same or similar data collection techniques. This would be achieved with a tool that can analyse free text fields to complete missing data, capture the sentiment of clinical notes, categorise the type of incident and assess if the treatment given was appropriate. It could be utilised as a backup process to manually reviewing data should there be a resourcing issue within the team. It will also evidence if any changes could be made to the clinical records to improve data capture.

+

We would like to consider how the free text could be searched to find positive and negative statements that impact if a record passes or fails a measure within each ACQI. In addition, creating a tool that would predict any changes to the ‘rules’ and their impact on the scores.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6006_DES_childrens_ADHD_diagnosis_and_treatment/index.html b/docs/previous_projects/hsma_6/H6_6006_DES_childrens_ADHD_diagnosis_and_treatment/index.html new file mode 100644 index 00000000..d4869f39 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6006_DES_childrens_ADHD_diagnosis_and_treatment/index.html @@ -0,0 +1,688 @@ + + + + + + + + + + +HSMA - Discrete Event Simulation modelling of childrens ADHD diagnosis and treatment + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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Discrete Event Simulation modelling of childrens ADHD diagnosis and treatment

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+ +
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Author
+
Affiliation
+ +
+

Heath McDonald

+
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+ Lancashire and South Cumbria NHS Foundation Trust +

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This project is using Discrete Event Simulation to model the children’s ADHD diagnostic and treatment pathway. The model will be used to identify delays and potential strategies to reduce waiting lists and waiting times for children accessing treatment.

+

The current assessment process is lengthy, proposing a new pathway that would include undertaking some of the diagnostic testing before undertaking a more comprehensive testing so that there is more confidence that patients who have assessments have ADHD.

+

The model will capture the changes to the pathway to assess the potential impact of these changes on waiting times and will also capture formal diagnosis and medication titration aspects of the pathway. The model may be extended to look at further 1:1 and group session appointments.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6007_Modelling_eye_injection_pathways/index.html b/docs/previous_projects/hsma_6/H6_6007_Modelling_eye_injection_pathways/index.html new file mode 100644 index 00000000..01ba9d35 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6007_Modelling_eye_injection_pathways/index.html @@ -0,0 +1,693 @@ + + + + + + + + + + +HSMA - Modelling eye injection pathways + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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Modelling eye injection pathways

+ + + + +
+
Agent Based Simulation
+
Discrete Event Simulation
+
Streamlit
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+ +
+ +
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Author
+
Affiliation
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Luke Herbert

+
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+ Surrey and Sussex Healthcare NHS Trust +

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This project aims to develop a comprehensive and flexible simulation model to optimise anti-VEGF treatment strategies in ophthalmology. The model will address the complex challenges in anti-VEGF treatments, including frequent injections, multiple treatment options, and diverse strategies, whilst considering the significant resource consumption and high costs associated with these treatments.

+

Key features of the project include: Dual modelling approach using Mesa and SimPy frameworks to determine the most suitable platform. Modular design to accommodate various treatment strategies, drug efficacies, and clinic capacities. User-friendly interface with interactive dashboards for stakeholders to explore different scenarios. Analysis of clinical effectiveness, costs, and resource requirements for various treatment approaches. Adaptability to incorporate new drugs and treatment paradigms as they emerge.

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Project Objectives: The project aims to provide a tool for data-driven decision-making in ophthalmology clinic management, potentially improving patient outcomes whilst optimising resource use and costs.

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6008_Modelling_the_impact_of_111_and_GP_access/index.html b/docs/previous_projects/hsma_6/H6_6008_Modelling_the_impact_of_111_and_GP_access/index.html new file mode 100644 index 00000000..78e091e5 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6008_Modelling_the_impact_of_111_and_GP_access/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Modelling the impact of 111 and GP access on Emergency Departments + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
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← Back to HSMA projects
+ +
+

Modelling the impact of 111 and GP access on Emergency Departments

+ + + + +
+
Discrete Event Simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Sarah Blincko

+
+
+

+ NHS England +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

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.

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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6009_ML_to_predict_future_fraility/index.html b/docs/previous_projects/hsma_6/H6_6009_ML_to_predict_future_fraility/index.html new file mode 100644 index 00000000..228f2edf --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6009_ML_to_predict_future_fraility/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Using machine learning models to predict future frailty + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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← Back to HSMA projects
+ +
+

Using machine learning models to predict future frailty

+ + + + +
+
Machine learning
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Emil Frances-Chi

+
+
+

+ NHS West Yorkshire ICB - Wakefield +

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+
+ +
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+ + + +
+ + +
+ + + +
+ + + + +

This project will use machine learning methods to estimate the size of the Wakefield District population who will likely be frail in 2 / 3 / 4 years time, and to understand the key features used to predict whether a person will become frail in the near future at a local level.

+

Planning for future population health needs - The ability to plan and design resource allocation (e.g. beds available) in an evidence-based manner is a crucial and consistent priority within the ICB - There is soon to be approximately 2 years’ worth of linked data, allowing us to begin building and evaluating the use of ML-based techniques aiming to predict the future prevalence of various LTCs or conditions - Pending the outcomes of this project, we anticipate there to be future scope to adapt similar approached and apply learning to predicting the future prevalence of other LTCs such as diabetes, hypertension, CVD conditions, respiratory conditions and so forth

+

The project will produce reports from the work and potentially a dashboard or other user interface.

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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6010_Understanding_drivers_of_increased_length_of_stay/index.html b/docs/previous_projects/hsma_6/H6_6010_Understanding_drivers_of_increased_length_of_stay/index.html new file mode 100644 index 00000000..572374f8 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6010_Understanding_drivers_of_increased_length_of_stay/index.html @@ -0,0 +1,695 @@ + + + + + + + + + + +HSMA - Understaning drivers of increased length of stay + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ +
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← Back to HSMA projects
+ +
+

Understaning drivers of increased length of stay

+ + + + +
+
Casual Analysis
+
Machine Leanring
+
Synthetic data
+
Streamlit
+
System Dynamics
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Jake Kealey

+
+
+

+ North Central London ICB +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

NCL has observed a consistent increase in long Length of Stay (LoS) over the past 5 years. Increasing length of stay means a higher capacity is needed for the same demand which causes system strain. Stakeholders are interested in understanding why length of stay has increased within NCL so that that can introduce appropriate measures to reduce it.

+

This project proposes to develop a causal model that can be used to identify relations between admission features, operations and length of stay. The model will be used to understand site level drivers to estimate the potential impact of proposed interventions.

+

Key Aims: To build a qualitative model of the drivers of Length of Stay in acute providers in NCL Build a quantitative model(s) of LoS Model changes in LoS in response to interventions (stretching)

+ + + +
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+   +
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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
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+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6011_Forecasting_modelling_for_A&E_attendance/index.html b/docs/previous_projects/hsma_6/H6_6011_Forecasting_modelling_for_A&E_attendance/index.html new file mode 100644 index 00000000..adb115bb --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6011_Forecasting_modelling_for_A&E_attendance/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Forecastubf modelling for A&E attendance + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
+ +
+

Forecastubf modelling for A&E attendance

+ + + + +
+
Forecasting
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Yu Qiao

+
+
+

+ Liverpool University Hospital Foundation Trust +

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+ + +
+ + + +
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The aim of this project is to forecast future A&E attendances 6 weeks in advance, accounting for seasonal patterns.

+

NHS A&E is under pressure nationwide for a long-time and often we do not know what lies ahead of us to manage the limited resources available and understand when interventions and helps are needed. This is in addition to seasonal pressures. Currently A&E has a challenging time looking ahead and manage staffing level needed to match the demand. Furthermore, this can help staff to assess if an escalation process is needed when the abnormal events occur. A&E is known for its unpredictability in demand that varies with seasonal trend, location, population, weather and various of other factors.

+

The project will use Time Series Forecasting methods and a Repeatable Analytical Pipeline for Deployment.

+ + + +
+ +
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6012_Using_classification_modelling_to investigate_changes_in_ HRG/index.html b/docs/previous_projects/hsma_6/H6_6012_Using_classification_modelling_to investigate_changes_in_ HRG/index.html new file mode 100644 index 00000000..a715c9e8 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6012_Using_classification_modelling_to investigate_changes_in_ HRG/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Using classification modelling technques to investigate changes in Healthcare Resources Group (HRG) coding over time + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
+ +
+

Using classification modelling technques to investigate changes in Healthcare Resources Group (HRG) coding over time

+ + + + +
+
Machine learning
+
Explainable AI
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Chris Todd

+
+
+

+ NHS England +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

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+   +
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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
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+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6013_Forecasting_acute_bed_occupancy/index.html b/docs/previous_projects/hsma_6/H6_6013_Forecasting_acute_bed_occupancy/index.html new file mode 100644 index 00000000..0390fd59 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6013_Forecasting_acute_bed_occupancy/index.html @@ -0,0 +1,694 @@ + + + + + + + + + + +HSMA - Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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+ +
← Back to HSMA projects
+ +
+

Forecasting acute bed occupancy, and simulating short term demand and capacity for acute beds

+ + + + +
+
Forecasting
+
Machine learning
+
Discrete Event Simulation
+
Streamlit
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Rey Tan

+
+
+

+ Royal United Hospitals Bath NHS Foundation Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

The aim of this project is to develop a tool to perform time series forecasting on bed occupancy based on historical data, incorporating variables like seasonality and growth. To build a web-based application that enables end users to simulate acute bed model, with the ability to include variables like closed beds, additional capacity, community availability etc, in turn aiding decision making.

+

The problem: Current bed occupancy forecast is done on Excel based on historical monthly averages High level position but lacking in flexibility Daily bed model is manually tweaked, and unable to simulate possible outcome

+

Using Machine learning and discrete event simulation the aim is for the project to predict values close to actual measures. For the user to utilise the web app on a day to day basis and find it a reliable tool for decision making.

+ + + +
+ +
+
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6014_Modelling_musculoskeletal_physiotherapy_services/index.html b/docs/previous_projects/hsma_6/H6_6014_Modelling_musculoskeletal_physiotherapy_services/index.html new file mode 100644 index 00000000..191607c4 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6014_Modelling_musculoskeletal_physiotherapy_services/index.html @@ -0,0 +1,694 @@ + + + + + + + + + + +HSMA - Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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    +
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← Back to HSMA projects
+ +
+

Modelling Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon

+ + + + +
+
Geographic modelling
+
Discrete Event Simulation
+
Streamlit
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Martin Greenslade

+
+
+

+ Royal Devon University Healthcare NHS Foundation Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

This project aims to explore the demand for and access to Musculoskeletal Physiotherapy services across Exeter, Mid and East Devon.

+

Using Geographic Modelling to demonstrate variation in demand and access times for Musculoskeletal Physiotherapy services across Exeter, Mid & East Devon.

+

Using DES to model how placement of resources (staffing) can reduce variation.

+

To construct a Streamlit App from the above as a tool for ongoing service development and review.

+ + + +
+ +
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+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6015_Forecasting_the_supply_of_medical_doctors/index.html b/docs/previous_projects/hsma_6/H6_6015_Forecasting_the_supply_of_medical_doctors/index.html new file mode 100644 index 00000000..fb1409b2 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6015_Forecasting_the_supply_of_medical_doctors/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Forecasting the supply of medical doctors + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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← Back to HSMA projects
+ +
+

Forecasting the supply of medical doctors

+ + + + +
+
Forecasting
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Sara Fundu

+
+
+

+ The Royal College of Pathologists +

+
+
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+ + +
+ + + +
+ + + + +

The problem: Not enough medical doctors by region and we need to establish the gap between what we have and what we need.

+

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

+ + + +
+ +
+
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6016_Optimising_the_location_of_breast_cancer_diagnostic_services/index.html b/docs/previous_projects/hsma_6/H6_6016_Optimising_the_location_of_breast_cancer_diagnostic_services/index.html new file mode 100644 index 00000000..9b1d99ea --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6016_Optimising_the_location_of_breast_cancer_diagnostic_services/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Optimising the location of Breast Cancer diagnostic services within RDUH + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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← Back to HSMA projects
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+

Optimising the location of Breast Cancer diagnostic services within RDUH

+ + + + +
+
Geographic modelling
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Gill Baker

+
+
+

+ Royal Devon University Healthcare NHS Foundation Trust +

+
+
+ +
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+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
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+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6017_Referral_to treatment_waiting_times_for_Neurosurgical_patients/index.html b/docs/previous_projects/hsma_6/H6_6017_Referral_to treatment_waiting_times_for_Neurosurgical_patients/index.html new file mode 100644 index 00000000..07056538 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6017_Referral_to treatment_waiting_times_for_Neurosurgical_patients/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Referral to treatment waiting times for Neurosurgical patients + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
+ +
+

Referral to treatment waiting times for Neurosurgical patients

+ + + + +
+
Discrete Event Simulation
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+
+ +
+ +
+
Author
+
Affiliation
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+

Andrew Sharrock

+
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+ The Walton Centre NHS Foundation Trust +

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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.

+

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.

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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6018_predicting_risk_of_injurious_falls/index.html b/docs/previous_projects/hsma_6/H6_6018_predicting_risk_of_injurious_falls/index.html new file mode 100644 index 00000000..52a7dcb5 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6018_predicting_risk_of_injurious_falls/index.html @@ -0,0 +1,692 @@ + + + + + + + + + + +HSMA - Predicting the risk of injurious falls in older people with atrial fibrillation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ +
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← Back to HSMA projects
+ +
+

Predicting the risk of injurious falls in older people with atrial fibrillation

+ + + + +
+
Machine learning
+
Explainable AI
+
+
+ +
+ +
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Author
+
Affiliation
+ +
+

Anneka Mitchell

+
+
+

+ University Hospitals Plymouth NHS Trust / University of Exeter +

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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.

+

The longer term aim of this project is to ascertain whether a tool could be developed to personalise anticoagulant treatment based on falls risk to help clinicians and patients make more informed treatment decisions.

+ + + +
+ +
+
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6019_clinical_coding_automation/index.html b/docs/previous_projects/hsma_6/H6_6019_clinical_coding_automation/index.html new file mode 100644 index 00000000..dfe16a3c --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6019_clinical_coding_automation/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Clinical coding automation using Natural Language Processing + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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+

Clinical coding automation using Natural Language Processing

+ + + + +
+
Natural Lanaguage Processing
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Sid Kumar

+
+
+

+ South West London ICB +

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+ + +
+ + + +
+ + + + +

The aim of this project is to use Natural Language Processing (NLP) to predict ICD-10 or OPCS-4 codes from doctor/patient notes, automating a task currently done manually by the clinical coding team.

+

Initially, we’ll predict 3-character ICD-10 chapters, with the goal of eventually predicting full 4-character codes. Collaborating with a provider that has access to these notes, this project will streamline coding and improve accuracy.

+ + + +
+ +
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+   +
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+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
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+   +
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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6020_modelling_111_option_2_call_centre/index.html b/docs/previous_projects/hsma_6/H6_6020_modelling_111_option_2_call_centre/index.html new file mode 100644 index 00000000..41c5c2fd --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6020_modelling_111_option_2_call_centre/index.html @@ -0,0 +1,694 @@ + + + + + + + + + + +HSMA - Modelling 111 option 2 call centre + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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← Back to HSMA projects
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+

Modelling 111 option 2 call centre

+ + + + +
+
Discrete Event Simulation
+
Forecasting
+
Streamlit
+
Quarto
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Richard Hall

+
+
+

+ Norfolk and Suffolk NHS Foundation Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

We operate 2 separate 111 call centres for mental health patients (for both Norfolk and Suffolk). These are both set up in different ways (one fully staffed with ‘qualified’ B6s, the other with junior staff who escalate to qualified staff when needed), but both are struggling with their performance.

+

Many callers abandon their calls before they can be reached and there are serious concerns about clinical safety. A piece of work is underway to transform these services, and a piece of demand and capacity analysis is required to ensure the best possible staffing model is in place, both in terms of structure and resource levels.

+

The aim of this project is to build a DES model to compare both staffing approaches and identify the level of resources required to safely staff the service. Further aims would be to build a useful helper tool to ensure planned rosters are safe, users could input current rosters again a forecasted level of calls and the model would show the potential effects of this.

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6021_predicting_future_demand_renal_replacement_therapy/index.html b/docs/previous_projects/hsma_6/H6_6021_predicting_future_demand_renal_replacement_therapy/index.html new file mode 100644 index 00000000..71a60eb5 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6021_predicting_future_demand_renal_replacement_therapy/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Predicting the future demand for Renal replacement therapy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Predicting the future demand for Renal replacement therapy

+ + + + +
+
Forecasting
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Sandhya Radha Krishnakumar

+
+
+

+ NHS England +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
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+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6022_Intelligent_pathway_management/index.html b/docs/previous_projects/hsma_6/H6_6022_Intelligent_pathway_management/index.html new file mode 100644 index 00000000..3e41b2d7 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6022_Intelligent_pathway_management/index.html @@ -0,0 +1,692 @@ + + + + + + + + + + +HSMA - Intelligent Pathway Management + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Intelligent Pathway Management

+ + + + +
+
Process Mining
+
Unsupervised learning
+
Discrete Event Simulation
+
Streamlit
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Portia Eze

+
+
+

+ Kings College Hospital +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6023_predicting_gestational_diabetes_using_ML/index.html b/docs/previous_projects/hsma_6/H6_6023_predicting_gestational_diabetes_using_ML/index.html new file mode 100644 index 00000000..77db49e1 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6023_predicting_gestational_diabetes_using_ML/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Predicting Gestational Diabeted and other maternity-related conditions using machine learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Predicting Gestational Diabeted and other maternity-related conditions using machine learning

+ + + + +
+
Machine Learning
+
Streamlit
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Rochelle Francis-Reid

+
+
+

+ Epsom and St Helier University Hospitals NHS Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6024_Streamlit_app_ for _theographs_of_patient_journeys/index.html b/docs/previous_projects/hsma_6/H6_6024_Streamlit_app_ for _theographs_of_patient_journeys/index.html new file mode 100644 index 00000000..6dadcfbb --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6024_Streamlit_app_ for _theographs_of_patient_journeys/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Developing a streamlit app for creating Theographs of patient journeys + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
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+ +
← Back to HSMA projects
+ +
+

Developing a streamlit app for creating Theographs of patient journeys

+ + + + +
+
Streamlit
+
Theographs
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Suprasad Gavhane

+
+
+

+ North of England Commissioning Support (NECS) +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6025_Modelling_delays_in_breast_head_neck_cancer_pathways/index.html b/docs/previous_projects/hsma_6/H6_6025_Modelling_delays_in_breast_head_neck_cancer_pathways/index.html new file mode 100644 index 00000000..5914cf81 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6025_Modelling_delays_in_breast_head_neck_cancer_pathways/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Modelling delays in breast, head and neck cancer pathways + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
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+ +
← Back to HSMA projects
+ +
+

Modelling delays in breast, head and neck cancer pathways

+ + + + +
+
Discrete Event Simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Michael Baser

+
+
+

+ NHS England +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6026_Modelling_secondary_care_psychological_/index.html b/docs/previous_projects/hsma_6/H6_6026_Modelling_secondary_care_psychological_/index.html new file mode 100644 index 00000000..376e2136 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6026_Modelling_secondary_care_psychological_/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Modelling secondary care psychological therapy flow + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Modelling secondary care psychological therapy flow

+ + + + +
+
Discrete Event Simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Helen Wharam

+
+
+

+ Solent NHS Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6027_Pharmacy_clinical_priortisation_tool/index.html b/docs/previous_projects/hsma_6/H6_6027_Pharmacy_clinical_priortisation_tool/index.html new file mode 100644 index 00000000..c4a0a74e --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6027_Pharmacy_clinical_priortisation_tool/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Pharmacy Clinical prioritisation tool + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Pharmacy Clinical prioritisation tool

+ + + + +
+
Machine Learning
+
Streamlit
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Dan Stevens

+
+
+

+ University Hospitals Plymouth NHS Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6028_Modelling_of_theatre_recovery_flow_requirements/index.html b/docs/previous_projects/hsma_6/H6_6028_Modelling_of_theatre_recovery_flow_requirements/index.html new file mode 100644 index 00000000..e98ab856 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6028_Modelling_of_theatre_recovery_flow_requirements/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Modelling of theatre recovery flow requirements + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Modelling of theatre recovery flow requirements

+ + + + +
+
Discrete Event Simulation
+
Machine learning
+
Agent based simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Matt Lavis

+
+
+

+ University of Plymouth +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6029_ML_to_identify_possible_outpatient_DNAs/index.html b/docs/previous_projects/hsma_6/H6_6029_ML_to_identify_possible_outpatient_DNAs/index.html new file mode 100644 index 00000000..4c2dc040 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6029_ML_to_identify_possible_outpatient_DNAs/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Machine learning to identify possible outpatient DNAs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Machine learning to identify possible outpatient DNAs

+ + + + +
+
Machine leanring
+
Discrete Event Simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Steph Kerr

+
+
+

+ Chesterfield Royal Hospital NHS Foundation TRust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6030_Exploring_factors_that_impact_opiate_cessation/index.html b/docs/previous_projects/hsma_6/H6_6030_Exploring_factors_that_impact_opiate_cessation/index.html new file mode 100644 index 00000000..1d0f98ba --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6030_Exploring_factors_that_impact_opiate_cessation/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Exploring factors that impact Opiate Cessation using explainable machine learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Exploring factors that impact Opiate Cessation using explainable machine learning

+ + + + +
+
Machine learning
+
Explainable AI
+
Geographic Modelling
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Richard Lee

+
+
+

+ North of England Commissioning Support Unit (NECSU) +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6031_ABS_to_understand_criminal_adoption_new_technologies/index.html b/docs/previous_projects/hsma_6/H6_6031_ABS_to_understand_criminal_adoption_new_technologies/index.html new file mode 100644 index 00000000..624d684d --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6031_ABS_to_understand_criminal_adoption_new_technologies/index.html @@ -0,0 +1,682 @@ + + + + + + + + + + +HSMA - Using Agent Based Simulation to understand criminal adoption of new technologies + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
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← Back to HSMA projects
+ +
+

Using Agent Based Simulation to understand criminal adoption of new technologies

+ + + + +
+
Agent Based Simulation
+
+
+ +
+ + +
+ +
+
Author
+
+

David Lewis

+
+
+ + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6032_Identifying_unpaid_carers/index.html b/docs/previous_projects/hsma_6/H6_6032_Identifying_unpaid_carers/index.html new file mode 100644 index 00000000..b526cd6e --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6032_Identifying_unpaid_carers/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Identifying unpaid carers in Norfolk + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Identifying unpaid carers in Norfolk

+ + + + +
+
Geographic modelling
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Elli Kontostoli

+
+
+

+ Norfolk County Council +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6033_111_downstream_forecasting/index.html b/docs/previous_projects/hsma_6/H6_6033_111_downstream_forecasting/index.html new file mode 100644 index 00000000..33e243c6 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6033_111_downstream_forecasting/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - 111 Downstream forecasting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

111 Downstream forecasting

+ + + + +
+
Machine learning
+
Forecasting
+
Discrete Event Simulation
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Dominic Rowney

+
+
+

+ North of England Commissioning Support Unit (NECSU) +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6034_Forcasting_blood_donation_sesion_capacity/index.html b/docs/previous_projects/hsma_6/H6_6034_Forcasting_blood_donation_sesion_capacity/index.html new file mode 100644 index 00000000..69a4b662 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6034_Forcasting_blood_donation_sesion_capacity/index.html @@ -0,0 +1,689 @@ + + + + + + + + + + +HSMA - Forecasting blood donation session capacity + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Forecasting blood donation session capacity

+ + + + +
+
Machine learning
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Sam Plimmer

+
+
+

+ NHS Blood and Transplant +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
+
+
+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6035_web_app_to_recommend_appropriate_tech_enabled_care/index.html b/docs/previous_projects/hsma_6/H6_6035_web_app_to_recommend_appropriate_tech_enabled_care/index.html new file mode 100644 index 00000000..0099d1d9 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6035_web_app_to_recommend_appropriate_tech_enabled_care/index.html @@ -0,0 +1,691 @@ + + + + + + + + + + +HSMA - Developing a web app to recommend appropriate technology enabled care + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+
+ +
+
    +
+
+ +
← Back to HSMA projects
+ +
+

Developing a web app to recommend appropriate technology enabled care

+ + + + +
+
Streamlit
+
Python
+
Economic modelling
+
+
+ +
+ +
+
Author
+
Affiliation
+ +
+

Radia Woodbridge

+
+
+

+ Torbay and South Devon NHS Foundation Trust +

+
+
+ +
+ + + + +
+ + + +
+ + +
+ + + +
+ + + + +

Here is some text about the project

+ + + +
+ +
+
+
+
+   +
+
+



This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

+
Cookie Preferences
+
+   +
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Forecasting demand in RDUH breast care services and the impact of urban development

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Discrete Event Simulation
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Forecasting
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Streamlit
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Gill Baker

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+ Royal Devon University Healthcare NHS Foundation Trust +

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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Mapping health inequalities, depreviation, ethnicities and crime across the UK

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Geographic modelling
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Abhinav Jindal

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+ NIHR Clinical Research Network +

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6038_Ml__identifying_deteminants_prostate_cancers/index.html b/docs/previous_projects/hsma_6/H6_6038_Ml__identifying_deteminants_prostate_cancers/index.html new file mode 100644 index 00000000..0aafe800 --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6038_Ml__identifying_deteminants_prostate_cancers/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Evaluating the use of machine learning calssifiers for the identification of the determinants of stage at diagnosis of prostate cancers using registry data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Evaluating the use of machine learning calssifiers for the identification of the determinants of stage at diagnosis of prostate cancers using registry data

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Machine Learning
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Explainable AI
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Alex Brokenshire

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+ National Disease Registration Service, NHS England +

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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+ + + + + \ No newline at end of file diff --git a/docs/previous_projects/hsma_6/H6_6039_NLP_to_automate_classification_congenital_anomaly/index.html b/docs/previous_projects/hsma_6/H6_6039_NLP_to_automate_classification_congenital_anomaly/index.html new file mode 100644 index 00000000..2b8fe23f --- /dev/null +++ b/docs/previous_projects/hsma_6/H6_6039_NLP_to_automate_classification_congenital_anomaly/index.html @@ -0,0 +1,690 @@ + + + + + + + + + + +HSMA - Applying Natural Language Processing to automate the extraction and classificiation of congenital anomaly diagnoses from free text and genetic data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Applying Natural Language Processing to automate the extraction and classificiation of congenital anomaly diagnoses from free text and genetic data

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Natural Language Processing
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Streamlit
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Charlotte Eversfield

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+ National Disease Registration Service, NHS England +

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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Modelling the benefit of MECC (Making Every Contact Count) Training using agent based simulation

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Dominic Rowney

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+ North of England Commissioning Support Unit (NECSU) +

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This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

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