diff --git a/docs/our_work/ai-deep-dive.md b/docs/our_work/ai-deep-dive.md index eba999f0..d05c7c24 100644 --- a/docs/our_work/ai-deep-dive.md +++ b/docs/our_work/ai-deep-dive.md @@ -6,147 +6,6 @@ origin: 'Skunkworks' tags : ['AI', 'GUIDANCE', 'BEST PRACTICE'] --- -## Playbook - -### Motivation - -A series of practical workshops designed to increase confidence, trust and capability of implementing AI within the NHS and Social Care sector, based on the experience of the AI Lab Skunkworks team. - -### Audience - -Clinicians, technology teams, operations teams, and other stakeholders from organisations interested in utilising AI - -### Pre-requisites - -* I understand there is great potential for AI in Health and Care -* I want to increase my understanding about the practical application of AI in Health and Care -* I understand the variety and quantity of data in my organisation -* I'm willing to embrace being experimental and open to learning from experience - -### Attendees - -10 or 12 attendees max - -### Your presenters - -Workshops run by NHS AI Lab Skunkworks team for one organisation (e.g. Trust) at a time. - -### Format - -A series of weekly 75 minute workshops, delivered online through Google Meet or Microsoft Teams - -### By the end of the workshop series, learners will be able to - -* Be confident in having more conversations about AI in Health and Care -* Embrace an experimental approach to AI in Health and Care -* Understand practical steps required for experimenting with AI in Health and Care -* Create a detailed plan for an AI project - -## Workshop 1: AI fundamentals - -### Aim - -Establish baseline understanding of AI and what is possible - -### Key topics - -* Define AI, Machine Learning and Data Science -* Understand the two AI families (Narrow and General) -* What's possible with ML -* Ethics considerations -* The AI Life Cycle -* Examples of AI in Health and Care -* Examples of projects we’ve worked on - -### By the end of this workshop, learners will - -* Have a baseline understanding of AI & Machine Learning -* Be familiar with AI case studies in health and care -* Be excited about the potential for AI in their organisation - -## Workshop 2: Problem Discovery - -### Aim - -Develop skills to identify and communicate problems - -### Key topics - -* Problem identification -* Identifying stakeholders -* Understanding user needs -* Writing a user story -* Capturing the user journey - -### By the end of this workshop, learners will - -* Have clearly defined problems they are facing -* Have identified stakeholder and user needs -* Documented the user journey - -## Workshop 3: Solution Discovery - -### Aim - -Identify solutions and potential AI technologies for a problem - -### Key topics - -* Solution identification -* Appropriate AI technologies -* Intended outcomes: Press Release - -### By the end of this workshop, learners will - -* Generate potential solutions for their problem -* Evaluate AI technologies as part of the solution -* Draft a “Press Release” for the future state - -## Workshop 4: Practicalities - -### Aim - -To understand the practical aspects of every AI project. - -### Key topics - -* Data Data Data: how much, where from -* Information Governance (IG) -* Regulatory frameworks -* Ethics approvals - -### By the end of this workshop, learners will - -* Identify the data needs of an AI project -* Understand how to work with Information Governance -* Understand the regulatory requirements for a project -* Understand ethical frameworks applicable to AI projects - -## Workshop 5: Launching your AI experiment - -### Aim - -To understand the next steps in launching your AI Experiment - -### Key Topics - -* Business and technical due diligence -* Build vs Buy? -* Team make up and roles -* Partnering with Skunkworks, AI Award, AHSN -* Keeping up to date with developments in AI - -### By the end of this workshop, learners will - -* Understand the need for business and technical due diligence -* Understand the balance of build vs buy -* Have a robust understanding of what they need to launch their AI experiment -* Be connected to the wider AI community within the NHS and care sector - -## Book your sessions - -If you'd like to arrange an AI Deep Dive with your team, please [get in touch](mailto:england.aiskunkworks@nhs.net?subject=AI%20Deep%20Dive%20enquiry). - # Case Study ## Info diff --git a/docs/our_work/clinical-coding.md b/docs/our_work/clinical-coding.md deleted file mode 100644 index 92bca905..00000000 --- a/docs/our_work/clinical-coding.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: 'Clinical coding automation with the Royal Free and Kettering General' -summary: 'Data scientists in the AI Lab Skunkworks team and the NHS Transformation Directorate Analytics unit are supporting this project to investigate whether the process of clinical coding (applying standard code words to health records) can be supported by artificial intelligence.' -category: 'Projects' -origin: 'Skunkworks' -tags: ['NLP','NEURAL NETWORKS'] ---- - -When you visit your doctor or attend hospital a lot of information is collected about you on computers, including your symptoms, tests, investigations, diagnosis, and treatments. Across the NHS, this represents a huge amount of information that could be used to help us learn how to tailor treatments more accurately for individual patients and to offer them better and safer healthcare. The challenge is that most of the information held within these records is in written form that is difficult to use. - -The process of reading health records and applying standardised codes based on particular words, conditions or treatments, is called "clinical coding". The process of clinical coding is time-consuming, expensive and carries the risk of mistakes. - -We are providing data science capability to a joint project with the Royal Free Hospital and Kettering General Hospital. This project aims to understand which open source models are best to support clinical coders by automating part of the clinical coding process using natural language processing (NLP) to teach computers to ‘read’ electronic health records. The aim is for the technology to summarise and suggest the standardised codes that will then be checked by clinical coders. - -NLP is a branch of AI used to interpret unstructured text data, such as free-text notes. - -The project was abandoned due to data access challenges. - -[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.) -# diff --git a/docs/our_work/nwas.md b/docs/our_work/nwas.md deleted file mode 100644 index 61f5f70b..00000000 --- a/docs/our_work/nwas.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: 'NWAS – Ambulance data exploration' -summary: 'Data exploration of ambulance service' -category: 'Projects' -origin: 'Skunkworks' -tags: ['EDA'] ---- - -The aim of this proof-of-concept project was to develop a machine learning model that could predict the triage outcome of emergency calls based on the information provided by the caller. The model was trained on a large dataset of emergency call data and triage outcomes to identify patterns and relationships between the information provided and the resulting triage classification. - -Two different AI approaches were involved in the developed models, including using a gradient-boosted decision trees model for the numerical and categorical type of data, and a NLP model to handle the free-text data. - -[comment]: <> (The below header stops the title from being rendered (as mkdocs adds it to the page from the "title" attribute) - this way we can add it in the main.html, along with the summary.) -# diff --git a/mkdocs.yml b/mkdocs.yml index e28e7bdf..1e9db29b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -35,11 +35,9 @@ nav: - Data Linkage Enhancement: our_work/linkage.md - Natural Language Processing Products: - Applying & Evaluating a Language Model to Patient Safety Data: our_work/p33_patientsafetylms.md - - Automating Clinical Coding: our_work/clinical-coding.md - Data Lens: our_work/data-lens.md - NHS Language Corpus: our_work/c250_nhscorpus.md - NHS.UK Automatic Moderation of Ratings & Reviews: our_work/ratings-and-reviews.md - - Emergency Call Triage: our_work/nwas.md - Text Analysis using Structural Topic Modelling: our_work/p23_stm.md - Tool to Asses Privacy Risk of Text Data: /our_work/c399_privfinger.md - Data Science Capability: @@ -68,11 +66,10 @@ nav: - Synthetic Data Generation Pipeline: our_work/synthetic-data-pipeline.md - TxtRayAlign: our_work/p22_txtrayalign.md - Understanding the Impact of Co-Morbidities: our_work/p34_hypergraphs.md - - Problems Solved: + - Problems Explored: - Healthcare Efficiency: - AI Models for Shortlisting Interview Candidates: our_work/casestudy-recruitment-shortlisting.md - Ambulance Handover Delay Predictor: our_work/ambulance-delay-predictor.md - - Automating Clinical Coding: our_work/clinical-coding.md - Bed Allocation: our_work/bed-allocation.md - Ease of Diagnosis: - CT Alignment & Lesion Detection: our_work/ct-alignment.md @@ -95,7 +92,6 @@ nav: - Data Linkage Enhancement: our_work/linkage.md - Understanding the Impact of Co-Morbidities: our_work/p34_hypergraphs.md - Resource Usage: - - Emergency Call Triage: our_work/nwas.md - Enriching Clinical Coding for Neurology Pathways using MedCAT: our_work/p43_medcat.md - Length of Hospital Day Prediction: our_work/long-stay.md - NHS.UK Automatic Moderation of Ratings & Reviews: our_work/ratings-and-reviews.md @@ -123,14 +119,10 @@ nav: - Synthetic Data From Real Data: our_work/casestudy-synthetic-data-pipeline.md - Synthetic Data Generation Pipeline: our_work/synthetic-data-pipeline.md - SynPath Simulator on Diabetes Pathway: our_work/p11_synpathdiabetes.md - - Gradient Boosting Decision Tree: - - Emergency Call Triage: our_work/nwas.md - NLP/LLM: - AI Models for Shortlisting Interview Candidates: our_work/casestudy-recruitment-shortlisting.md - Applying & Evaluating a Language Model to Patient Safety Data: our_work/p33_patientsafetylms.md - - Automating Clinical Coding: our_work/clinical-coding.md - Data Lens: our_work/data-lens.md - - Emergency Call Triage: our_work/nwas.md - NHS Language Corpus: our_work/c250_nhscorpus.md - NHS.UK Automatic Moderation of Ratings & Reviews: our_work/ratings-and-reviews.md - Text Analysis using Structural Topic Modelling: our_work/p23_stm.md @@ -155,7 +147,6 @@ nav: - Healthcare Domain: - Urgent & Emergency Care: - Ambulance Handover Delay Predictor: our_work/ambulance-delay-predictor.md - - Emergency Call Triage: our_work/nwas.md - Diagnostics: - CT Alignment & Lesion Detection: our_work/ct-alignment.md - Deep Learning to Detect Adrenal Lesions in CT Scans: our_work/adrenal-lesions.md @@ -173,7 +164,6 @@ nav: - Workforce: - AI Models for Shortlisting Interview Candidates: our_work/casestudy-recruitment-shortlisting.md - Applying & Evaluating a Language Model to Patient Safety Data: our_work/p33_patientsafetylms.md - - Automating Clinical Coding: our_work/clinical-coding.md - Nursing Placement Scheduled Optimisation: our_work/nursing-placement-optimisation.md - Text Analysis using Structural Topic Modelling: our_work/p23_stm.md - Primary Care: @@ -219,9 +209,7 @@ nav: - Data Lens: our_work/data-lens.md - Unknown Year: - AI Deep Dive Workshops: our_work/ai-deep-dive.md - - Automating Clinical Coding: our_work/clinical-coding.md - Creating a Generic Adversarial Attack for Synthetic Data: our_work/c339_sas.md - - Emergency Call Triage: our_work/nwas.md - Enriching Clinical Coding for Neurology Pathways using MedCAT: our_work/p43_medcat.md - NHS @Home Programme: our_work/open-safely.md - Understanding the Impact of Co-Morbidities: our_work/p34_hypergraphs.md