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NHS Hackathon

cRank - Smart Clinical Task Manager

Clinically informed, automated ranking of GP tasks in real time

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The problem

Task Management

Need for real time task management tool for GPs, nurses, and medical staff.

Priority

Medical tasks and events must be sorted and prioritised.

Accurate

Models and databases need to be updated and tested to ensure priority lists are fluid and accurate.

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Challenges deep-dive

Task Management

Preset Task/New Tasks

Tasks are received from phone calls, patients, other staff, etc.

Streamlined way to centralize the plethora of daily tasks required follow-up items.

Prioritising

Ranking task importance

Machine learning and training models can assist with this task.

However, medical staff may need to review and reassess importance of each task.

Accuracy

Ensure Accuracy

Data sets graphics, and test sets help to ensure the models are working as intended.

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Implementation

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Smart Task Ranking

Generate synthetic task data

Simulate ranking of groups of tasks

Train Machine Learning Model to predict ranking

Assign predicted ranking to each active task

Initial Training

Monitor user behaviour

Generate new training data

Retrain Machine Learning Model to predict ranking

Online Learning in Production

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Example Workflow

Task Allocated

Task Triage Category: 🟢

Time in pipeline: 0 hours

Task Priority Reordering

(If required)

Dynamic: Based on time in pipeline, time patient seen etc → 🟡or 🔴

Manual: Based on clinician discretion, patient has arrived for collection

Task Completion

Clinician: Checks item off list

Task Created

Task Creator: Admin

Task Type: Administrative Task

Description: Sick note

Allocated to: Dr X

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Conclusion

What are the next steps?

Integrate model into web app back end
Use real clinical data to train model
Integrate application with data sources (EHR etc)
Integrate into hospital workflows

What we have learnt?

Task delegation
Use case diagrams + user stories
Exposure to collaboration using GitHub
Multidisciplinary knowledge sharing

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09.05.XX