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NHS Data Analysis & Modelling: Key Concepts & Best Practices

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NHS Data Analysis & Modelling: Key Concepts & Best Practices

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NHS Data Analysis & Modelling: Key
Concepts & Best Practices

Describe a time you managed a large, complex dataset.—ANSWER--

Managed Harefields Clinical Outcomes dataset for Cardiac and Cardiology

services: Theatre activity, post op outcomes + incident data. Multiple

sources


Careful handling due to patient safety


SQL and Excel to validate record completeness + identify anomolies

Aggregated data for safe reporting in governance meetings


Improved accuracy + reliability of clinical reportings


Reduce rework for clinicians and admin


How would you extract insights from unstructured data (clinic letters)?—

ANSWER--Define the question first


Use NLP for keyword + topic extraction


Cluster themes

,Validate with clinicians to ensure insights are actionable.


How do you ensure dashboards are accessible to clinical and non-

technical audiences?—ANSWER--Understand audience needs + tailor to

them


Use simple visuals, clear definitions


Conduct user testing (small scale)


Focus on clarity and decision support.


Build confidence, create a culture where data is empowering and

accessible


Describe a data-driven project you led end-to-end.—ANSWER--Managed

project to redesign clinical performance


Document stakeholder needs


Mapped existing data flows to understand processes + locate single source

of truth


Extract, Clean, visualise using SQL


Compliant to Trust policies + GDPR


Provide regular progress reports to senior management



© 2026 Copyright. All Rights Reserved. This document is
protected by copyright law, Copyrighted By Brittie Donald

, Reduces billing Errors overall by 86%- Reduced admin's rework


How do you manage competing priorities and deadlines?—ANSWER--

Assess what's in my scope, risk + impact of each request


Prioritise based on impact to stakeholders + patients, urgency


Create timeline to ensure transparency


Offer short term solutions if possible


Describe a time you identified and resolved a data quality issue.—

ANSWER--Identified inconsistencies in outcomes- didn't align with expect

activity levels


Identified record updates and incorrect use of outcome fields caused

duplicated aggregated accounts


Worked with governance teams to confirm definitions


Refined validation checks to flag duplicate/ amended records


Ensured only validated and aggregated figures included in final report.


Final report submitted with improved accuracy + transparency. Reduced

repeat queries from clinical teams


How do you ensure compliance with Information Governance?—ANSWER--

Follow Trust IG policies + stay up to date

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