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Measures Editorial and Style Guide
speed-vm edited this page Nov 14, 2023
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- Definition: A measure on OpenPrescribing.net highlights a specific prescribing pattern that can be tracked over time and can be reported at Sub-ICB or individual practice level or at a national level for England.
- Types of Measures: With approximately 100 measures, we address various facets of prescribing, from cost-effectiveness, to greener prescribing to important safe prescribing practices.
- Clarity: Start with a simple statement explaining the measure's significance.
- Data-Driven: Back the importance of the measure with links to relevant clinical guidelines including from NICE, the MHRA or NHS England for example.
- Audience-Oriented: Ensure the section is clear to anyone with a basic understanding of prescribing. Try to avoid jargon.
- Definition: Deciles divide a dataset into ten equal parts, offering a comparative view of prescribing practices.
- Usage: We use deciles because they provide a transparent method to see variations and prevent misunderstandings caused by extreme values. For a deeper understanding of our choice, read our detailed blog post on why we use deciles.
- Interpretation: Seeing where a specific practice falls within the deciles helps understand if they're outliers or in line with the standard.
- Interpretation: Label measures clearly to ensure the plot and values are easy to interpret with no ambiguity. Use the "numerator_short" / "denominator_short" to label the numerator/denominator values shown in the text box as you hover over a plot with curser.
- Clarity: Quantity and items should be clearly distinguished when labelling.
- Criteria-Based: Categorise measures based on criteria such as therapeutic area, cost, or generic prescribing. Check out existing categories already available on OP and read the blog about how we use categories to help users navigate our measures.
- Consistency: Consistent categorisation using tags in the measures definition helps users quickly find and understand measures.
- Standardisation: To avoid confusion, we try to standardise the interpretation of our measures with a "low is good" approach. In almost all of our measures, a lower score indicates a better outcome.
- Ease of Interpretation: This consistency ensures everyone can quickly understand a measure's implications.
- Core Philosophy: OpenPrescribing is built on a foundation of openness and transparency. We believe these principles are key for reliable data analysis and fostering trust.
- Implementation: All our methods, datasets, and measures are openly available to the public. This approach invites scrutiny, collaboration, and continuous improvement. Learn more about our commitment to openness and transparency.
- Plain English: Aim to use plain English wherever possible. While technical language is sometimes necessary, prioritise clarity and simplicity to make the content accessible.
- Jargon-Free: Even though many readers will be pharmacists, ensure the content is understandable to anyone with a basic grasp of prescribing.
- Explanatory Links: Link to explanatory articles or blogs, such as posts from the Bennett blog, for nuanced topics.
- Community members are encouraged to suggest new measures or improvements to existing ones by contacting the team at [email protected].
This guide will evolve with OpenPrescribing.net's needs and its community. Regular reviews and refinements are important. Always prioritise clarity, user needs, and plain English in every measure developed.