Better decisions start below the dashboard.

A full diary is one number. What sits behind it is the useful part: who is attending, which new patients return, how care plans are progressing and where practitioner capacity is going. I build reporting around the decisions you need to make.

• Management dashboards shaped around your questions
• Agreed definitions for appointments, new patients and attendance
• Practitioner performance viewed alongside workload and available time

Power BI and DAX turn the agreed measures into reporting you can explore, from a practice overview down to the detail behind a number.

A dashboard should make the next question obvious. I design reports that bring the signal forward: a change in attendance, a break in the new-patient journey, or a gap between booked and available time.

• A clear hierarchy from practice overview to supporting detail
• Trends and comparisons with the right context
• Filters and drill-through views that let you investigate

Power BI report design connects the visual layer to the underlying measures, so a clear chart also has a clear explanation.

If two reports define a new patient differently, better charts will not settle the argument. I structure the data behind your reporting so appointments, patients, practitioners and care plans connect consistently—and the totals can be checked.

• Source mapping and documented reporting definitions
• A consistent data model across the reports that use it
• Reconciliation checks to explain missing, duplicated or conflicting records

SQL, Python and semantic modelling provide the foundation: how data is cleaned, connected, calculated and checked before it reaches a report.

First prove the reporting works. Then work out which manual steps are worth removing. I assess the path from source data to current reports, with a practical plan for refreshes, checks and the handover when something changes.

• Map the recurring work and the points where it can fail
• Choose an appropriate export, file-drop or API approach
• Build repeatable processing with clear checks and ownership

Python supports repeatable data preparation and validation. The refresh approach depends on what your source system supports and what your practice actually needs.