Case study · Healthcare
Fuller diaries and shorter waits, clinic by clinic
Clinic groups lose revenue to empty slots and no-shows, and patients lose patience with waiting lists. This is how we built Power BI dashboards from the practice management system that show utilisation, no-shows and waiting times per clinic, with patient data protected.
Project overview
Clinic operations analytics on Power BI.
- Industry
- Healthcare
- Client
- Confidential
- Solution
- Operations analytics
- Platform
- Power BI + Azure SQL
- Timeline
- 8–12 weeks
- Team
- 3 people
- Source
- Practice management system
- Privacy
- No patient-identifiable data in reports
- Users
- Clinic and operations managers
Expected business outcomes
What the client set out to gain
Utilisation and no-shows are where revenue is recovered.
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5–15 pts
Higher clinician utilisation
Empty slots and patterns become visible and are filled.
Percentage-point range; achieved by the clinics' scheduling changes
-
10–30%
Fewer no-shows targeted
No-show patterns by clinic and appointment type inform reminders.
Target range when reminder changes are made
-
Weekly
Waiting time reviews
Waiting lists by specialty and clinic reviewed weekly.
From practice management data
Business challenges
How clinic reporting worked before
The practice system records everything but reports very little.
Utilisation
Calculated occasionally from exported diaries.
No-shows
Known anecdotally by reception teams.
Waiting
Waiting lists checked by phone with each clinic.
Revenue
Revenue per clinician calculated by finance at month-end.
Privacy
Exports containing patient details shared in spreadsheets.
Proposed solution
What the Power BI build covers
Operational measures without exposing patient details.
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Data pipelines
Appointments and billing data loaded daily, with patient identifiers removed.
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Utilisation
Booked, available and used time per clinician and clinic.
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No-show analysis
No-show and late cancellation rates by clinic, day and appointment type.
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Waiting times
Waiting list length and time to first appointment by specialty.
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Revenue
Revenue per clinician hour and per clinic.
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Privacy by design
Aggregated and pseudonymised data only, with access by role.
Project requirements
What the client asked us for
The request was visibility of how clinics perform. Privacy is designed first: reports use aggregated, de-identified data so they can be shared safely.
We work with clinic managers to define utilisation, because different specialties book time differently.
What the project had to deliver
- 01Utilisation per clinician
- 02No-show patterns
- 03Waiting list visibility
- 04Privacy-safe reporting
Key challenges & considerations
What made it harder, and what we did
-
Practice systems with limited data access
What we did
Data was taken through scheduled exports where the practice system had no usable API.
-
Different booking practices per specialty
What we did
Utilisation was defined per specialty with the clinical leads.
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Strict health data regulations
What we did
No identifiable patient data left the source, and the reports used only aggregated figures.
-
Part-time clinicians and variable diaries
What we did
Diaries were modelled by session, so part-time clinicians were measured fairly.
What we watched for
- Agree data protection first
- Define utilisation per specialty
- Never export identifiable data
- Review weekly, not monthly
Development process
Privacy, measures, dashboards
Data protection is agreed before any data moves.
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Data protection design
Data flows, de-identification and access agreed with the data protection lead.
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Measures
Utilisation, no-show and waiting definitions agreed with clinic managers.
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Pipelines
Practice system data loaded and validated.
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Dashboards
Clinic and group dashboards built and reviewed.
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Rollout
Clinic managers trained and weekly reviews started.
Technology stack
Power BI with privacy built in
A small, secure data store and Power BI with role-based access.
Reporting
- Power BI
- Row-level security
Data
- Azure SQL
- Data pipelines
- De-identification
Source
- Practice management system API or exports
Governance
- Access by role
- Audit logs
Quality assurance & deployment
Every figure reconciled before anyone relied on it
A dashboard is only useful if its numbers are believed. Each measure was reconciled to its source, and to finance where money was involved, before the report was released.
- Each measure reconciled to its source system
- Financial figures signed off by finance
- Data quality checks on every refresh: completeness and freshness
- Row-level security tested with real user accounts
- Report performance tested at full data volume
Deployment
- 01DashboardsClinic and group dashboards built and reviewed.
- 02RolloutClinic managers trained and weekly reviews started.
- 03Deployment pipelinesReports moved from development to test to production through controlled pipelines.
- 04Adoption and warrantyTraining for report users and builders, usage reviewed, and six months of free bug fixing.
Final deliverables
Clinics that know where the time goes
Every clinic manager sees utilisation, no-shows and waiting times for their clinic, and the group compares performance fairly.
Reports contain no patient-identifiable data, so they can be shared without risk.
- Utilisation dashboards
- No-show insight
- Waiting list tracking
- Privacy-safe data
Handed over to the client
- 01Data model and pipelinesDocumented and scheduled
- 02Reports and dashboardsPublished with security roles
- 03Measure definitionsEvery figure documented
- 04TrainingFor report users and report builders
- 05Six-month warrantyFree bug fixing after delivery
Success metrics
How success is measured
| Measure | Outcome | How it is measured |
|---|---|---|
| Higher clinician utilisation | 5–15 pts | Percentage-point range; achieved by the clinics' scheduling changes |
| Fewer no-shows targeted | 10–30% | Target range when reminder changes are made |
| Waiting time reviews | Weekly | From practice management data |
Verified reviews
What clients say about our custom software work
Verified reviews from our clients on the same kind of work, published on Clutch.
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Custom Software Development
Web-Based Coupon App Development
The work produced by the team was of exceptional quality.
Jon Thies
CTO, Model Rocket Feb 2022 – Mar 2023
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Custom Software Development
Mobile & Web Platform Development & Design
Silver Scintilla was responsive, respectful, and enjoyable to work with, quickly addressing any questions or requests.
DeShawn Brown
CEO & Founder, Lithios Jun 2021 – Nov 2022
-
Custom Software Development
Web-Based HRM System Development
Silver Scintilla provided the best solution with scalable functionality, personalized features, and robust security.
Suheb Khan
Director, Collaborative Insight Technologies Jul 2020 – Jun 2022
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