Scalable BI for clinics
Power BI dashboard based on a standard data request for fast implementation of financial, clinical, operational and experience indicators in clinics.
Context
Clinics of different sizes need to make frequent decisions about revenue, scheduling, productivity, satisfaction, care delivery and professional performance. Even so, many still rely on manual reports, isolated exports and ad hoc analyses.
Problem addressed
The problem was not only to build a dashboard, but to create a replicable model. For the BI to be applied at scale, it was necessary to define a standard data request, with minimum fields, calculation rules and a common analytical structure for different clinics.
Project objective
Build a generic BI model for clinics, capable of receiving data in a standardized format and generating an executive view of financial, clinical, operational, patient and professional indicators.
Situation
Clinics need to monitor operations, revenue, care delivery, satisfaction, patients and professionals, but data are often scattered across systems, spreadsheets and manual reports.
Task
Create a generic, scalable and replicable BI model for different clinics, based on a standard data request to guide data collection, processing and visualization of key indicators.
Action
Structured a Power BI dashboard with executive overview, period filters, comparison with targets, previous period and previous year, as well as financial, clinical, patient and professional modules.
Result
The model helps accelerate BI implementation in clinics, reduce rework in data collection and standardize the interpretation of key management indicators.
Key metrics
Standard data request
The project was designed from a standard data request, defining which data the clinic needs to provide for BI implementation. The data request may include: - Patient registry - Professional registry - Schedule and appointments - Procedures performed - Revenue and payments - Payers and payment methods - Specialties and service types - Satisfaction indicators, such as CSAT and NPS - Operational and financial targets - Service, issue, accounting and payment dates
Dashboard structure
The dashboard was organized into four main modules: - Financial - Clinical - Patients - Professionals The initial screen also brings together key metrics such as revenue, appointment volume, CSAT and eNPS, allowing comparison with target, previous period and previous year.
Financial module
The financial module allows monitoring revenue, revenue variation, time comparison and decomposition of the effects explaining changes in results. The analysis can separate volume, price and mix effects, helping to understand whether variation came from higher quantity, ticket changes or shifts in the service mix.
Clinical module
The clinical module organizes indicators related to appointments, service types, care production, demand profile and monitoring of key procedures or care lines.
Patient module
The patient module allows monitoring patient volume, recurrence, panel profile, new patients, return visits, satisfaction and service usage behavior.
Professional module
The professional module supports analysis of productivity, scheduling, appointments performed, contribution by professional, specialty and care or operational performance.
Features
The BI allows: - Analysis period selection - Comparison with previous period - Comparison with previous year - Target monitoring - Revenue variation analysis - Decomposition by volume, price and mix - Drill-down by service, procedure or category - Navigation across management modules - Executive overview for quick monitoring - Standardized indicator interpretation
Technologies used
- Power BI - Power Query - Data modeling - DAX - Standard data request - Management indicators - Data visualization - Dashboard design
How the model scales
The main feature of the project is replicability. Based on a standard data request, different clinics can provide data in a similar structure, allowing the same BI model to be adapted with less implementation effort. This reduces the need to build from scratch for each client and supports a scalable analytics offer.
Limitations and caveats
- BI quality depends on the quality of data provided by the clinic. - The data request must be completed consistently. - Financial indicators depend on clear rules for accrual, payment and cancellation. - Satisfaction indicators depend on structured data collection. - The model may require adaptation for clinics with highly specific systems. - The dashboard does not replace data governance or clear target definition.
Deliverables
- Power BI dashboard - Executive indicator overview - Financial module - Clinical module - Patient module - Professional module - Standard data request - Replicable indicator model - Time comparison structure - Revenue variation analysis - Decision-support visualizations
Learnings
- Scalable BI starts before the dashboard, with the definition of the data request. - Data standardization reduces rework and accelerates implementation. - Clinics need simple, comparable and actionable indicators. - Result decomposition helps explain variations, not only display them. - A good dashboard should guide management conversations, not just show charts.