# Power BI: One Live View of a Multi-Site Clinic Network

> A governed Power BI model unifying scheduling, EHR, billing and finance across four systems into two role-based dashboards refreshed every morning.

Section: Power BI

Page: https://showcase.growlyze.cloud/case/powerbi-healthcare

## Power BI Case Study · Healthcare

Multi-site clinic network: scheduling, EHR, billing & finance unified · 6-week engagement

- 128,400: Appointments / yr
- 86.4%: Provider utilisation
- 7.8%: No-show rate
- 32 days: Days in AR

> Reports that used to take days across sites now land by 6am, and we act on them the same morning.
> Operations Director

The network ran on four disconnected systems: scheduling, the EHR, billing/claims and finance. Leadership could never see patient flow, provider utilisation and revenue-cycle health in the same place, and each site’s monthly report was rebuilt by hand. I consolidated all four into one governed Power BI model behind two role-based dashboards, refreshed automatically every morning, turning days of manual reporting into a screen the team reads with their coffee.

## What Was Leaking

- Siloed systems: Scheduling, EHR, billing and finance never lined up, so no-shows, utilisation and collections couldn’t be seen together.
- Manual per-site reporting: Every site’s pack was rebuilt by hand (weeks behind by the time it landed, and easy to get wrong).
- Invisible revenue leakage: Denials, AR aging and slow collections weren’t tracked in one place, so cash slipped through unnoticed.

## Dashboard One: Patient Flow & Utilisation

Appointments vs utilisation, department mix, top sites, a day-by-hour utilisation heat map and capacity gauge.

Image: Patient Flow & Utilisation: Executive Overview (https://showcase.growlyze.cloud/case-content/powerbi-healthcare/image1.png)

- Utilisation heatmap: Spots under-used clinic slots by day and hour at a glance.
- Sites & departments: Ranks volume and no-shows together, site by site.
- Volume vs utilisation: Pairs appointment growth with the utilisation trend.
- Capacity gauge: Tracks attainment against the plan, currently 96%.

## Dashboard Two: Revenue Cycle & Claims

Billed vs collection rate, a claims funnel, AR aging buckets and net revenue by payer.

Image: Revenue Cycle & Claims (https://showcase.growlyze.cloud/case-content/powerbi-healthcare/image2.png)

- Claims funnel: Shows where submissions fall out before they’re paid.
- AR aging: Flags the £84K stuck in 90+ days for chase-up.
- Billed vs collected: Tracks the collection rate month over month.
- Payer mix: Splits net revenue across NHS, insurers and self-pay.

## How It’s Built

| Stage | What We Built | Tools |
| --- | --- | --- |
| Sources | 4 systems: Scheduling · EHR · Billing · GL | REST / API / file connectors |
| Clean | Key standardisation | Power Query |
| Model | Star schema, shared date table, DAX | Power BI model + DAX |
| Secure | RLS by site, per-site access | RLS roles |
| Deliver | 06:00 refresh via gateway & service | Power BI Service + gateway |

## The Impact

**After six weeks**

- +5.0pts: Provider utilisation
- −2.1pts: No-show rate
- −6 days: Days in AR
- 3 days → 0: Manual reporting

With utilisation and no-shows visible by site and hour, the network reallocated clinic slots and tightened reminders, lifting utilisation five points while trimming no-shows, and replacing days of manual work with an automatic 6am refresh reconciled to the ledger.