Morning-Huddle BI Dashboards for a 32-Practice Dental Group
Let's Connect
Overview
What we built
A 32-practice dental partnership ran each office on gut feel and spreadsheets that were weeks out of date. We connected all three practice management systems into one nightly pipeline so every office sees the same trusted numbers each morning.
In plain terms: the 32 practices in this partnership were each running on instinct, because the numbers that should have guided decisions were weeks old by the time anyone saw them. Practice managers exported reports by hand from three different practice management systems, the totals rarely matched, and partners had no way to see production, recall or collections across the network on the same day the work happened.
We built a nightly pipeline that pulls production, scheduling and collections data out of all three systems into a single warehouse with one shared definition for every KPI, so a number means the same thing in every practice. Reporting lag fell from roughly 30 days to next-morning across all 32 practices, hygiene reappointment rate climbed from 61% to 72% within two quarters of huddle adoption, practice managers saved an estimated 6 hours a week previously spent building manual reports, and the collections-to-production ratio rose 3.5 percentage points as overdue balances surfaced daily instead of monthly.
The Problem
Month-old spreadsheet reporting
Running a 32-practice dental partnership on gut feel might have been tolerable at a smaller scale, but at this size it meant every office was, in effect, guessing. Reports were built from month-old spreadsheets, so by the time a number reached a partner's desk the situation on the ground had already moved on. Nobody could say with confidence how a given office was performing today, only how it had been performing weeks earlier.
The reporting itself was the bottleneck. Practice managers pulled production, scheduling and collections figures out of three different practice management systems by hand, then assembled them into spreadsheets on their own schedule. Because each system recorded things slightly differently and each manager compiled them their own way, the numbers that reached headquarters rarely matched from one office to the next, and nobody could fully trust a network-wide total.
The result was a partnership making decisions with no same-day view of its own performance. Partners had no way to see production, recall or collections across locations as they happened, only a delayed, inconsistent picture assembled weeks later. Staffing, scheduling and follow-up decisions that should have been made that morning were instead made on instinct, office by office, with no shared facts to anchor them.
Month-old numbers
Reports were built from spreadsheets that were already weeks out of date by the time partners saw them, so every decision looked backward instead of at the present.
Three separate systems
Production, scheduling and collections data lived in three different practice management systems, each recording things its own way with nothing tying them together.
Manual hand exports
Practice managers exported and assembled reports by hand on their own schedule, a process that consumed their time and left numbers rarely matching across offices.
No same-day visibility
Partners had no same-day view of production, recall or collections across the 32 practices, so network-wide decisions were made without current facts.
What it was costing them
Every week the partnership operated on month-old numbers was a week of decisions made without current facts: staffing, scheduling and follow-up work that should have responded to that morning's production and recall figures instead followed instinct. Practice managers lost hours to manual report-building across three systems, and partners overseeing 32 practices had no reliable way to compare performance or catch a slipping office early.
The Solution
Next-morning KPI dashboards
We designed the fix around a single idea: every practice should open the day already knowing the numbers, not waiting for someone to assemble them. A nightly pipeline now pulls production, scheduling and collections data out of all three practice management systems automatically, landing everything in one warehouse before the following morning, so no practice manager has to touch an export again.
Getting the numbers into one place was only half the job; the harder part was making sure a number meant the same thing everywhere. We built shared definitions for every KPI so that production, recall and collections figures are calculated identically whether they originated in one system or another, which is what finally let offices be compared side by side with confidence.
On top of the warehouse we built two views for two audiences. Each office opens its day with a morning-huddle dashboard on a wall display, giving staff the numbers before the first patient walks in. Partners get a separate, network-wide view with per-location drill-downs, so a slipping office can be spotted and addressed without waiting for the next spreadsheet.
Key decisions
Automate the nightly pull
A pipeline now pulls production, scheduling and collections data from all three practice management systems automatically overnight, replacing manual exports practice managers used to build by hand.
Standardise every KPI
We defined every KPI once in the shared warehouse, so a number means the same thing whether it originated in one practice management system or another.
Build the morning huddle view
Each office gets a wall-display dashboard ready before the day starts, giving staff current production, recall and collections figures at the morning huddle rather than a stale report.
Give partners network-wide visibility
Partners see a single network-wide view with per-location drill-downs, so performance across all 32 practices can be compared and a slipping office caught early.
Measurable Impact
What changed after launch
The clearest change is speed. Reporting lag fell from roughly 30 days to next-morning across all 32 practices, so every office now starts the day with figures from the day before rather than weeks behind. Practice managers saved an estimated 6 hours a week they used to spend assembling reports by hand out of three separate systems.
The numbers themselves moved too. Hygiene reappointment rate climbed from 61% to 72% within two quarters of huddle adoption, and the collections-to-production ratio rose 3.5 percentage points as overdue balances surfaced daily instead of once a month. Partners now have one trusted, network-wide view instead of weeks-old spreadsheets that rarely matched.
Reporting lag
Roughly 30 days behind, built by hand
Next-morning, automated across all 32 practices
Hygiene reappointment
61% reappointment rate before huddle adoption
72% reappointment rate within two quarters
Manager time
Hours lost weekly to manual report-building
An estimated 6 hours a week saved
Collections ratio
Overdue balances surfacing only once a month
Up 3.5 points, surfacing daily instead
Headline results
Reporting lag reduced from roughly 30 days to next-morning across all 32 practices
Hygiene reappointment rate improved from 61% to 72% within two quarters of huddle adoption
Practice managers saved an estimated 6 hours per week previously spent building manual reports
Collections-to-production ratio climbed 3.5 percentage points as overdue balances surfaced daily
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Airbyte
Runs the nightly connectors that pull production, scheduling and collections data out of all three practice management systems into the pipeline without manual exports.
dbt
Transforms the raw exports into the shared KPI definitions, so production, recall and collections figures mean the same thing across every practice.
Google BigQuery
Serves as the single warehouse holding standardised data from all 32 practices, ready for the dashboards each office and partner group opens every morning.
Python
Handles the scheduling and orchestration logic behind the nightly pipeline, coordinating each step from ingestion through to the finished dashboard data.
Node.js
Powers the backend services behind the dashboards, serving the standardised KPI data to both the morning-huddle displays and the partner-facing views.
React
Builds the morning-huddle dashboard and the partner network view, presenting production, recall and collections figures with per-location drill-downs.
Metabase
Gives partners a self-serve way to explore network-wide figures and drill into individual practices without waiting on a custom report.
Google Cloud Run
Hosts and runs the pipeline and dashboard services, scaling to serve all 32 practices each morning without anyone managing servers.
Terraform
Defines the cloud infrastructure behind the pipeline as code, keeping the environment serving 32 practices reproducible and easy to change safely.
Ready to Build your Dental & Orthodontics Business with Predictive Modeling & Forecasting
Ask Byte
Ask Byte
Typically replies instantly
just Now
Hi! I'm OrganByte's assistant. How can I help you today?
AI-generated content may be incorrect
