Practice Signals is a reporting platform that aggregates monthly performance metrics from Open Dental for dental practices and their advisors. It delivers a consolidated view of production, collections, accounts receivable aging, schedule utilization, patient flow, and provider contributions, highlighting outliers that need attention. The service refreshes after each closed month and uses only summary data, keeping patient records securely within the practice’s existing system.
Funding
Funding not disclosed
Founders
Product
Problem
Dental practice owners and advisors often struggle to compile and compare key performance metrics—such as production, collections, accounts receivable aging, schedule utilization, and patient flow—from Open Dental, requiring manual data extraction and time‑consuming report assembly.
Solution
Practice Signals connects directly to Open Dental and generates a refreshed monthly dashboard that aggregates essential summary metrics into a single view. The platform presents month‑over‑month trends, provider‑level contributions, and automatically highlights outlier changes that merit attention. By using only aggregated data, it preserves patient‑level records within the practice management system while still enabling shared, data‑driven conversations between owners and advisors. The dashboard is ready for review before meetings, streamlining performance discussions without the need for separate reporting tools.
Target Audience
Primary users are dental practice owners and external advisors or consultants who manage Open Dental practices and need a concise, shared performance report each month.
Features
- Automated monthly data pull from Open Dental with no patient‑level data export
- Consolidated view of production, collections, collection rate, and outstanding balances
- Accounts receivable aging totals, including amounts over 90 days
- Schedule utilization metrics for the closed month
- Patient flow statistics covering new patients, retention, and reactivation
- Provider‑specific production and collections breakdown
- Outlier detection that surfaces significant metric drifts for review