How it works: The threshold defines the acceptable window for clocking in before the first appointment and after the last appointment.
Any time outside this window is overage. All costs below recalculate dynamically when you change the threshold.
Overview
Position Breakdown
Overage Cost by Position
Staffing Curve — Employees vs. Appointments
Start of Day — Daily Overage Cost
End of Day — Daily Overage Cost
Location Comparison
Opportunity Prioritization: Every location ranked by annualized savings potential at the current 30-minute threshold.
Focus on the top of this list for the highest-impact interventions.
Top 25 Offices by Annualized Savings Opportunity
All Locations — Ranked by Opportunity
Employee Drill-Down: Select a location to see which employees are driving overage costs.
Each person's overage is calculated at the current 30-minute threshold.
Top Offenders — Overage Cost by Employee
SOD Offenders — Who Clocks In Too Early?
EOD Offenders — Who Stays Too Late?
All Employees at This Location
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Select a location above to see employee-level detail
Or click a location row on the Opportunity Summary page
Time Card Analysis: Mirrors the Power BI "TimeCard Employees Charted" report. Shows non-productive minutes, clock-in lead,
and clock-out lag using the same 30-minute grace period. Filter by position to focus on hygienists or other roles.
Staffing Curve — Clocked-In Employees vs. Appointments by Hour
Practice Summary — Non-Productive Time
Provider Detail
Non-Productive Minutes by Provider
Clock-In Lead vs Clock-Out Lag
Insights: Trend analysis, threshold sensitivity, and day-of-week patterns to identify persistent overage drivers.
All views respect the current filters (location, RD, date, threshold, first-appt cutoff).
Weekly Overage Trend
Week-by-week cost, so you can see where overage is accelerating vs improving.
Threshold Sensitivity
Network annualized cost at every threshold from 0 to 60 min. Use it to pick a defensible policy.
Day-of-Week Pattern
Average daily overage by weekday. Flat means uniform; spikes flag scheduling problems.
Persistent Offenders
Locations in the top quartile of overage for the most weeks in the filter range — these are the chronic underperformers, not one-off spikes.
Data Quality: Surfaces problems in the underlying data so you can decide what to trust.
Flags coverage gaps, appointment-window anomalies, and punches that can't be evaluated.
First-Appointment Time Distribution
How many days have first-appt at each hour. A dental practice should cluster at 7–9am. Anything 10am+ is suspicious.
Suspicious Appointment Days
Days where the first appointment starts at 10am or later — likely data-quality issues. These drive the 85%-SOD-dominated cost pattern you're seeing.
Punches Without Appointment Coverage
Punches on loc+date combos where no appointment record exists. These are silently skipped from overage calc.