Meridian Care Partners had been tracking turnover as a number on a monthly report. Dazie turned it into a live operational signal with names, teams, and specific risk factors attached.
Meridian Care Partners operates across three geographic service areas, managing a clinical team of roughly 40 nurses and therapists. Over 18 months, voluntary turnover had climbed from 17% annually to 28%. The clinical leadership team knew something was wrong. They did not know what, where, or who.
Monthly EMR reporting showed visit completion rates and documentation timing in aggregate. It told leadership how many notes were completed after hours across the whole agency. It did not show which teams were carrying unsustainable workloads, which clinicians were charting at 11 PM on weekends, or which managers were unaware that their staff was burning out underneath them.
The problem was not a lack of data. It was a lack of visibility. The information existed. No one had surfaced it in a format that leadership could act on.
When Meridian Care Partners activated the Burnout Risk Engine, Tamara entered baseline data for her clinical team across five dimensions: after-hours charting percentage, callout frequency per 30-day window, QA rework rate, caseload pressure relative to panel threshold, and weekend documentation rate.
Within the first week, two teams scored at elevated risk. One had an after-hours charting rate above 60%. The other had a callout frequency that had been climbing for two months without anyone connecting it to workload pressure.
Tamara brought the burnout heatmap into the weekly operations meeting. For the first time, the clinical directors could see the actual data for their specific teams. Two managers discovered their teams were operating well beyond sustainable limits without knowing it had been building that long.
Meridian Care Partners did not need a new system to collect data. They needed a way to organize what already existed into a decision-making format that clinical leaders could use on a weekly basis.
Burnout risk is not a mystery in home health. It is a pattern. After-hours charting climbs. Callouts follow. Quality dips. Then someone resigns and the agency pays the full cost of replacement, lost productivity, and coverage gaps. Dazie surfaces that pattern weeks earlier, when leadership still has time to intervene.
For Tamara and her team, the outcome was straightforward. Two managers who thought their teams were fine discovered they were not. Workloads were redistributed. The pattern was interrupted before it became a resignation. That is what operational intelligence is supposed to do.