A healthcare company had 7 disconnected reports across Tableau, Excel, and emailed PDFs. Instead of migrating to another paid BI tool, we eliminated the license dependency entirely — building a self-contained, interactive web dashboard that unified 4 departments into one source of truth.
A healthcare operations company with four departments — Operations, Clinical, Finance, and HR — was drowning in fragmented reporting. Operations tracked SLA compliance in Tableau. Clinical measured patient satisfaction in Excel. Finance ran revenue reports from a custom SQL query emailed as a PDF. HR tracked turnover in a Google Sheet. Each department used different date ranges, different definitions for "active staff," and different calculation methods for shared metrics like utilization.
The executive team received inconsistent numbers depending on who prepared the report. A monthly reporting cycle consumed 3–4 analyst-days of manual data pulling, reformatting, and reconciliation. By the time leadership saw the numbers, they were two weeks stale.
The initial proposal was to consolidate everything into Power BI. But after analyzing the situation, we proposed a different approach: skip the BI tool entirely. Build a lightweight, self-contained web dashboard that connects to the same data sources, costs nothing to license, and deploys on any static web host.
On the surface, the Operations department looks healthy — SLA compliance hovers around 87–91% across the year, consistently above the 85% threshold. But the unified dashboard revealed a hidden trend: call abandonment rate has been creeping upward from 4.2% in January to 7.8% in December, even as SLA held steady.
The explanation is subtle but important: the team was meeting SLA by triaging easier calls faster, while longer, more complex calls were abandoned. The old Tableau report only showed SLA %. The new dashboard surfaces both metrics side by side, making the trade-off visible to leadership for the first time.
Key insight: SLA compliance alone is a vanity metric. When paired with abandonment rate in the same view, it reveals that the team is optimizing for speed at the expense of complex-call completion — a pattern invisible in the old siloed Tableau report.
Clinical's patient satisfaction score has been stable at 4.1–4.3 out of 5 all year, but the appointment show rate tells a different story — declining from 82% in Q1 to 74% by Q4. This no-show increase directly impacts revenue per visit and clinical utilization.
However, the unified view also surfaces a positive counter-trend: referral completion time has dropped from 12 days to 7 days over the same period, suggesting that the care coordination pipeline is actually improving even as patient compliance weakens.
Revenue per visit has been trending upward ($142 → $168 over 12 months), and the collection rate stays above 92% — both healthy signals. But denied claims as a percentage of submissions have risen from 6.2% to 9.8%, representing a growing gap between billed and collected revenue.
In the old world, these lived in separate reports. Finance tracked collections in one PDF, and denied claims were buried in a separate SQL extract. Seeing them together for the first time prompted a root-cause investigation that identified a coding error in a specific procedure type responsible for 40% of the denials.
Key insight: The denied claims trend was invisible when reports were siloed. Once revenue per visit, collection rate, and denial rate appeared on the same dashboard, the divergence was immediately obvious — leading to a coding fix that recovered an estimated $180K annually.
The HR department's monthly turnover rate shows a clear seasonal pattern: spikes in March (post-annual-review departures) and September (back-to-school attrition). But overlaying training hours per employee reveals an inverse relationship — months with below-average training hours are followed by above-average turnover 60 days later.
This leading-indicator pattern was completely invisible when turnover lived in a Google Sheet and training hours in a separate system. The unified dashboard makes the correlation visually obvious.
The most valuable insights weren't inside any single department's data — they lived in the connections between departments. When HR turnover spikes, Operations SLA dips 6 weeks later (new hires ramp slowly). When Clinical show rates drop, Finance revenue per visit rises (only committed patients show up — selection bias). When Operations handle time increases, Clinical satisfaction actually improves (longer calls = more thorough service).
None of these cross-department correlations were visible in the old siloed world. The unified dashboard makes them obvious.
Select a department to see its full KPI dashboard with trends, compositions, and comparisons — all updating in real time.
For the first time, HR turnover, Clinical show rates, Operations SLA, and Finance revenue are visible on one normalized chart — exposing hidden connections between departments.
15 KPIs with agreed definitions, calculation methods, owners, and thresholds — eliminating the "my number vs your number" problem permanently.
Drill from company-wide overview to any single department and any quarter. All KPIs and charts recalculate instantly.
Normalized radar view lets leadership compare departments across performance dimensions — instantly identifying which departments are strong vs struggling.
Data pulls via JSON API on a daily cron schedule. The entire front end is static HTML — no Power BI, Tableau, or Looker subscription required.
All data is aggregated at the department/metric level. No PHI ever reaches the dashboard. Aggregation happens server-side before the JSON API exposes anything.
Monthly reporting cycle: 3.5 days → 1 day
Annual BI license costs eliminated
Cross-department insights previously invisible
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