Healthcare Analytics
Analytics for Behavioral Health Organizations
Your EHR is not the problem. The disconnection between your EHR, payroll, GL, and outcomes tools is the problem — and it can be fixed without replacing anything, in weeks.
The Reporting Problem
Behavioral health organizations face a reporting burden that is growing faster than their data infrastructure. CCBHC quality measures, SAMHSA block grant tables, Medicaid managed care outcome requirements, CARF accreditation documentation, and value-based contract compliance all depend on data that spans multiple systems — and no single system was built to produce it.
The EHR holds clinical data. Payroll holds workforce data. The GL holds financial data. Outcome measurement tools hold PHQ-9 and GAD-7 scores. Each system does its job. None of them talks to the others. The reports leadership needs — payer mix by program, staff productivity by caseload, grant deliverables by performance period, denial patterns by service type — require joining all of them. Without a data layer connecting these systems, that work falls to staff with spreadsheets.
We build the layer that connects them.
Where the Gaps Are
Payer Mix by Program
Medicaid, grant, and commercial coverage require different compliance and documentation. EHR billing modules record transactions — they do not produce program-level payer breakdowns on demand.
Staff Productivity vs. Caseload
Session counts live in the EHR. Hours worked and credential levels live in HR/payroll. No behavioral health EHR joins these natively. Staffing decisions get made without reliable data.
Grant Deliverable Tracking
Grant performance periods do not align with fiscal years or EHR defaults. CCBHC quality measures require custom date filtering across multiple systems. Manual extraction is the norm.
Denial Pattern Analysis
10–20% of behavioral health revenue is lost to preventable denials. Fewer than 1% of denied claims are appealed — because organizations cannot see which claims to pursue.
Clinical Outcomes
PHQ-9, GAD-7, and other outcome instruments typically live in a separate platform from the EHR. Scores exist. They cannot be analyzed alongside treatment data without a connecting layer.
Compliance Reporting
SAMHSA URS, 42 CFR Part 2, CARF MIC documentation, and value-based contract requirements all depend on cross-system data that organizations currently compile by hand.
How We Work
We follow a three-phase approach that most organizations complete in weeks, not quarters.
Assessment. We map your current stack, identify where each data element lives, and define the five to ten reports that would have the most operational impact if they ran automatically.
Build. We connect your source systems through a normalized data warehouse, build the reporting layer, and configure role-based access that satisfies HIPAA requirements from day one. No new EHR. No replacement of existing systems.
Adoption. We train your team to use the dashboards and own the data. The goal is an organization that does not need us to pull a report.
What You Get
Census Dashboards
Real-time and period-over-period census by program, location, and payer — updated automatically, available to all authorized staff.
CCBHC Quality Measure Reports
Automated calculation of required quality measures on calendar-year and custom grant-period bases, with drill-down by program and population.
Staff Productivity Views
Caseload size, billable hours, and session counts joined to payroll data — updated on a defined schedule without manual spreadsheet reconciliation.
Denial Trend Analysis
Denial rates and reasons by program, payer, and service type — so your billing team can see patterns, not just totals.
Grant Deliverable Tracking
Client counts, service units, and outcome data on any grant-year calendar — no manual extraction required at reporting time.
Outcome Measurement Visibility
PHQ-9, GAD-7, and other standardized instruments analyzed alongside treatment and program data. Required for CARF MIC documentation and value-based contracts.
Results in Practice
A behavioral health organization operating 90+ service locations across multiple regions was compiling all five report categories above manually — pulling from three disconnected systems every week. The billing summary alone projected 570 staff-hours per month.
After building the unified analytics layer, all five categories became automated. Projected annual savings in staff time: $736,920. In a post-implementation review, one regional leader said: "If the hours-worked numbers are accurate, this is a game changer."
Talk to Us About Your Data
Tell us what reports you cannot get from your current stack. We will tell you what a unified data layer would look like for your organization.