The Practice Consulting OS: Turning a Clinic into a System (With AI Inside)

TL;DR

• Healthcare practices run on chaos and Excel sheets - there's a better way
• Start with discovery: shadow staff for a day, map the actual workflow
• Build a KPI tree that matters: patient flow, revenue cycle, staff utilization
• Small automations beat big transformations every time
• Pilot with one workflow, socialize the win, then scale
• Legacy OBGYN went from 6-week billing lag to real-time dashboards

Discovery & Scope

Every practice thinks they're unique. They're not. After working with dozens of clinics, the patterns are predictable: scheduling chaos, billing delays, staff burnout, and providers who "just want to see patients."

My discovery process is simple but thorough:

• Day 1: Shadow the front desk. Watch check-in, scheduling, insurance verification
• Day 2: Follow the billing cycle. Find where claims get stuck
• Day 3: Map provider workflows. Identify documentation bottlenecks
• Day 4: Review financials. Spot revenue leaks
• Day 5: Present findings with quick wins highlighted

This isn't about finding problems - any consultant can do that. It's about finding the 20% of fixes that deliver 80% of impact.

Data Plumbing (EHR/billing/CRM)

Healthcare runs on disconnected systems. The EHR doesn't talk to billing. Billing doesn't talk to the practice management system. Nothing talks to marketing.

My approach:

• Map data flows between systems (usually 3-5 core platforms)
• Identify integration points (APIs, HL7, CSV exports)
• Build lightweight connectors using proven tools (Zapier for simple, custom scripts for complex)
• Create a single source of truth dashboard

The goal isn't to replace systems - that's a multi-year nightmare. It's to make existing systems work together.

KPI Tree & Ops Rhythm

Most practices track vanity metrics. Patient satisfaction scores are nice, but they don't pay bills. I focus on metrics that drive decisions:

Revenue Cycle:

• Days in AR by payer
• First-pass claim acceptance rate
• Collection rate by CPT code
• Time to payment by insurance type

Operations:

• Provider utilization (actual vs. capacity)
• No-show rate by day/time/provider
• Room turnover time
• Staff overtime hours

Growth:

• New patient acquisition cost
• Patient lifetime value
• Referral conversion rate
• Online booking adoption

These feed into a weekly ops rhythm: Monday metrics review, Wednesday course corrections, Friday planning.

Automations, Not Robots

AI in healthcare isn't about replacing doctors. It's about eliminating repetitive tasks that burn out staff:

• Insurance verification: Auto-check eligibility at booking
• Prior authorizations: Pre-populate forms, track status
• Appointment reminders: Smart scheduling based on no-show risk
• Clinical documentation: Voice-to-text with smart templates
• Billing codes: Suggest CPT codes based on visit notes

Each automation saves 10-30 minutes per day per staff member. Multiply that by 20 staff, 250 working days - that's real money.

Pilot → Rollout

Big bang implementations fail. I use a staged approach:

1. Pick one workflow: Usually appointment scheduling or insurance verification
2. Run a 2-week pilot: One provider, one staff member
3. Measure everything: Time saved, errors reduced, staff feedback
4. Refine and expand: Fix issues, add second provider
5. Socialize the win: Let early adopters evangelize
6. Full rollout: With champions in place

This builds trust and momentum. Staff see it working before they have to change.

Case Notes: Legacy OBGYN

Legacy OBGYN came to me with a simple problem: they didn't know how much money they were owed. Billing was 6 weeks behind, claims were getting denied, and the practice administrator was working 70-hour weeks.

Discovery findings:

• 3 different systems for scheduling, clinical, and billing
• Manual insurance verification taking 15 minutes per patient
• No visibility into claim status after submission
• Providers spending 2 hours daily on documentation

Quick wins (Week 1):

• Built automated insurance verification - saved 3 hours/day
• Created claims tracking dashboard - found $47K in stuck claims
• Implemented voice documentation - gave providers 1 hour back daily

3-month results:

• Billing current within 48 hours
• First-pass claim acceptance up from 72% to 91%
• Collections increased 23% with same patient volume
• Administrator back to 45-hour weeks

The playbook is now templatized and ready for the next practice.

What's Next

I'm packaging these systems into repeatable templates through GF Labs. The goal: any practice can implement the core operating system in 30 days.

Current focus areas:

• Multi-location practice orchestration
• Specialty-specific modules (OBGYN, ortho, primary care)
• Patient engagement automation
• Predictive analytics for capacity planning

If you're running a practice in DFW and want to explore what's possible, check out my other work or see how I'm applying similar principles to tax and financial operations.