Getting Started with AI
From the first two articles in this series, we’ve seen how innovative teams use AI to predict denials, triage workflow, and detect patterns that drive better revenue outcomes.
The next logical question is:
How do you get started with AI in your organization — without getting overwhelmed, overpromised, or oversold?
Good news: You don’t need a seven-figure IT budget or a team of data scientists to take advantage of AI.
You just need a practical roadmap that builds momentum without creating disruption.
Let’s walk through it.
Data Readiness and Preparation
Step 1: Clean Your House (Start with Data Readiness)
AI’s effectiveness hinges on one thing above all else: data quality.
Even the most innovative AI model will struggle if your claims data is messy, incomplete, or inconsistent.
Before you even consider an AI vendor or project, ask yourself:
- Are our denial codes standardized and consistently applied?
- Do we have clean historical data on claims, payments, and denials?
- Are authorization and eligibility records reliably captured?
Checklist for a Quick Data Readiness Review:
- Standardize denial reasons into common categories.
- Clean up payer ID mappings.
- Validate your historical claims and payment files for missing fields.
Tip: If you’re already struggling to pull clean monthly reports from your billing system, pause and fix that first. Good reporting hygiene sets the foundation for AI success.
Pilot Approach and Vendor Evaluation
Step 2: Start Small — Pilot Before You Scale
One of organizations’ most significant mistakes is doing too much at once.
Instead, start with a narrow, well-defined pilot:
- Focus on a specific denial category (e.g., authorization-related denials).
- Target a subset of payers (e.g., your top three by volume).
- Choose a single problem area (e.g., pre-bill denial prediction, or workflow routing for high-dollar claims).
Pilot Goal:
Prove you can improve a single KPI — such as reducing denial rates by 5% or decreasing days in AR for a specific claim type.
Small, early wins build internal confidence and make the case for broader investment later.
Step 3: Ask the Right Questions When Evaluating Vendors
The AI market is full of slick demos and promises.
Focus your evaluation on real-world performance, not theory.
Key Questions to Ask:
- How does your AI model learn from our data, not just generic data?
- What kind of outputs will we get — predictions, risk scores, worklists?
- Can you show examples of how your tool handled messy real-world claims data?
- How long until we see measurable improvements?
- What happens when the model makes mistakes — and how are those corrected?
Tip: Insist on seeing examples based on claims similar to yours — not just perfect data sets.
Change Management and Budgeting
Step 4: Focus on Change Management, Not Just Technology
Even the best AI tool will fail if your team doesn’t trust it or know how to use it.
Key to success: Position AI as an assistant, not a replacement.
- Billers and managers should be involved early in the pilot phase.
- Show side-by-side comparisons: “Here’s what the AI predicted, and here’s what happened.”
- Celebrate small wins — like finding denials early or speeding up payment on a complex claim.
Training Tip:
Use real case studies from your own claims to teach your team how to interpret AI outputs.
Focus on empowerment, not control.
Step 5: Budget Realistically (And Measure What Matters)
AI isn’t an instant cost-saver.
It’s an investment that typically pays off over time by:
- Reducing write-offs
- Speeding up collections
- Improving team productivity
- Enhancing cash flow predictability
When budgeting, plan for:
- An initial pilot (3–6 months)
- A ramp-up period for the AI model to learn
- Ongoing tuning and model optimization
Tip: Focus on KPIs that translate directly to dollars — like denial rate reduction, days in AR improvement, and high-dollar claim turnaround time.
Common Pitfalls and Key Takeaways
Common Pitfalls to Avoid
As you embark on the AI journey, watch out for these common mistakes:
- Starting too big: Pilot first. Prove value before expanding.
- Assuming AI will replace staff: It won’t. It complements them.
- Ignoring data readiness: Bad data equals bad predictions.
- Buying based on hype, not outcomes: Stay focused on measurable improvements, not flashy dashboards.
Closing Thought: Practical Wins Beat Grand Visions
If you take one thing away from this series, let it be this:
The best AI strategies aren’t revolutionary. They’re evolutionary.
Winning teams aren’t waiting for a massive overnight shift.
They’re making practical, targeted improvements that build momentum over time.
Start small. Stay focused. Grow smart.
That’s the real path to making AI a meaningful advantage in your revenue cycle.
At OncoSpark, we help healthcare organizations take this practical approach — identifying the right opportunities, setting realistic goals, and implementing AI solutions that drive measurable results over time. If you’re ready to explore how AI can strengthen your revenue cycle without the hype, we’re here to guide the way.
