Learn how to choose a provider data automation partner with this three-step framework.
"If you’re only investing to meet today’s rules, you’ll be out of date the moment the next rule comes down."
– David Finney, Co-Founder, Leap Orbit
Provider network managers face mounting pressure: maintain accurate directories, ensure compliance, satisfy providers, and deliver seamless member experiences—all with fragmented systems and outdated processes.
The urgency is real: Healthcare is adopting AI 2.4x faster than other industries. According to Menlo Ventures' 2025 report, 22% of healthcare organizations have implemented AI tools versus just 9% across the broader economy. For health plans, adoption stands at 14%, with buying cycles compressed from 12-18 months to under 6 months.
But here's the problem: every vendor claims to use AI, but few can explain how it works for healthcare. The difference between the right AI approach and the wrong one isn't just technical—it's the difference between regulatory compliance and audit risk.
What to Look for in a Provider Data Automation Partner:
✅ Proven health plan experience (not just healthcare IT)
✅ Uses the right type of AI for provider data
✅ Healthcare domain expertise on founding team
✅ Auditable algorithms
✅ Human-in-the-loop for quality control
✅ Transparent pricing and fast time-to-value
✅ Strong ongoing partnership and support
✅ Successful implementations in similar organizations
Manual provider data management is time-consuming and risky. The right automation partner can:
But the wrong partner—one that overpromises on AI or lacks healthcare domain expertise—can introduce new risks: hallucinated data, unexplainable errors, and compliance vulnerabilities.
"We operate in a space where you have some rich clinical experienced companies in the market that are really living under the Hippocratic oath but for AI, which is do no harm. And you see them leveraging the right tools and then you see technology entrants that don't have a lot of healthcare experience saying we're going to leverage LLM models for everything."– Mike Lang, SVP, Bingli (from The Next Orbit Podcast, Episode 3)
Manual workflows create three critical problems:
1. Operational Burdens
2. Compliance Risks
3. Member & Provider Friction
The workforce reality: By 2036, the U.S. will be short almost a million healthcare workers. Provider data management will not become easier with business as usual thinking.
The right automation partner can:
Not all AI is created equal. There are two fundamentally different types we need to think about, and using the wrong one for provider data can introduce new risks:
Deterministic AI delivers consistency, auditability, and compliance—exactly what provider data requires. Same input = same output, every time.
Non-Deterministic AI (like ChatGPT and LLMs) can assist but shouldn't power core workflows. They can produce different outputs for the same input, called "hallucination."
For provider data management:
Technology companies entering healthcare often lead with what their AI can do rather than understanding what healthcare needs.
"I would be much more cautious of technology companies entering healthcare saying 'this worked great in other industries.' It just means they have a much greater barrier to entry in understanding the true pain points."– Mike Lang, SVP, Bingli
Red flags:
Green flags:
The hard truth: Sophisticated AI means nothing if you can't see what's happening inside.
Leap Orbit once had a customer whose previous vendor's "smart algorithms" delivered bad data to their directory. When asked for an explanation, the vendor hand-waved with technical jargon and no real answers.
This is the danger of "black box" AI in healthcare.
Our CareLoaDr AI roster processing software uses a confidence score built from the match rate between your data and primary source validation. You can always trace back exactly where data came from and make real-time adjustments. Think of it as a glass box, not a black box.
Critical questions to ask vendors:
About AI approach:
About auditability:
About control:
Ask about founding principles:
The earlier-stage advantage:
Most impactful healthcare AI comes from earlier-stage companies. Don't automatically disqualify newer vendors—but DO adjust your criteria:
Mitigate risk with:
One health plan spent three years and lots of money on a massive overhaul. Even before go-live, executives admitted it wouldn't work. The problem? Too much riding on a single, high-risk launch.
We believe in modular, fast time-to-value implementations:
Good pilot structure:
About AI approach:
About healthcare expertise:
About transparency:
About implementation:
About risk:
Healthcare's AI adoption is accelerating. The window to be an early mover with the right partner is closing. But rushing into the wrong partnership creates more problems than it solves.
Choose a partner who:
Leap Orbit has helped health plans across the country automate, clean, and manage their provider data with confidence. Whether you're navigating new compliance mandates or scaling your network without burning out your ops team, we're here to help.
Let's talk about solving your provider data challenges, without adding another platform headache.