Before signing a contract or launching a pilot, it’s essential to ask the right questions, align stakeholders, and understand what an effective AI implementation looks like in the unique context of cardiology.
1) What Problem Are You Trying to Solve?
It may sound obvious, but many AI purchases fail because they start with the technology—not the problem. Cardiology teams should first identify where administrative bottlenecks exist. Common examples include:
- Delays in referral intake and patient scheduling
- Manual processing of faxes and prior authorizations
- Charting and documentation overload, especially for complex cases
- Prescription refill coordination and follow-up
Each of these pain points has different requirements—and may warrant different AI solutions.
2) Who Needs to Be Involved in the Decision?
AI touches multiple departments. While operations leads and IT directors often drive decisions, it’s critical to involve:
- Providers and care team members, who will work alongside AI agents
- Billing and compliance leads, who must ensure output is auditable
- Front office staff, who may be affected by workflow changes
Gaining cross-functional buy-in early makes adoption smoother later.
3) Is the AI Platform Trained on Healthcare Data?
Generic AI solutions may look good on paper, but if they’re not trained on clinical notes, fax documents, or healthcare-specific formats, they can fall short in real-world scenarios.
Look for platforms—like Honey Health—that have trained their models on tens of millions of clinical documents and built guardrails for specialty-specific use cases like cardiology.
4) Can It Work Within Your EHR?
If the AI doesn’t integrate with your existing EHR (Epic, Athena, eClinicalWorks, etc.), it creates workflow fragmentation. Cardiology teams don’t have time to toggle between systems or log into external dashboards.
The best platforms operate inside your EHR—submitting orders, routing notes, and processing referrals without requiring extra clicks or logins.
5) How Is Success Measured?
Cardiology clinics need measurable ROI. Before deploying, agree on KPIs like:
- Time saved per task (e.g., 3 minutes per referral)
- Volume of tasks automated per day/week
- Reduction in backlogs or missed handoffs
- Financial impact on margins or staffing needs
Ask the vendor for benchmarking data and a timeline for reporting outcomes.
6) What Happens When Something Goes Wrong?
No AI system is perfect. The real differentiator is how errors are detected and resolved.
Make sure the platform includes escalation paths, audit trails, and real-time error handling—especially important in cardiology where time-sensitive actions can impact outcomes.
7) Can You Start Small and Scale?
You don’t need to overhaul your entire clinic on day one. The best AI platforms allow for incremental adoption. Start with:
- Referral triage
- Device data filing (e.g., pacemaker uploads)
- Fax inbox cleanup
Once value is proven, scale to pre-charting, refills, prior auth, and full EHR workflow automation.
8) Will You Have a Partner or Just a Vendor?
Choose an AI company that acts like a partner. You want:
- Dedicated onboarding and support
- Custom workflow mapping for cardiology use cases
- Quarterly business reviews and optimization
This relationship matters when your team hits roadblocks or wants to grow.
Conclusion: Think Bigger—But Start Smarter
AI is not just another software tool—it’s a workforce transformation. For cardiology clinics to benefit, the decision-making process must be grounded in real needs, cross-functional alignment, and a focus on operational outcomes.
Take the time to assess your clinic’s readiness, ask tough questions, and choose a platform built specifically for healthcare—ideally one that understands the high-stakes environment of cardiology.
With the right foundation, AI co-workers can help your team scale care, reduce burnout, and drive sustainable growth.
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