Healthcare Technology12 min read

    AI in Medical Practice: What Actually Works in 2025

    Cut through the AI hype. Here's what artificial intelligence tools actually deliver value in medical practices today, and what's still more promise than reality.

    Andrew Radosevich•Managing Partner
    12 min read

    AI in Medical Practice: What Actually Works in 2025


    The healthcare AI landscape is noisy. Vendors promise the world, but what's actually delivering ROI for practices today? Let's separate signal from noise.


    AI That's Ready Now


    1. Ambient Clinical Documentation


    What it does: Listens to patient encounters and generates clinical notes automatically.


    Reality check:

  1. Best options reduce documentation time 50-70%
  2. Still requires physician review and editing
  3. Works best for straightforward visits
  4. Complex cases need more editing

    5. ROI calculation: If a physician saves 1 hour daily, at $200/hour productivity value, that's $50K+ annual value.


    Leading solutions: Nuance DAX, Suki, Abridge, Nabla


    2. Prior Authorization Automation


    What it does: Automates PA submissions, tracks status, predicts approvals.


    Reality check:

  5. Can reduce PA time by 70-80%
  6. Still need humans for peer-to-peers
  7. Effectiveness varies by payer
  8. ROI typically visible within 90 days

    9. Leading solutions: Olive, Infinx, Infinitus


    3. Patient Communication


    What it does: Appointment reminders, follow-up messages, basic triage.


    Reality check:

  9. Reduces no-shows by 20-40%
  10. Handles routine inquiries automatically
  11. Must be carefully monitored
  12. Patients generally accept AI for admin tasks

    13. 4. Coding Assistance


    What it does: Suggests codes based on documentation, identifies missed charges.


    Reality check:

  13. Can increase capture by 5-10%
  14. Reduces coding errors
  15. Still needs coder review
  16. Works better with structured documentation

    17. AI That's Still Emerging


    Clinical Decision Support

  17. Promising for risk stratification
  18. Diagnostic AI still needs more validation
  19. Liability questions remain
  20. Best used as "second opinion," not primary

    21. Revenue Cycle Prediction

  21. Predicting denials before they happen
  22. Identifying at-risk accounts
  23. Still early but improving rapidly

    24. Patient Risk Stratification

  24. Identifying high-risk patients proactively
  25. Population health applications
  26. Requires good data foundation

    27. How to Evaluate AI Vendors


    Questions to Ask


  27. What's the actual implementation timeline? (Hint: If they say 2 weeks, be skeptical)
  28. What does integration with our EHR actually look like?
  29. What ongoing training/maintenance is required?
  30. What's the total cost of ownership? (Not just license fees)
  31. Can we talk to 3 similar-sized practices using this?

    32. Red Flags


  32. No clear ROI calculation
  33. Vague integration claims
  34. No references in your specialty
  35. Pricing that's "custom" without transparency
  36. Over-promising on timeline

    37. Building Your AI Strategy


    Start Here


  37. Document current pain points Where do you lose the most time?
  38. Quantify the problem Hours, dollars, patient impact
  39. Prioritize by ROI Documentation and PA typically highest
  40. Pilot before committing Insist on trial periods
  41. Plan for change management Staff adoption is critical

    42. Budget Expectations


    For a 3-5 physician practice:

  42. Ambient documentation: $300-500/provider/month
  43. PA automation: $500-2000/month depending on volume
  44. Patient communication: $200-500/month
  45. Coding assistance: $200-400/provider/month

    46. The Bottom Line


    AI is real and delivering value, but it's not magic. The practices winning with AI are:

  46. Starting with specific, measurable problems
  47. Piloting carefully before scaling
  48. Investing in training and adoption
  49. Measuring actual ROI, not vendor promises

    50. Want to develop an AI strategy for your practice? Let's identify where technology can have the biggest impact.


    AIartificial intelligencehealthcare technologypractice efficiencyautomation

    About the author

    Andrew Radosevich

    Managing Partner

    Andrew Radosevich is a visionary executive with over 15 years of experience driving innovation, growth, and operational excellence across diverse industries, including a role as Chief Experience Officer at Forefront Concierge Medicine.

    Read full bio
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