Lean AI Framework for Health Care
How Healthcare Leaders Can Beat the Odds
Hey AI Productivity Explorer,
I was talking to a friend of mine who is in the healthcare space. During the conversation, we got into a short debate on how to best approach AI in healthcare.
He argued that AI is everywhere in healthcare right now. It’s on conference stages, in product demos, and in every vendor pitch promising to revolutionize diagnostics, workflows, and patient outcomes.
I said to him “I agree AI is everywhere but still 80% of AI projects in healthcare will fail”.
Why?
Because AI isn’t magic and without a focused strategy, it becomes a money pit, especially in healthcare, where regulations are tight, workflows are complex, and staff are already stretched thin.
He asked me “So Sameer what’s the reason for this failure?”
The 5 Reasons AI Fails in Healthcare Organizations
1. Undefined Problems
Telling your team “Let’s try AI” is like saying “Let’s cure something.” Without a clearly defined use case like reducing no-show rates, optimizing billing cycles, or automating patient intake you’re throwing darts in the dark.
2. Bad or Fragmented Data
Most health systems are sitting on mountains of siloed, incomplete, or unstructured data. Garbage in still means garbage out even with AI. You can’t expect usable insights from messy EHRs, paper records, and inconsistent codes.
3. Overengineered Solutions
Trying to build “Enterprise AI” before solving real issues is a classic mistake. We’ve seen smaller but fast-growing healthcare orgs spend millions chasing buzzwords instead of outcomes. AI that doesn’t connect directly to patient care or operations? Useless.
4. Workflow Misalignment
AI tools that don’t integrate with your EHR or require your staff to learn new systems often get ignored. If AI isn’t embedded into your team’s existing flow think Epic, Cerner, or Athena it won’t get used.
5. No Iteration Plan
Healthcare moves fast. Regulations shift. Patient behavior changes. If your AI model isn’t tuned over time, it becomes stale, irrelevant, or worse dangerous. One-time setup?
That’s not enough.
The Cure: Lean AI for Healthcare
AI in healthcare doesn’t need to be big, expensive, or risky. It needs to be Lean.
Lean AI is a 6-step framework that strips away complexity and focuses only on solving meaningful, high-impact problems. It brings the “build-measure-learn” mindset of Lean Startup into your organization’s AI adoption without needing a massive data science team.
You don’t need to be a hospital system or a billion-dollar health tech startup to use it.
Whether you’re running a private practice, a specialty clinic, a behavioral health group, or a multi-location provider business this framework works.
Let’s walk through it using a real pain point:
Use Case: Patient Intake and Pre-Visit Workflows
Imagine you’re managing a growing primary care network or a behavioral health clinic. Your team is buried in pre-visit forms, insurance verifications, and repetitive patient questions.
Your nurses are spending hours tracking down data before the patient ever walks in.
Your admin team is juggling phone calls, portal logins, and “Did you complete your intake?” emails.
Sound familiar?
Now, let’s apply the Lean AI Framework:
Step-by-Step: Lean AI in Action for Healthcare
Step 1: Define the Problem
Start with what’s broken. Not what’s trendy.
• Patients are missing steps in the pre-visit process.
• Manual intake is eating up staff hours.
• Delays cause rescheduling and increase no-show rates.
Your goal: Reduce admin load and improve intake completion rate by 50%.
Step 2: Build an AI MVM (Minimum Viable Model)
Instead of an expensive custom solution, you start small.
You deploy an AI-powered intake assistant using ChatGPT or a healthcare-safe NLP tool.
It guides patients through intake via SMS, web, or patient portal. It answers FAQs and collects structured responses.
MVM Tech Stack Example
• ChatGPT for dialogue
• Twilio for SMS
• Make.com or Zapier for automation
• Airtable or Google Sheets for structured data
Step 3: Measure Impact and Gather Feedback
Test it on 30 new patients. Track:
• Intake completion rate
• Staff time saved
• Patient satisfaction (“Was this easier than our old process?”)
Step 4: Iterate Based on Insights
You find patients over 60 prefer voice calls. So you add a voice assistant.
You discover people drop off at the insurance upload step. Add reminders + clearer instructions.
Step 5: Scale Gradually
Now that it’s working, you roll it out to all clinics.
You connect it to your EHR system to auto-fill patient fields.
You add smart triage logic to flag red flags before the visit.
Step 6: Continuously Improve
Every quarter, you review system performance.
You use analytics to track time saved per visit.
You retrain the AI with the latest FAQs, payer changes, and consent form updates.
Now here’s the best part…
You don’t have to figure this all out yourself.
The Mega Prompt: Your AI Blueprint Builder
This prompt is designed for healthcare leaders like you who want clarity before committing to a full AI buildout. Just copy-paste this into ChatGPT/Copilot/Gemini or Claude, answer the questions, and get a custom AI implementation blueprint tailored to your organization.
ChatGPT Mega Prompt for Lean AI in Healthcare
#Role
You are a healthcare-focused AI consultant guiding me, a healthcare leader, through the Lean AI Framework to create a patient-facing or back-office AI solution.
#Tasks
Your job is to help me define, design, and iterate an AI-driven solution that solves a real problem in our healthcare business (11–100 employees). Ask me specific, strategic questions at each of the following steps:
1. Define the Problem
2. Build a Minimum Viable Model (MVM)
3. Measure Impact and Gather Feedback
4. Iterate Based on Insights
5. Scale Gradually
6. Continuously Improve
Only proceed once I answer each step. No skipping.
#Step 1: Define the Problem
• What exact issue are you trying to fix? (e.g., intake delays, missed follow-ups, billing errors)
• How does this affect staff time, revenue, or patient satisfaction?
• What’s your ideal outcome?
#Step 2: Build a Minimum Viable Model (MVM)
• What’s the simplest tool we could use to test a solution?
• Do you already use EHRs, SMS tools, forms, or automation tools?
• What could AI automate or assist with today?
#Step 3: Measure Impact and Gather Feedback
• Who will use this? (Patients, admin, nurses?)
• What metrics matter? (Time saved, completion rates, no-shows reduced?)
• How will we gather feedback?
#Step 4: Iterate Based on Insights
• What feedback might indicate a need for change?
• What new feature or logic could improve performance?
#Step 5: Scale Gradually
• Once it works, where else could we use it?
• Should we connect this with EHR, billing, or other internal tools?
#Step 6: Continuously Improve
• How often will we review data?
• What signs will show the AI is out of date?
• How do we future-proof this solution?
#Blueprint Summary
At the end of the interaction, summarize the user’s inputs into a clear blueprint:
•Problem Definition: [Insert user’s description of the problem]
•MVM Design: [Describe the AI solution, its tasks, and expected outcomes]
•Success Metrics: [List metrics and user feedback plans]
•Iteration Plan: [Describe how improvements will be made]
•Scaling Strategy: [Summarize plans for expanding the solution]
•Continuous Improvement: [Outline plans for ongoing monitoring and updates]
#Example Output (for a Customer Onboarding Use Case)
Problem Definition:
Manual customer onboarding delays the start of projects by an average of two weeks due to repetitive tasks and missed document submissions.
MVM Design:
An AI-powered chatbot to automate document collection, answer FAQs, and send reminders. Expected outcome: Reduce onboarding time by 50%.
Success Metrics:
Time saved per onboarding, completion rates for document submissions, and customer satisfaction scores.
Iteration Plan:
Enhance chatbot FAQ responses based on customer queries and add document upload tracking for better monitoring.
Scaling Strategy:
Integrate the chatbot with the CRM system to automate data entry and enable personalized messaging for different customer segments.
Continuous Improvement:
Monitor chatbot performance monthly and update based on new onboarding trends or customer feedback.
#Rules:
1. Do not go to the next step until the user has completed the each step. Check with the user first to get their confirmation.
2. Do not skip any stepsFinal Thoughts
Healthcare doesn’t need more tools. The real power of AI in healthcare isn’t in flashy dashboards or futuristic predictions. It’s in solving daily operational pain points quietly, consistently, and with zero drama.
It needs better outcomes, faster decisions, and less burnout.
The Lean AI Framework gives you a clear, strategic way to introduce AI that doesn’t waste resources and actually works with the reality of running a healthcare business.
So here’s your next step:
Pick one broken process (like intake, billing, appointment reminders, or follow-ups)
Drop the Lean AI prompt into ChatGPT
Let it walk you through creating a solution tailored to your workflow, tools, and goals
Once you’ve done that comment or email me.
Let’s showcase how smart healthcare teams are doing AI right.
Best,
Creator of Solve with AI.