The Promise vs The Reality
AI in healthcare is one of the most hyped areas of technology. Every conference talks about AI diagnosing diseases, AI predicting patient outcomes, AI revolutionizing medicine. The promise is exciting. The reality is more nuanced.
Having worked closely with AI/ML teams at TachyHealth for over four years, building intelligent claims management systems and integrating AI into healthcare workflows, I have seen both the transformative potential and the practical challenges. Here is what I have learned.
Where AI Actually Works in Healthcare
1. Claims Processing and RCM
This is where we have seen the most immediate ROI. AI excels at:
- Claim validation: Catching errors before submission
- Denial prediction: Identifying claims likely to be denied
- Code suggestion: Recommending appropriate diagnosis and procedure codes
- Fraud detection: Identifying anomalous patterns
Why it works: High volume of structured data, clear feedback loops (claim approved or denied), and measurable business impact.
2. Document Processing
Healthcare generates mountains of documents. AI helps with:
- OCR and data extraction from medical records
- Classification of document types
- Entity extraction (patient names, dates, medications)
- Summarization of lengthy clinical notes
Why it works: Repetitive tasks with clear right/wrong answers, reducing manual data entry.
3. Predictive Analytics
Using historical data to predict future events:
- Patient no-show prediction: Optimizing scheduling
- Readmission risk: Identifying patients needing follow-up
- Resource planning: Predicting patient volumes
- Revenue forecasting: Financial planning
Why it works: Clear business value, actionable insights, and measurable outcomes.
The Challenges Nobody Talks About
1. Data Quality is Everything
The saying garbage in, garbage out is amplified in healthcare AI:
- Medical records are messy, inconsistent, and often incomplete
- Different systems use different coding standards
- Historical data reflects historical biases
- Data labeling requires clinical expertise (expensive)
We spent more time cleaning and preparing data than building models.
2. Explainability Matters
In healthcare, black box AI is problematic:
- Clinicians need to understand why a recommendation was made
- Regulatory requirements demand explainability
- Trust requires transparency
- Wrong predictions need to be debuggable
We prioritized interpretable models over marginally more accurate black boxes.
3. Integration is Harder Than the AI
Building a model is maybe 20% of the work:
- Integrating with existing EMR workflows
- Ensuring real-time performance
- Handling edge cases gracefully
- Training users on new workflows
- Monitoring and maintaining production models
4. Validation is Complex
Unlike typical software testing, AI validation requires:
- Clinical validation with real patient outcomes
- Bias testing across patient populations
- Performance monitoring over time (model drift)
- Regulatory compliance documentation
Our Implementation Approach
Start with the Problem, Not the Technology
We never start with we want to use AI. We start with:
- What problem are we solving?
- What is the current process?
- What data do we have?
- What would success look like?
Sometimes the answer is not AI. Sometimes it is better workflow design or simple rule-based automation.
Build the Data Pipeline First
Before any ML:
- Understand the data sources
- Build reliable data extraction
- Implement data quality checks
- Create feedback loops for labeling
Start Simple, Iterate
Our approach:
- Baseline: Rule-based system or simple heuristics
- Simple ML: Logistic regression, decision trees
- Complex ML: Only if simple models are not sufficient
- Deep Learning: Only for specific use cases (NLP, imaging)
Often, simple models with good features outperform complex models with poor data.
Human in the Loop
For high-stakes decisions:
- AI provides recommendations, humans decide
- Confidence scores help prioritize human review
- Feedback improves the model over time
- Clear escalation paths for uncertain cases
Technology Stack
What we use:
- Python: Primary language for ML (scikit-learn, pandas, numpy)
- Azure ML: Model training and deployment
- MLflow: Experiment tracking and model registry
- Docker: Containerized model serving
- .NET: Integration with existing applications
- Kafka/Service Bus: Event-driven model invocation
Model Serving Architecture
[Application] -> [API Gateway] -> [Model Service]
|
[Model Registry]
|
[Monitoring]
Models run as separate services, versioned and deployable independently.
Lessons for AI Implementation
1. Manage Expectations
AI is not magic. Set realistic expectations with stakeholders about:
- What AI can and cannot do
- Timeline for results
- Need for ongoing maintenance
- Potential failure modes
2. Invest in Data Infrastructure
Data platforms are more important than AI platforms. You cannot do good AI without good data.
3. Build Cross-Functional Teams
Successful healthcare AI requires:
- ML engineers who understand the algorithms
- Software engineers who can build production systems
- Domain experts who understand healthcare
- Clinicians who can validate results
4. Plan for the Long Term
AI is not deploy and forget:
- Models degrade over time
- Healthcare practices change
- New edge cases emerge
- Continuous monitoring is essential
5. Prioritize Trust
Healthcare professionals are rightfully skeptical of AI. Build trust through:
- Transparency about how models work
- Clear communication about limitations
- Demonstrating value incrementally
- Respecting clinical expertise
The Future
I believe AI will transform healthcare, but not overnight and not without challenges. The organizations that succeed will be those that:
- Invest in data infrastructure now
- Build AI capabilities iteratively
- Keep humans in the loop
- Focus on practical problems with clear value
The hype cycle will fade, but the real work of making AI useful in healthcare will continue. That is where the opportunity lies.