Bad 
Healthcare
Data.

TLDR: 

The problem:

A mobile kidney stone treatment business had outgrown its third-party software, but simply upgrading to the latest cloud version would not solve the real issues—broken workflows, bad data, and untrustworthy AI models.

The solution?
Fix the data by aligning the structure and redesigning data capture workflows.

The outcome:
Created a scalable, AI-ready data infrastructure—the key ingredient to supporting long-term growth & technological advancements across divisions and partnerships.

How did we know it was bad data?


  • Data quality failures manifested: persistent scheduling conflicts, billing errors, and incomplete treatment records.
  • Legacy systems created inefficiencies—slow processes, persistent data silos, and inconsistent treatment protocols.
  • Poor-quality data prevented AI models from performing effective predictive analysis.


The company had two divisions with conflicting needs:


  • Traditional healthcare operations required structured workflows, strict regulatory compliance, and consistent, error-free patient records.
  • AI & software engineering teams—focused on treatment prediction and data analytics.


As a result, each grew independently—without a unified data strategy. Workarounds became the norm, increasing errors, inefficiencies, and compliance risks.



Approach


Consequently, instead of migrating to new software, we fixed the data first—eliminating errors, inefficiencies, and compliance risks before layering AI-driven optimizations.



1. Redesigning Workflows & Capturing Context


  • Mapped & streamlined workflows to fix data inconsistencies across scheduling, treatment, and billing
  • Used NLP (Natural Language Processing) to extract valuable insights from previously unstructured intake & treatment notes


2. Standardizing Data & Securing Integrations


  • Developed a unified data model that worked across clinical teams & AI researchers
  • Built a secure, API-driven framework for real-time data exchange across mobile treatment units, hospitals, and AI research teams



3. Ensuring Compliance & Boosting Efficiency


    • Implemented data governance & security protocols to maintain HIPAA compliance
    • Automated scheduling & error validation—reducing admin work, treatment delays, and billing errors



The Results


  • Fixed broken data pipelines—ensuring data accuracy for treatment tracking, billing, and AI models
  • Eliminated compliance risks—removing error-prone workarounds that threatened HIPAA security
  • Enabled AI-driven treatment models—creating structured, high-quality datasets for predictive analytics
  • Reduced operational costs by optimizing scheduling and resource allocation
  • Created a scalable, AI-ready data infrastructure—supporting long-term growth & technological advancements



Where We Ended Up


A software licensing decision sparked a transformation, uniting clinical care and AI analytics.