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.

CONTEXT:


How did we know it was bad data?


  • Inconsistent, unreliable data—scheduling conflicts, billing errors, and missing treatment records
  • Outdated, rigid systems—slow processes that created data silos and treatment inconsistencies
  • AI models blocked by bad data—poor-quality records prevented predictive modelling from being effective


The company had two divisions with conflicting needs:


  • Traditional healthcare operations—structured workflows and regulatory compliance
  • AI & software engineering teams—focused on treatment prediction and data analytics


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


Approach


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


What started as a software licensing decision became a full-scale transformation—bridging the gap between clinical care and AI-driven predictive analytics.


BOttom LIne: 


Bad data kills AI-driven healthcare. Outdated workflows waste money and create risk. If your data capture, workflows, and AI models don’t align, your healthcare system isn’t ready for the future.


Why Work With Me?


You don’t need another generic consultant or a one-size-fits-all solution—you need someone who knows how to break down complex systems, fix what’s broken, and build something that works.


I specialize in navigating chaos to find clarity, eliminating inefficiencies, and designing scaled workflows. Whether it’s optimizing healthcare systems, integrating AI-driven insights, or fixing broken operations, I cut through the stagnating effects of disconnected teams to deliver impact.


What You Get:

  •      Clear, actionable solutions—no fluff, just results
  •     Cross-functional expertise—bridging business, tech, and AI seamlessly
  •      A partner who navigates complexity—no endless strategy decks, just execution that moves the needle


If you’re stuck with broken workflows, unreliable data, and outdated systems, it’s time to fix it.


Let’s get to work.