Selected work & example outcomes in healthcare analytics and automation
The examples below reflect a range of analytics, automation, and workflow optimization consulting work delivered across complex and regulated environments, including projects involving custom data applications and multi-step analytical or automation workflows.
To respect confidentiality, examples focus on problems addressed, approaches taken, and outcomes achieved rather than specific organizations or proprietary details. These scenarios reflect work across a range of teams and contexts, and I’m happy to discuss relevant details with prospective clients.
Example outcomes
Automated regulatory reporting pipeline (State funded safety-net behavioral health program)
Replaced a 10–20 hour weekly manual data entry process—performed in Excel across approximately 5,000 monthly encounters—with an automated Python pipeline that completes the same work in under 5 minutes with built-in validation and audit-friendly outputs.
The solution eliminated recurring error risk and is being extended to a new statewide reporting system that will cover tens of thousands of monthly encounters across all behavioral health service lines.
Automation · Regulatory reporting · Python · SQL
Operational analytics suite & visual management (Behavioral healthcare operations)
Designed and built a suite of operational Tableau dashboards from scratch for a behavioral health department where most metrics had never been systematically tracked, covering provider utilization, appointment volumes, intake and discharge trends, no-show and cancellation analysis, external referral follow-up, waitlist monitoring, grant compliance metrics, and population health indicators stratified by payor, clinic, and program.
Dashboards are being consolidated into a Lean-style Visual Management Board serving as the primary tool for leadership decision-making and continuous quality improvement.
Tableau · Dashboards · Operational analytics · Python · SQL
Applied AI for knowledge work acceleration (Healthcare & research operations)
Developed three custom agentic AI workflows in Python: for intelligent email management, RAG-based research synthesis using internal and external document libraries, and an academic writing assistant that safely anonymizes PHI before interfacing with commercial LLMs.
Each tool addressed a specific recurring bottleneck and reduced multi-hour tasks to minutes while maintaining compliance and governance standards.
AI · NLP · Agentic Workflows · Python · HIPAA compliance
Demographic & equity analysis for health policy research (National health policy journal - in progress)
This engagement involves designing and implementing a large-scale analytic framework to evaluate authorship and peer review patterns across four years of submission data for one of the nation's leading health policy journals, covering demographic representation, publication outcomes, and equity trends across thousands of manuscript and reviewer records.
Data infrastructure, cleaning, and validation are complete; full analysis is underway, with a potential follow-on deliverable of a standalone Python dashboard for the client's ongoing internal workflows.
Demographic analysis · Health policy · Equity analytics · Statistical analysis
State regulatory compliance data infrastructure (Large safety-net health system - in progress)
Currently underway: building end-to-end data infrastructure to support a large health system's compliance with a new statewide behavioral health reporting mandate, replacing two legacy systems that had operated in parallel for years and consolidating mental health and SUD reporting into a single pipeline covering all behavioral health service lines.
The mandate is live with monthly submissions underway via an interim process; full automation—including validation logic, QI checks, and Lean-aligned clinical workflows—is in active development.
Regulatory compliance · Data infrastructure · Python · Automation · Behavioral health
How to read these examples
The examples above are drawn from real projects delivered in production environments. To respect confidentiality and regulatory requirements, details have been generalized and identifying information has been removed.
Examples may include work involving custom dashboards or data applications built in Python, as well as the use of responsible, agentic AI workflows to support analysis, research, compliance, or operational tasks. The intent is to illustrate problem types, approaches, and outcomes—rather than serve as one-to-one blueprints.
Similar patterns and methods can often be adapted to different organizations, data environments, and constraints. I’m happy to provide additional context or discuss how comparable work might apply to your situation during a consultation.
Interested in a similar outcome?
If these examples resonate, a short conversation can help determine whether a similar approach would be useful in your context.