Health Care Information Systems

Helping you manage through HIPPA compliance with data modeling and enterprise resource solutions.

Revenue Cycle Management

• Revenue cycle business operations leadership and direction including requesting/tracking authorizations, utilization tracking, capturing provider services into billable charges, preparing accurate health care claims specific to the payer, applying proper/accurate diagnosis and procedure codes, claims processing (preparation/submission), remittance processing, patient/consumer account reconciliation, and patient/payer payment collections.
• Development, testing, and implementation of a customized enterprise resource planning package supporting revenue cycle operations to track patient/consumer visits and caregiver expenses, track patient/consumer authorizations, convert labor and expense charges into payer-specified health care claims, transmit claims using EDI or other electronic means, receive electronic payer remittances, and auto-reconcile patient/consumer accounts. This HIPPA 5010 compliant package handles a variety of payer-specific health care claims for state Medicaid, MCOs, insurance companies, and third-party Medicare and Medicaid claims processors.
• Lead a multi-functional team tasked with transitioning from the HIPPA 4010 to HIPPA 5010 standard. This effort required a complete overhaul of business processes, computer systems and software.
• Development and implementation of enterprise business rules, billing systems, operational processes, and procedures to comply with multiple health care agency authorization and claims requirements/specifications.
• Development and implementation of electronic data interchange (EDI) transaction protocols in partnership with multiple state Medicaid agencies, Managed Care Organizations (MCOs), insurance companies and third-party claims processors.

Data Science/Analytics/BI

• Lifecycle of Data Science Project: Problem Identification, Data Mining, Data Preparation, Data Analysis and Modeling, Data Visualization, and reporting findings in a clear, concise and defensible manner.
• Statistical analysis and development of descriptive and predictive analytics models.
• ETL Process: Extract, Transform and Load.
• Development, program and validate machine learning models using best practices.
• Code using SQL, Python, R, SAS, and VBA.
• Developing visualizations using a variety of software packages to help explain statistical, descriptive, and predictive modeling results.