Vijaykumar Viradia
Healthcare Transformation Leader with AI, Project Management, Agile
Milwaukee, Wisconsin, United States
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Delivery-focused, PMP, SAFe, ICAgile, Microsoft, and CSM-certified product leader with strong business acumen and proven success leading cross-functional teams across PMO, SaaS, Go-To-Market, Analytics, and Enterprise Software in Digital Health, Medical Device, and Healthcare AI.
Project Management Responsibilities:
Managed and oversaw PMO functions, aligning project objectives with strategic goals to ensure effective execution.
Implemented processes to deliver projects on time, within budget, and to quality standards.
Created Jira dashboards to aggregate data across projects, accounts, departments, and company levels, enhancing delivery effectiveness, resource utilization, and quality management.
Defined and translated market needs into technical requirements, quickly architecting, designing, and delivering high-quality, cost-effective solutions.
Led and developed Agile mindsets within teams through Lean-Agile leadership practices and principles.
Directed global cross-functional teams to achieve seamless system and subsystem integration.
Technical Oversight:
Drove Healthcare AI initiatives, including ED strategies, ACO pricing, care gap strategies, and provider scribing.
Implemented Digital Health & IT solutions in Value-Based Care, Point of Care, and Clinical Decision Support, with a focus on Patient Engagement, Care Management, Quality Management, and Analytics.
Integrated and implemented EPIC, eClinical Works, Health Plan, NextGen, Kaiser Health Plan, KERN, MedPoint, and LANES, managing data across Clinical, Claim, Behavioral Health, Pharmacy, SDOH, and ADT domains.
Led enterprise IT solutions in medical imaging and IT, specializing in New Product Introduction (NPI) for ultrasound devices.
Adapted Agile/Scrum development processes for global teams, improving product quality and reducing costs.
Authored eight research papers and scholarly articles on AI, healthcare, Agentic AI, and project management, published by PMI, IEEE, and renowned journals.
Active peer reviewer for the Journal of Evaluation in Clinical Practice (Wiley), IEEE Conferences, and the International Journal of Global Innovations and Solutions (IJGIS).
Contributing author for the Project Management Institute (PMI), Association for Project Management (APM), and IEEE.
Speaker at PMI, Cognizant, University of Wisconsin-Milwaukee on "PMI & PMO," American Association of Information Technology Professionals (AAITP), and The New World Foundation (TNWF).
Panel judge for prestigious awards, including the Globee Awards, Claro AI Awards, Vega Awards, Fierce Healthcare Innovation Award, PMI - PMO of the Year Award, and NASA's Tech Rising Challenge.
Certification
With Project Management Institute (PMI):
-Project Management Professional (PMP)® – Project Management Institute, 2013
-Generative AI Overview for Project Managers
-Data Landscape of GenAI for Project Managers
-Talking to AI: Prompt Engineering for Project Managers
-Practical Application of Generative AI for Project Managers
Certified ScrumMaster (CSM) – Scrum Alliance, 2014
SAFe® Agilist – Scaled Agile, 2021
ICAgile Certified Professional in Agile Team Facilitation (ICP-ATF) – ICAgile
ICAgile Certified Professional in Agile Coaching (ICP-ACC) – ICAgile
Area of Expertise
Topics
Artificial Intelligence to detect Healthcare Fraud.
Healthcare insurance fraud is a growing global issue that undermines trust, increases healthcare costs, and causes financial strain on insurers. Fraud occurs in various forms, including false claims, upcoding, duplicate billing, and collusion between healthcare providers and patients. Traditional fraud detection methods—like manual reviews and rule-based systems—are limited by inefficiency, human error, and inability to adapt to evolving fraud schemes.
Artificial Intelligence (AI) has emerged as a promising solution, offering powerful tools for detecting complex and hidden fraud patterns. AI models, especially machine learning (ML) and deep learning techniques, can analyze large volumes of data, identify anomalies, and adapt to new fraud behaviors over time. Supervised ML uses labeled data to classify claims, while unsupervised learning identifies outliers without predefined labels. Deep learning further enhances detection by uncovering high-level, non-obvious patterns within health records and claim data.
AI-based systems can integrate with healthcare information systems like electronic health records (EHRs) and claim platforms to provide real-time fraud detection. Despite their promise, these systems face challenges including data privacy concerns, bias in training data, lack of interpretability in deep models, and ethical issues related to the use of sensitive health information.
Real-world implementations of AI in healthcare insurance fraud detection have already shown success—reducing fraudulent claims, improving operational efficiency, and saving millions. As AI technology evolves, trends like predictive analytics, blockchain integration, and advanced neural networks are expected to further enhance fraud detection capabilities. However, to fully realize AI’s potential, organizations must address data quality, ensure regulatory compliance, and maintain transparency in decision-making processes.
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