# WellSky's Journey to Responsible AI Implementation in Healthcare ## Company Background and Challenge WellSky is a healthcare technology company bridging the gap across different parts of the care continuum, from acute to post-acute and community care. Their operations span: - 2,000+ hospitals - 130,000+ providers - 100 million+ forms processed annually The company faced several critical challenges: - Severe clinician shortage and high attrition rates - Quality of care issues due to documentation errors - "Pajama time" phenomenon affecting 75% of nurses - Overwhelming administrative burden on healthcare providers ## Partnership with Google Cloud ### Selection and Trust Factors - Initial partnership focused on data center migration - Expanded into generative AI implementation - Key factors in choosing Google: ## Technical Implementation Approach ### Incubation Team Structure - Cross-functional team composition: ### Foundation Building - Two primary focus areas: ### Technical Workstreams - AI Capabilities Integration: - Tooling Development: - Production Monitoring: ## Responsible AI Implementation ### Governance Framework - AI Use Case Registry - Security and Privacy Controls: ### Risk Management - Development Lifecycle Integration: - Incident Management: ## Product Development and Deployment ### User Experience Considerations - AI Transparency: - Progressive Rollout Strategy: ### Technical Infrastructure - Integration with Google Cloud Services: ## Key Learnings and Best Practices ### Implementation Strategy - Start with lower-risk use cases - Gradual expansion of capabilities - Evidence-based approach - Mandatory citation requirements - Optional AI assistance ### Customer Adoption - Variable client readiness levels - Need for flexible deployment options - Importance of user training - Progressive rollout strategies ### Technical Considerations - Data strategy significance - API integration importance - Platform selection impact - Monitoring requirements ## Risk Mitigation Strategies ### Evidence Citation - Mandatory evidence linking - Prevention of hallucination - Transparent source attribution - Clinical accuracy verification ### System Design - Optional AI assistance - Progressive feature rollout - Power user testing - Client-specific customization ## Future Considerations - Model evolution adaptation - Expanding use cases - Enhanced monitoring capabilities - Continued focus on responsible AI - Ongoing user feedback integration