What does it really take to make this happen? Data leaders in the sector are working with the same slide deck, join us to hear how the combination of regulatory compliance, technology, architecture, business model and change management, can come together to allow you to execute your vision. Beyond just compliance with TEFCA, FHIR and HL7 how do you ensure effective implementation and execution considering the deterrents for data sharing and movement in the sector? Data standards and APIs are thought to enable seamless data exchange across stakeholders (payers, providers, patient advocates). The issue comes out of the practical requirements of the sector to invest in purpose-built solutions to solve issues specific to their enterprise.
From centralising data by implementing data lakes to migrating to EPIC and looking at metadata features for data governance, CDOs in the healthcare provider space aim to converge on new data strategies, using modern cloud computing paradigms. The emphasis looks to be on the importance of acquiring and cataloguing data for AI readiness, shifting from reporting-focused data strategies to a more holistic approach. Data leaders are actively looking at changing gears and working on data strategies to improve health outcomes, enhance data exchange, and promote health equity.
As technology evolves, roles in data and analytics shift but to be able to stay up to date the team’s competencies need to be upskilled to include a high degree of business acumen to be able to bridge technical ability with organizational goals and integrate in the wider business.
Jump into the debate between Data Fabric and Data Mesh. Examine the pros and cons of each approach and how they could impact sector specific data governance, ownership, and scalability. Give real-world examples and examine the suitability of each for your organisations
Explore the impact and cost benefit of cloud migration for different healthcare organizations. Discuss use cases of robust data architecture implementation that enabled a more efficient data utilization. Discuss adoption, outcomes and best practices
Discuss the democratization of AI development. Explore how low code and no code platforms empower business users to create AI solutions without deep technical expertise. Low code vs. no code: Understanding the trade-offs. Use cases: Automating workflows, predictive analytics, and personalized care. Future impact: Balancing customization with governance
Embracing digital twins involves integrating data from various sources (IoT devices, electronic health records, wearables) to create accurate models. Use cases include patient-specific treatment optimization, predictive maintenance for medical equipment, and clinical simulations.
CDOs in the sector are fighting an uphill battle to overcome enterprise-wide resistance to change and encourage stakeholders to embrace data driven transformation. Focusing on data literacy programs, data leaders are starting with small, high-impact projects to demonstrate tangible benefits whilst engaging leadership to champion the cultural shift towards data-driven practices. On this journey your peers are taking onboard the lessons learned from other industries and adapting them to fit the sector by understanding the fundamental stressors, emphasizing data-driven approaches that can improve patient outcomes and quality of care.
Data leaders in the sector are taking a gradual approach to AI/ML innovation, starting with lower risk use cases while developing frameworks to address the unique requirements of healthcare. CDOs are exploring modalities to build internal automation models to enhance patient data analysis by incorporating behavioral data sets from multiple sources.
In this session, we will explore the holistic data strategy implemented at Moffitt, addressing the diverse data needs across administration, research, and clinical domains. We will discuss the approach to surveying the landscape of data use cases, focusing on a North Star use case to guide improvements in various data domains. By strategically investing in data curation—whether through manual efforts, NLP-derived methods, or innovative workflow integrations—the aim is to enhance the quality and efficiency of downstream analytics. Gain insights into the development and execution of the data strategy, highlighting the role of AI as a tool for driving the use cases and the importance of a strategic approach to data investment.
Look beyond AI and explore disruptive technologies. From blockchain to quantum computing, discover how these advancements can transform healthcare. Discuss practical applications and potential breakthroughs