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.
As healthcare data continues to grow at an exponential rate, the ability to access, connect, integrate, and analyze these datasets has become a critical priority for the industry. Whether addressing regulatory requirements like price transparency, interoperability, and data portability, supporting industry goals such as value-based care and improved outcomes, or responding to competitive pressures like customer acquisition and retention, the demand for secure, accessible, and interoperable data remains one of the sector's most pressing challenges. Join our experts from IBM Consulting to explore the technology and business solutions that are driving real change and tangible outcomes across the healthcare industry.
Artificial Intelligence (AI) has the potential to significantly empower traditional Business Intelligence (BI) reporting but replacing it entirely might not always be practical or desirable. In this session debate the issues with AI for BI reporting your organization needs to be cognizant of.
Join Fivetran at the CDO Healthcare Exchange for a dynamic roundtable discussion on the pivotal role of centralized patient data in powering predictive analytics to improve patient outcomes. Learn from other senior data leaders as we discuss how to leverage predictive analytics to enhance care delivery while navigating its critical challenges such as ensuring data quality, managing vast and diverse datasets, and delivering real-time insights.
In this discussion, you will discover:
With real-world examples and contrasting viewpoints on AI, this roundtable will ignite a dynamic debate about the hard questions of strategic prioritization, weighing patient outcomes against financial imperatives, and short-term gains against long-term innovation.
- Are we automating the worst parts of the patient and member journey instead of focusing on the essence of care?
- Does AI open up new economic opportunities for health systems and insurers or is that fools gold?
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.
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.