Transform the Data Foundations Holding Back Healthcare

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Bindu Chanagala

VP Performance Insights/Data Analytics for the Population Health Services Org CommonSpirit Health

Bindu Chanagala is the Vice President of Performance Insights/Data Analytics for the Population Health Services Organization at CommonSpirit Health, one of the largest nonprofit health systems in the United States. In this role, she leads enterprise performance intelligence strategy—architecting advanced analytics, predictive insights, and reporting capabilities that enable value-based care delivery across a national footprint. She partners closely with clinical, operational, and financial leaders to translate complex healthcare data into measurable improvements in quality, utilization, and cost outcomes.

Bindu brings a rare blend of large-scale health system leadership and startup execution. She is a Strategic Advisor - Product & Operations for an AI driven Cancer Navigation company. She co-founded a Techstars-backed maternal mental health startup, where she led product strategy, AI-driven innovation, and operational design to improve perinatal outcomes and expand equitable access to care. Her entrepreneurial experience spans product-market validation, fundraising strategy, regulatory navigation, and cross-functional team leadership.

Previously, Bindu held senior technology and operations roles at Intermountain Health and SelectHealth, where she led modernization initiatives across analytics platforms, application development, cloud infrastructure, and performance improvement programs. Her work focused on bridging clinical care delivery with scalable technology solutions in highly regulated healthcare environments.

An MIT Sloan MBA and technologist by training, Bindu combines deep systems thinking, data fluency, and operational rigor. She is passionate about supporting innovators who are tackling complex healthcare challenges and advancing equitable, technology-enabled care models.

Agenda Day 1

5:25 PM PANEL DISCUSSION | WHY ‘SHARING DATA’ FAILS IN THE REAL WORLD AND WHAT ACTUALLY FIXES IT

Interoperability has been “solved” on paper for over a decade, yet data sharing still breaks in the moments that matter most. The issue is not willingness or standards adoption, it’s usability. What arrives is often late, inconsistently formatted, or incomplete, forcing teams back into manual work just to keep care and revenue moving. It’s not an issue of data access and visibility, but rather a question of whether data can be used immediately, without rework. Progress comes from agreeing on what actually needs to move, how fresh it must be, and who owns failure when the pipeline breaks.

• Define a “minimum valuable dataset” by separating must-have operational data from context that otherwise slows action.

• Expose where payer-provider incentives undermine data liquidity, and where commercial alignment has actually improved data timeliness and usability.

• Move from regulatory compliance to SLAs that work, clarifying refresh expectations, ownership, and dictionary alignment across organizations.

Agenda Day 2

11:50 AM PANEL DISCUSSION | BUILDING TEAMS THAT UNDERSTAND BOTH HEALTHCARE AND DATA - BRIDGE DOMAIN EXPERTISE WITH ANALYTICS SKILLS

The so-called “unicorn” candidate, fluent in modern data stacks and deeply grounded in healthcare operations, is effectively non-existent at scale. Leaders are realizing they cannot hire their way out of this crunch; they have to manufacture capability deliberately. This panel examines how organizations are redesigning the talent supply chain itself, moving beyond salary debates to structural solutions. From internal data academies that convert domain experts into analytics talent, to global delivery models that expand capacity without losing context, the focus is on building teams that can actually sustain data and AI ambition.

• Compare “build vs buy” talent strategies, from internal data academies that upskill clinicians and operators, to external hiring and global delivery models, and where each breaks down in practice.

• Identify the roles that benefit most from domain-first upskilling, and where deep healthcare context outperforms raw technical strength in driving usable analytics and AI outcomes.

• Share retention and progression models that keep newly trained “bilingual” talent engaged, including career paths, incentives, and governance structures that prevent loss to other industries

Check out the incredible speaker line-up to see who will be joining Bindu.

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