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Interoperability is a key driver of payer-provider collaboration and cost efficiency, but putting it into practice requires strategy, alignment, and persistence. This session takes you inside one health plan’s ongoing journey to modernize its interoperability infrastructure to support stronger partnerships with providers and deliver a more seamless member experience. From breaking down data silos to enabling real-time exchange and smarter workflows, discover how targeted process improvements are driving better outcomes, reducing administrative friction, and enabling more informed, value-based decisions.

Author:

Saikiran (Sai) Vodela, MPA, MS, PharmB

Advisor and Manager, HealthIT
L.A. Care Health Plan

Saikiran (Sai) Vodela is an Advisor and Manager of HealthIT at L.A. Care Health Plan, where he leads strategic efforts in Health Information Exchange (HIE), California’s Data Exchange Framework (DxF), and interoperability initiatives that support care coordination for Los Angeles County’s safety net population.

Sai previously spearheaded the implementation of a clinical data repository under the Transforming Clinical Practice Initiative (TCPi), a nationwide effort by the Center for Medicare & Medicaid Innovation (CMMI) aimed at improving healthcare delivery through data-driven transformation. The program achieved $196 million in cost avoidance for the health plan. Earlier in his career, he developed performance monitoring solutions for Accountable Care Organizations (ACOs) and Medicare providers at an electronic health record company.

He holds a Master of Public Administration (MPA) in Healthcare Management, a Master of Science (MS) in Pharmaceutical Chemistry, and a Bachelor of Pharmacy (PharmB).

Saikiran (Sai) Vodela, MPA, MS, PharmB

Advisor and Manager, HealthIT
L.A. Care Health Plan

Saikiran (Sai) Vodela is an Advisor and Manager of HealthIT at L.A. Care Health Plan, where he leads strategic efforts in Health Information Exchange (HIE), California’s Data Exchange Framework (DxF), and interoperability initiatives that support care coordination for Los Angeles County’s safety net population.

Sai previously spearheaded the implementation of a clinical data repository under the Transforming Clinical Practice Initiative (TCPi), a nationwide effort by the Center for Medicare & Medicaid Innovation (CMMI) aimed at improving healthcare delivery through data-driven transformation. The program achieved $196 million in cost avoidance for the health plan. Earlier in his career, he developed performance monitoring solutions for Accountable Care Organizations (ACOs) and Medicare providers at an electronic health record company.

He holds a Master of Public Administration (MPA) in Healthcare Management, a Master of Science (MS) in Pharmaceutical Chemistry, and a Bachelor of Pharmacy (PharmB).

 

Michael Steinbaugh

Director, Data, AI & Genome Sciences
Merck

Michael Steinbaugh

Director, Data, AI & Genome Sciences
Merck

Michael Steinbaugh

Director, Data, AI & Genome Sciences
Merck
 

David Kombo

Principal Scientist
Sanofi

David Kombo

Principal Scientist
Sanofi

David Kombo

Principal Scientist
Sanofi
 

Justin Scheer

Vice President, In Silico Discovery
Johnson & Johnson Innovative Medicine

Justin Scheer

Vice President, In Silico Discovery
Johnson & Johnson Innovative Medicine

Justin Scheer

Vice President, In Silico Discovery
Johnson & Johnson Innovative Medicine
 

Morten Sogaard

Senior Vice President & Head, Astellas Innovation Lab
Astellas Pharma

Morten Sogaard

Senior Vice President & Head, Astellas Innovation Lab
Astellas Pharma

Morten Sogaard

Senior Vice President & Head, Astellas Innovation Lab
Astellas Pharma
 

Philip Tagari

Chief Scientific Officer
Insitro

Philip Tagari

Chief Scientific Officer
Insitro

Philip Tagari

Chief Scientific Officer
Insitro

Developing a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA.

Author:

Nathan Stover

PhD Student, Chemical Engineering
Massachusetts Institute of Technology

Nathan Stover

PhD Student, Chemical Engineering
Massachusetts Institute of Technology

Explore how machine learning techniques, such as supervised learning and deep learning, predict critical ADME properties like solubility, permeability, and DDI risk.
Discover how computational methods, including molecular docking and quantum chemistry simulations, optimize high-affinity drug-target interactions for enhanced efficacy.

Author:

David Kombo

Principal Scientist
Sanofi

David Kombo

Principal Scientist
Sanofi