In The Loop: Doug Blackwell
Doug Blackwell, formerly the SVP and CIO of Horizon (Blue Cross Blue Shield New Jersey), and Carol McCall, Chief Health Analytics Officer discuss the biggest challenges in wrangling healthcare data, data interoperability, the impact of AI, and much more.
In this installment of In The Loop, ClosedLoop’s thought leadership series, Carol McCall, Chief Health Analytics Officer, and Doug Blackwell, formerly the SVP and CIO of Horizon (Blue Cross Blue Shield New Jersey), discuss the biggest challenges in wrangling healthcare data, data interoperability, the impact of AI, and much more.
Doug is a strategic advisor to ClosedLoop among other companies and helped to found and guide Abacus Insights. Previously, he was the Senior Vice President and Chief Information Officer for Horizon Blue Cross Blue Shield of New Jersey (BCBSNJ), the largest health insurance company in the state. Before joining Horizon BCBSNJ, Mr. Blackwell served as Senior Vice President for Remote Application Hosting Services, a division of the Allscripts/Eclipsys Corporation. Prior to that he was the SVP for Service Operations IT at CIGNA Corporation and held several senior IT positions during his 5-year tenure there. In addition, he held executive positions at several software companies including SVP of Operations, VP of Operations and Client Delivery, and VP of Global Operations.
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In The Loop: Doug Blackwell
Doug Blackwell, formerly the SVP and CIO of Horizon (Blue Cross Blue Shield New Jersey), and Carol McCall, Chief Health Analytics Officer discuss the biggest challenges in wrangling healthcare data, data interoperability, the impact of AI, and much more.
In The Loop: John Bertko
John Bertko, Chief Actuary with Covered California and Carol McCall, Chief Health Analytics Officer dive into risk adjustment, alternate payment models, and much more.
AI = ROI How AI Drives Health Outcomes and Tangible ROI in Healthcare
In this webinar with Massachusetts Health Data Consortium, ClosedLoop discusses measuring tangible ROI for predictive systems, creating explainable AI, addressing algorithmic bias, and overcoming the deployment challenges of machine learning models.