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Livongo CEO Zane Burke talks about how artificial intelligence can make care more personal from both the clinical and consumer perspective.
Andrew Rebhan, consultant at The Advisory Board, discusses new advances in artificial intelligence and other emerging tech for which hospitals should prepare: biopsychosocial IT, internet of things, 3D printing, augmented reality and more.
Todd Schlesinger, VP of business development at Jvion, says AI has already moved beyond its status as a novel technology with exciting potential use cases, to one that is already helping solve real-world challenges and improving patient outcomes.
Joseph Dorocak, manager of population health analytics at Cleveland Clinic, describes some of the staffing and process strategies the health system uses as it puts AI to work for two intertwined aims: better patient outcomes and financial return.
Machine learning is only a means to an end, helping organizations learn from data more quickly and intuitively, says Leonard D'Avolio, founder and CEO of Cyft.
Westchester Medical Center integrated data from the EHR using a machine learning algorithm to connect patients to the right sources of care, according to Simer Sodhi, the organization's director of data management & analytics.
Dr. Sujay Kakarmath, digital health scientist at Partners HealthCare Pivot Labs, explains how the Boston organization is enabling collaboration between hospitals, health systems and other stakeholders such as big tech and pharma.
Tej Anand, professor of practice at the University of Texas' McCombs School of Business, explains how the most promising use case for blockchain in healthcare is the elimination of "redundant work, rework and reconciliation."
Badrul Husain, CEO of Infolytx, explains how his company's natural language processing technology is helping with clinical trial cohorts, and discusses how AI is gaining real traction elsewhere across healthcare.
(Sponsored) Kevin Shah, head of Enterprise New Business at Fujifilm Europe, says automating some processes can deliver diagnoses more quickly, increasing the chance of better outcomes for patients while reducing clinicians' workloads.
Interventions are the hardest part as disparate data has to be interwoven for behavior change if risk is to be mitigated, says Duke University Health System Chief Analytics Officer Stephen Blackwelder.
Rob Wellen, regional vice president at KenSci, talks about common data analytics questions from providers, including how to operationalize machine learning and AI.