Harvard and Aetna Ally for Targetted Health Care Treatments

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"Major advances in research and clinical care can be made by applying new bioinformatics techniques to large, aggregated clinical databases," said Isaac (Zak) Kohane, MD, a professor of pediatrics and health sciences and technology at Harvard Medical School and co-director of CBI, in a statement. Kohane, along with Brian Kelly, MD, head of informatics and strategic alignment at Aetna, will supervise the research.
The researchers will focus on:
• Evaluating the outcomes of various treatments for specific conditions based on quality and cost.
• Determining factors that predict adherence to medical and drug treatments for chronic diseases.
• Studying how claims data and clinical data available through electronic health records can best be used to predict disease and follow outcomes.
• Improving the ability to predict adverse events through the proactive study of claims and clinical data.
“If our healthcare system is going to become a ‘learning’ healthcare system, we need to better use the enormous amount of information we derive from healthcare to develop tools to understand what is happening today – such as which drugs are not working as safely as we thought, which therapies have unexpected benefits, what are the predictors of effective diabetes management and which genetic tests are likely to usefully guide therapy,” Kohane added. “Major advances in research and clinical care can be made by applying new bioinformatics techniques to large, aggregated clinical databases. We are excited about the opportunity to work with Aetna to rapidly develop and deploy algorithms and applications that can make a real impact on biomedical discovery and patient care."
The researchers will focus on:
• Evaluating the outcomes of various treatments for specific conditions based on quality and cost.
• Determining factors that predict adherence to medical and drug treatments for chronic diseases.
• Studying how claims data and clinical data available through electronic health records can best be used to predict disease and follow outcomes.
• Improving the ability to predict adverse events through the proactive study of claims and clinical data.
“If our healthcare system is going to become a ‘learning’ healthcare system, we need to better use the enormous amount of information we derive from healthcare to develop tools to understand what is happening today – such as which drugs are not working as safely as we thought, which therapies have unexpected benefits, what are the predictors of effective diabetes management and which genetic tests are likely to usefully guide therapy,” Kohane added. “Major advances in research and clinical care can be made by applying new bioinformatics techniques to large, aggregated clinical databases. We are excited about the opportunity to work with Aetna to rapidly develop and deploy algorithms and applications that can make a real impact on biomedical discovery and patient care."