Digital Health Frontier
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By John Halamka and Paul Cerrato - The medical Internet of Things is filled with many valuable devices--and a few questionable ones. It’s critically important to separate clinically validated tools from marketing hype.
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By John Halamka and Paul Cerrato - Working “in the cloud” may not be a panacea to solve all your organization’s problems, but the benefits outweigh the risks.
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By John Halamka and Paul Cerrato - The Interoperability and data blocking regulations mandated by the 21st Century Cures Act may pose a challenge to providers, IT developers, and health information exchanges, but implementing the rules has the potential to significantly improve patient care.
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By John Halamka and Paul Cerrato - Combining genetic sequencing, life-style modification, and personalized drug therapy will transform health care.
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By John Halamka and Paul Cerrato - An analysis of over 2 billion lab test results suggests a deep learning model can help create personalized reference ranges, which in turn would enable clinicians to monitor health and disease better.
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By John Halamka and Paul Cerrato - AI and machine learning have the potential to redefine the management of several GI disorders.
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By John Halamka and Paul Cerrato - We must make a serious commitment to increase financial resources and provide better analytics for real world evidence/real time data in support of public health.
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By John Halamka and Paul Cerrato - In addition to evaluating the safety of software as a medical device (SaMD), the agency needs to devote more resources to evaluating its efficacy and quality.
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By John Halamka and Paul Cerrato - By providing a safe, secure environment, novel approaches enable health care innovators to share data without opening the door to snoopers and thieves.
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By John Halamka and Paul Cerrato - The three risk assessment tools now in use fall far short. Using the latest deep learning techniques, investigators are developing more personalized ways to locate women at high risk.
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The evidence is mixed but suggests that these overlooked variables have a profound impact on each patient’s journey.
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By John Halamka and Paul Cerrato - To convince physicians and nurses that deep learning algorithms are worth using in everyday practice, developers need to explain how they work in plain clinical English.