Digital Health Frontier
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.
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.
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.
By John Halamka and Paul Cerrato - Combining genetic sequencing, life-style modification, and personalized drug therapy will transform health care.
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.
By John Halamka and Paul Cerrato - AI and machine learning have the potential to redefine the management of several GI disorders.
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.
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.
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.
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.
The evidence is mixed but suggests that these overlooked variables have a profound impact on each patient’s journey.
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.