"Study Finds the Risks of Sharing Health Care Data Are Low"

Scientists have made significant progress in developing Artificial Intelligence algorithms that can analyze patient data and create new ways to diagnose a disease or predict which treatments work best for different patients. The success of those algorithms is based on having access to patient health data stripped of personal information that could be used to identify individuals in the dataset. However, privacy advocates are concerned about the possibility of individuals being identified through other means. A team of researchers led by MIT Principal Research Scientist Leo Anthony Celi quantified the potential risk of this type of patient re-identification and discovered that it is currently significantly low in comparison to the risk of a data breach. There were no reports of patient re-identification through publicly available health data in the period examined in this study, which is between 2016 and 2021. According to Celi, the findings indicate that the potential risk to patient privacy is greatly outweighed by the benefits to patients, who benefit from better diagnosis and treatment. He hopes that these datasets will become more widely available and include a more diverse group of patients in the near future. This article continues to discuss the MIT study finding that the potential risk of patient re-identification from publicly available health data is extremely low. 

MIT News reports "Study Finds the Risks of Sharing Health Care Data Are Low"