Jan January 29 Wed 2020
Stanford Strong. Diversity & Inclusion Week 2020 #BoussardLab
Dec December 03 Tue 2019
We are hosting a monthly workshop for clinical informatics: DDAMES: Data Divas for AI in Medcine at Stanford. More information can be found here.
Sep September 17 Tue 2019
Dr. Hernandez-Boussard received a new R01 award from NLM, Advancing Knowledge Discovery for Postoperative Pain Management. This work will apply deep learning techniques to identify patients at high risk for adverse postoperative pain outcomes. Check out our recruiting page to join our team!
Sep September 04 Wed 2019
Dr. Hernandez-Boussard discusses the use of real world data for clinical assertions in a recent interview with Stanford Medicine based on findings from her recent JAMIA article. More details can be found here.
Putting the data before the algorithm in big data addressing personalized healthcare.
Technologies leveraging big data are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system. Blame for these deficiencies has often been placed on the algorithm—but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.
Real world evidence in cardiovascular medicine: ensuring data validity in electronic health record-based studies
With growing availability of digital health data and technology, health-related studies are increasingly augmented or implemented using real world data (RWD). Recent federal initiatives promote the use of RWD to make clinical assertions that influence regulatory decision-making. Here we assess the credibility of clinical assertions in cardiovascular medicine using electronic health records (EHR). In this study, recall varied greatly between EHR data types; EHR structured data did not meet regulatory grade criteria, while EHR unstructured data did. These results suggest that recall should be routinely measured in EHR-based studes intended for regulatory use. Furthermore, advanced data and technologies may be required to achieve regulatory grade results.