Wisdom of the Crowd or Tyranny of the Mob?

Date

February 21, 201702/21/2017 10:00am 02/21/2017 10:00am Wisdom of the Crowd or Tyranny of the Mob?

About this Presentation:

Medical decision making is fraught with both uncertainty and undesirable variability. The vast majority of our clinical decisions lack adequate evidence to determine their efficacy and inconsistent implementation compromises quality and efficiency. The current standards in clinical decision support reinforce best-practices but are limited in scalability by manual production. “Grand challenges” thus include mining clinical data sources to automatically generate decision support content. Statistical approaches allow us to learn patterns that reflect real-world standards of care vs. outliers. This can range from my evaluation of the national distribution of opioid prescriptions to my current NIH Big Data 2 Knowledge K01 Career Development Award directed to empower individual clinicians with the collective experience of the many.

In this presentation, I will review my efforts developing a collaborative filtering machine-learning approach to clinical order entry, analogous to Netflix or Amazon.com’s “Customer’s who bought A also bought B” algorithm. This automatically generated decision support content can reproduce, and even optimize, manual constructs like order sets while remaining largely concordant with guidelines and avoiding inappropriate recommendations. This has even more important implications for prevalent cases where well-defined guidelines do not exist. The same methodology is predictive of clinical outcomes comparable to state-of-the-art risk prediction models. Embedded randomization of such decision support interventions could then allow us to explicitly build knowledge for the future, even as we enhance care today, in a closed-loop learning health system.

Jonthan H. Chen, MD, PhD - Bio:

Dr. Jonathan H. Chen an Instructor in the Stanford University Department of Medicine. He is a practicing physician with research interests focused on data-mining electronic medical records for insights to inform medical decision making.
 
Chen co-founded a company to translate his Computer Science graduate work into an expert system to solve organic chemistry problems, with applications from drug discovery to a practical education tool distributed to students across the world. To gain first-hand perspective in tackling the greater societal problems in health care, he completed medical training in Internal Medicine and a VA Research Fellowship in Medical Informatics.
 
A current focus is automated generation of personalized decision support content. With the support of an NIH Big Data 2 Knowledge K01 Career Development Award, he is developing this approach to systematically extract and disseminate the undocumented collective wisdom of practicing clinicians. This will translate endpoint clinical data into a reproducible and executable form of expertise and, deploying this right at the point-of-care, will close the loop of a continuously learning health system.

1001 America/Los_Angeles public

Type

Lecture

Time Duration

10:00am - 12:00pm

About this Presentation:

Medical decision making is fraught with both uncertainty and undesirable variability. The vast majority of our clinical decisions lack adequate evidence to determine their efficacy and inconsistent implementation compromises quality and efficiency. The current standards in clinical decision support reinforce best-practices but are limited in scalability by manual production. “Grand challenges” thus include mining clinical data sources to automatically generate decision support content. Statistical approaches allow us to learn patterns that reflect real-world standards of care vs. outliers. This can range from my evaluation of the national distribution of opioid prescriptions to my current NIH Big Data 2 Knowledge K01 Career Development Award directed to empower individual clinicians with the collective experience of the many.

In this presentation, I will review my efforts developing a collaborative filtering machine-learning approach to clinical order entry, analogous to Netflix or Amazon.com’s “Customer’s who bought A also bought B” algorithm. This automatically generated decision support content can reproduce, and even optimize, manual constructs like order sets while remaining largely concordant with guidelines and avoiding inappropriate recommendations. This has even more important implications for prevalent cases where well-defined guidelines do not exist. The same methodology is predictive of clinical outcomes comparable to state-of-the-art risk prediction models. Embedded randomization of such decision support interventions could then allow us to explicitly build knowledge for the future, even as we enhance care today, in a closed-loop learning health system.

Jonthan H. Chen, MD, PhD - Bio:

Dr. Jonathan H. Chen an Instructor in the Stanford University Department of Medicine. He is a practicing physician with research interests focused on data-mining electronic medical records for insights to inform medical decision making.
 
Chen co-founded a company to translate his Computer Science graduate work into an expert system to solve organic chemistry problems, with applications from drug discovery to a practical education tool distributed to students across the world. To gain first-hand perspective in tackling the greater societal problems in health care, he completed medical training in Internal Medicine and a VA Research Fellowship in Medical Informatics.
 
A current focus is automated generation of personalized decision support content. With the support of an NIH Big Data 2 Knowledge K01 Career Development Award, he is developing this approach to systematically extract and disseminate the undocumented collective wisdom of practicing clinicians. This will translate endpoint clinical data into a reproducible and executable form of expertise and, deploying this right at the point-of-care, will close the loop of a continuously learning health system.

Speakers

Jonathan Chen, MD, PhD
Instructor
Department of Medicine
Stanford University