Precision population analytics: population management at the point-of-care
Authors Paul Tang and Kenney Ng discuss this month's JAMIA Journal Club selection:
Tang PC, Miller S, Stavropoulos H, Kartoun U, Zambrano J, Ng K. Precision population analytics: population
management at the point-of-care. J Am Med Inform Assoc. 2021;28(3):588-595. doi:10.1093/jamia/ocaa247 [Article]
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Paul Tang, MD, MS
Internist, Palo Alto Medical Foundation
Clinical Excellence Research Center
Paul Tang, MD, MS is an adjunct professor in the Clinical Excellence Research Center at Stanford University and an internist at the Palo Alto Medical Foundation. He was formerly vice president, chief innovation and technology officer at the Palo Alto Medical Foundation and vice president, chief health transformation officer at IBM Watson Health. He has over 25 years of executive leadership experience in health information technology within medical groups, health systems, and corporate settings.
Most recently, he led the creation, development, deployment, and evaluation of an AI-based clinical decision support application integrated within an electronic health record system. He also led a corporate enterprise-wide design team.
Dr. Tang has chaired numerous federal and private sector advisory and professional association groups related to health information technology and policy. He is an elected member of the National Academy of Medicine, and has served on numerous NAM study committees, including a patient-safety committee he chaired that published two reports: Patient Safety: A New Standard for Care, and Key Capabilities of an Electronic Health Record System. He is a member of the Health and Medicine Division committee of the National Academies of Science, Engineering, and Medicine. Dr. Tang was co-chair of the federal Health Information Technology Policy committee from 2009-2017. He has served as board chair for several health informatics professional associations, including the American Medical Informatics Association (AMIA). He received his B.S. and M.S. in Electrical Engineering from Stanford University and his M.D. from the University of California, San Francisco.
Kenney Ng, PhD
Principal Research Staff Member, Center for Computational Health
Manager, Health Analytics Research Group
Kenney Ng is a Principal Research Staff Member in the Center for Computational Health and Manager of the Health Analytics Research Group at IBM Research Cambridge. His research focus is on the development and application of data mining, machine learning, and AI techniques to analyze, model and derive actionable insights from real world health data to improve patient outcomes. His prior research areas include information retrieval, speech recognition, probabilistic modeling, topic modeling, and statistical language modeling. Before IBM Research, he was a senior software engineer and architect in the IBM Software Group.
Prior to IBM, he was a principal software engineer at iPhrase Technologies and held research positions at the MIT
Laboratory for Computer Science, BBN Technologies, and MIT Lincoln Laboratory. He received Bachelors, Masters, and
Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. He is a
member of IEEE and AMIA.
- 35-minute presentation by article author(s) considering salient features of the published study and its potential impact on practice.
- 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members
JAMIA Journal Club managers and monthly moderators are JAMIA Student Editorial Board members:
Hannah Burkhardt, PhD candidate, University of Washington School of Medicine, Biomedical Informatics and Medical Education, Seattle, WA
James Rogers, PhD Student, Department of Biomedical Informatics, Columbia University, New York City, NY
Statement of Purpose
Machine learning methods have been applied to clinical data from electronic health records (EHRs), primarily in the
inpatient and intensive-care settings. We applied machine-learning techniques to support outpatient personalized
management of three common chronic diseases by creating precision cohorts of similar patients in similar clinical
situations. We presented our dynamic population analysis of treatment outcomes for the index patient’s
relevant precision cohort at the point-of-care, integrated within the EHR. We also applied the same precision-cohort
analysis to the organization’s entire patient population for each disease. The reports calculate the
organization’s potential clinical performance if the best-practice personalized treatments were adopted for
Our methods focused on developing a repeatable workflow that can be applied to each chronic disease. The workflow includes: (1) defining disease-specific clinical treatment “decision points” that can be automatically extracted from the longitudinal patient EHR data and used for modeling and analysis, (2) identifying appropriate confounding variables for patient similarity matching based on both knowledge-driven clinical guidelines and data-driven feature selection from EHR data, (3) dynamically constructing a precision cohort of patient events that are clinically similar to the current state of the index patient for analysis, and (4) presenting the patient-specific analysis to the clinician at the point-of-care within the EHR.
The target audience for this activity is professionals and students interested in health informatics.
The general learning objective for all of the JAMIA Journal Club webinars is that participants will
- Use a critical appraisal process to assess article validity and to gauge article findings' relevance to practice
After participating in this webinar, the listener should be able to:
- Understand limitations of applying RCT results in clinical practice
- Learn a method for analyzing observational data to create precision cohorts of patients similar to an index patient in a similar clinical situation
- Formulate an approach to visualize and integrate observational data analysis in clinicians’ EHR workflow to inform clinical decisions at the point-of-care
- Apply analysis of precision cohorts within an organization to derive best practices for common chronic conditions (precision population analytics)
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Credit Designation Statement
The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The live webinar only offers CME credit. The recording on our website will be openly available for learners but will not offer CME credit.
No commercial support was received for this activity.
Disclosures for this Activity
Kenney Ng discloses that he is an employee of IBM.
The following presenters, planners, and staff who are in a position to control the content of this activity disclose that they and their life partners have no relevant financial relationships with commercial interests:
Presenter: Paul Tang
JAMIA Journal Club planners: Hannah Burkhardt, Kirk E. Roberts, James Rogers
AMIA Staff: Susanne Arnold, Pesha Rubinstein
For questions about webinar access, email Susanne@amia.org.
Instructions for Claiming CME Credit
Use the link in the webinar’s chat area to claim credit; in a day or two you will receive an email with your CME certificate.
If you require a certificate of participation, contact Pesha@amia.org.