AMIA's Annual Symposium is the premier learning and networking conference attended by more than 2,500 health informaticians from across the world. Now, you can access full presentations and slides from the live event at your convenience while earning CME/CNE online.
AMIA 2024 Annual Symposium On Demand is designed to provide you with the very latest health informatics content with maximum value and convenience. Revisit one or all top 20 sessions from the conference, featuring leading voices from across the informatics field. Choose the format that fits your preferred learning style. Take up to two years to claim your education credits. Recorded at AMIA’s Annual Symposium, held November 9-13, 2024, in San Francisco, CA.
Choose Your Format
Presenter, Slides, and Audio
Members: $190 $152*
Nonmembers: $270 $216*
*Includes 20% discount through March 31. Log in to see your discount.
Slides and Audio
Members: $150 $120*
Nonmembers: $210 $168*
*Includes 20% discount through March 31. Log in to see your discount.
Exceptional Jo: A Semi-Automated and Scalable System to Share Personalized Patient Positive Feedback with Employees
The Exceptional Jo recognition program, initiated by Vanderbilt University Medical Center in 2020, utilizes an automated system to match employees with positive patient feedback, fostering a culture of recognition and addressing caregiver burnout. By cross-referencing patient feedback with employee access to medical records, personalized recognition emails are sent, boosting employee morale. With almost 70,000 emails sent to date, the initiative has garnered overwhelmingly positive responses, showcasing its effectiveness in enhancing workforce engagement and well-being.
Learning Outcomes
- Understand how the Exceptional Jo Recognition Program at Vanderbilt University Medical Center (UVMC) uses patient feedback to enhance employee engagement and reduce burnout, highlighting the impact of personalized recognition on workforce satisfaction.
Speaker
- Peyton Larson, MPA, Vanderbilt University Medical Center
Configure or Integrate? Tradeoffs for Remote Symptom Monitoring Innovation with Electronic Health Records
There are two competing approaches for innovation with electronic health records (EHR): “configure” leverages EHR’s existing capabilities as much as possible; “integrate” views the EHR as a platform for integrating third-party tools. We compared technical feasibility and user experience implications of these approaches when implementing an asthma symptom monitoring intervention in two different health systems. We found fewer technical challenges implementing user requirements with the integrate, and pros and cons of each for user experience.
Learning Outcomes
- Understand tradeoffs between two approaches for innovation with electronic health records
Speaker
- Robert Rudin, RAND Corporation
A Case Study of Digital Phenotyping in a Large Integrated Healthcare System: An Evaluation of Veterans Sharing Unsolicited Patient-Generated Health Data
The Veterans Health Administration (VHA) recently launched a new mobile health app, allowing patients to voluntarily share patient-generated health data with their care teams. We examined early users of this app, including how they compared to the general VHA population and common digital phenotypes shared. We found that users of the SMHD had higher annual health care costs than non-users, despite being younger in age and living in more urban and higher income zip codes.
Learning Outcomes
- Gain new knowledge about a patient-generated health data (PGHD) collection effort in a large integrated health care system, potential for future clinical applications, and to better understand how veterans who share PGHD may differ from veterans who do not share PGHD.
Speaker
- Mark Zocchi, PhD, Veterans Health Administration
Learning Interpretable, Temporal Health Status Phenotypes from Self-Tracked Patient Data
Endometriosis is a debilitating, systemic chronic illness where unpredictable week-to-week variations care. We hypothesize that unsupervised probabilistic phenotype approaches can enable meaningful, interpretable representations of health status over time in the context of self-tracked data, independently of an individual’s level of engagement with self-tracking. We generate and evaluate temporal phenotypes from self-tracking data to represent individuals’ illness states over time, which have the potential to support new tools for tracking and management.
Learning Outcomes
- Explore the use of machine learning alongside patient self-tracked data to facilitate analysis that can support personal informatics tools to support patients with chronic illness. The focus of this podium abstract is constructing a learned phenotype model that can be used to characterize the health status of a poorly understood chronic illness.
Speaker
- Adrienne Pichon, Columbia University, Department of Biomedical Informatics
Examining Oral Anti-Cancer Medication Continuation Using Questionnaires, Prescription Refills, and Structured Electronic Health Records
Medication persistence is essential for the efficacy of treatment and patient health outcomes. This study investigates the discontinuation of oral anticancer medications (capecitabine, ibrutinib, or sunitinib) in a cohort that is well-characterized by medication discontinuation questionnaires, prescription refill data, and structured electronic health records (EHRs). We categorized discontinuation reasons based on the questionnaire of patients taking medication, revealing that 38% of 257 patients completed therapy, while discontinuation was due primarily to no response to therapy and/or progression of disease leading to discontinuation (33%) and side effects/complication (9%). Survival analysis showed variable medication persistence, with capecitabine persistence decreasing significantly over time, to 0.08 in two years. A logistic regression model demonstrated strong capability (with an AUC of 0.835) to identify patients at risk for medication discontinuation. The study shows the complexities of medication persistence and emphasizes the importance of understanding medication discontinuation patterns and leveraging predictive analytics to inform future research and clinical monitoring in the treatment of cancer.
Learning Outcomes
- Understand the primary reasons for discontinuation of oral anticancer medications and learn how predictive models can help identify patients at risk for early medication cessation.
Speaker
- Congning Ni, Ph.D. student, Vanderbilt University
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.25 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
Nurses
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation.
- Approved Contact Hours: 1.25 participant maximum
- Nurse planner for this activity: Jenna Thate, PhD, RN, CNE
- Jenna Thate discloses that she has no financial relationships with ACCME/ANCC-defined ineligible companies.
Upon completion of each video and corresponding evaluation portion of this activity, all learners will be able to download the appropriate credit certificate, or a certificate of participation.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.