Description

An onsite recording of this session will be included in the Symposium OnDemand offering. 

Exceptional Jo: A Semi-Automated and Scalable System to Share Personalized Patient Positive Feedback with Employees

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Bioinformatics, Patient Engagement and Preferences, Data Sharing, Information Extraction
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

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.

Speaker(s):
Peyton Larson, MPA
Vanderbilt University Medical Center

Author(s):
Daniel Fabbri, PhD - Vanderbilt; Brian Carlson, MBA, MHSA - Vanderbilt University Medical Center; Peyton Larson, MPA - Vanderbilt University Medical Center;

Concordance Between Electronic Health Record Data and Patient Recall of Disease and Treatment Details

Presentation Time: 03:45 PM - 04:00 PM

Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Information Retrieval, Real-World Evidence Generation
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics

We aimed to assess the concordance between self-reported recall of disease history (prior treatment and diagnostic prostate specific antigen [PSA]) and Electronic Health Records (EHRs) in patients with prostate cancer using TrueNTH’s Community of Wellness. Sixty patients were included. Among patients with complete questionnaire and EHR data, there was strong concordance between recall and EHR for prior treatment (up to 82%), and PSA (83%). EHR had a substantial number of missing diagnostic PSA values (86%).

Speaker(s):
Ali Sabbagh, MD
University of California San Francisco

Author(s):
Ali Sabbagh, MD - University of California San Francisco; Isabel Friesner, BA - University of Colorado; Kerri Winters-Stone, PhD - Oregon Health & Science University; Anobel Odisho, MD, MPH - University of California, San Francisco; Rebecca Graff, ScD - University of California San Francisco; Erin Van Blarigan, ScD - University of California San Francisco; Stacey Kenfield, ScD - University of California San Francisco; June Chan, ScD - University of California San Francisco; Julian Hong, M.D., M.S. - UCSF;

Configure or Integrate? Tradeoffs for Remote Symptom Monitoring Innovation with Electronic Health Records

Presentation Time: 04:00 PM - 04:15 PM

Abstract Keywords: Interoperability and Health Information Exchange, Informatics Implementation, Usability
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

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.

Speaker(s):
Robert Rudin
RAND Corporation

Author(s):
Robert Rudin - RAND Corporation; Erick Hinson, MS - Reliant Medical Group; Wilson Pace, MD - University of Colorado; Lawrence Garber, MD - Reliant Medical Group;

A Case Study of Digital Phenotyping in a Large Integrated Healthcare System: An Evaluation of Veterans Sharing Unsolicited Patient-Generated Health Data

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Personal Health Informatics, Real-World Evidence Generation, Mobile Health
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

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.

Speaker(s):
Mark Zocchi, PhD
Veterans Health Administration

Author(s):
Stephanie Shimada, PhD - Department of Veterans Affairs; Felicia Bixler, MS - Department of Veteran Affairs; Saige Calkins, MA - Department of Veterans Affairs; Bella Etingen, PhD - Department of Veterans Affairs; Timothy Hogan, PhD - Department of Veterans Affairs; Jessica Lipschitz, PhD - Bringham and Women's Hospital; Ndindam Ndiwane, MPH - Department of Veterans Affairs; Stephanie Robinson, PhD - Department of Veterans Affairs; Nilesh Shah, MD - Department of Veterans Affairs; Terry Newton, MD - Department of Veterans Affairs;

Learning Interpretable, Temporal Health Status Phenotypes from Self-Tracked Patient Data

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Personal Health Informatics, Patient / Person Generated Health Data (Patient Reported Outcomes), Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics

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.

Speaker(s):
Adrienne Pichon
Columbia University, Department of Biomedical Informatics

Author(s):
Adrienne Pichon - Columbia University, Department of Biomedical Informatics; Noemie Elhadad, PhD - Columbia University;

Examining Oral Anti-Cancer Medication Continuation Using Questionnaires, Prescription Refills, and Structured Electronic Health Records

Presentation Time: 04:45 PM - 05:00 PM

Abstract Keywords: Machine Learning, Surveys and Needs Analysis, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications

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.

Speaker(s):
Congning Ni, Ph.D. student
Vanderbilt University

Author(s):
Congning Ni, Ph.D. student - Vanderbilt University; Qingyuan Song, Master of Engineering - Vanderbilt University; Jeremy Warner, MD, MS - Brown University; Qingxia Chen, PhD - Vanderbilt University Medical Center; Lijun Song, Ph.D. - Vanderbilt University; S. Trent Rosenbloom, MD, MPH - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center; Zhijun Yin, Ph.D. - Vanderbilt University Medical Center;