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  • Abstracts for Presentation and Poster

    Plenary Sessions (Days 1 and 2)

    Day 1 - Plenary Session #1

    Complexity of Decision-Making in Surgical Care Provided in Patient Portals

    Authors: Jamie R Robinson1, Alissa Valentine2, Cathy Carney1, Daniel Fabbri1, Gretchen P. Jackson1, 1.

    Vanderbilt University, 2. Vanderbilt University Medical Center

    Abstract: Messages exchanged between patients and providers through patient portals contain diverse

    types of communications. The types of communications and complexity of medical decision-making in

    portal messages sent to surgeons has not been studied. We obtained all message threads initiated by

    patients and exchanged with surgical providers through the Vanderbilt University Medical Center patient

    portal from June 1 to December 31, 2014. Five hundred randomly selected messages were manually

    analyzed. In total, 9,408 message threads were sent to 401 surgical providers during the study period. In

    the 500 threads selected for detailed analysis, 453 message threads (90.6%) involved medical needs or

    communications. Further, 339 (67.8%) of message threads contained medical decision-making. The

    overall complexity of medical decision-making was straightforward in 210 threads (62%), low in 102

    threads (30%), and moderate in 27 threads (8%). No highly complex decisions were made over portal

    messaging. Through patient portal messages, surgeons deliver substantial medical care with varied levels

    of medical complexity. Models for compensation of online care must be developed as consumer and

    surgeon adoption of these technologies increases.

    Longitudinal Changes in Psychological States in Online Health Community

    Members: Understanding the Long-Term Effects of Participating in Online

    Depression Health Community

    Authors: Albert Park, Mike Conway, University of Utah

    Abstract: Online health communities have shown the potential to reduce the symptoms of depression,

    however, emotional contagion theory suggests that negative emotion can spread within a community and

    prolonged interactions with other depressed individuals has potential to worsen the symptoms of

    depression. We investigate longitudinal changes in psychological states that are manifested through

    linguistic changes in depression community members who are interacting with other depressed

    individuals. We examine emotion-related language usages using the Linguistic Inquiry and Word Count

    (LIWC) program for each member of a depression community from Reddit. To measure the changes, we

    applied the linear least-squares regression to the LIWC scores against the interaction sequence for each

    member. We measured the differences in linguistic changes against three online health communities

    focusing on positive emotion, diabetes, and irritable bowel syndrome from Reddit as control for

    comparison purpose only. On average, members of an online depression community showed

    improvement in 9 of 10 pre-specified linguistic dimensions. Moreover, these members improved either

    significantly or at least as much as members of other online health communities. These results are an important step towards providing insights into the long-term psychosocial well-being of members.

  • Visual Analytics Approach for Identifying Diabetes Self-Management Trends

    Using Patient-Generated Data

    Authors: Daniel Feller, Lena Mamykina, PhD, Columbia University

    Abstract: Mobile health technologies have generated vast amounts of patient-generated data (PGD)

    raising significant concerns regarding information overload among clinicians. We developed a visual

    analytics tool to reveal patterns of glycemic response in diabetes self-monitoring data.

    Using participatory design, we developed Glucotype, an interactive tool utilizing hierarchical clustering

    and heatmap visualization to allow dietitians to visually inspect patterns of associations between nutrition

    in meals and blood glucose levels. Ten registered dietitians evaluated the interactive tool in comparison to

    a static tabular representation of 1 month of diabetes self-monitoring data. The ‘think-aloud’ protocol was

    employed to compare approaches to data analysis across conditions.

    Observations generated using Glucotype were more detailed and numerous compared to those generated

    by a static HTML-based representation. Participants using the HTML-based representation reported being

    overwhelmed by the volume of PGD and often based their conclusions on a small sample of meals. 9/10

    participants considered hierarchical clustering helpful in revealing patterns of glycemic response.

    Our findings suggest that visual analytics and computational learning can support the meaningful use of

    patient-generated data in diabetes. Interfaces that allow clinicians to filter and manipulate large amounts

    of patient-generated data may enhance user engagement and limit the cognitive biases resulting from

    information overload.

    Using Modern Optical See-Through Augmented Reality (OSTAR) to Improve

    Medical Practices

    Author: Ryan C James, University of Washington

    Abstract: Medical images are the cornerstone of pre-procedural planning and surgical guidance.

    However, these images are viewed in two-dimensions (2D), which limits comprehension of the true 3-

    dimensional (3D) structure for the majority of viewers. Research has demonstrated that stereoscopic

    displays can improve comprehension, leading to clearer delineation of near and far structures, improved

    identification of tumours, more accurate preparation for complex operations, and improved navigation of

    surgical tools. However, prior research is just the tip of the iceberg, and not enough to safely integrate

    optical see-through augmented reality (OSTAR), a relatively new and less-studied type of stereoscopic

    display, into medical workflows.

    We hypothesize that OSTAR will improve the interpretation of medical imaging data. To test this

    hypothesis, we will 1) develop an application that displays medical imaging data as truly 3D holograms

    seen through an OSTAR head mounted display (HMD), 2) evaluate OSTAR’s effect on decision making

    by conducting a comparative study with 6-8 interventional cardiologists (ICs) who plan with and without

    the developed application, and 3) understand OSTAR’s potential influence on surgical guidance by

    developing an OSTAR surgical guidance system, then formatively evaluating it while 6-8 ICs use it to

    perform a transseptal puncture (TS).

  • Day 1 Plenary Session #2

    Integrative Cancer Patient Stratification via Subspace Merging

    Authors: Michael Sharpnack, The Ohio State University

    Abstract: Technologies that generate high-throughput 'omics data are flourishing, creating enormous,

    publicly available repositories of multi-omics data. As many data repositories continue to grow, there is

    an urgent need for computational methods that can leverage these data to create comprehensive clusters of

    patients with a given disease. Our proposed approach creates a patient-to-patient similarity graph for each

    data type as an intermediate representation of each omics data type and merges the graphs through

    subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more

    informative clusters by preserving the complementary information from each level of 'omics data. We

    applied our approach to a TCGA breast cancer data set and show that by integrating gene expression,

    microRNA, and DNA methylation data, our proposed method can produce clinically useful subtypes of

    breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a

    highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA

    breast cancer patients and validate these results in three additional breast cancer datasets. We also

    compare our approach with previous integrative clustering approaches and obtain comparable or superior

    results.

    Novel Pedigree Analysis Implicates DNA Repair and Chromatin Remodeling

    in MM Risk

    Authors: Rosalie G Waller, Todd M. Darlington, Myke Madsen, Karen Curtin, Nicola J. Camp,

    University of Utah

    Abstract: In an age of precision medicine, understanding the genetic factors of common, complex

    disease is paramount. Missing heritability exists for the majority of complex disease; rare, risk-variants

    have been suggested as a missing source. Identifying these variants remains a challenge in complex

    disease due to genetic heterogeneity and complex inheritance models. To address these issues, we

    developed a new high-risk pedigree (HRP) strategy that identifies segregating segments. These segments

    capture inherited coding variants, and non-coding variants important for mapping regulatory risk. We

    applied this strategy in 11 large, multiple myeloma (MM) HRPs, with subsequent exome sequencing in

    the 11 HRPs and 57 smaller pedigrees from a collaborative resource. One genome-wide significant, 1.8

    Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious

    variants in USP45, a gene known to i