PSM in Biomedical and Health Informatics Program – Internship Presentations

Students in the Professional Science Master’s in Biomedical and Health Informatics Program complete an internship that synthesizes knowledge gleaned from the program curriculum. The purpose of the internship is to expand classroom learning to include “hands-on” experience in health IT.

In this occasion Trusha Taneja, Farrah Hermes and Halina Krzystek will be presenting their internship projects via Zoom.

Tiltles and abstracts below:

A Socio-Technical Assessment of Hospital Systems to Improve Provider Well-Being using Contextual Inquiry

Presenter: Trusha Taneja

Abstract: Physician burden and burnout are at epidemic proportions. This study explores causes and contributors of workplace stress and provider burden, with a specific focus on technological factors, including the use of electronic health records. The purpose of this project was to identify system breakdowns that cause provider burnout and propose solutions to overcome technological barriers to provider well-being.

Digital Health Transformation + Community Discovery 

Presenter: Farrah Hermes

Abstract: A look into the next generation of healthcare that utilized cloud technology, heatlh informatics and social determinates of health through digital platforms to tell a meaningful story about community and the individuals within them. By making meaningful connections through community discovery and outreach creates a better understanding of the pain points and strengths of the communities we are trying to serve as healthcare professionals and this approach is not always used which creates gaps. At the Digital Health Institute of Transformation (DHIT), our team focused on three counties in Eastern North Carolina to learn more about how the digital transformation played a role within their communities. With public libraries as the main access point for information, we conducted community discovery sessions by having informational interviews with librarians within these counties and a few in Western North Carolina to gain an understanding of their current state and where they would like to be as a community. These efforts helped define a plan of action for the Community Health Utility Grid (HUG) that will be used to better serve these communities.

Part I: An Evaluation of Copy Number Variant Calling Algorithms for a Clinical Genomics Pipeline Using Exome Sequencing

Part II: The Application of Machine Learning Clustering on MicroRNAs as a Quality Analysis and Control Tool for Large Cancer Genomics Projects

Presenter: Halina Krzystek

Abstract:

Part I-

The NCGENES2 Project at UNC aims to generate evidence for the use of exome sequencing as a first-line diagnostic tool. A source of significant genetic variation is the larger copy number variants (CNVs). My project evaluated CNV-calling computational methods available for exome sequencing, determined which were appropriate for NCGENES2 pipeline, and compared their performance on test data. The results suggest that integrating multiple callers into the pipeline and requiring a CNV call to be confirmed by the pipeline’s current CNV calling tool, ExomeDepth, plus at least one additional caller, had the least tradeoff between sensitivity and decreasing the number of false positives. The results of my project suggest integrating multiple CNV algorithms into the pipeline.

Part II-

MicroRNAs are small non-coding RNAs that play a significant role in cancer. The machine learning approach of clustering has been applied to gene expression in cancer genomics but its application to microRNAs is still new. My project created a microRNA clustering pipeline and evaluated it as a QA/QC tool on retrospective and pilot data from large cancer genomics projects which UNC’s HTSF (High Throughput Sequencing Facility) is contracted for by the National Cancer Institute at the NIH.  My project showed that certain tumor types, like Amyloid Leukemia and Glioblastoma, clustered well based on microRNA expression, and that normal adjacent samples clustered separately from tumor samples. The results are promising and HTSF plans to incorporate my clustering pipeline into the QA/QC pipeline for its cancer genomics projects.