Leadership Auditorium, 2501 Student Center
Using Social Network Analysis to Describe Communication Practices in Healthcare
The Electronic Medical Record (EMR) serves different purposes including documentation of care and billing. The EMR is used to document care delivery, monitor ongoing clinical conditions, and it is also the repository of the patients’ healthcare story. Many people, including healthcare providers, nurses, social workers, therapists, office staff, and nurse care managers (known as the interdisciplinary team, or IDT) work together to deliver healthcare. Members of the IDT use the secure messaging application “Message Center” via the EMR to communicate with, and receive communication from, patients via eHealth (patient portal). An essential part of care coordination is communication, and an analysis of social networks of care providers offers insight into the complexity of care coordination. This research aims to describe how the EMR is used in primary care by nurse care coordinators and other actors across settings to document care coordination activities. Social Network Analysis is used here to expose, map and extract the communication between different users of the EMR.
2206A Student Center
Challenges for the analysis of healthcare reports using natural language processing
Healthcare professionals generate, transmit, and store healthcare records as free-text documents these are the traditional“physician’s reports” or “physician’s notes”. These reports contain complex biomedical data, demographic information, location data, etc. However, free text data are a poor starting point for complex data management, aggregation and processing tasks with computational models. For data-based applications, information from healthcare reports, biomedical tests, radiology impressions and the like should be available in discrete and machine-processable form. Natural language processing (NLP), a subfield of artificial intelligence, includes techniques for manipulating and interpreting free text data for analyses with computers. Here, we briefly discuss free-textpreprocessing, an early challenge for the analysis of healthcare reports using NLP.
2206A Student Center
Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials
“Blockchain” is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of Health Information Exchange and clinical trials; conducted design thinking processes with clinicians, trial managers, informaticians, and blockchain professionals; and implemented a blockchain model to tackle known issues. We used coded Smart Contract regulations to simulate several scenarios in healthcare processes. This proof-of-concept work provides a feasible simulation for potential solutions to monitor clinical trials across different census regions persistently. Various levels of data access privileges have been designed to utilize a suite of customized Smart Contract settings. These settings emulate the workflow protocols for the monitoring entities, trial sponsors, clinical sponsors and participating subjects.
Dr. Erin L. CrowgeyDate:
Leadership Auditorium, 2501 MU Student Center
The promise of precision medicine: A focus on genomics and bioinformatics
Chromosomal rearrangements leading to the generation of gene fusions are more common in pediatric malignancies compared to adults and possess diagnostic and prognostic value. Identification of novel gene fusions provides a means for patient stratification and the foundation for the development of targeted therapeutics. The innovations discussed will include: the development of custom Next Generation Sequencing (NGS) gene panels for pediatric leukemias, and a newborn screening assay that detects germline events in high-risk populations.
2501 Student Center, Leadership Auditorium
Development and Application of a high throughput multiomics pipeline to uncover molecular signatures in drought stressed Maize Nodal Roots
One of the major focus areas in agricultural research is reducing the major limiting factor of drought for agricultural production worldwide. In the maize plant, water uptake is mainly acquired by the nodal root system after the seedling stage. These roots grow from multiple stem nodes, initially from below-ground and later from above-ground nodes. In drought conditions, nodal roots have shown to grow through dry topsoil to access water found at significant depth and transport water to other parts. This means these roots are able to grow under low water potential levels which normally inhibit leaf and stem growth. However, the molecular and genetic mechanisms underlying nodal root elongation maintenance in drought conditions and under low water potentials remain largely uncharacterized.
To characterize these mechanisms and signatures – transcriptomics, proteomics and metabolome datasets where generated from nodal root samples from two different genotypes of the maize plant, B73 and FR697, the later demonstrated superior maintenance of nodal root elongation under drought conditions relative to B73. These datasets will be run through a high throughput multiomics pipeline.Multiomics analysis combines omics datasets and different data analysis methods to be used to look at all the signatures together instead of traditional approach of looking at subsets of interesting signatures. Combined with the multiple connected time point datasets, this gives the opportunity to generate highly accurate systems biology maps which can then be used to make models for root elongation and detect unique signatures previously not known to be linked to root elongation. We showcase the progress in the collection of the omics datasets and the development of the multiomics pipeline which is being tested using previously published plant omics data.
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