Natural-Setting PHR Usability Evaluation using the NASA TLX to Measure Cognitive Load of Patients

While personal health records (PHRs) carry an array of potential benefits such as increased patient engagement, poor usability remains a significant barrier to patients’ adoption of PHRs. In this mixed methods study, we evaluate the usability of one PHR feature, an intake form called the pre-visit summary, from the perspective of cognitive load using real cardiovascular patients in a natural setting. A validated measure for cognitive load, the NASA Task Load Index, was used along with retrospective interviews to identify tasks within the pre-visit summary that increased participants’ cognitive load. We found that the medications, immunizations, active health concerns, and family history pages induced a higher cognitive load because participants struggled to recall personal health information and also due to user interface design issues. This research is significant in that it uses validated measures of cognitive load to study real patients interacting with their PHR in a natural environment

Identification of genes involved in diauxic shift of saccharomyces cerevisiae through gateway node analysis.

The use of high-throughput assays, or experiments yielding large data sets, in biological research has become a standard practice in laboratories throughout the world. Because such investigations have the ability to produce high volume and comprehensive data sets, it is then important to develop methods that allow researchers to quickly pull meaningful information from an overwhelming amount of data. Network modeling has become a popular technique for visualizing and analyzing large biological data sets. A network is a basic graph with nodes and edges (i.e. social networks) that also integrates complex principles of graph theory for deeper analysis and pattern discovery. In biological research, networks have successfully modeled protein interactions within a cell, gene expression rates, and the correlation or relationships between gene expression when zooming into a specific biological pathway or process. In my research, I have collected gene expression data for network modeling and pattern discovery in the hope of identifying key genes involved in the shift that yeast undergo from active proliferation and development to a state of dormancy. Using a method called gateway node analysis, I am aiming to analyze gene expression networks for dense regions, or clusters, which may represent genes under similar gene expression regulation. Then, gateway node analysis will allow me to predict specific genes that may be responsible or important for this shift. This analysis technique, once further validated, could serve to predict genes involved in many biological pathways for disease research and other medical applications

Identification of Genes Involved in Diauxic Shift of Saccharomyces cerevisiae through Gateway Node Analysis

The use of high-throughput assays, or experiments yielding large data sets, in biological research has become a standard practice in laboratories throughout the world. Because such investigations have the ability to produce high volume and comprehensive data sets, it is then important to develop methods that allow researchers to pull meaningful information from an overwhelming amount of data. Network modeling has become a popular technique for visualizing and analyzing large biological data sets. A network is a basic graph with nodes and edges (i.e. social networks) that also integrates complex principles of graph theory for deeper analysis and pattern discovery. In biological research, networks have successfully modeled protein interactions within a cell, gene expression rates, and the correlation or relationships between gene expression when zooming into a specific biological pathway or process. In my research, I have collected gene expression data for network modeling and pattern discovery in the hope of identifying key genes involved in the shift that yeast undergo from active proliferation and development to a state of dormancy, or the diauxic shift. Using a method called gateway node analysis, I am aiming to analyze gene expression networks for dense regions, or clusters, which may elude to genes under similar gene expression regulation. Then, gateway node analysis will allow me to predict specific genes that may be responsible or important for this shift. This analysis technique, once further validated, could serve to predict genes involved in many biological pathways for disease research and other medical applications

Measuring cognitive load using eye tracking to evaluate the usability of an electronic patient intake form

Personal health records (PHRs) provide patients secure and confidential access to their personal health information and carry an array of potential benefits such as increased patient engagement and improved health outcomes. Unfortunately, poor system usability has been recognized to be a significant barrier to patients’ adoption and use of PHRs. In this mixed methods research study, we will evaluate the usability of an online patient intake form intended to be part of cardiovascular patients’ PHR at Nebraska Medicine. Usability will be assessed from the perspective of cognitive load using validated measures including eye tracking and the NASA Task Load Index questionnaire. Both eye tracking data and subjective questionnaires have been chosen to be used in tandem in order to support the predicted cognitive load with objective, real-time and subjective, post-hoc data. This will provide deeper, more accurate measurements of cognitive load. The System Usability Scale will then be administered, which is a valid and reliable tool for measuring overall system usability. These data will be integrated with qualitative interview responses to further explain usability issues and support conclusions drawn from the cognitive load measurements. To complete this exploratory study, a goal of 15 cardiovascular patients from Nebraska Medicine will be recruited. The results of this research will inform efforts for improving the usability of this online patient intake form and will serve as guidance for future usability studies of health information technology systems

Understanding System-induced Cognitive Load with Eye Tracking

Cognitive load is one important contributor to system usability, and minimizing unnecessary cognitive load can help users better process system information. Cognitive load theory, an educational theory, defines three types of cognitive load: intrinsic, extraneous, and germane (Sweller et al. 1998). Intrinsic cognitive load is a necessary part of completing a task. Extraneous cognitive load is introduced by the format and presentation of material, and can be minimized with design help. Germane cognitive load is the effect of the effort learners put into understanding. Important to our study here are intrinsic and extraneous cognitive load. Systems can be designed to minimize the negative influences of extraneous cognitive load. Using eye tracking, we can understand how system design influences cognitive load, and correlating with reported cognitive load can clarify differences between extraneous and intrinsic cognitive load in system design. We have designed a novel personal health record pre-visit form. Data from the pre-visit form helps the healthcare team understand the patient’s current status and needs, and ensures needs are met. The system consists of 10 sequential screens where patients input their information such as past surgical history, medications, allergies, and family history. As a part of the development of the system, we have conducted usability tests with 32 adult cardiovascular patients from the University of Nebraska Medical Center’s Heart and Vascular Center. Patients used the pre-visit form to enter their personal health information. After each screen, patients filled out measures of cognitive load using the NASA Task Load Index (TLX). Throughout the interaction, their gaze was tracked using an EyeTech eye tracker. Using the combination of self-reported cognitive load (e.g. NASA TLX) during system interaction and eye tracking, we will gain an understanding of how gaze behavior is influenced by cognitive load. Our initial analysis of the TLX data has shown that cognitive load varies depending on the type of information requested from patients, and that tasks requiring significant information recall create cognitive load in patients (Pachunka et al. 2019). By combining this information with our eye tracking data, we will be able to better understand how system design and cognitive load influence gaze behavior