The project revolves around understanding the impact of the integration of telehealth technologies on the clinician’s workflow is vital for the achievement of a fully functional system. A lack of understanding of such important aspect of the system design could lead to underutilization of the technology, or even rejection, from the clinician’s’ perspective. The application of a systems engineering holistic approach to this problem provides an opportunity to identify ways to minimize the clinician workflow disruption while efficiently communicating the data collected from the telehealth technologies. Additionally, it aids in synthesizing the clinician’s perspectives into a set of requirements for the design of the information visualization displays and interfaces.
Sponsors/ Partners: Coordination Centric
In this project, a means to assess and proactively warn clinicians about a patient’s withdrawal status through the use of wearable smart sensing technology is being developed. By identifying withdrawal markers before the patient is acutely symptomatic the care trajectory can be altered from one of relapse and abuse to recovery and return to a non-opioid dependent life. The rapid response will enable clinicians to assist patients suffering from withdrawal in identifying their condition and administering the appropriate care rapidly, increasing the likelihood of an end to the patient’s opioid dependency.
Sponsors/ Partners: Houston Methodist Hospital
Drowsy Driving Among Shift Work Nurses
This research utilizes a user centered approach to develop educational and technological interventions to prevent drowsy driving among shift work nurses. Moreover, it involves a naturalistic driving study and analyzes data gathered from the study in order to develop algorithms that can detect drowsy driving.
Sponsors/ Partners: Human Factors and Machine Learning (HFML) Lab; Houston Methodist Research Institute, National Safety Council (NSC),Houston Methodist Research Institute (HMRI), Road to Zero Safe Systems Innovation Grant
PTSD Smart Systems
In this project, an easy-to-use and efficient PTSD information system is being developed. Biometric data will be collected using a sensor-based mobile app designed for veterans and then be presented to clinicians to provide them with additional information about a patient’s therapeutic progress. The system will provide sensor-based monitoring of important mental state information and will help veterans to communicate patients’ key mental state changes, thus enabling clinicians to access and monitor patients by presenting them with information that supports their work and meets their expectation.
Sponsors/Partners: Texas A&M Engineering Experiment Station (TEES), Veterans Administration (VA), HERO Trak Inc.
Nursing Smart Systems
This project is centered around design of a smart connected system to help nurses who experience high workload and stress. The tool uses wearable sensors to measure biometric information and infers periods of high workload and stress. This information is then transmitted to other parts of systems such as displays that provide information about availability or interruptability of the nurse. This is a collaborative project with UHCL and Memorial Hermann South Hospital.
Sponsors/Partners: TEES, Memorial Hermann, Baylor Scott & White
Interruption Mitigation in ICU
ICU nurses are often interrupted during high-severity tasks. In this project we are developing interruption mitigation technologies to reduce unnecessary interruptions or interruptions at inopportune times in several ICU settings. Our previous research provides strong support for efficacy of smart systems that improve unit’s awareness of nurses’ task-at-hand and task severity. Using a combination of observational and laboratory studies we are also investigating the effects of interruptions on working memory and task resumption.
Sponsor/Partners: TEES, Baylor Scott & White
This research explores how readmission decisions are made in a large hospital. We are evaluating technological mitigations or redesign of the system to support readmission decision-making and to assure the most appropriate level of care for patients. Some health systems have developed ‘ED rerouting’ initiatives to urgent care centers as part of addressing this problem, but access to the most appropriate level of care still remains a problem in the U.S.
Sponsor/Partners: NSF, Mainline Health
Team Decision-Making in Emergency Response
This collaborative project between ACE-lab, School of Public Health, Mary Kay O’Connor Process Safety Center, and TAMU Emergency Operations Training Center aims at investigating how team decisions are made in a complex emergency response high-fidelity simulator to inform the design of technologies and methodologies that support team cognition.
Sponsor/Partners: NSF, Mary Kay O’Connor Process Safety Center (MKOPSC)
Smart Hypoglycemia Support Tool
This project investigates the efficacy of a novel hypoglycemic detection and warning system. The system involves a secure, non-invasive wearable proactive technology that detects early onsets of hypoglycemic tremors. This collaborative project involves research teams from Industrial and System Engineering, Center for Remote Health Technologies and Systems, and School of Public Health at Texas A&M University as well as Sidra Hospital and Texas A&M University at Qatar.
Sponsor/Partners: Qatar National Research Foundation (QNRF), Sidra Hospital
Process Control Accident Visualization
Process control accidents documentations are extremely voluminous. A nuclear accident documentation may include hundreds of pages qualitative information that are maintained in databases that are often non-searchable or suffer from poor usability. In this project, we investigate visualization of accidents using network theory. Accident visual networks’ emergent properties would simplify understanding the main contributors and interactions between events.
Resilient Human factors Engineering
In order to cope with increasing complexity and business pressure of modern complex systems, this research project examines principles and methods for enhancing resilient performance such as early hazard detection, continuous adaptation to emerging changes and quick recovery from disruptions. This project is a multidisciplinary effort that involves ACE-lab, School of Public Health and Mary Kay O’Connor Process Safety Center. This research is conducted in the domain of large-scale emergency management simulation operated by TEEX (Texas A&M Engineering Extension Service).
Sponsor/Partners: NSF, Mary Kay O’Connor Process Safety Center (MKOPSC)
Supervisory-level Decision Support
supervisors in in complex control room settings such as emergency response, space operations, and command and control are not only in charge of assessing the “mission” but also monitoring the performance of personnel and making tactical decisions. This project involves the design of supervisory-level displays to support detecting the changes and maintaining situation awareness (SA) of an ongoing mission.
Remote Health Technologies for Pediatric Population
Remote health technologies vary in terms of cost, patient adherence and utility, and effectiveness in terms of implementation success, desired health outcomes, and impact on capacity. The multi-institutional research project will explore the benefits of patient monitoring in various settings, including complex pediatric patients and patients with chronic diseases in the U.S.
Sponsor/Partners: NSF, Texas Children’s Hospital