Matthias Goerges
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Affiliations to Research Centres, Institutes & Clusters
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Biography
Matthias Görges is a Principal Investigator at the Research Institute, BC Children’s Hospital, where he co-leads the Clinical & Community Data, Analytics & Informatics group. He is a biomedical engineer with extensive clinical research experience, and an Associate Professor (Partner) in the Anesthesiology, Pharmacology & Therapeutics department at the University of British Columbia. As part of an inter-disciplinary team of engineers, computer scientists, and health care providers, he is involved in a wide range of projects focusing on the development and application of new technologies in the pediatric anesthesia and intensive care setting. His research interests are patient monitoring alarms, medical displays, decision support systems, mobile health applications, and data integration/communication platforms. Matthias’ goal is to extract clinically-useful information from vital signs and other clinical data, and to transform these data into information for better, more timely, and more efficient decision-making by clinicians.
Research Methodology
Recruitment
The introduction of information technology in medicine has enabled a transformational change in recent decades. The coronavirus pandemic further accelerated the introduction of virtual care and digital health solutions. Thus, most of my work focuses on designing, implementing, and evaluating ways that leverage technology to improve patient-relevant outcomes.
Clinical context is essential, as understanding the real-world setting where technical innovations are applied is crucial in any engineering discipline. I collaborate with medical experts, including nurses and physicians across several disciplines, engineers, data scientists, and patients and their families, to solve real-world problems. I believe it is essential to appreciate a) that technology supports and enables people, b) we need to integrate data from a wide variety of sources to generate meaningful information to guide decision-making, c) good usability is vital to workflow, and d) high levels of situational awareness are required to ensure patient safety.Ìý
My research program focuses on two key objectives:
- Digital health platforms: Develop a research data integration and exchange platform that leverages data from multiple sources, including patient-generated health data and patient-reported outcomes. It will enable (future) linking against electronic medical record data.
- Applications for predictive analytics: Design and implement outcome prediction pipelines that integrate and analyze clinical, patient-provided, and physiological data and create risk stratification models using statistical and machine learning techniques. This is primarily focusing on pediatric anesthesia and pediatric critical care.
Previously, I also worked on a) data displays for clinical decision support, b) vital signs collection infrastructure, and c) closed-loop control of anesthesia.
I consider trainees withÌýbiomedical engineeringÌýorÌýclinical informaticsÌýbackgrounds, as my program focuses on predictive analytics and digital health applications inÌýBiomedical Engineering. I occasionally recruit trainees inÌýExperimental MedicineÌýfor digital health research projects. Pharmacology is too far from my research interests and core training; therefore,ÌýI do NOT recruit Pharmacology students.Ìý
Next, it is helpful for you to outline which projects you are interested in and why you are an excellent fit for my program. Finally, a CV makes it easier for me to assess your suitability as a candidate, so please include this (and, ideally, a transcript and, if applicable, English test scores) with your application as you contact me.
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Background: Type 1 diabetes (T1D) is associated with various psychological struggles, making the management of this condition even more challenging. Integrating digitally enabled peer support interventions into diabetes care may improve mental health outcomes, such as diabetes distress. Objective: The objectives of this study are three-fold: (1) to examine the impact of digitally enabled peer support interventions on mental health outcomes in people living with type 1 diabetes, (2) to explore the mental health support needs of adolescents with T1D, and (3) to elicit feedback for translating REACHOUT, a mobile app originally designed to deliver peer-led mental health support to T1D adults, to meet the unique needs of adolescents with T1D.Methods: For the first objective, we conducted a comprehensive systematic review of the literature on the impact of digitally enabled peer support platforms on mental health outcomes for individuals living with T1D. For the second and third objectives, we utilized focus groups to explore the mental health support needs of adolescents with T1D (from the perspective of adolescents and their parents) and to determine preferences for translating REACHOUT to NexGEN. Results: In the systematic review, out of 3,623 abstracts screened, 34 studies underwent full-text evaluation, and nine were selected for synthesis. Of these, three showed significant reductions in diabetes distress, and two reported improvements in depression. Key factors linked to these outcomes included participatory intervention development, diabetes education, longer duration, theory-based frameworks, and peer mentor involvement.Participants in the focus group were ten adolescents (16 ± 1 years, 80% female) and ten parents (51 ± 7 years, 90% female). Four core themes (with subthemes) emerged, including 1) Experience: Navigating adolescence with T1D; 2) Empowerment: Support systems that enable better management of their T1D; 3) Obstacles: Societal barriers that affect adolescent’s T1D management; and 4) Innovation: Adolescent-driven preferences for virtual peer support platforms. Conclusion: Understanding T1D adolescents' specific support needs is crucial for addressing their emotional and social challenges. Involving them and their parents in tailoring NexGEN to their diabetes management and developmental needs is key. Future research should pilot NexGEN to assess its feasibility and acceptability before evaluative studies.
Publications
- (2022)
Journal of Clinical Monitoring and Computing, - (2022)
JMIR Pediatrics and Parenting, 5 (1), e34967 - (2022)
JMIR Formative Research, - (2022)
Health Services Insights, - (2022)
JMIR Pediatrics and Parenting, - (2021)
JCI Insight, - (2021)
JMIR Medical Informatics, 9 (8), e24079 - (2021)
Pediatric Anesthesia, - (2021)
Pediatric Anesthesia, 31 (11), 1170--1178 - (2021)
Pediatric Critical Care Medicine, Publi - (2021)
Wellcome Open Research, 4, 121 - (2021)
Journal of Clinical Monitoring and Computing, 35 (3), 557--567 - (2021)
Telemedicine Reports, 2 (1), 108--117 - (2021)
Studies in Health Technology and Informatics, - (2021)
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, - (2021)
Applied clinical informatics, - (2021)
The Spine Journal, 21 (7), 1047--1048 - (2021)
Journal of Clinical Monitoring and Computing, - (2021)
Anesthesia & Analgesia, - (2020)
- (2020)
Current Opinion in Anaesthesiology, , 1 - (2020)
Wellcome Open Research, - (2020)
Canadian Journal of Anesthesia/Journal canadien d'anesthésie, 67 (3), 324--335 - (2020)
PLOS ONE, - (2020)
JMIR Perioperative Medicine, - (2020)
Pediatric Critical Care Medicine, - (2020)
Pediatric Anesthesia, 30 (8), 874--884 - (2020)
Pediatric Anesthesia, - (2020)
Anesthesia & Analgesia, , 1 - (2020)
JMIR Formative Research, - (2020)
Journal of Clinical Monitoring and Computing, 34 (5), 1121--1129 - (2019)
JMIR Perioperative Medicine, - (2019)
Computing in Cardiology, 2019- - (2019)
Wellcome Open Research, 4, 121 - (2019)
- (2019)
JMIR Perioperative Medicine, 2 (2), e13559 - (2019)
Anesthesia and analgesia, - (2019)
Anesthesia and analgesia, - (2019)
Computing in Cardiology, 2019- - Human Computer Interaction Design for Mobile Devices Based on a Smart Healthcare Architecture (2019)
Advances in Computers and Software Engineering: Reviews, , 99--132 - Human Computer Interaction Design for Mobile Devices Based on a Smart Healthcare Architecture (2019)
CoRR, abs/1 - (2019)
Pediatric Anesthesia, 29 (1), 27--37 - (2019)
IEEE Transactions on Biomedical Engineering, , 1--1 - (2019)
IEEE Transactions on Biomedical Engineering, 66 (10), 2918--2923 - (2019)
Pediatric Critical Care Medicine, 20 (8), 790--792 - (2018)
IEEE Journal of Translational Engineering in Health and Medicine, , 1--1 - (2018)
JMIR Medical Informatics, - (2018)
Canadian Journal of Anesthesia/Journal canadien d'anesthésie, 65 (9), 1029--1040 - (2018)
Pediatric Critical Care Medicine, , 1 - (2018)
Journal of Clinical Monitoring and Computing, 32 (6), 1081--1091 - (2018)
Pediatric Anesthesia, - (2018)
Pediatric Critical Care Medicine, , 1 - (2017)
A & A Case Reports, , 1 - (2017)
Anesthesia & Analgesia, , 1 - (2017)
Anesthesia & Analgesia, 124 (5), 1735--1736 - (2017)
Pediatric Anesthesia, 27 (3), 240--247 - (2017)
Canadian Journal of Anesthesia/Journal canadien d'anesthésie, - (2016)
Anesthesia & Analgesia, , 1 - (2016)
Anesthesia & Analgesia, 122 (4), 1132--1140 - (2016)
Pediatric Anesthesia, - (2016)
Pediatric Anesthesia, - (2015)
Pediatric Anesthesia, 25 (12), 1287--1293 - (2014)
- (2014)
IEEE J. Biomed. Health Inform., 18 (6), 1857--1864 - (2014)
- (2014)
- No BP During NIBP (2014)
- (2014)
2014 International Conference on the Internet of Things (IOT), - (2013)
- (2013)
Anesthesia & Analgesia, 117 (2), 380--391 - (2013)
- (2013)
- (2013)
- (2013)
- (2012)
- (2012)
- (2012)
- (2012)
Anesthesia & Analgesia, 114 (3), 584--589 - (2011)
Dimensions of Critical Care Nursing, 30 (4), 206--217 - (2011)
Biomedizinische Technik/Biomedical Engineering, 56 (2), 73--83 - (2010)
- (2010)
- Signal processing, human factors, and modelling to support bedside care in the intensive care unit (2010)
- (2009)
- (2008)
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Partner appointment
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