Browsing by Subject "Data collection"

Now showing 1 - 5 of 5
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    A research definition and framework for acute paediatric critical illness across resource-variable settings: a modified Delphi consensus
    (Elsevier, 2024) Arias, Anita V.; Lintner-Rivera, Michael; Shafi, Nadeem I.; Abbas, Qalab; Abdelhafeez, Abdelhafeez H.; Ali, Muhammad; Ammar, Halaashuor; Anwar, Ali I.; Appiah, John Adabie; Attebery, Jonah E.; Diaz Villalobos, Willmer E.; Ferreira, Daiane; González-Dambrauskas, Sebastián; Habib, Muhammad Irfan; Lee, Jan Hau; Kissoon, Niranjan; Tekleab, Atnafu M.; Molyneux, Elizabeth M.; Morrow, Brenda M.; Nadkarni, Vinay M.; Rivera, Jocelyn; Silvers, Rebecca; Steere, Mardi; Tatay, Daniel; Bhutta, Adnan T.; Kortz, Teresa B.; Agulnik, Asya; Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network on behalf of the PALISI Global Health Subgroup; Pediatrics, School of Medicine
    The true global burden of paediatric critical illness remains unknown. Studies on children with life-threatening conditions are hindered by the absence of a common definition for acute paediatric critical illness (DEFCRIT) that outlines components and attributes of critical illness and does not depend on local capacity to provide critical care. We present an evidence-informed consensus definition and framework for acute paediatric critical illness. DEFCRIT was developed following a scoping review of 29 studies and key concepts identified by an interdisciplinary, international core expert panel (n=24). A modified Delphi process was then done with a panel of multidisciplinary health-care global experts (n=109) until consensus was reached on eight essential attributes and 28 statements as the basis of DEFCRIT. Consensus was reached in two Delphi rounds with an expert retention rate of 89%. The final consensus definition for acute paediatric critical illness is: an infant, child, or adolescent with an illness, injury, or post-operative state that increases the risk for or results in acute physiological instability (abnormal physiological parameters or vital organ dysfunction or failure) or a clinical support requirement (such as frequent or continuous monitoring or time-sensitive interventions) to prevent further deterioration or death. The proposed definition and framework provide the conceptual clarity needed for a unified approach for global research across resource-variable settings. Future work will centre on validating DEFCRIT and determining high priority measures and guidelines for data collection and analysis that will promote its use in research.
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    Evolving availability and standardization of patient attributes for matching
    (Oxford University Press, 2023-10-12) Deng, Yu; Gleason, Lacey P.; Culbertson, Adam; Chen, Xiaotian; Bernstam, Elmer V.; Cullen, Theresa; Gouripeddi, Ramkiran; Harle, Christopher; Hesse, David F.; Kean, Jacob; Lee, John; Magoc, Tanja; Meeker, Daniella; Ong, Toan; Pathak, Jyotishman; Rosenman, Marc; Rusie, Laura K.; Shah, Akash J.; Shi, Lizheng; Thomas, Aaron; Trick, William E.; Grannis, Shaun; Kho, Abel; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.
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    Metrics Toolkit: an online evidence-based resource for navigating the research metrics landscape
    (Medical Library Association, 2018-10) Champieux, Robin; Coates, Heather L.; Konkiel, Stacy; Gutzman, Karen; University Library
    While research metrics may seem well established in the scholarly landscape, it can be challenging to understand how they should be used and how they are calculated. The Metrics Toolkit is an online evidence-based resource for researchers, librarians, evaluators, and administrators in their work to demonstrate or assess the impact of research.
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    Towards Fair Cross-Domain Adaptation via Generative Learning
    (IEEE, 2021) Wang, Tongxin; Ding, Zhengming; Shao, Wei; Tang, Haixu; Huang, Kun; Medicine, School of Medicine
    Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. Existing DA normally assumes the well-labeled source domain is class-wise balanced, which means the size per source class is relatively similar. However, in real-world applications, labeled samples for some categories in the source domain could be extremely few due to the difficulty of data collection and annotation, which leads to decreasing performance over target domain on those few-shot categories. To perform fair cross-domain adaptation and boost the performance on these minority categories, we develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification. Specifically, generative feature augmentation is explored to synthesize effective training data for few-shot source classes, while effective cross-domain alignment aims to adapt knowledge from source to facilitate the target learning. Experimental results on two large cross-domain visual datasets demonstrate the effectiveness of our proposed method on improving both few-shot and overall classification accuracy comparing with the state-of-the-art DA approaches.
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    Understanding Informational Practices and Exploring Data Collection Approaches for Quality of Life in Brain Injury Illness Management
    (2023-07) Masterson, Yamini Lalama Patnaik; Brady, Erin; Miller, Andrew D.; Toscos, Tammy; Hong, Youngbok; Gunter, Tracy D.
    Brain injury, a combination of medical injury, chronic illness, and impairment, affects more than 3.5 million people in the United States every year through an interplay of physiological, psychological, environmental, and cultural factors spanning clinical recovery, illness management, and personal recovery phases. The lack of collaborative and integrated understanding from healthcare and accessibility communities led to treating brain injury as a localized damage rather than individual response to ever-changing impairment and symptoms, focusing primarily on clinical recovery until recently. While self-tracking and management technologies have been widely successful in measuring individual symptoms, they have struggled to facilitate sensemaking and problem solving to achieve a consistent biopsychosocial awareness of illness. My dissertation addresses this gap through three aims: (1) investigate the current informational practices of individuals undergoing post-acute brain injury recovery, (2) explore technology-agnostic approaches for data collection and their impact on sensemaking processes and conceptual understanding of brain injury, and (3) develop guidelines for designing data collection tools that facilitate sensemaking in brain injury self-management. I achieve this through two longitudinal studies – an interview study that introduced participants to the framework on quality of life after traumatic brain injury (QoLIBRI) and a narrative study that used QoLIBRI framework to do structured journaling and co-design individualized data collection tools. The goal of this work is to improve self-awareness of individuals with brain injury enabling them to anticipate or recognize the occurrence of a challenge caused by impairment and then, utilize assistive technologies to bypass the limitation. It also has implications for involving neurodiverse populations in research and technology design.