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(searched for: Developing a Shared Mental Model of Team Functioning in the Context of Research)
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Sciprofile linkSimeone Marino, Yi Zhao, Nina Zhou, Yiwang Zhou, Arthur W. Toga, Lu Zhao, Yingsi Jian, Yichen Yang, Yehu Chen, Qiucheng Wu, et al.
Published: 28 August 2020
PLoS ONE, Volume 15; doi:10.1371/journal.pone.0228520

Abstract:
Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA (1) identifies salient features and key biomarkers enabling reliable and reproducible forecasting of binary, multinomial and continuous outcomes (i.e., feature mining); and (2) suggests the most accurate algorithms/models for predictive analytics of the observed data (i.e., model mining). The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions for observed univariate outcomes. The novelty of this study is highlighted by a new and expanded set of CBDA features including (1) efficiently handling extremely large datasets (>100,000 cases and >1,000 features); (2) generalizing the internal and external validation steps; (3) expanding the set of base-learners for joint ensemble prediction; (4) introducing an automated selection of CBDA specifications; and (5) providing mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. To ground the mathematical model and the corresponding computational algorithm, CBDA 2.0 validation utilizes synthetic datasets as well as a population-wide census-like study. Specifically, an empirical validation of the CBDA technique is based on a translational health research using a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, variable heterogeneity, feature multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions. Our results show the scalability, efficiency, and usability of CBDA to interrogate complex data into structural information leading to derived knowledge and translational action. Applying CBDA 2.0 to the UK Biobank case-study allows predicting various outcomes of interest, e.g., mood disorders and irritability, and suggests new and exciting avenues of evidence-based research in the context of identifying, tracking, and treating mental health and aging-related diseases. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.
John Vonrosenberg
Published: 1 July 2020
Air Medical Journal, Volume 39; doi:10.1016/j.amj.2020.05.004

The publisher has not yet granted permission to display this abstract.
Frode Heldal, Endre Sjøvold, Kenneth Stålsett
Team Performance Management: An International Journal, Volume 26, pp 211-226; doi:10.1108/tpm-06-2019-0051

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Sciprofile linkSimeone Marino, Yi Zhao, Sciprofile linkNina Zhou, Sciprofile linkYiwang Zhou, Sciprofile linkArthur W. Toga, Sciprofile linkLu Zhao, Yingsi Jian, Yichen Yang, Sciprofile linkYehu Chen, Qiucheng Wu, et al.
Published: 20 January 2020
Abstract:
Health advances are contingent on continuous development of new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences. Open-science offers hope for tackling some of the challenges associated with Big Data and team-based scientific discovery. Domain-independent reproducibility, area-specific replicability, curation, analysis, organization, management and sharing of health-related digital objects are critical components.This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA identifies salient features and key biomarkers for reliable and reproducible forecasting of binary or multinomial outcomes. The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions of observed univariate outcomes. In this manuscript, we extend the CBDA technique by (1) efficiently handling extremely large datasets, (2) generalizing the internal and external validation steps, (3) expanding the set of base-learners for joint ensemble prediction, (4) introduce an automated selection of CBDA specifications, and (5) provide mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency.We validated the CBDA 2.0 technique using synthetic datasets as well as a population-wide census-like study, which grounds the mathematical models and the computational algorithm into translational health research settings. Specifically, we empirically validated the CBDA technique on a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, feature heterogeneity, multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions that require novel health analytical approaches.Our results showcase the scalability, efficiency and potential of CBDA to compress complex data into structural information leading to derived knowledge and translational action. The results of the real case-study suggest new and exciting avenues of research in the context of identifying, tracking, and treating mental health and aging-related disorders. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.
Dr. Sarah Gehlert
Journal of Psychiatry and Psychiatric Disorders, Volume 4, pp 74-81; doi:10.26502/jppd.2572-519x0092

Abstract:
Despite the fact that the number of disciplines and specializations in universities has burgeoned over time, education and training remain discipline-specific. Consequently, investigators attempting to address complex problems like the prevalence and severity of psychiatric disorders are hampered by the absence of a template for working together to capture the complex, and often multi-level, nature of these problems and turn their jointly-acquired knowledge into practice and policy solutions. Effective team functioning relies on the ability of investigators from different backgrounds to communicate with one another, understand one another’s work, and develop a shared identity, mission, and goals. This is facilitated by the development of a shared mental model of the research endeavor. Here, after describing various modes of disciplinary collaboration, we describe the development of such a shared mental model in one site of a transdisciplinary team-based research initiative of the U.S. National Institutes of Health. This site that was located at the University of Chicago included investigators from the social, behavioral, and biological sciences, some of whom were clinician-scientists. We describe the stages of the team’s development and mechanisms that were used successfully to create a shared mental model. This model took individual investigators outside their home disciplines to create new intellectual space and provided evidence of its success in achieving transdisciplinary functioning. We also provide evidence that the approach increased the item’s ability to work collaboratively.
Strategies for Team Science Success pp 401-406; doi:10.1007/978-3-030-20992-6_30

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Sciprofile linkChristina Hackett, Kelsey Brennan, Heather Smith Fowler, Chad Leaver, Tracie Risling, Joanne Maxwell, Colene Byrne
Journal of Medical Internet Research, Volume 21; doi:10.2196/12277

Abstract:
In publicly funded health systems, digital health technologies are strategies that aim to improve the quality and safety of health care service delivery and enhance patient experiences and outcomes. In Canada, governments and health organizations have invested in digital health technologies such as personal health records (PHRs) and other electronic service functionalities and innovation across provincial and territorial health systems. Patients' access to their own information via secure, Web-based PHRs and integrated virtual care services are promising mechanisms for supporting patient engagement in health care. We draw on current evidence to develop an economic model that estimates the demonstrated and potential value of these digital health initiatives. We first synthesized results from a variety of Canadian and international studies on the outcomes for patients and service providers associated with PHRs across a continuum of services, ranging from viewing information (eg, laboratory results) on the Web to electronic prescription renewal to email or video conferencing with care teams and providers. We then developed a quantitative model of estimated value, grounded in these demonstrated benefits and citizen use (2016-2017). In addition to estimating the costs saved from patient and system perspectives, we used a novel application of a compensating differential approach to assess the value (independent of costs) to society of improved health and well-being resulting from PHR use. Patients' access to a range of digital PHR functions generated value for Canadians and health systems by increasing health system productivity, and improving access to and quality of health care provided. As opportunities increased to interact and engage with health care providers via PHR functions, the marginal value generated by utilization of PHR functionalities also increased. Web-based prescription renewal generated the largest share of the total current value from the patient perspective. From the health systems perspective, Canadians' ability to view their information on the Web was the largest value share. If PHRs were to be implemented with more integrated virtual care services, the value generated from populations with chronic illnesses such as severe and persistent mental illness and diabetes could amount to between Can $800 million and Can $1 billion per year across Canadian health systems. PHRs with higher interactivity could yield substantial potential value from wider implementation in Canada and increased adoption rates in certain target groups-namely, high-frequency health system users and their caregivers. Further research is needed to tie PHR use to health outcomes across PHR functions, care settings, and patient populations.
Business Process Management Forum pp 87-98; doi:10.1007/978-3-030-21297-1_8

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Sciprofile linkJody Hoffer Gittell, Lauren Hajjar
Journal of General Internal Medicine, Volume 34, pp 7-10; doi:10.1007/s11606-019-04996-7

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