Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2023A standardized workflow for long-term longitudinal actigraphy data processing: One year of continuous actigraphy from the CAN-BIND Wellness Monitoring Studycitations
  • 2021Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician–patient interaction48citations

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Li, Qingqin S.
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Placenza, Franca
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Minarik, Anna
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Turecki, Gustavo
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Ho, Keith
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Co-Authors (by relevance)

  • Li, Qingqin S.
  • Placenza, Franca
  • Uher, Rudolf
  • Lukus, Patricia K.
  • Minarik, Anna
  • Daros, Alexander R.
  • Slyepchenko, Anastasiya
  • Hassel, Stefanie
  • Turecki, Gustavo
  • Ho, Keith
  • Soares, Claudio N.
  • Milev, Roumen
  • Kennedy, Sidney H.
  • Müller, Daniel J.
  • Quilty, Lena C.
  • Taylor, Valerie H.
  • Rotzinger, Susan
  • Frey, Benicio N.
  • Lam, Raymond W.
  • Foster, Jane A.
  • Matthews, Craig
OrganizationsLocationPeople

article

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician–patient interaction

  • Rosenfeld, Katherine
  • Karp, Jordan F.
  • Martins, Ruben
  • Benrimoh, David
  • Fratila, Robert
  • Linnaranta, Outi
  • Snook, Emily
  • Turecki, Gustavo
  • Israel, Sonia
  • Vahia, Ipsit V.
  • Soufi, Ghassen
  • Margolese, Howard C.
  • Heller, Katherine
  • Wakid, Marina
  • Williams, Jérôme
  • Myhr, Gail
  • Parikh, Sagar V.
  • Miresco, Marc
  • Blumberger, Daniel
  • Karama, Sherif
  • Tunteng, Jingla-Fri
  • Armstrong, Caitrin
  • Rollins, Colleen
  • Cardona, Liliana Gomez
  • Tanguay-Sela, Myriam
  • Popescu, Christina
  • Lundrigan, Eryn
  • Mehltretter, Joseph
  • Perlman, Kelly
  • Vigod, Simone
Abstract

<jats:sec id="S2056472420001271_sec_a1"><jats:title>Background</jats:title><jats:p>Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.</jats:p></jats:sec><jats:sec id="S2056472420001271_sec_a2"><jats:title>Aims</jats:title><jats:p>Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.</jats:p></jats:sec><jats:sec id="S2056472420001271_sec_a3" sec-type="methods"><jats:title>Method</jats:title><jats:p>Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.</jats:p></jats:sec><jats:sec id="S2056472420001271_sec_a4" sec-type="results"><jats:title>Results</jats:title><jats:p>All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.</jats:p></jats:sec><jats:sec id="S2056472420001271_sec_a5" sec-type="conclusions"><jats:title>Conclusions</jats:title><jats:p>The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.</jats:p></jats:sec>

Topics
  • impedance spectroscopy
  • simulation
  • size-exclusion chromatography