Participatory methods to support team science development for predictive analytics in health

Armen C Arevian¹, Doug Bell², Mark Kretzman¹, Connie Kasari¹, Shrikanth Narayanan³, Carl Kesselman⁴, Shinyi Wu⁵, Paul Di Capua⁶, William Hsu⁷, Mathew Keener⁸, Joshua Pevnick⁹, Kenneth B Wells¹, Bowen Chung^{1

10}

Affiliations

¹ Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
² General Internal Medicine, Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
⁴ Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA.
⁵ School of Social Work, University of Southern California, Los Angeles, CA, USA.
⁶ Baptist Health Medical Group, Miami, FL, USA.
⁷ Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Blackbird Health, Pittsburgh, PA, USA.
⁹ Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
¹⁰ LA Biomed, University of California, Los Angeles, Los Angeles, CA, USA.

PMID: 30370071
PMCID: PMC6199545
DOI: 10.1017/cts.2018.313

Participatory methods to support team science development for predictive analytics in health

Armen C Arevian et al. J Clin Transl Sci. 2018 Jun.

. 2018 Jun;2(3):178-182.

doi: 10.1017/cts.2018.313. Epub 2018 Aug 30.

Authors

Affiliations

¹ Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
² General Internal Medicine, Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
⁴ Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA.
⁵ School of Social Work, University of Southern California, Los Angeles, CA, USA.
⁶ Baptist Health Medical Group, Miami, FL, USA.
⁷ Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Blackbird Health, Pittsburgh, PA, USA.
⁹ Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
¹⁰ LA Biomed, University of California, Los Angeles, Los Angeles, CA, USA.

PMID: 30370071
PMCID: PMC6199545
DOI: 10.1017/cts.2018.313

Abstract

Predictive analytics in health is a complex, transdisciplinary field requiring collaboration across diverse scientific and stakeholder groups. Pilot implementation of participatory research to foster team science in predictive analytics through a partnered-symposium and funding competition. In total, 85 stakeholders were engaged across diverse translational domains, with a significant increase in perceived importance of early inclusion of patients and communities in research. Participatory research approaches may be an effective model for engaging broad stakeholders in predictive analytics.

Keywords: Predictive analytics; participatory research; stakeholder engagement; team science; translational research.

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References

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Participatory methods to support team science development for predictive analytics in health

Affiliations

Participatory methods to support team science development for predictive analytics in health

Authors

Affiliations

Abstract

References

Grants and funding

LinkOut - more resources

Full Text Sources