Clinical data-mining: Learning from practice in international settings

Marina Lalayants; Irwin Epstein; Gail K. Auslander; Wallace Chi Ho Chan; Christa Fouché; Ros Giles; Lynette Joubert; Hadas Rosenne; Anne Vertigan

doi:10.1177/0020872811435370

Clinical data-mining: Learning from practice in international settings

Marina Lalayants^*, Irwin Epstein, Gail K. Auslander, Wallace Chi Ho Chan, Christa Fouché, Ros Giles, Lynette Joubert, Hadas Rosenne, Anne Vertigan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

This article describes a practice-based, mixed-method research methodology - Clinical Data-Mining (CDM) - as a strategy for engaging international practitioners for describing, evaluating and reflecting upon endogenous forms of practice with the ultimate goal of improving practice and contributing to knowledge (Epstein, 2010). These knowledge contributions are perforce 'local', but through conceptual reflection and/or empirical replication they may also be 'globalized'. More specifically, the article defines, describes, and details CDM methodology; discusses its strengths and limitations; and illustrates international applications in Australia, Hong Kong, Israel, New Zealand, and the United States. It also describes various infra-structural support platforms and CDM's primary and secondary organizational benefits.

Original language	English
Pages (from-to)	775-797
Number of pages	23
Journal	International Social Work
Volume	56
Issue number	6
Early online date	27 Mar 2012
DOIs	https://doi.org/10.1177/0020872811435370
Publication status	Published - 1 Nov 2013
Externally published	Yes

Keywords

clinical data-mining
clinical information
international exemplars of data-mining
mixed methods
practice-based research
practitioner studies

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10.1177/0020872811435370

Cite this

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AU - Fouché, Christa

AU - Giles, Ros

AU - Joubert, Lynette

AU - Rosenne, Hadas

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Clinical data-mining: Learning from practice in international settings

Abstract

Keywords

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