Interpreting Health Events in Big Data Using Qualitative Traditions. (8th December 2020)

• Record Type: Journal Article
• Authors: Fritz, Roschelle L.; Dermody, Gordana
• Is Part Of: International journal of qualitative methods. Volume 19(2020)
• Publication Date: 2020-12-08
• Subjects: data collection and management -- descriptive methods -- knowledge transfer -- nursing -- research -- interdisciplinary -- research -- mixed methods -- technology; Qualitative research -- Periodicals; Social sciences -- Philosophy -- Periodicals; Social sciences -- Research -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Qualitative Research -- Periodicals; Qualitative research; Social sciences -- Philosophy; Social sciences -- Research; Periodicals; 300.72
• DOI: 10.1177/1609406920976453
• Languages: English
• ISSNs: 1609-4069

Abstract

The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant's description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.