Ethically Building Business Intelligence in Healthcare: A Value-Sensitive Perspective

Ethically Building Business Intelligence in Healthcare: A Value-Sensitive Perspective

Majid Dadgar (University of San Francisco, USA) and K. D. Joshi (University of Nevada, Reno, USA)
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-2310-0.ch012

Abstract

This chapter advocates the use of a value-sensitive design (VSD) approach toward deriving patient intelligence by illustrating that the insights provided by the healthcare data that captures patients' concerns, needs, and desires—known as values—provide more sustainable care. Authors examine three cases extracted from top information systems (IS) peer-reviewed journals in which medical data is collected and analyzed and in which intelligence is derived through a VSD framework. VSD is a three-part methodology that comprises conceptual, empirical, and technical investigations. This chapter investigates the value sensitivity of the following key activities and tasks that result in intelligence from data: data collection, data analysis, and data reporting.
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Background: Healthcare Data & Ethics

Healthcare ethics primarily involves policies, regulations, guidelines, and activities to ensure ethical and professional conduct and providing medical and social benefits (Concannon et al., 2019). Healthcare data is collected from multiple sources to be used by different stakeholders for different purposes. The healthcare data collected to improve patients’ care should take into account patients’ values in forms of feelings, needs, and desires. It is the ethical responsibility of those collecting, analyzing, and reporting patients’ healthcare data to ensure that the patients have a say in the process. The intelligence derived from patients’ data for their care and treatment, should be sensitive to their values and not just focus on clinical outcomes. The patients’ intelligence will be more congruent with ethical considerations if the important steps of the data lifecycle, data collection, analysis, and reporting, are sensitive to the values of the patients as humans.

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