Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients. Issue 4 (July 2018)

• Record Type: Journal Article
• Authors: Spruit, Marco; Lytras, Miltiadis
• Is Part Of: Telematics and informatics. Volume 35:Issue 4(2018)
• Page Start: 643
• Page End: 653
• Publication Date: 2018-07
• Subjects: Applied data science -- Knowledge discovery process -- Patient-centric healthcare -- Adaptive analytic system -- Meta-algorithmic modelling -- Big data analytics -- Natural language processing; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2018.04.002
• Languages: English
• ISSNs: 0736-5853

Abstract

Graphical abstract: Highlights: Applied data science for healthcare empowers physicians and patients. Applied data science focuses on adaptive analytic systems to improve daily care. Adaptive analytic systems for patient-centredness enable personalised medicine. Empowerment of physicians and patients accelerates healthcare innovation. Meta-algorithmic modelling provides a framework for the post-algorithmic era. Abstract: We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging "post-algorithmic era" of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.