Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

[Display omitted] •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...

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Bibliographic Details
Published in:Telematics and informatics Vol. 35; no. 4; pp. 643 - 653
Main Authors: Spruit, Marco, Lytras, Miltiadis
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.07.2018
Elsevier Science Ltd
Subjects:
Adaptive analytic system
Adaptive systems
Algorithms
Applied data science
Big data analytics
Data
Data processing
Data science
Deployment
Discovery
Health care
Health care industry
Health services
Knowledge
Knowledge discovery
Knowledge discovery process
Mathematical analysis
Medicine
Meta-algorithmic modelling
Methodological problems
Modelling
Natural language processing
Patient-centered care
Patient-centric healthcare
Patients
Physicians
Practitioner patient relationship
Precision medicine
Prototypes
Research design
Systems analysis
Transparency
Applied data science
Knowledge discovery process
Big data analytics
Adaptive analytic system
Natural language processing
Patient-centric healthcare
Meta-algorithmic modelling
ISSN:0736-5853, 1879-324X
Online Access:Get full text
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Summary:[Display omitted] •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. 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.
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ISSN:0736-5853
1879-324X
DOI:10.1016/j.tele.2018.04.002