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OUT: Applied Data Science in Patient-Centric Healthcare

posted May 23, 2018, 5:38 AM by Marco Spruit   [ updated Oct 30, 2018, 5:13 AM ]
Even though my research is frequently being published, I now have one paper out that I am particularly happy with and proud of, in a collaboration with my Greek friend Miltiadis: 
  • Spruit,M., & Lytras,M. (2018). Applied Data Science in Patient-centric Healthcare: Adaptive Analytic Systems for Empowering Physicians and Patients. Telematics and Informatics, 35(4), 643–653.[ISI impact factor: 3.398] [pdf] [online]
This strategic paper defines and positions my research theme as a research framework for Applied Data Science research on the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. It introduces Adaptive Analytic Systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: 
  1. domain and data understanding for physician- and patient-centric healthcare, 
  2. data preprocessing and modelling using natural language processing and big data analytic techniques, and 
  3. 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, noting that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. 

We then explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare, through the included manuscripts in this special issue of the high-impact journal Telematics and Informatics.

Last but certainly not least, 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.

NB: Elsevier provides free access to the paper until July 4, 2018!