Arnob Zahid1, Jennifer Kay Poulsen2, Ravi Sharma3, Stephen C Wingreen4. 1. Department of Accounting and Information Systems, College of Business and Law, University of Canterbury, Christchurch, New Zealand. Electronic address: arnob07@yahoo.com. 2. Help 4 U Limited, Christchurch, New Zealand. Electronic address: kayp@help4u.co.nz. 3. College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates. Electronic address: rs.sharma.sg@gmail.com. 4. Department of Accounting and Information Systems, College of Business and Law, University of Canterbury, Christchurch, New Zealand. Electronic address: stephen.wingreen@canterbury.ac.nz.
Abstract
BACKGROUND: Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. OBJECTIVE: This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. FINDINGS: A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. CONCLUSION: Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.
BACKGROUND: Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. OBJECTIVE: This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. FINDINGS: A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. CONCLUSION: Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.
Authors: David Novillo-Ortiz; Yuri Quintana; John H Holmes; Damian Borbolla; Heimar De Fatima Marin Journal: Int J Med Inform Date: 2021-05-15 Impact factor: 4.730