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Comprehending the Health Informatics Spectrum: Grappling with System Entropy and Advancing Quality Clinical Research

We outline three scenarios from across the health spectrum where issues with health informatics are exemplified.

Citation:
Bellgard MI, Chartres N, Watts GF, Wilton S, Fletcher S, Hunter A, Snelling T. Comprehending the Health Informatics Spectrum: Grappling with System Entropy and Advancing Quality Clinical Research. Front Public Health. 2017;5(224).

Keywords: 
clinical practice; clinical research; health; informatics; information communication technology

Abstract: 
Clinical research is complex. The knowledge base is information and data rich where value and success depend upon focused, well designed connectivity of systems achieved through stakeholder collaboration. Quality data, information, and knowledge must be utilized in an effective, efficient, and timely manner to affect important clinical decisions and communicate health prevention strategies. In recent decades, it has become apparent that information communication technology (ICT) solutions potentially offer multidimensional opportunities for transforming health care and clinical research. However, it is also recognized that successful utilization of ICT in improving patient care and health outcomes depends on a number of factors such as the effective integration of diverse sources of health data; how and by whom quality data are captured; reproducible methods on how data are interrogated and reanalyzed; robust policies and procedures for data privacy, security and access; usable consumer and clinical user interfaces; effective diverse stakeholder engagement; and navigating the numerous eclectic and non-interoperable legacy proprietary health ICT solutions in hospital and clinic environments (1, 2). This is broadly termed health informatics (HI).

We outline three scenarios from across the health spectrum where these issues are exemplified: (i) for a given clinical trial methodology and study design, the nature of how quality data is captured, by whom, how it is aggregated, reused and repurposed is just as critical as the data content itself.