What Clinics are Expecting From Data Scientists? A Review on Data Oriented Studies Through Qualitative and Quantitative Approaches

Abstract

Ensuring healthy lives and promoting well-being for all, at all ages, is one main objective for sustainable development proposed by the United Nations. The concept of connected health (CH) has been proposed to achieve that goal by connecting all the stakeholders through enabling Telehealth technologies. This paper has first presented an overview of the whole picture of CH along with the data collection process in CH. In the whole picture of CH, translational medicine (TM), as a rapidly growing discipline in biomedical research, aims to expedite the discovery of new diagnostic tools and treatments by using a multi-disciplinary and highly collaborative approach. It has been introduced to bridge the technique gap between the clinics and data scientists, particularly targeting on health related data analysis and evidence medicine. What clinicians are expecting and what researchers can offer will/should all be defined and clarified through TM. To further facilitate the communication between the clinicians and the researchers, electronic health records (EHRs) are often applied in place. This paper first reviews the evolution history of EHR and its current status and standards. Then a detailed and comprehensive discussion on data analysis techniques applied in TM through both quantitative and qualitative approaches is elaborated. We reveal that future work in TM should put an emphasis on data oriented qualitative analysis, using advanced techniques from the artificial intelligence domain to predict health risk, such as heart attacks and early stages of cancers. Multidisciplinary research in the Internet of Medical Things across health science, data science, and engineering will be the main challenge in TM.

Keywords

• Connected health (CH)
• translational medicine (TM)
• electronic health record (EHR)
• qualitative and qualitative data analysis
• Internet of Medical Things (IoMT)