BACKGROUND
Simple visualizations in health research data, such as scatter plots, heat maps and bar charts typically present relationships between two variables. Interactive visualization methods allow for multiple related facets, such as multiple risk factors, to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big healthcare data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.
OBJECTIVE
The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods and tools being employed in population health and HSR, and their sub-domains in the last 15 years, from 1 January 2005 to 30 March 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals and co-design of applications.
METHODS
We adapted standard scoping review guidelines, with a peer reviewed search strategy, two independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sector. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and health services research, and their sub-domains such as epidemiologic surveillance, health resource planning, access, utilization and costs, among diverse clinical and demographic populations.
RESULTS
As a companion review to our earlier systematic synthesis of literature on visual analytic applications, we present findings in six major themes of interactive visualization applications developed for eight major problem categories. We found a wide application of interactive visualization methods, the major being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality and studying medication use patterns. Data sources included mostly secondary administrative and electronic medical record data. Additionally, at least two-third applications involved participatory co-design approaches, while introducing a distinct category ‘embedded research’ within co-design initiatives. These applications were in response to an identified need for data-driven insights towards knowledge generation and decision support. We further discuss the opportunities from the use of interactive visualization methods towards studying global health, inequities including social determinants of health, and other related areas. We also allude to the challenges in the uptake of these methods.
CONCLUSIONS
Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and health services research. Such applications are being fast utilized by academic and health care agencies for knowledge discovery, hypotheses generation and decision support.
CLINICALTRIAL
Protocol registration: RR1-10.2196/14019
Related first review: RR2-10.2196/14019
INTERNATIONAL REGISTERED REPORT
RR2-10.2196/14019
Intersections of machine learning and epidemiological methods for health services research
