JMIR Publications

ABSTRACT

Background:

Visual analytics promotes the understanding of data with visual, interactive techniques, using ‘analytic’ and ‘visual’ engines. The analytic engine includes machine learning and other automated techniques, while common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The scoping review aims at addressing a gap in literature on the contemporary use of visual analytic techniques in population health and health services research.

Objective:

The main objective is to synthesize literature on the use of visual analytic methods, particularly tools, techniques and frameworks to population health and health services research issues.

Methods:

Using Tricco et al’s 2018 PRISMA-ScR guidelines, our scoping review focused on peer reviewed sources including journal articles and conference papers published between January 2005 and March 2019. Using the Covidence platform, two independent researchers were involved at all stages including during title, abstract and full text screening and data abstraction. Another independent researcher served as the arbitrator in cases of disagreement during screening and for validation of abstracted data. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer and health sciences, with the findings categorized for reporting.

Results:

After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of: visual and analytic engines; visual presentation characteristics; tools used and their capabilities; application to healthcare areas; data types and sources; VA frameworks; frameworks used for VA applications; availability and innovation; and co-design initiatives. We found a wide application of VA methods used in areas of epidemiology, surveillance and modelling, health services access, utilization, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided on each. There were seven articles presenting analytic frameworks. Related to knowledge translation, 7 articles targeted policy and decision makers. Most articles included tools that were prototypes with 5 in use at publication time.

Conclusions:

With increasing availability and generation of healthcare data, visual analytics is a fast growing method applied to complex healthcare data, such as from administrative sources and electronic medical records. What makes VA innovative is its capability to process multiple, varied data sources for providing demonstrating trends and patterns for exploratory analysis from big healthcare data, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and health services, which further indicates possible avenues for the adoption of these methods in future. The review is especially important in the wake of the Covid-19 surveillance and response initiatives, where many VA products have taken centerstage. Clinical Trial: Not applicable.