Using Maps to Promote Data-Driven Decision-Making: One Library's Experience in Data Visualization Instruction

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency

OBJECTIVE

While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system.

MATERIALS AND METHODS

Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency.

RESULTS

Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling & optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12).

DISCUSSION

Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects.

CONCLUSION

Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.

Data-driven decision making in graduate students’ research topic selection

Purpose

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

Design/methodology/approach

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

Findings

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

Practical implications

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

Originality/value

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

What is genomic medicine?

Genomic medicine is rapidly changing the future of medicine. Medical librarians need to understand this field of research and keep current with its latest advancements. Even if they are not directly involved in genomic medicine, librarians can play an integral role by helping health care consumers and practitioners who may also need to expand their knowledge in this area. This article provides a basic introduction to genomic medicine, gives a brief overview of its recent advancements, and briefly describes some of the ethical, legal, and social implications of this emerging area of research and practice.