Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Design and evaluation of a dashboard to support the comprehension of the progression of patients with dementia in day centers

BACKGROUND AND OBJECTIVE

In day centers, people with dementia are assigned to specific groups to receive care according to the progression of the disease. This article presents the design and evaluation of a dashboard aimed at facilitating the comprehension of the progression of people with dementia to support decision-making of healthcare professionals (HCPs) when determining patient-group assignment.

MATERIALS AND METHOD

A participatory design methodology was followed to build the dashboard. The grounded theory methodology was utilized to identify requirements. A total of 8 HCPs participated in the design and evaluation of a low-fidelity prototype. The perceived usefulness and perceived ease of use of the high-fidelity prototype was evaluated by 15 HCPs (from several day centers) and 38 psychology students utilizing a questionnaire based on the technology acceptance model.

RESULTS

HCPs perceived the dashboard as extremely likely to be useful (Mdn=6.5 out of 7) and quite likely to be usable (Mdn=6 out of 7). Psychology students perceived the dashboard as quite likely to be useful and usable (both with Mdn=6).

CONCLUSIONS

Making use of a participatory design helped foster in HCPs a sense of ownership of the dashboard, thus facilitating its acceptance. The creation of low-fidelity and high-fidelity prototypes led to identifying valuable, timely, and specific feedback at different stages of the development process as well as to establishing a set of lessons learned for the development of dashboards in the healthcare domain.

VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.

Visual Analytics for Electronic Health Records: A Review

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.