Data Visualizations to Support Health Practitioners’ Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

Backonja, Uba; Haynes, Sarah C.; Kim, Katherine

doi:10.2196/11826

Cited by 28 publications

(36 citation statements)

References 52 publications

(51 reference statements)

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“…Previous attempts have explored the application of data-driven assistive technologies in the field of cancer care [ 19 ], physiotherapy [ 20 ], cardiovascular disease [ 21 ], and elderly care at home [ 22 ]. Conversational agents [ 23 ] and data visualizations [ 19 ] are being developed for facilitating the interactions between the technologies and their users. However, few studies have investigated this type of assistive technology in personalizing dementia care.…”

Section: Introductionmentioning

confidence: 99%

A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study

Wang¹,

Albayrak²,

Kortuem³

et al. 2021

JMIR Form Res

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Background Care personalization is key to the well-being of people with dementia according to person-centered care. With the development of the internet of things, a large quantity of personal data can be collected securely and reliably, which has the potential to facilitate care personalization for people with dementia. Yet, there are limited assistive technologies developed for this purpose, and the user acceptance of assistive technologies is low in nursing homes. Therefore, through a data-enabled design approach, a digital platform was developed for helping the care team in a nursing home to personalize dementia care, specifically in the management of behavioral and psychological dementia symptoms. Objective This study aimed to evaluate the digital platform in a real-life context with potential users from the following two aspects: (1) to explore if the digital platform could help with generating insights on the current state of each person with dementia and (2) to gather feedback on the digital platform from the care team. Methods The digital platform was deployed in the nursing home for 7 weeks and the data collected were visualized and presented to the care team via the digital platform. The visualizations were analyzed by the researchers for pattern detection. Meanwhile, the care team was asked to examine the visualizations and were interviewed for the following: (1) if any insights and actions were generated from the examination, (2) the usefulness of the digital platform, and (3) the improvements they would like to see. Results The data collected on the digital platform demonstrated its potential for pattern detection. Insights were generated by the care team and categorized into “client level,” “ward level,” and “team level.” The corresponding actions taken by the care team were classified into “investigation” and “implementation.” User acceptance varied across the care team, and three aspects of improvement for the digital platform were identified. Conclusions By evaluating the digital platform, this study gained insights on applying data-enabled design for personalizing dementia care; besides, it offers future researchers some recommendations on how to integrate assistive technologies in the nursing home context.

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Section: Introductionmentioning

confidence: 99%

A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study

Wang¹,

Albayrak²,

Kortuem³

et al. 2021

JMIR Form Res

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show abstract

“…This includes studying expectations and customs of the target audience, how this affects their perception and interpretation of data visualizations, and how this consequently impacts their decision-making or behaviour. The importance of assessing visual habits and perceptions in data visualization has been demonstrated before in other medical fields [21,42]. It was shown that personal preferences and the familiarity of a target audience with certain visualization types can result in tensions with data visualization recommendations and novel data visualization approaches.…”

Section: Discussionmentioning

confidence: 98%

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Keizer

Luz

Sinha

et al. 2021

Preprint

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Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed. The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n=54; 36.0%) and line charts (n=42; 28.1%) were preferred visualization types. Visualization problems comprised colour scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve colour use for grouping/stratifying, improve the display of magnitude, and bring visualizations to match scientific standards. Conclusions: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship towards more effective visualizations that improve data insights, knowledge, and clinical decision-making.

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“…It can support better aligning of known disease biomarkers with established treatments necessary for real-time personalized care. However, one of the existing challenges includes timely high-throughput genomics and transcriptomics data interpretation and visualization to support health practitioners in the provision of personalized care [ 53 ]. It requires integration and understanding of data with various types, structures, velocity, and magnitude [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

et al. 2021

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Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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Data Visualizations to Support Health Practitioners’ Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

Cited by 28 publications

References 52 publications

A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study

A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

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