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Huot S, Fortin PR, Godbout A, Laflamme C, Pouliot M. A tool to assist rheumatologists to engage their lupus patients: the Purple Butterfly. Rheumatol Adv Pract 2024; 8:rkae075. [PMID: 38915844 PMCID: PMC11194531 DOI: 10.1093/rap/rkae075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/22/2024] [Indexed: 06/26/2024] Open
Abstract
Objective Translating the highly technical medical jargon of SLE into understandable concepts for patients, their families and individuals without expertise in SLE is a serious challenge. To facilitate communication and enable self-management in SLE, we aimed to create an innovative visual tool, the Purple Butterfly. Methods We selected clinically representative criteria for SLE and transposed them as graphical features in an attractive and meaningful visual. We developed a script in R programming language that automatically transposes clinical data into this visualization. We asked SLE patients from a local cohort about the relevance, usefulness and acceptability of this visual tool in an online pilot survey. Results The innovative Purple Butterfly features 11 key clinical criteria: age; sex; organ damage; disease activity; comorbidities; use of antimalarials, prednisone, immunosuppressants and biologics; and patient-reported physical and mental health-related quality of life. Each Purple Butterfly provides the health portrait of one SLE patient at one medical visit, and the automatic compilation of the butterflies can illustrate a patient's clinical journey over time. All survey participants agreed that they would like to use the Purple Butterfly to visualize the course of their SLE over time, and 9 of 10 agreed it should be used during their medical consultations. Conclusion The Purple Butterfly nurtures effective doctor-patient communication by providing concise visual summaries of lupus patients' health conditions. We believe the Purple Butterfly has the potential to empower patients to take charge of their condition, enhance healthcare coordination and raise awareness about SLE.
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Affiliation(s)
- Sandrine Huot
- Département de Microbiologie-Infectiologie et Immunologie, Université Laval, Québec, Canada
- Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec, Canada
- Centre ARThrite-UL, Québec, Canada
| | - Paul R Fortin
- Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec, Canada
- Centre ARThrite-UL, Québec, Canada
- Division de Rhumatologie, Département de Médecine Spécialisée, CHU de Québec-Université Laval, Québec, Canada
| | | | - Cynthia Laflamme
- Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec, Canada
- Centre ARThrite-UL, Québec, Canada
| | - Marc Pouliot
- Département de Microbiologie-Infectiologie et Immunologie, Université Laval, Québec, Canada
- Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec, Canada
- Centre ARThrite-UL, Québec, Canada
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Prabhune A, Bhat S, Mallavaram A, Mehar Shagufta A, Srinivasan S. A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia. Cureus 2023; 15:e48977. [PMID: 38111408 PMCID: PMC10726017 DOI: 10.7759/cureus.48977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/20/2023] Open
Abstract
The objective of this paper was to evaluate and compare the quantity and sustainability of digital health initiatives in the South Asia region before and during the COVID-19 pandemic. The study used a two-step methodology of (a) descriptive analysis of digital health research articles published from 2016 to 2021 from South Asia in terms of stratification of research articles based on diseases and conditions they were developed, geography, and tasks wherein the initiative was applied and (b) a simple and replicable tool developed by authors to assess the sustainability of digital health initiatives using experimental or observational study designs. The results of the descriptive analysis highlight the following: (a) there was a 40% increase in the number of studies reported in 2020 when compared to 2019; (b) the three most common areas wherein substantive digital health research has been focused are health systems strengthening, ophthalmic disorders, and COVID-19; and (c) remote consultation, health information delivery, and clinical decision support systems are the top three commonly developed tools. We developed and estimated the inter-rater operability of the sustainability assessment tool ascertained with a Kappa value of 0.806 (±0.088). We conclude that the COVID-19 pandemic has had a positive impact on digital health research with an improvement in the number of digital health initiatives and an improvement in the sustainability score of studies published during the COVID-19 pandemic.
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Affiliation(s)
- Akash Prabhune
- Health and Information Technology, Institute of Health Management Research, Bangalore, IND
| | - Sachin Bhat
- Health and Information Technology, Institute of Health Management Research, Bangalore, IND
| | | | | | - Surya Srinivasan
- Health and Information Technology, Institute of Health Management Research, Bangalore, IND
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Vennemeyer S, Kinnear B, Gao A, Zhu S, Nattam A, Knopp MI, Warm E, Wu DT. User-Centered Evaluation and Design Recommendations for an Internal Medicine Resident Competency Assessment Dashboard. Appl Clin Inform 2023; 14:996-1007. [PMID: 38122817 PMCID: PMC10733060 DOI: 10.1055/s-0043-1777103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Clinical Competency Committee (CCC) members employ varied approaches to the review process. This makes the design of a competency assessment dashboard that fits the needs of all members difficult. This work details a user-centered evaluation of a dashboard currently utilized by the Internal Medicine Clinical Competency Committee (IM CCC) at the University of Cincinnati College of Medicine and generated design recommendations. METHODS Eleven members of the IM CCC participated in semistructured interviews with the research team. These interviews were recorded and transcribed for analysis. The three design research methods used in this study included process mapping (workflow diagrams), affinity diagramming, and a ranking experiment. RESULTS Through affinity diagramming, the research team identified and organized opportunities for improvement about the current system expressed by study participants. These areas include a time-consuming preprocessing step, lack of integration of data from multiple sources, and different workflows for each step in the review process. Finally, the research team categorized nine dashboard components based on rankings provided by the participants. CONCLUSION We successfully conducted user-centered evaluation of an IM CCC dashboard and generated four recommendations. Programs should integrate quantitative and qualitative feedback, create multiple views to display these data based on user roles, work with designers to create a usable, interpretable dashboard, and develop a strong informatics pipeline to manage the system. To our knowledge, this type of user-centered evaluation has rarely been attempted in the medical education domain. Therefore, this study provides best practices for other residency programs to evaluate current competency assessment tools and to develop new ones.
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Affiliation(s)
- Scott Vennemeyer
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
| | - Benjamin Kinnear
- Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
| | - Andy Gao
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
| | - Siyi Zhu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- School of Design, College of Design, Architecture, Art, and Planning (DAAP), University of Cincinnati, Ohio, United States
| | - Anunita Nattam
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
| | - Michelle I. Knopp
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
| | - Eric Warm
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
| | - Danny T.Y. Wu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
- School of Design, College of Design, Architecture, Art, and Planning (DAAP), University of Cincinnati, Ohio, United States
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Eslami M, Tabarestani S, Adjouadi M. A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease. Artif Intell Med 2023; 140:102543. [PMID: 37210151 PMCID: PMC10204620 DOI: 10.1016/j.artmed.2023.102543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/28/2023] [Accepted: 04/02/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal study. The main aim of this study is to help capture visually in 2D and 3D renderings the diagnosis and prognosis of AD, therefore augmenting our understanding of the processes of multiclass classification and regression analysis. METHOD The proposed method, Machine Learning for Visualizing AD (ML4VisAD), is designed to predict disease progression through a visual output. This newly developed model takes baseline measurements as input to generate a color-coded visual image that reflects disease progression at different time points. The architecture of the network relies on convolutional neural networks. With 1123 subjects selected from the ADNI QT-PAD dataset, we use a 10-fold cross-validation process to evaluate the method. Multimodal inputs* include neuroimaging data (MRI, PET), neuropsychological test scores (excluding MMSE, CDR-SB, and ADAS to avoid bias), cerebrospinal fluid (CSF) biomarkers with measures of amyloid beta (ABETA), phosphorylated tau protein (PTAU), total tau protein (TAU), and risk factors that include age, gender, years of education, and ApoE4 gene. FINDINGS/RESULTS Based on subjective scores reached by three raters, the results showed an accuracy of 0.82 ± 0.03 for a 3-way classification and 0.68 ± 0.05 for a 5-way classification. The visual renderings were generated in 0.08 msec for a 23 × 23 output image and in 0.17 ms for a 45 × 45 output image. Through visualization, this study (1) demonstrates that the ML visual output augments the prospects for a more accurate diagnosis and (2) highlights why multiclass classification and regression analysis are incredibly challenging. An online survey was conducted to gauge this visualization platform's merits and obtain valuable feedback from users. All implementation codes are shared online on GitHub. CONCLUSION This approach makes it possible to visualize the many nuances that lead to a specific classification or prediction in the disease trajectory, all in context to multimodal measurements taken at baseline. This ML model can serve as a multiclass classification and prediction model while reinforcing the diagnosis and prognosis capabilities by including a visualization platform.
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Affiliation(s)
- Mohammad Eslami
- Harvard Ophthalmology AI lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA; Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States.
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Ignatenko E, Ribeiro M, Oliveira MD. Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11012. [PMID: 36078728 PMCID: PMC9517757 DOI: 10.3390/ijerph191711012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/07/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.
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Affiliation(s)
- Ekaterina Ignatenko
- Centre for Management Studies of Instituto Superior Técnico (CEG-IST), Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
| | - Manuel Ribeiro
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
| | - Mónica D. Oliveira
- Centre for Management Studies of Instituto Superior Técnico (CEG-IST), Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
- iBB—Institute for Bioengineering and Biosciences and i4HB—Associate Laboratory Institute for Health and Bioeconomy, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
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