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Pilipczuk, Olga, Dmitri Eidenzon, and Olena Kosenko. "Patient Postoperative Care Data Visualization." International Journal of Computer Applications 156, no. 7 (December 15, 2016): 27–33. http://dx.doi.org/10.5120/ijca2016912469.
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Stonbraker, Samantha, Tiffany Porras, and Rebecca Schnall. "Patient preferences for visualization of longitudinal patient-reported outcomes data." Journal of the American Medical Informatics Association 27, no. 2 (October 31, 2019): 212–24. http://dx.doi.org/10.1093/jamia/ocz189.
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Abstract Objective The study sought to design symptom reports of longitudinal patient-reported outcomes data that are understandable and meaningful to end users. Materials and Methods We completed a 2-phase iterative design and evaluation process. In phase I, we developed symptom reports and refined them according to expert input. End users then completed a survey containing demographics, a measure of health literacy, and items to assess visualization preferences and comprehension of reports. We then collected participants’ perspectives on reports through semistructured interviews and modified them accordingly. In phase II, refined reports were evaluated in a survey that included demographics, validated measures of health and graph literacy, and items to assess preferences and comprehension of reports. Surveys were administered using a think-aloud protocol. Results Fifty-five English- and Spanish-speaking end users, 89.1% of whom had limited health literacy, participated. In phase I, experts recommended improvements and 20 end users evaluated reports. From the feedback received, we added emojis, changed date and font formats, and simplified the y-axis scale of reports. In phase II, 35 end users evaluated refined designs, of whom 94.3% preferred reports with emojis, the favorite being a bar graph combined with emojis, which also promoted comprehension. In both phases, participants literally interpreted reports and provided suggestions for future visualizations. Conclusions A bar graph combined with emojis was participants’ preferred format and the one that promoted comprehension. Target end users must be included in visualization design to identify literal interpretations of images and ensure final products are meaningful.
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Zhu, Zhecheng, Bee Hoon Heng, and Kiok Liang Teow. "Interactive Data Visualization to Understand Data Better." International Journal of Knowledge Discovery in Bioinformatics 4, no. 2 (July 2014): 1–10. http://dx.doi.org/10.4018/ijkdb.2014070101.
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This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.
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Grossman, Lisa, Steven Feiner, Elliot Mitchell, and Ruth Masterson Creber. "Leveraging Patient-Reported Outcomes Using Data Visualization." Applied Clinical Informatics 09, no. 03 (July 2018): 565–75. http://dx.doi.org/10.1055/s-0038-1667041.
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Background Health care organizations increasingly use patient-reported outcomes (PROs) to capture patients' health status. Although federal policy mandates PRO collection, the challenge remains to better engage patients in PRO surveys, and ensure patients comprehend the surveys and their results. Objective This article identifies the design requirements for an interface that assists patients with PRO survey completion and interpretation, and then builds and evaluates the interface. Methods We employed a user-centered design process that consisted of three stages. First, we conducted qualitative interviews and surveys with 13 patients and 11 health care providers to understand their perceptions of the value and challenges associated with the use of PRO measures. Second, we used the results to identify design requirements for an interface that collects PROs, and designed the interface. Third, we conducted usability testing with 12 additional patients in a hospital setting. Results In interviews, patients and providers reported that PRO surveys help patients to reflect on their symptoms, potentially identifying new opportunities for improved care. However, 6 out of 13 patients reported significant difficultly in understanding PRO survey questions, answer choices and results. Therefore, we identified aiding comprehension as a key design requirement, and incorporated visualizations into our interface design to aid comprehension. In usability testing, patients found the interface highly usable. Conclusion Future interfaces designed to collect PROs may benefit from employing strategies such as visualization to aid comprehension and engage patients with surveys.
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Plaisant, Catherine. "Visualization of temporal patterns in patient record data." Fundamental & Clinical Pharmacology 32, no. 1 (October 17, 2017): 85–87. http://dx.doi.org/10.1111/fcp.12322.
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Turchioe, Meghan Reading, Annie Myers, Samuel Isaac, Dawon Baik, Lisa V. Grossman, Jessica S. Ancker, and Ruth Masterson Creber. "A Systematic Review of Patient-Facing Visualizations of Personal Health Data." Applied Clinical Informatics 10, no. 04 (August 2019): 751–70. http://dx.doi.org/10.1055/s-0039-1697592.
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Abstract Objectives As personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data. Methods We searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized. Results In 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation. Conclusion In this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.
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Ha, Hyoji, Jihye Lee, Hyunwoo Han, Sungyun Bae, Sangjoon Son, Changhyung Hong, Hyunjung Shin, and Kyungwon Lee. "Dementia Patient Segmentation Using EMR Data Visualization: A Design Study." International Journal of Environmental Research and Public Health 16, no. 18 (September 16, 2019): 3438. http://dx.doi.org/10.3390/ijerph16183438.
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(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach.
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Warner, Jeremy L., Joshua C. Denny, David A. Kreda, and Gil Alterovitz. "Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization." Journal of the American Medical Informatics Association 22, no. 2 (October 21, 2014): 324–29. http://dx.doi.org/10.1136/amiajnl-2014-002965.
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Abstract Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome. Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations.
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Shaffer, Victoria A., Pete Wegier, K. D. Valentine, Jeffery L. Belden, Shannon M. Canfield, Mihail Popescu, Linsey M. Steege, Akshay Jain, and Richelle J. Koopman. "Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control." Medical Decision Making 40, no. 6 (July 22, 2020): 785–96. http://dx.doi.org/10.1177/0272989x20940999.
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Objective. Uncontrolled hypertension is driven by clinical uncertainty around blood pressure data. This research sought to determine whether decision support—in the form of enhanced data visualization—could improve judgments about hypertension control. Methods. Participants (Internet sample of patients with hypertension) in 3 studies ( N = 209) viewed graphs depicting blood pressure data for fictitious patients. For each graph, participants rated hypertension control, need for medication change, and perceived risk of heart attack and stroke. In study 3, participants also recalled the percentage of blood pressure measurements outside of the goal range. The graphs varied by systolic blood pressure mean and standard deviation, change in blood pressure values over time, and data visualization type. Results. In all 3 studies, data visualization type significantly affected judgments of hypertension control. In studies 1 and 2, perceived hypertension control was lower while perceived need for medication change and subjective perceptions of stroke and heart attack risk were higher for raw data displays compared with enhanced visualization that employed a smoothing function generated by the locally weighted smoothing algorithm. In general, perceptions of hypertension control were more closely aligned with clinical guidelines when data visualization included a smoothing function. However, conclusions were mixed when comparing tabular presentations of data to graphical presentations of data in study 3. Hypertension was perceived to be less well controlled when data were presented in a graph rather than a table, but recall was more accurate. Conclusion. Enhancing data visualization with the use of a smoothing function to minimize the variability present in raw blood pressure data significantly improved judgments about hypertension control. More research is needed to determine the contexts in which graphs are superior to data tables.
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Macedo, Márcio C. F., and Antônio L. Apolinário. "Focus plus context visualization based on volume clipping for markerless on-patient medical data visualization." Computers & Graphics 53 (December 2015): 196–209. http://dx.doi.org/10.1016/j.cag.2015.09.007.
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Ragg, Susanne, Marc B. Rosenman, Eve M. Doucette, Zhong Yan, Julie C. Haydon, Jada H. Paine, Nadine D. Lee, Terry Vik, Ketan Mane, and Katy Borner. "Data Visualization of Multiparameter Information in Acute Lymphoblastic Leukemia Expands the Ability To Explore Prognostic Factors." Blood 106, no. 11 (November 16, 2005): 862. http://dx.doi.org/10.1182/blood.v106.11.862.862.
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Abstract In clinical studies and for patient care, data collections have become more and more complex. Clinical studies often include millions of data points, and even data collections about individual patients can include several thousand data points. To enable searches for meaningful relationships and patterns, and to gain understanding and knowledge of the data, state-of-the-art visualization approaches have to be adapted to the needs of clinicians and clinical researchers, in order to best reveal relevant patterns in the data. However, despite the progress that has been made in the field of information visualization, none of the currently available high dimensional visualization tools are used in clinical research or practice. The goal of this research was to develop visualization tools that allow clinical researchers to explore multidimensional datasets, as well as temporal clinical datasets. The dataset used for this presentation involves 300 pediatric patients with acute lymphoblastic leukemia diagnosed at Indiana University between 1992 and 2000. Clinical and laboratory data were extracted electronically from the Regenstrief Medical Records System. The dosages of all medications during the treatment period were extracted from the patients’ charts. This information was supplemented, for a subset of 78 patients for whom Indiana Medicaid claims data were available, with actual fill dates and quantities dispensed. Cytogenetic data were extracted from the clinical genetic database, and immunophenotype data were extracted from the pathology database at Indiana University. Temporal patient data, such as laboratory data, prescription fill dates, and medication dosage, are visualized through custom-designed multiple layer graphics. The visualization tools developed allow the user to interactively visualize and query the data. For exploratory analysis, the application offers an overview of the data through visual representations such as parallel coordinates and matrix methods. The user can interact with the data set in diverse ways, e.g, the order in which the variables are visualized can be changed; interactivity augments the insight that can be gained from visually exploring such data. The visualizations are dynamically linked, so that the user can obtain coordinate views of the data. Dynamic querying interactively filters data in all views. In addition, the user can highlight or select a subset of data elements in one view and thereby highlight data for the same subset in other views. For example, we show that patient data with a specific pattern in the parallel coordinate view can be selected, and then clinical, laboratory, and prescription data for the entire treatment period can be viewed through multiple layer graphics. In summary, the adaptation of temporal and multidimensional visualization tools to clinical data allows clinicians or clinical researchers to better explore these datasets. These tools improve understanding of the complex prognostic features of acute lymphoblastic leukemia, including type of leukemia, initial risk factors, therapy, adherence to therapy, and host factors that affect tolerance of therapy.
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Aigner, Wolfgang, and Silvia Miksch. "CareVis: Integrated visualization of computerized protocols and temporal patient data." Artificial Intelligence in Medicine 37, no. 3 (July 2006): 203–18. http://dx.doi.org/10.1016/j.artmed.2006.04.002.
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West, Vivian L., David Borland, and W. Ed Hammond. "Innovative information visualization of electronic health record data: a systematic review." Journal of the American Medical Informatics Association 22, no. 2 (October 21, 2014): 330–39. http://dx.doi.org/10.1136/amiajnl-2014-002955.
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Abstract Objective This study investigates the use of visualization techniques reported between 1996 and 2013 and evaluates innovative approaches to information visualization of electronic health record (EHR) data for knowledge discovery. Methods An electronic literature search was conducted May–July 2013 using MEDLINE and Web of Knowledge, supplemented by citation searching, gray literature searching, and reference list reviews. General search terms were used to assure a comprehensive document search. Results Beginning with 891 articles, the number of articles was reduced by eliminating 191 duplicates. A matrix was developed for categorizing all abstracts and to assist with determining those to be excluded for review. Eighteen articles were included in the final analysis. Discussion Several visualization techniques have been extensively researched. The most mature system is LifeLines and its applications as LifeLines2, EventFlow, and LifeFlow. Initially, research focused on records from a single patient and visualization of the complex data related to one patient. Since 2010, the techniques under investigation are for use with large numbers of patient records and events. Most are linear and allow interaction through scaling and zooming to resize. Color, density, and filter techniques are commonly used for visualization. Conclusions With the burgeoning increase in the amount of electronic healthcare data, the potential for knowledge discovery is significant if data are managed in innovative and effective ways. We identify challenges discovered by previous EHR visualization research, which will help researchers who seek to design and improve visualization techniques.
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Abou El-Seoud, Samir, Amr Mady, and Essam Rashed. "An Interactive Mixed Reality Ray Tracing Rendering Mobile Application of Medical Data in Minimally Invasive Surgeries." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 03 (March 25, 2019): 29. http://dx.doi.org/10.3991/ijim.v13i03.9893.
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<p class="0abstract">Visualization of patient’s anatomy is the most important pre-operation process in surgeries, minimally invasive surgeries are among these types of medical operations that counts totally on medical visualization before operating on a patient. However, medicine has a problem in visualizing patients’ through looking through multiple slices of scans, trying to understand the three-dimensional (3D) anatomical structure of patients. With Mixed Reality (MR) the developments in medicine visualization will become much easier and creates a better environment for surgeries. This will help reduce the excessive effort and time spent by surgeons to locate where the problem lies with patients without looking through multiple of two-dimensional (2D) slices, but to see patients’ bodies in 3D in front of them augmented in their reality, and to interact with it whatever pleases them. Moreover, this will reduce the number of scans that doctors will ask their patient’s for, which will result in less harmful x-ray dosages for both the patient and the radiologist. Biomedical development in medical visualization is an active research topic as it provides the physicians with required devices for clinically feasible way for diagnosis, follow-up and take decisions in different disease life line. Current clinical imaging facility can provide a 3D imaging that can be used to guide different interventional procedures. The main challenge is how to map the information presented in the digital image with the real object. This is commonly implemented by mental processing that requires skills from the medical doctor. This paper contributes to this problem by providing a mixed reality system to merge the digital image of the patient anatomy with the patient visual image. Anatomical image obtained from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) is mapped over the patient body using virtual reality (VR) head-mounted device (HMD).</p>
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Abou El-Seoud, Samir, Amr S. Mady, and Essam A. Rashed. "An Interactive Mixed Reality Ray Tracing Rendering Mobile Application of Medical Data in Minimally Invasive Surgeries." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 06 (March 29, 2019): 4. http://dx.doi.org/10.3991/ijoe.v15i06.9933.
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Visualization of patient’s anatomy is the most important pre-operation process in surgeries, minimally invasive surgeries are among these types of medical operations that counts totally on medical visualization before operating on a patient. However, medicine has a problem in visualizing patients’ through looking through multiple slices of scans, trying to understand the three-dimensional (3D) anatomical structure of patients. With Mixed Reality (MR) the developments in medicine visualization will become much easier and creates a better environment for surgeries. This will help reduce the excessive effort and time spent by surgeons to locate where the problem lies with patients without looking through multiple of two-dimensional (2D) slices, but to see patients’ bodies in 3D in front of them augmented in their reality, and to interact with it whatever pleases them. Moreover, this will reduce the number of scans that doctors will ask their patient’s for, which will result in less harmful x-ray dosages for both the patient and the radiologist. Biomedical development in medical visualization is an active research topic as it provides the physicians with required devices for clinically feasible way for diagnosis, follow-up and take decisions in different disease life line. Current clinical imaging facility can provide a 3D imaging that can be used to guide different interventional procedures. The main challenge is how to map the information presented in the digital image with the real object. This is commonly implemented by mental processing that requires skills from the medical doctor. This paper contributes to this problem by providing a mixed reality system to merge the digital image of the patient anatomy with the patient visual image. Anatomical image obtained from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) is mapped over the patient body using virtual reality (VR) head-mounted device (HMD).
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Macedo, Márcio Cerqueira de Farias, Antônio Lopes Apolinário Júnior, Antonio Carlos dos Santos Souza, and Gilson Antônio Giraldi. "High-Quality On-Patient Medical Data Visualization in a Markerless Augmented Reality Environment." Journal on Interactive Systems 5, no. 3 (December 30, 2014): 1. http://dx.doi.org/10.5753/jis.2014.725.
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To provide on-patient medical data visualization, a medical augmented reality environment must support volume rendering, accurate tracking, real-time performance and high visual quality in the final rendering. Another interesting feature is markerless registration, to solve the intrusiveness introduced by the use of fiducial markers for tracking. In this paper we address the problem of on-patient medical data visualization in a real-time high-quality markerless augmented reality environment. The medical data consists of a volume reconstructed from 3D computed tomography image data. Markerless registration is done by generating a 3D reference model of the region of interest in the patient and tracking it from the depth stream of an RGB-D sensor. From the estimated camera pose, the volumetric medical data and the reference model are combined allowing a visualization of the patient as well as part of his anatomy. To improve the visual perception of the scene, focus+context visualization is used in the augmented reality scene to dynamically define which parts of the medical volume will be visualized in the context of the patient’s image. Moreover, context-preserving volume rendering is employed to dynamically control which parts of the volume will be rendered. The results obtained show that the markerless environment runs in real-time and the techniques applied greatly improve the visual quality of the final rendering.
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Bashyam, Vijayaraghavan, William Hsu, Emily Watt, Alex A. T. Bui, Hooshang Kangarloo, and Ricky K. Taira. "Problem-centric Organization and Visualization of Patient Imaging and Clinical Data." RadioGraphics 29, no. 2 (March 2009): 331–43. http://dx.doi.org/10.1148/rg.292085098.
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Lötsch, Jörn, and Alfred Ultsch. "Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data." International Journal of Molecular Sciences 21, no. 1 (December 20, 2019): 79. http://dx.doi.org/10.3390/ijms21010079.
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Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor embedding (t-SNE). We analyzed its performance on artificial and real biomedical data sets. t-SNE introduced a cluster structure for homogeneously distributed data that did not contain any subgroup structure. In other data sets, t-SNE occasionally suggested the wrong number of subgroups or projected data points belonging to different subgroups, as if belonging to the same subgroup. As an alternative approach, emergent self-organizing maps (ESOM) were used in combination with U-matrix methods. This approach allowed the correct identification of homogeneous data while in sets containing distance or density-based subgroups structures; the number of subgroups and data point assignments were correctly displayed. The results highlight possible pitfalls in the use of a currently widely applied algorithmic technique for the detection of subgroups in high dimensional cytometric data and suggest a robust alternative.
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Noël, Guillermina, Janet Joy, and Carmen Dyck. "Improving the quality of healthcare data through information design." Information Design Journal 23, no. 1 (July 20, 2017): 104–22. http://dx.doi.org/10.1075/idj.23.1.11noe.
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Improving the quality of patient care, generally referred to as Quality Improvement (QI), is a constant mission of healthcare. Although QI initiatives take many forms, these typically involve collecting data to measure whether changes to procedures have been made as planned, and whether those changes have achieved the expected outcomes. In principle, such data are used to measure the success of a QI initiative and make further changes if needed. In practice, however, many QI data reports provide only limited insight into changes that could improve patient care. Redesigning standard approaches to QI data can help close the gap between current norms and the potential of QI data to improve patient care. This paper describes our study of QI data needs among healthcare providers and managers at Vancouver Coastal Health, a regional health system in Canada. We present an overview of challenges faced by healthcare providers around QI data collection and visualization, and illustrate the advantages and disadvantages of different visualizations. At present, user– centred and evidence–based design is practically unknown in healthcare QI, and thus offers an important new contribution.
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Popow, C., L. Unterasinger, and W. Horn. "Support for Fast Comprehension of ICU Data: Visualization using Metaphor Graphics." Methods of Information in Medicine 40, no. 05 (2001): 421–24. http://dx.doi.org/10.1055/s-0038-1634202.
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Summary Objectives: The time-oriented analysis of electronic patient records on (neonatal) intensive care units is a tedious and time-consuming task. Graphic data visualization should make it easier for physicians to assess the overall situation of a patient and to recognize essential changes over time. Methods: Metaphor graphics are used to sketch the most relevant parameters for characterizing a patient’s situation. By repetition of the graphic object in 24 frames the situation of the ICU patient is presented in one display, usually summarizing the last 24 h. Results: VIE-VISU is a data visualization system which uses multiples to present the change in the patient’s status over time in graphic form. Each multiple is a highly structured metaphor graphic object. Each object visualizes important ICU parameters from circulation, ventilation, and fluid balance. Conclusion: The design using multiples promotes a focus on stability and change. A stable patient is recognizable at first sight, continuous improvement or worsening condition are easy to analyze, drastic changes in the patient’s situation get the viewers attention immediately.
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Loginova, A. A., D. A. Tovmasian, A. P. Chernyaev, D. A. Kobyseva, A. O. Lisovskaya, and A. V. Nechesnyuk. "EVALUATION OF DOSE DELIVERY FOR TOTAL MARROW IRRADIATION USING IMAGING DATA OBTAINED WITH TOMOTHERAPY DEVICE." Russian Electronic Journal of Radiology 11, no. 1 (2021): 230–37. http://dx.doi.org/10.21569/2222-7415-2021-11-1-230-237.
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Fractionated total marrow irradiation and lymphoid irradiation (TMLI) is a highly conformal method of radiotherapy, requiring a high degree of dose delivery accuracy. This study presents a quantitative assessment of the delivered dose while taking into account the influence of daily positioning using one patient receiving TMLI as an example. Material and methods. Before each treatment session on TomoTherapy preliminary visualization is performed by megavoltage computer tomography (MVCT). The resulting images are used to correct the position of the patient and thereby to minimize the error of dose adjustment. In this study dose was recalculated for each treatment fraction, taking into account the current radiation geometry based on the MVCT images of the patient. The planned and delivered total dose distributions were compared. Results. The difference between the delivered and planned average dose in target comprising bone marrow and lymphoid tissue was less than 0.5%. The volume of the lungs, receiving a dose of 8 Gy did not exceed 39.3% of the total delivered dose, at the same time the coverage of the targets met prescribed requirements. Discussion. Appropriate immobilization, visualization with subsequent correction of the patient's position prior to each fraction allowed for reliable and accurate dose delivery. The evaluation of the delivered dose provides opportunity for an objective analysis of the therapy. Conclusion. The analysis of the delivered dose distribution based on MVCT visualization of the patient's body demonstrated the safety of TMLI method in terms of dose to the organs at risk, as well as the acceptable quality of the target coverage
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Foo, Jung-Leng, Thom Lobe, and Eliot Winer. "A Virtual Reality Environment for Patient Data Visualization and Endoscopic Surgical Planning." Journal of Laparoendoscopic & Advanced Surgical Techniques 19, s1 (April 2009): s211—s217. http://dx.doi.org/10.1089/lap.2008.0159.supp.
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Shee, Kevin, Sumanta K. Pal, J. Connor Wells, Jose Manuel Ruiz-Morales, Kenton Russell, Shaan Dudani, Toni K. Choueiri, Daniel Y. Heng, John L. Gore, and Anobel Y. Odisho. "Interactive Data Visualization Tool for Patient-Centered Decision Making in Kidney Cancer." JCO Clinical Cancer Informatics, no. 5 (August 2021): 912–20. http://dx.doi.org/10.1200/cci.21.00050.
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PURPOSE Patients and providers often lack clinical decision tools to enable effective shared decision making. This is especially true in the rapidly changing therapeutic landscape of metastatic kidney cancer. Using the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria, a validated risk prediction tool for patients with metastatic renal cell carcinoma, we created and user-tested a novel interactive visualization for clinical use. METHODS An interactive visualization depicting IMDC criteria was created, with the final version including data for more than 4,500 patients. Usability testing was performed with nonmedical lay-users and medical oncology fellow physicians. Subjects used the tool to calculate median survival times based on IMDC criteria. User confidence was surveyed. An iterative user feedback implementation cycle was completed and informed revision of the tool. RESULTS The tool is available at CloViz—IMDC. Initially, 400 lay-users and 15 physicians completed clinical scenarios and surveys. Cumulative accuracy across scenarios was higher for physicians than lay-users (84% v 74%; P = .03). Eighty-three percent of lay-users and 87% of physicians thought the tool became intuitive with use. Sixty-eight percent of lay-users wanted to use the tool clinically compared with 87% of physicians. After revisions, the updated tool was user-tested with 100 lay-users and 15 physicians. Physicians, but not lay-users, showed significant improvement in accuracy in the updated version of the tool (90% v 67%; P = .008). Seventy-two percent of lay-users and 93% of physicians wanted to use the updated tool in a clinical setting. CONCLUSION A graphical method of interacting with a validated nomogram provides prognosis results that can be used by nonmedical lay-users and physicians, and has the potential for expanded use across many clinical conditions.
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Nouri, Maede, Daniel Lizotte, Kamran Sedig, and Sheikh Abdullah. "VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data." Data 6, no. 8 (August 4, 2021): 85. http://dx.doi.org/10.3390/data6080085.
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Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.
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Kraft, Valentin, Christian Schumann, Daniela Salzmann, Hans Nopper, Thomas Lück, Dirk Weyhe, and Andrea Schenk. "Towards realistic organ models for 3D printing and visualization." Current Directions in Biomedical Engineering 7, no. 1 (August 1, 2021): 166–70. http://dx.doi.org/10.1515/cdbme-2021-1036.
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Abstract Three-dimensional visualizations and 3D-printed organs are used increasingly for teaching, surgery planning, patient education, and interventions. Hence, pipelines for the creation of the necessary geometric data from CT or MR images on a per-patient basis are needed. Furthermore, modern 3D printing techniques enable new possibilities for the models with regard to color, softness, and textures. However, to utilize these new features, the respective information has to be derived from the medical images in addition to the geometry of the relevant organ structures. In this work, we propose an automatable pipeline for the creation of realistic, patientspecific 3D-models for visualization and 3D printing in the context of liver surgery and discuss remaining challenges.
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Lor, Maichou, Theresa A. Koleck, and Suzanne Bakken. "Information visualizations of symptom information for patients and providers: a systematic review." Journal of the American Medical Informatics Association 26, no. 2 (December 7, 2018): 162–71. http://dx.doi.org/10.1093/jamia/ocy152.
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AbstractObjectiveTo systematically synthesize the literature on information visualizations of symptoms included as National Institute of Nursing Research common data elements and designed for use by patients and/or healthcare providers.MethodsWe searched CINAHL, Engineering Village, PsycINFO, PubMed, ACM Digital Library, and IEEE Explore Digital Library to identify peer-reviewed studies published between 2007 and 2017. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT) and a visualization quality score, and organized evaluation findings according to the Health Information Technology Usability Evaluation Model.ResultsEighteen studies met inclusion criteria. Ten of these addressed all MMAT items; 13 addressed all visualization quality items. Symptom visualizations focused on pain, fatigue, and sleep and were represented as graphs (n = 14), icons (n = 4), and virtual body maps (n = 2). Studies evaluated perceived ease of use (n = 13), perceived usefulness (n = 12), efficiency (n = 9), effectiveness (n = 5), preference (n = 6), and intent to use (n = 3). Few studies reported race/ethnicity or education level.ConclusionThe small number of studies for each type of information visualization limit generalizable conclusions about optimal visualization approaches. User-centered participatory approaches for information visualization design and more sophisticated evaluation designs are needed to assess which visualization elements work best for which populations in which contexts.
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Kunjir, Ajinkya, Jugal Shah, Navdeep Singh, and Tejas Wadiwala. "Big Data Analytics and Visualization for Hospital Recommendation using HCAHPS Standardized Patient Survey." International Journal of Information Technology and Computer Science 11, no. 3 (March 8, 2019): 1–9. http://dx.doi.org/10.5815/ijitcs.2019.03.01.
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Enciso, R., A. Memon, and J. Mah. "Three-dimensional visualization of the craniofacial patient: volume segmentation, data integration and animation." Orthodontics & Craniofacial Research 6 (August 2003): 66–71. http://dx.doi.org/10.1034/j.1600-0544.2003.237.x.
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Gisladottr, Undina, Drashko Nakikj, Rashi Jhunjhunwala, Nils Gehlenborg, and Gabriel Brat. "Data Visualization for Surgical Informed Consent to Communicate Personalized Risk and Patient Preferences." Journal of the American College of Surgeons 231, no. 4 (October 2020): S136—S137. http://dx.doi.org/10.1016/j.jamcollsurg.2020.07.263.
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Dixit, Ram A., Stephen Hurst, Katharine T. Adams, Christian Boxley, Kristi Lysen-Hendershot, Sonita S. Bennett, Ethan Booker, and Raj M. Ratwani. "Rapid development of visualization dashboards to enhance situation awareness of COVID-19 telehealth initiatives at a multihospital healthcare system." Journal of the American Medical Informatics Association 27, no. 9 (July 3, 2020): 1456–61. http://dx.doi.org/10.1093/jamia/ocaa161.
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Abstract The COVID-19 pandemic has led to the rapid expansion of telehealth services as healthcare organizations aim to mitigate community transmission while providing safe patient care. As technology adoption rapidly increases, operational telehealth teams must maintain awareness of critical information, such as patient volumes and wait times, patient and provider experience, and telehealth platform performance. Using a model of situation awareness as a conceptual foundation and a user-centered design approach we describe our process for rapidly developing and disseminating dashboard visualizations to support telehealth operations. We used a 5-step process to gain domain knowledge, identify user needs, identify data sources, design and develop visualizations, and iteratively refine these visualizations. Through this process we identified 3 distinct stakeholder groups and designed and developed visualization dashboards to meet their needs. Feedback from users demonstrated the dashboard’s support situation awareness and informed important operational decisions. Lessons learned are shared to provide other organizations with insights from our process.
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Peral, Jesús, Eduardo Gallego, David Gil, Mohan Tanniru, and Prashant Khambekar. "Using Visualization to Build Transparency in a Healthcare Blockchain Application." Sustainability 12, no. 17 (August 20, 2020): 6768. http://dx.doi.org/10.3390/su12176768.
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With patients demanding services to control their own health conditions, hospitals are looking to build agility in delivering care by extending their reach into patient and partner ecosystems and sharing relevant patient data to support care continuity. However, sharing patient data with several external stakeholders outside a hospital network calls for the development of a digital platform that is trusted by both hospitals and stakeholders, given that there is often no single entity supporting such coordination. In this paper, we propose a methodology that uses a blockchain architecture to address the technical challenge of linking disparate systems used by multiple stakeholders and the social challenge of engendering trust by using visualization to bring about transparency in the way in which data are shared. We illustrate this methodology using a pilot implementation. The paper concludes with a discussion and directions for future research and makes some concluding comments.
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Woods, Susan S., Neil C. Evans, and Kathleen L. Frisbee. "Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications." Journal of the American Medical Informatics Association 23, no. 3 (February 5, 2016): 491–95. http://dx.doi.org/10.1093/jamia/ocv199.
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Electronic health record content is created by clinicians and is driven largely by intermittent and brief encounters with patients. Collecting data directly from patients in the form of patient-generated data (PGD) provides an unprecedented opportunity to capture personal, contextual patient information that can supplement clinical data and enhance patients’ self-care. The US Department of Veterans Affairs (VA) is striving to implement the enterprise-wide capability to collect and use PGD in order to partner with patients in their care, improve the patient healthcare experience, and promote shared decision making. Through knowledge gained from Veterans’ and healthcare teams’ perspectives, VA created a taxonomy and an evolving framework on which to design and develop applications that capture and help physicians utilize PGD. Ten recommendations for effectively collecting and integrating PGD into patient care are discussed, addressing health system culture, data value, architecture, policy, data standards, clinical workflow, data visualization, and analytics and population reach.
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Hildebrand, C., J. Stausberg, K. H. Englmeier, and G. Kopanitsa. "Visualization of Medical Data Based on EHR Standards." Methods of Information in Medicine 52, no. 01 (2013): 43–50. http://dx.doi.org/10.3414/me12-01-0016.
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SummaryBackground: To organize an efficient interaction between a doctor and an EHR the data has to be presented in the most convenient way. Medical data presentation methods and models must be flexible in order to cover the needs of the users with different backgrounds and requirements. Most visualization methods are doctor oriented, however, there are indications that the involvement of patients can optimize healthcare.Objectives: The research aims at specifying the state of the art of medical data visualization. The paper analyzes a number of projects and defines requirements for a generic ISO 13606 based data visualization method. In order to do so it starts with a systematic search for studies on EHR user interfaces.Methods: In order to identify best practices visualization methods were evaluated according to the following criteria: limits of application, customizability, re-usability. The visualization methods were compared by using specified criteria.Results: The review showed that the analyzed projects can contribute knowledge to the development of a generic visualization method. However, none of them proposed a model that meets all the necessary criteria for a re-usable standard based visualization method. The shortcomings were mostly related to the structure of current medical concept specifications.Conclusion: The analysis showed that medical data visualization methods use hard-coded GUI, which gives little flexibility. So medical data visualization has to turn from a hardcoded user interface to generic methods. This requires a great effort because current standards are not suitable for organizing the management of visualization data. This contradiction between a generic method and a flexible and user-friendly data layout has to be overcome.
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N. D., Dr Oye,, and Emeje, G.D. "Designing Framework for Data Warehousing of Patient Clinical Records using Data Visualization Technique of Nigeria Medical Records." International Journal of Computer Applications Technology and Research 8, no. 4 (April 16, 2019): 122–34. http://dx.doi.org/10.7753/ijcatr0804.1007.
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Pisanelli, D. M., F. L. Ricci, F. Consorti, A. Piermattei, and F. Ferri. "Toward a General Model for the Description of Multimedia Clinical Data." Methods of Information in Medicine 37, no. 03 (July 1998): 278–84. http://dx.doi.org/10.1055/s-0038-1634537.
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AbstractThe patient folder integrates information originating from heterogeneous sources. For this reason computerized tools for patient data management should exploit the advantages of multimediality and offer an integrated environment for data presentation, and image and biosignal visualization. Object-oriented modeling is the best approach for designing systems for multimedia patient folder management.We propose an object-oriented model, able to define the entities constituting the patient folder and their logical organization. This model has sufficient flexibility to adapt to the most varied clinical environments. It allows the physician to structure the information needed for his/her patient folder without employing a programming language.
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Bauchwitz, Benjamin, Spencer Lynn, Peter Weyhrauch, Raj Ratwani, Danielle Weldon, Jessica Howe, and James Niehaus. "Thematic Issues in Analysis and Visualization of Emergency Department Patient Flow." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 7, no. 1 (June 2018): 132–39. http://dx.doi.org/10.1177/2327857918071034.
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Emergency Department (ED) congestion is a significant problem affecting clinical outcomes, patient satisfaction and hospital costs. Identifying and resolving bottlenecks in the flow of patients from the ED to eventual admission or discharge has the potential to reduce wait times, improve care for individual patients, and increase the volume of patients treated at the hospital over time. Our objective was to review methods commonly used to measure, analyze, and visualize patient flow, characterize drawbacks to these methods, and identify areas in which analysis and visualization can be improved to make bottlenecks easier to identify and resolve. Sixty-five articles obtained from PubMed and Google Scholar searches were reviewed to identify: (1) variables used to measure ED throughput; (2) downstream effects of ED congestion; (3) factors contributing to ED congestion; (4) techniques used to predict or respond to ED congestion; and (5) tools used to visualize data on ED throughput. Hospital resource availability, patient demographics, and environmental factors have all been used to predict contributors to ED congestion. Unfortunately, the hospital practices most critical to ED congestion are unlikely to change as they involve increasing the number of beds and providers or modifying protocols with EMS, insurance, and other care facilities. Therefore, interventions addressing optimization of ED resource allocation and visualization of ED data are the best avenue to yield more efficient ED operation.
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Abdelmoula, Walid M., Benjamin Balluff, Sonja Englert, Jouke Dijkstra, Marcel J. T. Reinders, Axel Walch, Liam A. McDonnell, and Boudewijn P. F. Lelieveldt. "Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data." Proceedings of the National Academy of Sciences 113, no. 43 (October 10, 2016): 12244–49. http://dx.doi.org/10.1073/pnas.1510227113.
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The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.
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Raslan, Osama, James Matthew Debnam, Leena Ketonen, Ashok J. Kumar, Dawid Schellingerhout, and Jihong Wang. "Stereoscopic Visualization of Diffusion Tensor Imaging Data: A Comparative Survey of Visualization Techniques." Radiology Research and Practice 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/780916.
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Diffusion tensor imaging (DTI) data has traditionally been displayed as a grayscale functional anisotropy map (GSFM) or color coded orientation map (CCOM). These methods use black and white or color with intensity values to map the complex multidimensional DTI data to a two-dimensional image. Alternative visualization techniques, such asVmaxmaps utilize enhanced graphical representation of the principal eigenvector by means of a headless arrow on regular nonstereoscopic (VM) or stereoscopic display (VMS). A survey of clinical utility of patients with intracranial neoplasms was carried out by 8 neuroradiologists using traditional and nontraditional methods of DTI display. Pairwise comparison studies of 5 intracranial neoplasms were performed with a structured questionnaire comparing GSFM, CCOM, VM, and VMS. Six of 8 neuroradiologists favoredVmaxmaps over traditional methods of display (GSFM and CCOM). When comparing the stereoscopic (VMS) and the non-stereoscopic (VM) modes, 4 favored VMS, 2 favored VM, and 2 had no preference. In conclusion, processing and visualizing DTI data stereoscopically is technically feasible. An initial survey of users indicated thatVmaxbased display methodology with or without stereoscopic visualization seems to be preferred over traditional methods to display DTI data.
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Park, Jihwan, Mi Jung Rho, Anatoly Dritschilo, Sean P. Collins, Simeng Suy, In Young Choi, and Seong K. Mun. "Prostate clinical outlook visualization for patients and clinicians considering cyberknife treatment: A personalized approach." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e16549-e16549. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e16549.
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e16549 Background: When a patient presents with localized prostate cancer, referral for radiation oncology consultation includes a discussion of likely outcomes of therapy. Among current radiation treatments for prostate cancers, hypo-fractionated stereotactic body radiation therapy (SBRT) has gained clinical acceptance based on efficacy, short duration of treatment and potential radiobiological advantages. The prostate Clinical Outlook (PCO) visualization system was developed to provide the patient and the clinician with a tool to visualize probable treatment outcomes using institutional, patient specific data for comparing results of treatment. Methods: We calculated the prostate cancer outlooks for each prospective patient using the EPIC-26 quality of life parameters based on clinical outcomes data of 580 cyberknife SBRT treated prostate cancer patients. We applied Kaplan-Meier analysis using the ASTRO method for disease free likely outcome and the PCO nomogram to calculate parameters for quality of life. Open source R, R/Shiny, and MySQL were used to develop a modularized architecture system. Results: PCO presents patient specific risk scores in a gauge chart style and risk free probability bar plots to compare treatment data of cyberknife patients. PCO generates reports, in PDF and HTML, which consist of a comparison chart of risk free probabilities and gauge charts of risk scores. This system is now being expanded as a web-based service to patients. Conclusions: PCO visualized patient specific likely outcomes were compared to treatment data from a single department, helping the patient and the clinician to visualize likely outcomes. The PCO approach can be expanded to other specialties of oncology with the flexible, modularized architecture which can be customized by changing parameter variables for the modules.
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Ahmed, Ryan, Tammy Toscos, Romisa Rohani Ghahari, Richard J. Holden, Elizabeth Martin, Shauna Wagner, Carly Daley, Amanda Coupe, and Michael Mirro. "Visualization of Cardiac Implantable Electronic Device Data for Older Adults Using Participatory Design." Applied Clinical Informatics 10, no. 04 (August 2019): 707–18. http://dx.doi.org/10.1055/s-0039-1695794.
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AbstractPatients with heart failure (HF) are commonly implanted with cardiac resynchronization therapy (CRT) devices as part of their treatment. Presently, they cannot directly access the remote monitoring (RM) data generated from these devices, representing a missed opportunity for increased knowledge and engagement in care. However, electronic health data sharing can create information overload issues for both clinicians and patients, and some older patients may not be comfortable using the technology (i.e., computers and smartphones) necessary to access this data. To mitigate these problems, patients can be directly involved in the creation of data visualization tailored to their preferences and needs, allowing them to successfully interpret and act upon their health data. We held a participatory design (PD) session with seven adult patients with HF and CRT device implants, who were presently undergoing RM, along with two informal caregivers. Working in three teams, participants used drawing supplies and design cards to design a prototype for a patient-facing dashboard with which they could engage with their device data. Information that patients rated as a high priority for the “Main Dashboard” screen included average percent pacing with alerts for abnormal pacing, other device information such as battery life and recorded events, and information about who to contact with for data-related questions. Preferences for inclusion in an “Additional Information” display included a daily pacing chart, health tips, aborted shocks, a symptom list, and a journal. These results informed the creation of an actual dashboard prototype which was later evaluated by both patients and clinicians. Additionally, important insights were gleaned regarding the involvement of older patients in PD for health technology.
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Struikman, Bas, Nadine Bol, Annelijn Goedhart, Julia C. M. van Weert, Esther Talboom-Kamp, Sanne van Delft, Anne E. M. Brabers, and Liset van Dijk. "Features of a Patient Portal for Blood Test Results and Patient Health Engagement: Web-Based Pre-Post Experiment." Journal of Medical Internet Research 22, no. 7 (July 20, 2020): e15798. http://dx.doi.org/10.2196/15798.
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Background The use of patient portals for presenting health-related patient data, such as blood test results, is becoming increasingly important in health practices. Patient portals have the potential to enhance patient health engagement, but content might be misinterpreted. Objective This study aimed to discover whether the way of presenting blood test outcomes in an electronic patient portal is associated with patient health engagement and whether this varies across different blood test outcomes. Methods A 2x3 between-subjects experiment was conducted among members of the Nivel Dutch Health Care Consumer Panel. All participants read a scenario in which they were asked to imagine themselves receiving blood test results. These results differed in terms of the presented blood values (ie, normal vs partially abnormal vs all abnormal) as well as in terms of whether the results were accompanied with explanatory text and visualization. Patient health engagement was measured both before (T0) and after (T1) participants were exposed to their fictive blood test results. Results A total 487 of 900 invited members responded (response rate 54%), of whom 50.3% (245/487) were female. The average age of the participants was 52.82 years (SD 15.41 years). Patient health engagement saw either a significant decrease or a nonsignificant difference in the experimental groups after viewing the blood test results. The mean difference was smaller in the groups that received blood test results with additional text and visualization (meanT0 5.33, SE 0.08; meanT1 5.14, SE 0.09; mean difference 0.19, SE 0.08, P=.02) compared with groups that received blood test results without explanatory text and visualization (meanT0 5.19, SE 0.08; meanT1 4.55, SE 0.09; mean difference 0.64, SE 0.08, P<.001). Adding text and visualization, in particular, reduced the decline in patient health engagement in participants who received normal results or mixed results (ie, combination of normal and abnormal results). Conclusions Adding text and visualization features can attenuate the decrease in patient health engagement in participants who receive outcomes of a blood test via a patient portal, particularly when blood test results are (partly) normal. This suggests that explanatory text and visualization can be reassuring. Future research is warranted to determine whether these results can be generalized to a patient population who receive their actual blood test results.
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Monsen, Karen, Sung-Heui Bae, Wenhui Zhang, and Kavita Radhakrishnan. "Visual Analytics for Pattern Discovery in Home Care." Applied Clinical Informatics 07, no. 03 (July 2016): 711–30. http://dx.doi.org/10.4338/aci-2016-03-ra-0049.
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SummaryVisualization can reduce the cognitive load of information, allowing users to easily interpret and assess large amounts of data. The purpose of our study was to examine home health data using visual analysis techniques to discover clinically salient associations between patient characteristics with problem-oriented health outcomes of older adult home health patients during the home health service period.Knowledge, Behavior and Status ratings at discharge as well as change from admission to discharge that was coded using the Omaha System was collected from a dataset on 988 deidentified patient data from 15 home health agencies. SPSS Visualization Designer v1.0 was used to visually analyze patterns between independent and outcome variables using heat maps and histograms. Visualizations suggesting clinical salience were tested for significance using correlation analysis.The mean age of the patients was 80 years, with the majority female (66%). Of the 150 visualizations, 69 potentially meaningful patterns were statistically evaluated through bivariate associations, revealing 21 significant associations. Further, 14 associations between episode length and Charlson co-morbidity index mainly with urinary related diagnoses and problems remained significant after adjustment analyses. Through visual analysis, the adverse association of the longer home health episode length and higher Charlson co-morbidity index with behavior or status outcomes for patients with impaired urinary function was revealed.We have demonstrated the use of visual analysis to discover novel patterns that described high-needs subgroups among the older home health patient population. The effective presentation of these data patterns can allow clinicians to identify areas of patient improvement, and time periods that are most effective for implementing home health interventions to improve patient outcomes. Citation: Radhakrishnan K, Monsen KA, Bae S-H, Zhang W. Visual analytics for pattern discovery in home care: Clinical relevance for quality improvement.
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Eichelberger, Jenna, Christine Zirges, Madison Himelright, and Cara Wiskow. "Implementation of Surgical Site Infection (SSI) Gap Analysis and Data Visualization Dashboards to Drive Organizational Change." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s279. http://dx.doi.org/10.1017/ice.2020.848.
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Background: Surgical site infections (SSIs) are a major healthcare quality issue; they lead to increased morbidity and mortality rates. They also prolong the length of stay and increase the cost to the patient and the healthcare system. Depending on the procedure, the risk of death is 2 to 11 times greater for patients with an SSI than for patients without an SSI. Additionally, the financial burden and patient burden is considerable; it ranks as the most common and costly of hospital-acquired infections (HAIs) and can extend a patient’s length of stay by 11.2 days. The risk of developing an SSI is affected by multiple factors at the patient, operative, and institutional levels. Methods: A Midwestern healthcare system conducted a review of the recommended best-practice guidelines that are currently accepted as the standards of care in US healthcare facilities. A gap analysis instrument for colorectal SSI prevention was drafted and reviewed for content validity and accuracy by field experts. Hospital infection preventionists worked in conjunction with operating room leaders to disseminate the survey to staff. Responses were collected from June 5 to June 30, 2019. Concurrently, the system infection preventionist team developed a standardized SSI dashboard template that could be used at the hospital, regional, and system level to visualize SSI infection counts, standardized infection ratios (SIRs) as well as procedure count data. These dashboard reports are updated and distributed on a monthly basis to each hospital’s campus executive team and other leaders. Federal- and state-required procedures were included and additional procedures were included based on hospital risk. Results: In total, 35 responses were recorded from 8 ministries across the system. Infection preventionists, operating room directors, physicians, nurses, and surgical technologists were represented among the respondents. The following areas were identified areas for improvement: use of chlorhexidine gluconate (CHG) bathing kit, mechanical bowel preparation with preoperative oral antibiotics, hair removal practices, use of fascial wound protector, maintenance of patients’ blood glucose levels, glove and gown changing procedures, and use of antimicrobial-coated sutures. The development and distribution of the SSI dashboard increased awareness and knowledge of SSIs by hospital and system-level leaders. Conclusions: The implementation of both the gap analysis and dashboard reports improved the awareness areas needed for improvement and knowledge of the burden of SSIs. These findings will drive discussions within the hospitals and at the system-level to implement evidence-based practice to improve care and decrease infections as well as guide the development of SSI patient care bundles.Funding: NoneDisclosures: None
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Ye, Jiancheng. "The impact of electronic health record–integrated patient-generated health data on clinician burnout." Journal of the American Medical Informatics Association 28, no. 5 (April 2, 2021): 1051–56. http://dx.doi.org/10.1093/jamia/ocab017.
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Abstract Patient-generated health data (PGHD), such as patient-reported outcomes and mobile health data, have been increasingly used to improve health care delivery and outcomes. Integrating PGHD into electronic health records (EHRs) further expands the capacities to monitor patients’ health status without requiring office visits or hospitalizations. By reviewing and discussing PGHD with patients remotely, clinicians could address the clinical issues efficiently outside of clinical settings. However, EHR-integrated PGHD may create a burden for clinicians, leading to burnout. This study aims to investigate how interactions with EHR-integrated PGHD may result in clinician burnout. We identify the potential contributing factors to clinician burnout using a modified FITT (Fit between Individuals, Task and Technology) framework. We found that technostress, time pressure, and workflow-related issues need to be addressed to accelerate the integration of PGHD into clinical care. The roles of artificial intelligence, algorithm-based clinical decision support, visualization format, human-computer interaction mechanism, workflow optimization, and financial reimbursement in reducing burnout are highlighted.
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Faiola, Anthony, Preethi Srinivas, and Simon Hillier. "Improving Patient Safety: Integrating Data Visualization and Communication Into Icu Workflow to Reduce Cognitive Load." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 4, no. 1 (June 2015): 55–61. http://dx.doi.org/10.1177/2327857915041013.
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Del Fiol, Guilherme, Jorie Butler, Yarden Livnat, Jeanmarie Mayer, Matthew Samore, Makoto Jones, Charlene Weir, and Don Roosan. "Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain." Applied Clinical Informatics 07, no. 02 (April 2016): 604–23. http://dx.doi.org/10.4338/aci-2015-12-ra-0182.
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SummaryBig data or population-based information has the potential to reduce uncertainty in medicine by informing clinicians about individual patient care. The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population’s database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes.We used the Veteran’s Affairs (VA) database to identify similar complex patients based on a similar complex patient case. Study outcomes measures were 1) preferences for population information display 2) time looking at the population display, 3) time to read the chart, and 4) appropriateness of plans with pre-and post-presentation of population data. Finally, we redesigned the population information display based on our findings from this study.The qualitative data analysis for preferences of population information display resulted in four themes: 1) trusting the big/population data can be an issue, 2) embedded analytics is necessary to explore patient similarities, 3) need for tools to control the view (overview, zoom and filter), and 4) different presentations of the population display can be beneficial to improve the display. We found that appropriateness of plans was at 60% for both groups (t9=-1.9; p=0.08), and overall time looking at the population information display was 2.3 minutes versus 3.6 minutes with experts processing information faster than non-experts (t8= -2.3, p=0.04).A population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care. The preferences identified for the population information display will guide future health information technology system designers for better and more intuitive display.
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Tscholl, David Werner, Julian Rössler, Sadiq Said, Alexander Kaserer, Donat Rudolf Spahn, and Christoph Beat Nöthiger. "Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology: A Qualitative Review of the Primary Research." Sensors 20, no. 7 (April 9, 2020): 2112. http://dx.doi.org/10.3390/s20072112.
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Visual Patient technology is a situation awareness-oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements—a language humans can easily perceive and interpret—on a patient avatar model in real time. In this review, we summarize the current state of the research on the Visual Patient, including the technology, its history, and its scientific context. We also provide a summary of our primary research and a brief overview of research work on similar user-centered visualizations in medicine. In several computer-based studies under various experimental conditions, Visual Patient transferred more information per unit time, increased perceived diagnostic certainty, and lowered perceived workload. Eye tracking showed the technology worked because of the way it synthesizes and transforms vital sign information into new and logical forms corresponding to the real phenomena. The technology could be particularly useful for improving situation awareness in settings with high cognitive demand or when users must make quick decisions. This comprehensive review of Visual Patient research is the foundation for an evaluation of the technology in clinical applications, starting with a high-fidelity simulation study in early 2020.
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Wong, Hui-Li, Koen Degeling, Azim Jalali, Jeremy David Shapiro, Suzanne Kosmider, Rachel Wong, Belinda Lee, et al. "Answering real-world questions using real-world data: Understanding dynamic treatment decisions and outcomes in metastatic colorectal cancer (mCRC)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e18061-e18061. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18061.
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e18061 Background: The wide range of possible combinations and sequences available for mCRC treatment presents a major challenge to clinicians, who need to determine the optimal approach for an individual patient or patient subset. In the absence of clinical trial evidence, real world data are an increasingly valuable resource that can be utilized not only to understand treatment patterns and outcomes in routine practice, but also to define an optimal treatment strategy for individual patients across multiple lines of therapy. Methods: Real world data from an Australian mCRC registry were used to develop an interactive data visualization tool that displays treatment variation, customizable to different levels of detail and specific patient subsets, based on patient and disease characteristics. Next, a discrete event simulation model was developed to predict progression-free (PFS) and overall survival (OS) for first line palliative treatment with doublet chemotherapy alone or with bevacizumab, based on data of 867 patients that were treated accordingly. Results: Of 2694 Australian patients enrolled, 2057 (76%) started 1st line treatment with chemotherapy and/or a biologic agent, 1087 (40%) and 428 (16%) received 2nd and 3rd line therapy, respectively. Combined, these 3 lines of treatment accounted for 733 unique sequences. After recoding treatment to the most intensive chemotherapy and the first exposed biologic, 472 unique sequences remained. In exploratory analyses, the simulation model estimated that median 1st line PFS (95% CI) of 219 (25%) patients could be improved from 175 (156, 199) to 269 days (247, 293) by targeting a different treatment. Conclusions: This was an initial exploration of the potential for data visualization and simulation modeling to inform understanding of practice in mCRC and to guide clinical decision making. Such tools allow clinicians and health system providers to define variation in practice patterns and to identify opportunities to improve care and outcomes. Ultimately, the aim is to improve the delivery of personalized cancer care, where other applications such as conditional survival and cost-effectiveness analyses may be useful.
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Derozier, Vincent, Sylvie Arnavielhe, Eric Renard, Gérard Dray, and Sophie Martin. "How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management." Journal of Diabetes Science and Technology 13, no. 4 (May 21, 2019): 698–707. http://dx.doi.org/10.1177/1932296819847739.
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Background: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. Method: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient’s overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. Results: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. Conclusions: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.
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Pevnick, Joshua M., Yaron Elad, Lisa M. Masson, Richard V. Riggs, and Ray G. Duncan. "Patient-Initiated Data: Our Experience with Enabling Patients to Initiate Incorporation of Heart Rate Data into the Electronic Health Record." Applied Clinical Informatics 11, no. 04 (August 2020): 671–79. http://dx.doi.org/10.1055/s-0040-1716538.
Full textAbstract:
Abstract Background Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. Objective This study aimed to develop a protocol to address millions of unreviewed heart rates. Methods As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. Results We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. Conclusion We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology.