Figure 5 - uploaded by Filip Dabek
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(Left) When a user mouses over a node or a path, they are presented with detailed information in a tooltip about the number of times the specific node/path had occurred, the number of patients that took that route, and the average occurrences per patient. (Middle) In this panel the information for a node has been displayed in tabs: a chart, raw data, and inbound/outbound paths. The selected tab, chart, displays an area chart of how many times the diagnosis occurred over time. (Right) In this panel the user had selected the "Outbound Paths" tab which displays all of the paths from the selected node to every other node and the count and percentage of each path occurring. In addition, each column in the table of outbound paths is sortable in ascending and descending order.
Source publication
The adoption of electronic health records (EHRs) and the increased participation of hospitals and clinics in health information exchange systems have resulted in unique longitudinal data that describes a patient's clinical trajectory. In-depth analysis of that information is important to better understand the general course to recovery or the evolu...
Contexts in source publication
Context 1
... we give the user the ability to manipulate the graph further by altering the shape, color, and outline of nodes to their own encodings, as can be seen in Figure 4. Using these encodings in just a simple graph provides a user with a deeper understanding of the flow of patients between diagnoses. Along with the encodings, users are able to hover their mouse over a node and/or a path to be presented with the raw data that is encoding into that aspect of the graph (Fig- ure 5 Left). Furthermore, by clicking on a node users are presented with a new panel that contains an area chart, data, and inbound and outbound paths. ...
Context 2
... this technique, users will be able to identify that a node in one graph is larger than another or that a certain group of patients follows through a different path flow than the other based on the line thicknesses. With the data panel that was previously explained, shown in Figure 5 Left, users are able to manipulate the data that is used for each individual graph allowing for comparisons such as: male vs female, army vs navy, 20-30 vs 40-50, as well as many other combinations which will aid in the process of identifying the differences in patients and the varying effects. ...
Similar publications
Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records:...
Citations
... Graph layout and visualization: There have been several studies into the visualization and lay outing of graph-structured data, e.g., biomedical or healthcare data, for aiding human interpretability and pattern recognition [18]. ...
Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.
... More details about the items and their concrete sets of attributes are available in Multimedia Appendix 2 [15,16,18,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. ...
... Medical context in the corpus was mainly clinical research (13/22, 59%), clinical care only (4/22, 18%), and both areas (5/22, 22%; Table 1). [45], Klimov and Shahar [15], Wang et al [16], and Borhani et al [38] Clinical care 13 (59) Gschwandtner et al [18], Gotz and Wongsuphasawat et al [41], Stubbs et al [35], Tao et al [46], Gotz et al [42], Cho et al [47], Browne et al [48], Dabek et al [49], Kamaleswaran et al [40], Gomov et al [39], Wildfire et al [34], Nickerson et al [50], and Polack et al [37] Clinical research 5 (22) Guo et al [43], Rogers et al [36], van Dortmont et al [33], Magallanes et al [44], and Dahlin et al [51] Clinical research or clinical care ...
... Medical data types in included articles. Klimov and Shahar[15], Wang et al[16], Borhani et al[38], Gschwandtner et al[18], Stubbs et al[35], Gotz and Stavropoulos[42], Browne et al[48], Gomov et al[39] Wildfire et al[34], Guo et al[43], van Dortmont et al[33], and Magallanes et al[44] Stubbs et al[35], Cho et al[47], Browne et al[48], Gomov et al[39], Wildfire et al[34], Nickerson et al[50], Polack et al[37], and van Dortmont et al[33] Wang et al[16], Gotz and Wongsuphasawat[41], Stubbs et al[35], Tao et al[46], Gomov et al[39], Guo et al[43], Rogers et al[36], van Dortmont et al[33], and Dahlin et al[51] Stubbs et al[35], Tao et al[46], Gotz and Stavropoulos[42], Dabek et al[49], Gomov et al, 2017[39], Guo et al[43], van Dortmont et al[33], and Dahlin et al[51] ...
Background
Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns.
Objective
This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques.
Methods
This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding.
Results
Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively.
Conclusions
The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.
... In recent years, several data mining and statistical approaches have been proposed to extract patterns from sequential data of CTs, such as formal concept analysis [15], latent class analysis [16,17], neural network [18], multi-state Markov model [12] and exponential proportional hazards mixture model [19]. However, getting the picture of complex temporal event sequences is not straightforward for non-experts, despite the variety of strategies available to develop visual analytic tools and graph-based approaches [20][21][22]. This emphasizes the need for appropriate methods to describe and visualize sequential patterns of real-world CTs for evidence-based decision-making. ...
Background:
Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts. Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients' characteristics for each CT type.
Methods:
This is an observational cohort study extracted from Quebec's medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a 'tailored' multidimensional State Sequence Analysis, based on the "6W" model of Care Trajectories. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why"), and the speciality of care providers ("which"). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients' characteristics using the usual descriptive statistics.
Results:
The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization.
Conclusions:
The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.
... A graph-based method for detecting anomalous blood glucose patterns from a diabetes dataset is a novel research area. Dabek et al. [9] previously built an interactive, graph-based visualization system for analyzing the content of electronic health records. Although the system was capable of comparing the clinical trajectory of an individual patient against that of the rest of the group, it was not specifically designed to detect anomalous patterns from graphs. ...
... percutaneous transluminal coronary angioplasty). Similar to Dabek et al. [9], the method was not tailored for anomaly detection applications. ...
Diabetes mellitus is a multifactorial chronic disease with many possible contributing factors. Performing anomaly detection on datasets collected from large epidemiological diabetes studies has the potential to unearth previously unknown factors contributing to the pathogenesis of diabetes mellitus. This paper proposes a novel method for detecting anomalous blood glucose trajectories of individuals in a longitudinal diabetes dataset. We formulate the anomaly detection problem as the problem of discovering contextually homogeneous communities in diabetes similarity graphs, from which individuals exhibiting unexpected progression of blood glucose could then be identified. Specifically, we employ community detection and Bayesian techniques to identify communities with the highest degree of anomaly. Our results successfully pointed to individuals with contrasting blood glucose trajectories, even though they demonstrated highly similar social demographics and lifestyle characteristics. Domain expert evaluation supports the efficacy of our proposed method.
... The following diagram shows the crisp of the VA result. Filip Dabek et al [6] presented web-based graph visualization method to interactively analyse clinical trajectories of group of patients affected by same disease. It allows the user to set different conditions, thresholds to exert patient related critical information. ...
... Several approaches have been undertaken in the literature that empower visualization and visual modeling. In [3], patient clustering based on data attributes and cohort comparison is explored, while [4] uses highlighting of similar and of missing data in correlated patient records. Visual diagnosis of diseases is also common, as is the case of range-based, trajectory-based and pattern-based diagnosis [5], where the performance of the disease is visualized to predict the best diagnosis. ...
Visualisations in Electronic Health Records (EHRs) are crucial for clinical care. Since clinicians need to quickly diagnose and treat their patients, having appropriate ways to visualise patients’ characteristics and issues documented in the EHR, can be instrumental. However, the existing literature has not yet summarised the characteristics and lessons learned from the studies on patient dashboards for clinical care. Our review analysed patient dashboards, that visualised EHR data to support clinical care, and which were evaluated with end-users. We read papers from Human-Computer Interaction, Information Visualisation, and Medical Informatics, focusing on the user interfaces and the end-user evaluation results. From a set of 3545 articles, we selected 30 studies, which were analysed using Thematic Analysis. Results provide an understanding of the patient dashboard designs, the visualisation techniques employed, the data represented, as well as the lessons learned from this body work; which should contribute to future designs.
Background
Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.
Methods
A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.
Results
254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.
Discussion
Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.
Conclusion
This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.
