TimeLine: Visualizing Integrated Patient Records

ArticleinIEEE Transactions on Information Technology in Biomedicine 11(4):462-73 · August 2007with 162 Reads
Abstract
An increasing amount of data is now accrued in medical information systems; however, the organization of this data is still primarily driven by data source, and does not support the cognitive processes of physicians. As such, new methods to visualize patient medical records are becoming imperative in order to assist physicians with clinical tasks and medical decision-making. The TimeLine system is a problem-centric temporal visualization for medical data: information contained with medical records is reorganized around medical disease entities and conditions. Automatic construction of the TimeLine display from existing clinical repositories occurs in three steps: 1) data access, which uses an eXtensible Markup Language (XML) data representation to handle distributed, heterogeneous medical databases; 2) data mapping and reorganization, reformulating data into hierarchical, problemcentric views; and 3) data visualization, which renders the display to a target presentation platform. Leveraging past work, we describe the latter two components of the TimeLine system in this paper, and the issues surrounding the creation of medical problems lists and temporal visualization of medical data. A driving factor in the development of TimeLine was creating a foundation upon which new data types and the visualization metaphors could be readily incorporated.
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    To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level. We utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support. In both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F1 score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F1 score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy. The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited. Results contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy.
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    As our ability to access the abundance of clinical data grows, it is imperative that methods to organize and to visualize this information be in place so as not to overwhelm users: increasingly, users are faced with information overload. Moreover, the manner of presentation is fundamental to how such information is interpreted, and can be the turning point in uncovering new insights and knowledge about a patient or a disease. And of course, medical imaging is itself an inherently visual medium. This chapter presents work related to the visualization of medical data, focusing on issues related to navigation and presentation by drawing upon imaging and other disciplines for examples of display and integration methods. We first cover different visual paradigms that have been developed (e.g., icons, graphs), grouped along dimensions that emphasize the different types of data relationships and workflow. Subsequently, issues related to combining these visualizations are given. As no single graphical user interface (GUI) can accommodate all users and the spectrum of tasks seen in the healthcare environment, the ultimate goal is to create an adaptive graphical interface that integrates clinical information so as to be conducive to a given user's objectives: efforts in this direction are discussed. Throughout, we describe applications that illustrate the many open issues revolving around medical data visualization.
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    Problem-oriented charting is form of medical documentation that organizes patient data by a diagnosis or problem. In this review, we discuss the history and current use of problem-oriented charting by critically evaluating the literature on the topic. We provide insights with regard to our own institutional use of problem-oriented charting and potential opportunities for research.
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    This chapter explores the mining, visualization, and analysis of healthcare data, and examines ways such data can be managed efficiently to positively affect an organization's ability to generate revenue, control costs, and mitigate risks. Data mining can help patients get tailored health alerts, help physicians detect disease earlier, and help insurers reduce fraud and abuse. Knowledge discovery in databases (KDD) was one of the early methodologies to encompass data mining and connect it with other elements. The prevalence of data visualization (DV) found its way into healthcare practice. The high volume of medical data and complex nature of the medical knowledge require an efficient tool to share and communicate these types of information between patients and physicians. Within the healthcare context, social network analysis (SNA) models are useful for understanding human behavior, particularly health behaviors. The chapter also discusses the data envelopment analysis (DEA) and multiple-criteria decision making (MCDM).
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    Timelines are often used for summarizing complex, time-evolving, sequences of events. In this work, we propose Linea, a tool that helps users build timelines from unstructured text. Besides providing interactive tools for browsing, selecting, editing, and filtering events, Linea includes mechanisms to build smart defaults, thus providing good starting points for users to create their own timelines. The core component is a novel visualization widget called event matrix, a matrix designed to explore events over time and in different time scales. We illustrate the strengths of our tool with a case study showing that important events can be browsed, timelines can be easily built, and show the result of an information user evaluation.
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    Over the last two decades there has been considerable deliberation, experience, and research in the arena of Health Information Technology (HIT), Electronic Medical Records (EMR), Electronic Health Records (EHR), and more recently, Electronic Personal Health Records (PHR). Despite the challenges involved in adopting these systems and technologies, there is consensus that they bring significant value to the delivery of trusted and affordable healthcare. The investment involved and the impact on customers, clinical and non-clinical staff, and processes are significant and far reaching. This chapter attempts to synthesize the vast amount of information, experience, and implementation perspectives related to Electronic Health Records with the intent of assisting healthcare institutions and key stakeholders make informed choices as they embark on designing, developing, and implementing an EHR. EHR considerations, challenges, opportunities, and future directions are also addressed. The chapter highlights the power of management engineering to facilitate planning, implementation, and sustainability of the EHR, a critical asset for a healthcare organization and the overall healthcare industry.
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    Thus far, discussion has focused on issues related to collecting and analyzing clinical data. Yet central to the challenge of informatics is the organization of all of this information to enable a continuum of healthcare and research applications: the type of attributes supported in characterizing an entity within a data model and the scope of relationships defined between these objects determine the ease with which we can retrieve information and ultimately drive how we come to perceive and work with the data. This chapter overviews several data models that have been proposed over the years to address representational issues inherent to medical information. Three categories of data models are covered: spatial models, which are concerned with representing physical and anatomical relations between objects; temporal models that embody a chronology and/or other time-based sequences/patterns; and clinically-oriented models, which systematically arrange information around a healthcare abstraction or process. Notably, these models no longer serve the sole purpose of being data structures, but are also foundations upon which rudimentary logical reasoning and inference can occur. Finally, as translational informatics begins to move toward the use of large clinical datasets, the context under which such data are captured is important to consider; this chapter thus concludes by introducing the idea of a the phenomenon-centric data model (PCDM) that explicitly embeds the principles of scientific investigation and hypotheses with clinical observations.
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    Background Regional and epidemiological cancer registries are important for cancer research and the quality management of cancer treatment. Many technological solutions are available to collect and analyse data for cancer registries nowadays. However, the lack of a well-defined common semantic model is a problem when user-defined analyses and data linking to external resources are required. The objectives of this study are: (1) design of a semantic model for local cancer registries; (2) development of a semantically-enabled cancer registry based on this model; and (3) semantic exploitation of the cancer registry for analysing and visualising disease courses. Results Our proposal is based on our previous results and experience working with semantic technologies. Data stored in a cancer registry database were transformed into RDF employing a process driven by OWL ontologies. The semantic representation of the data was then processed to extract semantic patient profiles, which were exploited by means of SPARQL queries to identify groups of similar patients and to analyse the disease timelines of patients. Based on the requirements analysis, we have produced a draft of an ontology that models the semantics of a local cancer registry in a pragmatic extensible way. We have implemented a Semantic Web platform that allows transforming and storing data from cancer registries in RDF. This platform also permits users to formulate incremental user-defined queries through a graphical user interface. The query results can be displayed in several customisable ways. The complex disease timelines of individual patients can be clearly represented. Different events, e.g. different therapies and disease courses, are presented according to their temporal and causal relations. Conclusion The presented platform is an example of the parallel development of ontologies and applications that take advantage of semantic web technologies in the medical field. The semantic structure of the representation renders it easy to analyse key figures of the patients and their evolution at different granularity levels.
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    Medical data are generated in large quantities every day. There are many aspects to medical data, including clinical information, administration data, and time granularity, and the number of chronic disease patients increases yearly. However, clinicians have limited time to review and process patient data. Information visualization is therefore required for the efficient management and utilization of the data. The management of chronic disease requires information technology if it is to improve the quality and efficiency of health care. In this paper, we consider the visualization of medical data, focusing on the diversity of medical data and chronic disease care.
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    Multiple electronic health records (EHR) have been interactively represented using information visualization (IV) formats for different scenarios. Based on physicians’ requirements, database professionals develop query sets and perform database operations but they are less involved in the development process of IV tools. Professional use of IV tools is less adapted by physicians due to incompatibility of database administrator’s (DBA) lack of understanding with their end user requirements. Absence of training, poor understanding on IV requirement, and incomplete database knowledge oriented DBA generates incomplete data visualization. The key features with DBA are understanding end user requirements, database management system (DBMS) limitations knowledge, and future improvement perspective from IV point of view. This paper presents the outcome of a survey-based questionnaire study conducted on DBAs and provides an outline of their knowledge factors identification and performance to IV applications.
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    Information visualization is a significant, measured and potential advantage of Electronic Health Records (EHR) for understanding patient data to physicians. Doctors take primarily interest in understanding a complete knowledge from different portions of visualization that is comprised of numbers, pictures, texts and colored icons. However, complex presentation due to non-identification of different knowledge driven factors in EHR tools results in lesser attraction for its daily use in public health care units. Understanding and analysis of these factors that affect the utilization and sole understanding of visualization in EHR by physicians is main issue. Based on previous work by different researchers in same domain, these factors are shortlisted and compared to analyze the use of such tools by doctors. A survey based questionnaire study with a group of doctors is conducted using a approach to understand the deficiency areas for EHR tools. This figure out the requirements and expectations of doctors with EHR that may also assist other stakeholders like database professionals and visualization designers to align the tools based upon physician’s requirements. Results are analyzed based on feedback of doctors from emergency and outdoor departments of hospitals as they are first to deal with patients and their data in daily routine. Facts are represented in two different categories where first is mentioning the rate of knowledge skills of physicians about visualization and second is mentioning future expectations from such tools. Results concluded that EHR tools should facilitate in more insight about multiple patients history and more skills improvement is required for doctors to use such tools. This research paper is also an integral part of our ongoing effort for developing an integrated model, CARE1.0 that has been proposed in one of the previous work.
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    Information Visualization (IV) in Electronic Health Records (EHR) is a significant source of patient knowledge using available medical data set(s). Although IV applications most focus on demands of doctors as considering primary stake holders for this use but unavailability of simplified, ease to use and a user friendly application is due to non-consideration and absence of database contextual factors in EHR tools development. Efficient query handling and data retrieval is carried out using various visual health data related queries based on structure and hierarchical flow of event based information. Conventional visual EHR applications ignore the fact of heterogeneity within the different data entities, i.e., text, number, figure and other form of data and more focus on presentation of data rather than considering the pre alignment and configuration of database affecting results. This research work is carried out solely for highlighting the needs of database professional and influencing factors affecting the efficacy of query based visualization results in EHR. More emphasis is on future needs of database professionals point of view in context of developing a Visual Analytic System that can address the needs of doctors and other stake holders based on capacity and performance ability of given database based on encoupled factors. It also represents the gaps areas of databases within visualizations and determines the solution by providing highly demanded scope areas for visualization in EHR. This will yield to a contribution to formulate database model within ongoing research for CARE1.0 as a complete EHR visualization model.
  • Chapter
    Information Technologies can play a major role in how healthcare services are designed, offered and utilized. In this chapter, we show how IT can be utilized in healthcare. More specifically, the topics of health informatics, electronic health, electronic health records, and telemedicine are discussed. Also, HIPAA is discussed in the context of IT and healthcare. Detailed discussion on medical records and several challenges in the wide-scale deployment are also presented. The future of telemedicine is also addressed.
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    Background: Visualization 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. Methods: 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 de-identified 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. Results: 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. Conclusions: 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.
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    Visualization of the timelines is commonly used in many different areas, such as historical education, medical systems, criminal investigations or social networks. In order to understand presented data, it is desirable to display not only the information itself, but also the relations between visualized entities and other, more detailed information, that will simplify orientation in the created timeline. As many of such information can be obtained from automatic sources, the amount of displayed data can be quite large and it is necessary to find a ways how to facilitate the navigation in the visualized data set. We present a method based on the PageRank evaluation of importance of each visualized entity, along with the tagging that allow user to specify which topics are interested to him. We describe the way how the weight of each entity is calculated and how is the whole visualization created. Along with this, we also present a small user study that we performed to validate our approach.
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    In Web environments for collaborative problem solving or collaborative learning, the process of a collaborative activity can be as important as or even more important than the outcome of the activity. Furthermore, processes need to be flexible to enable free exploration and creativity. Many online systems fail to optimally support those activities, because they are document-centric, or provide not enough flexibility regarding processes and meaning negotiation. In this paper, we propose a timeline metaphor that enables flexible processes and permits users to see the current state of a process "at a glance", i.e. what already has been done and what are possible next steps. We describe how literature and participatory practices informed the design of a low-fidelity prototype. As a first result, we present and discuss the prototype that is contextualized to the domain of lifelong learning among special education teachers.
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    Using electronic rather than paper-based record systems improves clinicians' information retrieval from patient narratives. However, few studies address how data should be organized for this purpose. Information retrieval from clinical narratives containing free text involves two steps: searching for a labeled segment and reading its content. The authors hypothesized that physicians can retrieve information better when clinical narratives are divided into many small, labeled segments ("high granularity"). The study tested the ability of 24 internists and 12 residents at a teaching hospital to retrieve information from an electronic medical record--in terms of speed and completeness--when using different granularities of clinical narratives. Participants solved, without time pressure, predefined problems concerning three voluminous, inpatient case records. To mitigate confounding factors, participants were randomly allocated to a sequence that was balanced by patient case and learning effect. Compared with retrieval from undivided notes, information retrieval from problem-partitioned notes was 22 percent faster (statistically significant), whereas retrieval from notes divided into organ systems was only 11 percent faster (not statistically significant). Subdividing segments beyond organ systems was 13 percent slower (statistically significant) than not subdividing. Granularity of medical narratives affected the speed but not the completeness of information retrieval. Dividing voluminous free-text clinical narratives into labeled segments makes patient-related information retrieval easier. However, too much subdivision slows retrieval. Study results suggest that a coarser granularity is required for optimal information retrieval than for structured data entry. Validation of these conclusions in real-life clinical practice is recommended.
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    A 1991 Institute of Medicine report called computer-based patient records (CPRs) an essential technology for health care and recommended widespread implementation of CPRs within a decade. Although a broader understanding of CPRs has been achieved and more leadership for CPR development exists today, substantial work remains to be accomplished. Critical tasks include developing a detailed specification of the CPR concept, strengthening standards development efforts through greater federal funding and involvement, developing national policy on key issues, and identifying funding sources for CPR system implementation. This article reviews the major views and issues of the 1991 IOM report and relates them to subsequent developments.
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    Poring over the medical record of brain tumor patients for pertinent history can be an overwhelming task for the neuroradiologist. The evaluation of an imaging study in a brain tumor patient involves examining the prior imaging and clinical documents for recent intervention potentially affecting the appearance of the brain and then drawing a conclusion and rendering a report based on the contextual information obtained. In complex cases, the radiologist can spend much of his/her time trying to locate the appropriate documents. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information. Our research involves three areas in improving the clinical workflow for neuroradiologists: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.
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    Episode creation describes the task of clas-sifying medical events and related clinical data to a high-level concept, such as a disease, illness or care. Traditional approaches to the problem have been lim-ited to feature-poor claims records utilizing simplis-tic strategies such as rules, filters and code transfor-mations. However, these approaches have suffered a number of shortcomings including: inconsistencies in defining episodes; lack of sufficient information to infer episodes; and differences in methods for diag-nosing and resolving episodes. With the advent of the electronic medical record, which contains mul-tiple sources of patient-related information, data is now accessible to construct more accurate and refined episodes. A probabilistic model is described that uti-lizes features extracted from different medical reposi-tories (e.g., claims records, structured medical reports) to guide a context-sensitive combinatorial approach for associating medical data contained in a patient's record with their underlying episodes. Results from a preliminary evaluation of our prototype on a set of knee pain episodes is presented.
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    Poring over the medical record of brain tumor patients for pertinent history can be an overwhelming task for the neuroradiologist. The evaluation of an imaging study in a brain tumor patient involves examining the prior imaging and clinical documents for recent intervention potentially affecting the appearance of the brain and then drawing a conclusion and rendering a report based on the contextual information obtained. In complex cases, the radiologist can spend much of his/her time trying to locate the appropriate documents. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information. Our research involves three areas in improving the clinical workflow for neuroradiologists: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.
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    The collision of computer-based technologies and the medical environment is resulting in an increasingly electronic multimedia patient record, consisting of not only the traditional types of data (e.g., clinic notes and laboratory reports), but also digital images (e.g., computed tomography and magnetic resonance imaging) and other visual representations of patient data (e.g., pulmonary function graphs and urodynamic charts). Given the increasing amount of data made available to physicians, it is not only critical that the totality of a patient's medical record be accessible to a clinician, but that the diverse data be integrated and presented in a manner conducive to patient management: key information should be easily discovered. This paper describes a problemcentric time-based visualization of urologic conditions, whereby a patient's medical history is automatically organized around a medical problem and presented as a graphic chronology. Urology-related data in the patient medical record is organized in accord with an expert constructed knowledge-base, and plotted on a timeline using iconic representations. The user interface permits the physician to quickly view multimedia data and to visualize relationships between events in the patient's history.
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    Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, as successful infovis applications are often difficult to author and require domain-specific customization. To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data, prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. To evaluate prefuse we have built both existing and novel visualizations testing the toolkit's flexibility and performance, and have run usability studies and usage surveys finding that programmers find the toolkit usable and effective.
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    The problem-oriented electronic medical record has been investigated as an alternative to source-oriented organization of patient data. At the core of a problem-oriented view is the medical problem list. Maintenance of the medical problem list is often manual, making it highly user dependent. We detail the beginnings of an automated medical problem list generator based on ICD-9: given a set of ICD-9 codes associated with a patient record, the system maps the codes (and related data) to an anatomy-centric hierarchy. 1 million patient encounters from an outpatient setting were reviewed to generate a unique set of 7,890 ICD-9 codes. Natural language processing of the ICD-9 string descriptions identified 1,981 anatomical terms, which were subsequently mapped to one of 21 anatomical categories. The output of the medical problem list generator was then used to create a problem-oriented, gestalt view of a patient's medical record. Preliminary evaluation of the generator revealed 100% recall, but only 60% precision. This initial work has highlighted several issues in defining a medical problem list, including questions of granularity and performance trade-offs.
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    A computerized patient tracking system with an intuitive graphical interface was deployed in a large multidisciplinary pediatric outpatient clinic and has been enthusiastically embraced by both physicians and non-physician staff. Usage of the computerized patient tracking system has been associated with a significant decrease in the total time patients spend in the clinic. The total duration of an average patient visit has declined from 99.25 +/- 57.44 minutes to 66.94 +/- 30.47 minutes (p = 0.03). Most of this decrease has been due to a decrease in total waiting time from 54.17 +/- 37.61 minutes to 27.29 +/- 14.05 minutes (p = 0.006). Usage of the patient tracking system has also been associated with a significant increase in examination room utilization during peak business hours from 58.47 +/- 21.18% to 64.49 +/- 13.38% (p = 0.0329).
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    This paper outlines the methodologies that can be used to perform an intelligent analysis of diabetic patients' data, realized in a distributed management context. We present a decision-support system architecture based on two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We stress the necessity to resort to temporal abstraction techniques, combined with time series analysis, in order to provide useful advice to patients; finally, we outline how data analysis and interpretation can be cooperatively performed by the two modules.
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