From multimodality digital imaging to multimedia patient record

Analysis and visualization of disease courses in a semantically-enabled cancer registry

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.

Context-based electronic health record: toward patient specific healthcare

Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed clinical information from a multitude of sources. However, applying this information to guide medical decisions for a specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge sources, and generates a tailored presentation of the record based on the information needs of the user. We have implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging disease models to improve the access, integration, and interpretation of clinical patient data.

TimeLine: visualizing integrated patient records

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.

From PACS to integrated EMR

The integration of medical images as part of the patient record has always been a critical component of documentation and information supporting clinical decisions. In the past two decades the increased number of imaging procedures that allows a more accurate and more specific diagnosis has significantly increased and their role in patient management has grown rapidly. With the evolution toward digital modalities and management of medical images in a fully digital environment with the deployment of enterprise wide Picture Archiving Communication Systems (PACS) a wider and more rapid access to the images by referring physicians and clinicians has become possible. The parallel evolution of electronic medical records (EMR) supporting all other documents and clinical data in electronic format led to the necessity of integrating medial image data with the rest of the patient record. Although the marriage of medical images and patient record data in electronic format seems a very natural and necessary combination it has often been very slow in development due to the lack of standardization and clear understanding of clinical workflows and clinical requirements. Several early implementations demonstrated the added value of combining medical images with the patient record and have shown that the availability of data and images facilitates and improves the accuracy and efficiency of patient management. Recent efforts in industry and the academic community to harmonize and improve the integration of medical images with patient record, with the promotion of new standards and better definitions of clinical workflows and standard mechanism of integration of different types of data into unified data models, has facilitated the deployment of modern EMR. Also, a shift in paradigm due to recent technological revolutions such as the development of the World Wide Web and the concepts of portal servers for accessing data for multiple sources has significantly boosted the trends into open systems which allows easier and more functional integration of medical data from different sources. Furthermore, the emergence of a new strategy for software development, based on open source components, allows software programs to be shared and exchanged between different institutions leading to more rapid deployment of standardized electronic patient record.

A review of medical imaging informatics

This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

Integrated visualization of problemcentric urologic patient records

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.

Problem-oriented prefetching for an integrated clinical imaging workstation

Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.

An object-oriented taxonomy of medical data presentations

A variety of methods have been proposed for presenting medical data visually on computers. Discussion of and comparison among these methods have been hindered by a lack of consistent terminology. A taxonomy of medical data presentations based on object-oriented user interface principles is presented. Presentations are divided into five major classes-list, table, graph, icon, and generated text. These are subdivided into eight subclasses with simple inheritance and four subclasses with multiple inheritance. The various subclasses are reviewed and examples are provided. Issues critical to the development and evaluation of presentations are also discussed.

Clinical Information Systems for Intensive Care, Pediatric Critical Care, and Neonatology

Computer information systems are expected to soon take the place of current paper charting practices, and they offer great promise to assist management of the considerable amounts of data encountered in the information-rich environment of intensive care units (ICUs). Efforts to create an electronic medical record (EMR) have been underway for more than two decades, and major national organizations, such as the Institute of Medicine, have issued recommendations on standards. Benefits of an EMR include a legible patient record, enhanced communication, provision of timely reminders and alerts to clinicians, reduction of calculation errors, access to data bases for quality assurance and research, reduced healthcare costs, and improved patient outcomes. Despite these benefits, successful EMR implementations have been confined to a few committed institutions, and expensive failures have occurred. Practitioners of neonatology and pediatric intensive care are likely to have substantial difficulty implementing an EMR to fit their specialized needs because most experience in this area has been gained through care of adult patients, and systems being developed are oriented toward nonpediatric patients. It is therefore important to examine experience thus far with the functional components of an EMR so practitioners will be able to evaluate systems better as they become available. System components discussed include nursing charting facilities, lab reporting, physician order entry, physician progress notes, structured reports, decision support systems, and problem list management. Other concerns discussed include research and quality assurance functions, data access and confidentiality issues, and electronic mail. Maximizing the “structured data” content, as opposed to narrative content of an EMR, is an important priority, and progress on developing a uniform medical language is discussed. An approach to evaluating clinical information systems for use in the ICU is presented; it should assist practitioners of pediatric critical care and neonatology in identifying computer-based charting solutions that are optimal for infants and children, while cooperating with medical center-wide needs for compatibility and a common data base.

A medical workstation for the display and analysis of multi-frame digital cineradiographic data

A medical workstation has been developed for the efficient display and analysis of large sets of digital cineradiographic images. Various features aid the clinician in quickly identifying and extracting the image information relevant for diagnosis: animated viewing of image frames, a digital magnifying glass for local image enlargement and enhancement, a special review queue for critical image frames, and task-oriented image processing. Double frame buffering and direct memory addressing ensure fast, artifact-free image display and transfer. Much freedom is provided for adapting the system to one's own preferences. A statistical analysis of extensive tests conducted by eight clinical expert reviewers is given.