Integration of computer-assembled digital images and text data as evidence for the oncological record

Mapping Out Point-of-Care Review Screens for Omaha System Data

Omaha System data are text data that consist of standardized terminology and customized descriptions. The customized descriptions related to patient care reveal changes in a client's status over time. These data help public health nurses to understand the patient's progress and to plan future care. However, most electronic health records do not provide clinicians with efficient displays of stored text data. The purpose of this study is to develop point-of-care review screens for Omaha System data on an individual patient level and examine nurse perceptions of the usefulness of the displayed data in improving patient care. Individual patients' data were organized on a Web-based overview page to present all of the health problems that a client had and on detailed pages to present all records of Omaha System data regarding each health problem. Nurse survey results indicated the usefulness of at-a-glance displays of text data on patient care and nurses' decision making. The meaningful review of patient data using a health information system supports patient-data-driven, evidence-based practice and decision making.

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.

Comprehensive breast cancer adjuvant digital summary

BACKGROUND

Patients with breast cancer often fail to recall the details of their original diagnosis and adjuvant therapy with the passage of time. Subsequent follow-up and treatment at a later time and a different institution wastes valuable time and effort to retrieve the original data.

PATIENTS AND METHODS

Twenty-five consecutive patients with breast cancer of all stages admitted for adjuvant/neoadjuvant treatment and surgical excision were entered on study. An individualized comprehensive visual evaluation summary sheet (VESS) was created that detailed initial diagnosis, preceding relevant investigations, drug scheduling, and dosages of adjuvant therapy. Completion of a VESS required a computer, a digital camera with connection to a microscope, and radiology images over the PACS system. The completed one-page summary can be printed or stored.

RESULTS

A VESS takes up an average of 4.4 MB (1.24-8 MB), each containing 11.5 images (range, 4-23 images), spanning a time period of around 216 days (range, 125-558 days).

CONCLUSIONS

Patients received a complete summary of pertinent information concerning their diagnosis and adjuvant therapy.

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.

Advances in biomedical informatics for the management of cancer

Increased access to health care, and advances in education and technology have resulted in a larger proportion of the population having longer life expectancy. The strong correlation between age and cancer has resulted in a major healthcare problem for this century, and until recently cancer has defied any long-lasting cure. However, progress, especially in the field of biomedical informatics, promises a successful prediction and possibly a permanent cure for cancer within the next two decades. Biomedical informatics-with its roots in computer science, biomedical engineering, biostatistics, and mathematics-helps to bring the patient closer to the physician, facilitates access to specialist information and knowledge bases across the world, and makes it possible to identify genetic expression profiles for malignant or cancerous cells. This paper reviews the new research findings in biomedical informatics, working toward the ultimate goal of successfully predicting cancer, solving complex problems in prevention and treatment of cancer, and perhaps completely curing the scourge of cancer.

Histopathology and the 'third great lie'. When is an image not a scientifically authentic image?

This article considers the issues of image manipulation within the context of electronic/digital images. The possibility for such alterations has occurred with the increasing availability of simple and cheap computer software, permitting modification of digitized images as a whole, or in part. The concepts of acceptable, through to unacceptable, manipulations are discussed using macroscopic and histological illustration examples. The legal and editorial problems that this poses for medical and scientific publishing are also discussed, alongside examples of previous fraudulent image publications and the historical context of valid scientific images.