DataServer: an infrastructure to support evidence-based radiology

Interactive Radiology teaching file system: the development of a MIRC-compliant and user-centered e-learning resource

Radiology teaching usually employs a systematic and comprehensive set of medical images and related information. Databases with representative radiological images and documents are highly desirable and widely used in Radiology teaching programs. Currently, computer-based teaching file systems are widely used in Medicine and Radiology teaching as an educational resource. This work addresses a user-centered radiology electronic teaching file system as an instance of MIRC compliant medical image database. Such as a digital library, the clinical cases are available to access by using a web browser. The system has offered great opportunities to some Radiology residents interact with experts. This has been done by applying user-centered techniques and creating usage context-based tools in order to make available an interactive system.

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.

Problem-centric organization and visualization of patient imaging and clinical data

A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient's record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis.

A methodology to integrate clinical data for the efficient assessment of brain-tumor patients

Careful examination of the medical record of brain-tumor patients can be an overwhelming task for the neuroradiologist. The number of clinical documents alone may approach 100 for a patient that has a 3-year-old brain tumor. The neuroradiologist's evaluation of a patient's brain tumor involves examining the current imaging exam and checking for previous imaging exams that may occur pre- or post-treatment. The goal of this research is to develop an effective method to review all of the pertinent patient information from the medical record. We have designed and developed a medical system that incorporates Hospital Information Systems, Radiology Information Systems, and Picture Archiving and Communications Systems information. Our research improves clinical review of patient's data by organizing image display, removing unnecessary documents, and mining for key clinical scenarios that are important in the assessment and care of brain-tumor patients.

A field theoretical approach to medical natural language processing

A parser for medical free text reports has been developed that is based on a chemistry/physics inspired "field theory" for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of "work" required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9% and 89.9%, respectively.

O3-DPACS Open-Source Image-Data Manager/Archiver and HDW2 Image-Data Display: an IHE-compliant project pushing the e-health integration in the world

After many years of study, development and experimentation of open PACS and Image workstation solutions including management of medical data and signals (DPACS project), the research and development at the University of Trieste have recently been directed towards Java-based, IHE compliant and multi-purpose servers and clients. In this paper an original Image-Data Manager/Archiver (O3-DPACS) and a universal Image-Data Display (HDW2) are described. O3-DPACS is also part of a new project called Open Three (O3) Consortium, promoting Open Source adoption in e-health at European and world-wide levels. This project aims to give a contribution to the development of e-health through the study of Healthcare Information Systems and the contemporary proposal of new concepts, designs and solutions for the management of health data in an integrated environment: hospitals, Regional Health Information Organizations and citizens (home-care, mobile-care and ambient assisted living).

Inpatient Radiology Utilization: Trends over the Past Decade

OBJECTIVE

The objective of our study was to assess patterns of use of radiology services for inpatients at our institution between 1993 and 2002.

MATERIALS AND METHODS

We retrospectively reviewed the administrative data about adult inpatients for fiscal years 1993-2002 in a 721-bed tertiary care institution. Examinations were coded according to imaging technique: conventional (plain films and fluoroscopy), sonography, nuclear medicine, CT, or MRI. We assessed workload trends using relative value units (RVUs). Linear regression analysis was used to assess the significance of trends for the number of examinations and RVUs per case-mix-adjusted admission (CMAA).

RESULTS

There was a significant decrease in the total number of examinations per CMAA (p < 0.001). This was due to significant decreases in the use of conventional studies (p < 0.001) and sonograms (p < 0.001), despite significant increases in the number of nuclear medicine (p = 0.046), CT (p < 0.001), and MRI (p < 0.002) examinations per CMAA. The RVUs per CMAA increased significantly (p < 0.01) during the study period.

CONCLUSION

Newer imaging technologies (nuclear medicine, CT, and MRI) are replacing older ones in the evaluation of inpatients. Despite the significant decrease in the total number of imaging examinations per CMAA, we observed a significant increase in RVUs per CMAA during the 10-year study period. Understanding the impact of this change in practice on the quality of care would be useful in justifying the increasing use of these new technologies, and decreasing their inappropriate use should be a priority in efforts that focus on controlling imaging expenditures.

Effect of an imaging-based streamlined electronic healthcare process on quality and costs1

RATIONALE AND OBJECTIVES

A streamlined process of care supported by technology and imaging may be effective in managing the overall healthcare process and costs. This study examined the effect of an imaging-based electronic process of care on costs and rates of hospitalization, emergency room (ER) visits, specialist diagnostic referrals, and patient satisfaction.

MATERIALS AND METHODS

A healthcare process was implemented for an employer group, highlighting improved patient access to primary care plus routine use of imaging and teleconsultation with diagnostic specialists. An electronic infrastructure supported patient access to physicians and communication among healthcare providers. The employer group, a self-insured company, manages a healthcare plan for its employees and their dependents: 4,072 employees were enrolled in the test group, and 7,639 in the control group. Outcome measures for expenses and frequency of hospitalizations, ER visits, traditional specialist referrals, primary care visits, and imaging utilization rates were measured using claims data over 1 year. Homogeneity tests of proportions were performed with a chi-square statistic, mean differences were tested by two-sample t-tests. Patient satisfaction with access to healthcare was gauged using results from an independent firm.

RESULTS

Overall per member/per month costs post-implementation were lower in the enrolled population (126 dollars vs 160 dollars), even though occurrence of chronic/expensive diseases was higher in the enrolled group (18.8% vs 12.2%). Lower per member/per month costs were seen for inpatient (33.29 dollars vs 35.59 dollars); specialist referrals (21.36 dollars vs 26.84 dollars); and ER visits (3.68 dollars vs 5.22 dollars). Moreover, the utilization rate for hospital admissions, ER visits, and traditional specialist referrals were significantly lower in the enrolled group, although primary care and imaging utilization were higher. Comparison to similar employer groups showed that the company's costs were lower than national averages (119.24 dollars vs 146.32 dollars), indicating that the observed result was not attributable to normalization effects. Patient satisfaction with access to healthcare ranked in the top 21st percentile.

CONCLUSION

A streamlined healthcare process supported by technology resulted in higher patient satisfaction and cost savings despite improved access to primary care and higher utilization of imaging.