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A computer-aided sculpting system for use in prosthetics is described. The prosthetist's sculpting tools now consist of a computer, a graphics terminal, a mouse and an on-screen moveable cursor. Accompanied by the system software, these tools allow systematic modification of a primitive socket using techniques analogous to those used by a prosthetist working with rasps and plaster.
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OBJECTIVE: To create an electronic procedural logbook with enhanced user interactivity and usefulness as an educational resource. DESCRIPTION: From our own work on electronic student logbooks and other studies, it is clear that compliance with complete and valid data entry remains a challenge. Without direct and visible benefit, students are reluctant to spend time logging all their cases. We developed an interactive procedural logbook using Microsoft Embedded Visual Basic and Metrowerks CodeWarrior. Interface design focused on rapid data entry with minimum requirement for text, and field-level automation where possible. We loaded it onto a mixed platform of personal digital assistants (PDAs)--Compaq iPaq Pocket PCs and HandEra 330 PalmOS devices. Our rural residents were supplied with the devices of their choice. Various built-in educational reference resources included: (1) contextual help options about each procedure, which contained diagrams and pictures; (2) diagnostic and fee coding so they could see how poorly some procedures pay; and (3) Quick Tips relevant to each procedure, which can be easily modified by the preceptor. Preceptor evaluations and comments can be entered rapidly. Using built-in database conduits, data are automatically collected from each device on every HotSync with the desktop. Data can then be collated and analyzed using Microsoft Access or via secure Web access. DISCUSSION: Improved compliance has been dramatic-one resident logged 250 procedures in just two months. However, not all residents have been successful in establishing seamless synchronization, and the resulting data loss has caused frustration. The evidence indicates the need to implement central data collection and backup right from the outset. Central data collection provides many advantages. The program director has better information for future applications. Preceptor evaluations are now spread over many interactions and yet can be collated and analyzed. Quick Tips have been very popular-we have been able to collect the tips and redistribute them. Focus-group feedback from the residents has shown that the rich data in the logbook's reference component improved its usefulness and popularity as an educational tool. Choice of device type is important for user acceptance because devotees of one platform are reluctant to switch to another. Cross-platform development does slow the process but is increasingly easy with the latest software design tools, such as AppForge. These new tools have enabled us to explore further improvements in data entry. Digital ink provides the ability to capture annotated diagrams and preceptor signatures. Voice input is built in with these devices, and our software now allows for voice annotation for more detailed commentary by preceptors or student. The compressed digital sound file is collected along with the data and transcribed centrally (on-device voice recognition is not feasible yet). Point-of-care accessibility has been the key attraction of using these devices for logging encounter data. This project demonstrates that it is important to explore all multimodal interactive capabilities to provide a truly rich educational tool.
Single program provides practice management, human resources, and even medical records applications.
Healthcare managers are demanding ever more flexible and sophisticated capabilities from their management information systems, without sacrificing software reliability. This is paradoxical, since, all things being equal, the number of bugs residing in a program is a function of how complex it is. One method of addressing the problem of software reliability is to focus on software testing tools and techniques within the framework of an effective Quality Assurance methodology. The purpose of this article is to discuss the integral role Quality Assurance plays in the software life cycle that forges the development of new and enhanced software. An in-depth discussion of the software life cycle is beyond the scope of this article; it will be discussed only in its relation to the role of Quality Assurance.
At University Hospital, interfaces are being designed to link-stand-alone patient care devices to Hospital Information System (HIS). The objective is to have an integrated HIS comprising of data from bedside patient monitors and from stand-alone devices such as infusion pumps, fetal monitors, and pulse oximeters. Interfaces are implemented with standard hardware and software design tools. The feasibility of designing a general purpose interface hardware based on a common microprocessor has been demonstrated for an IMED model 960 infusion pump. The present paper describes the implementation of the software component of the interface using standard microcomputer software packages. The device of choice was the Corometrics model 115 fetal monitor for implementing the software because it is used extensively at University Hospital and has built-in data communication hardware. The Corometrics model 115 fetal monitor was interfaced to an IBM Personal Computer (PC) model XT. Data acquired from the fetal monitor was used to provide data tables and graphs. The feasibility of designing and implementing a software interface capable of acquiring and processing data received from a stand-alone device (fetal monitor) was demonstrated. A description of the important aspects of the interface software design is presented in this paper. This design will eventually be part of the HIS. A brief discussion about the design modifications necessary to acquire data from multiple devices simultaneously is also included.
A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.
A rating scale was developed to assess the contribution made by computer software towards the delivery of a quality consultation, with the purpose of informing the development of the next generation of systems. Two software programmes were compared, using this scale to test their ability to enable or inhibit the delivery of an ideal consultation with a patient with heart disease. The context was a general practice based, nurse run clinic for the secondary prevention of heart disease. One of the programmes was customized for this purpose; the other was a standard general practice programme. Consultations were video-recorded, and then assessed by an expert panel using the new assessment tool. Both software programmes were oriented towards the implementation of the evidence, rather than facilitating patient-centred practice. The rating scale showed, not surprisingly, significantly greater support from the customized software in the consultation in five out of eight areas. However, the scale's reliability measured by Cronbach's Alpha, was sub-optimal. With further refinement, this rating scale may become a useful tool that will inform software developers of the effectiveness of their programmes in the consultation, and suggest where they need development.
The U.S. Department of Defense (DOD) has long pursued applied research concerning fatigue in sustained and continuous military operations. In 1996, Hursh developed a simple homeostatic fatigue model and programmed the model into an actigraph to give a continuous indication of performance. Based on this initial work, the Army conducted a study of 1 wk of restricted sleep in 66 subjects with multiple measures of performance, termed the Sleep Dose-Response Study (SDR). This study provided numerical estimation of parameters for the Walter Reed Army Institute of Research Sleep Performance Model (SPM) and elucidated the relationships among several sleep-related performance measures. Concurrently, Hursh extended the original actigraph modeling structure and software expressions for use in other practical applications. The model became known as the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model, and Hursh has applied it in the construction of a Fatigue Avoidance Scheduling Tool. This software is designed to help optimize the operational management of aviation ground and flight crews, but is not limited to that application. This paper describes the working fatigue model as it is being developed by the DOD laboratories, using the conceptual framework, vernacular, and notation of the SAFTE Model. At specific points where the SPM may differ from SAFTE, this is discussed. Extensions of the SAFTE Model to incorporate dynamic phase adjustment for both transmeridian relocation and shift work are described. The unexpected persistence of performance effects following chronic sleep restriction found in the SDR study necessitated some revisions of the SAFTE Model that are also described. The paper concludes with a discussion of several important modeling issues that remain to be addressed.
BACKGROUND: Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). However, with the increasing number of samples analysed using different software, no tool is currently available to integrate and facilitate the extensive and efficient data retrieval and analyses, such as correlation of LOH data with various clinical data sets. RESULTS: An algorithm to identify prognostic disease markers is devised and implemented as novel software called LDMAS. LDMAS is a software suite designed for data retrieval, management and integrated analysis of the clinico-pathological data and molecular results from independent databases. LDMAS is used in stratification of disease stages according to clinical stage or histological features and correlation of various clinico-pathological features with molecular findings to obtain relevant prognostic markers such as those used in predicting the outcome of cervical intraepithelial neoplasia (CIN). This approach lead to the identification of novel prognostic cervical cancer markers and extraction of useful clinical information such as correlation of Human Papilloma Virus (HPV) status with CIN lesions. CONCLUSIONS: A novel software called LDMAS is implemented and used to extract and identify prognostic disease markers. The software is used to successfully identify 4 novel prognostic markers that can be used to predict the outcome of CIN. LDMAS provides an essential platform for the extraction of useful information from large amount of data generated by LOH studies. LDMAS provides three unique and novel features for LOH analysis: (1) automatic extraction of relevant data from patient records and reports (2) correlation of LOH data with clinico-pathological data and (3) storage of complex data in flexible format. The first feature automates the creation of database of clinically relevant information from huge amount of data, the second feature extracts useful biomedical information such as prognostic markers in CIN and the third feature simplifies the statistical analyses of the data and allows non-statisticians to carry out the analysis. Additionally, LDMAS can be used to extract clinically useful markers from other diseases and interface to high throughput genotyping analysis software such as GDAS used to generate LOH data from Affymetrix GeneChip Mapping arrays.
Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the "View-Primitive-Data Model framework," [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.
The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.
A Radiology Engineering program was initiated in the Department of Radiology at the Albany Medical Center, Albany, New York, in the summer of 1981. The program has been successful in attaining its goals of containing costs, providing minimal equipment downtime, and giving high-quality service. This report presents the job functions and duties that the department found necessary to provide this level of service. In addition, two techniques for managing malfunction and service reports, techniques for scheduling PM's and service calls, and software management tools that assist the department with service are described.
Robot Assisted Surgery (RAS) Systems win more and more recognition in the field of orthopaedics. Especially in Hip Surgery RAS has proved to be suited for application in medical routine. Often Robot Assisted Surgery Systems consist of a planning- and an interoperative component. According to specifications done with the planning software the tools are driven. Benefits of the robot assisted surgery should be higher precision and a better surgical outcome. In the co-operation project of several Fraunhofer Institutes "RoMed" (Robots and Manipulators for Medical Application) an exemplary application of robot aided spine surgery is developed [1]. The planning software used in this context is proposed in this article.
The pro7-communication server is a new approach to manage communication between different applications on different hardware platforms in a hospital environment. The most important features are the use of OSF/DCE for realising remote procedure calls between different platforms, the use of an SQL-92 compatible relational database and the design of a new software development tool (called protocol definition language compiler) for describing the interface of a new application, which is to integrate in a hospital environment.
Visual field is one of the important visual functions; it is the extent of the visual field defect that can be employed in judging whether the visual function is impaired. The rapid achievements in computer technologies do provide an impulse for improvement of visual field detection, making possible the automatic, rapid, accurate, detailed and large-scaled visual field detection. This paper gives a thorough description about development of the visual field analyzer, model TEC-2A, which is based on PC windows platform, Visual Basic software developing tool, ISA peripheral circuits, standard Goldmann visual field half-ball and standard stimulus.
The academic health center information environment is saturated with information of varying quality and overwhelming quantity. The most significant challenge is transforming data and information into knowledge. The University of Cincinnati Medical Center's (UCMC) focus is to develop an information architecture comprising data structures, Web services, and user interfaces that enable individuals to manage the information overload so that they can create new knowledge. UCMC has accomplished much of what is reported in this article with the help of a four-year Integrated Advanced Information Management Systems (IAIMS) operation grant awarded by the National Library of Medicine in 2003. In the UCMC vision for knowledge management, individuals have reliable, secure access to information that is filtered, organized, and highly relevant for specific tasks and personal needs. Current applications and tool sets will evolve to become the next generation knowledge management applications or smart digital services. When smart digital services are implemented, silo applications will disappear. A major focus of UCMC's IAIMS grant is research administration. Testing and building out existing and new research administration applications and digital services is underway. The authors review UCMC's progress and results in developing a software architecture, tools, and services for research administration. Included are sections on the evolution to full integration, the impact of the work at UCMC to date, lessons learned during this research and development process, and future plans and needs.
It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.