Information Systems and Healthcare XXI: A Dynamic, Client-Centric, Point-Of-Care System for the Novice Nurse

Nurse clinicians need to make complex decisions on a continual basis, while delivering cost-effective treatments. The rapid proliferation of medical and nursing knowledge complicates the decision-making process, particularly for novice nurses. We describe a Clinical Decision Support System (CDSS) for the novice nurse that combines evidence-based nursing knowledge with specific patient information to create a real-time guide through the nursing diagnostic care process. The goal of the paper is to describe how an appropriately designed and evidence-based CDSS can aid the nursing practice. An off-the-shelf handheld computer is utilized to deliver clinical knowledge to the nurse, via wireless link to a central server and a data repository. In describing the software architecture of the system, particular emphasis is paid to the issue of appropriate design by discussing the steps taken to address system extensibility, performance, reliability, and security, which are important factors in the design of a CDSS

Using Feature Predictive Power as a Guide for the Clinical Decision Making Process

ABSTRACT: Given the hectic nature of patient care environments, it is important for the clinician to reach the correct patient diagnosis as soon as possible. One of the challenges that clinicians face is to identify which patient data are salient to the identification of the correct patient status. In this paper, we propose an algorithm that was developed for incorporation in a decision-support tool for the novice nurse. The algorithm guides the patient data collection process using an iterative two-step process: (a) at each stage of the clinical decision process and based on the current partial patient information, it identifies the most probable diagnoses (b) it prompts the nurse to measure the clinical indicators with the highest predictive power of the aforementioned diagnoses. Results are presented as to the effectiveness of two predictive power measures, mutual information and the divergence measure, in guiding the data collection process

T. Mitsa and K. J. Parker Digital halftoning technique using a blue-noise mask

A novel digital halftoning technique, by which the halftoning is achieved by a pixelwise comparison of the grayscale image to an array (halftone screen), the blue-noise mask, is presented. This mask is designed so that the halftone image has blue-noise (high-frequency) characteristics in the frequency domain. The algorithm for the construction of the blue-noise mask and an algorithm for the construction of binary patterns with the same first-order but different second-order statistics are presented. Two psychovisual tests in which human subjects rated halftone patterns and images according to various criteria are also described. 1

Computer Science and Engineering Research Review 1991-1992

Table of Contents: Introduction / p. 5; The Rochester Checkers Player: Multi-Model Parallel Programming for Animate Vision / Brian D. Marsh, Christopher M. Brown, Thomas J. LeBlanc, Michael L. Scott, Timothy G. Becker, Prakash Ch. Das, Jonas Karlsson, Cesar A. Quiroz p. 7; A Novel Halftoning Technique; the Blue Noise Mask / Theophano Mitsa , Kevin J. Parker p. 16; Quasi-Injective Reductions / Lane A. Hemachandra, Edith Spaan p. 21; An Asynchronous Multiplier / Brenda Luderman, Alexander Albicki p. 24; An Overview of the TRAINS Project James F. Allen, Lenhart K. Schubert p. 29; Faculty p. 36; Publications p. 38; Doctoral and Master's Theses p. 46; Seminars p. 51; Grant and Industrial Support p. 52