BIODS--OUTCOMES TRACKING DATABASE FOR PEOPLE WITH SPMI

DESCRIPTION (adapted from applicant's abstract): In this era of changing health care reform, there is a need for community-based agencies involved in the rehabilitation of individuals with serious and persistent mental illnesses (SPMI) to document the outcomes achieved through their programs. To this end, a computerized database for tracking the progress of individuals with SPMI has been developed (called BIODS or Biographical Information and Ongoing Data System). The objectives of this application are to train four community-based agencies on the use of the program and to implement the system at each agency through the use of TQM principles. During this time, a group of experts will be developing a second piece of the program for the purposes of Quality Assurance Improvements (QAI) to measure program integrity while meeting the needs of managed care. The analysis will include a feasibility study to assess the viability of the program and its implementation. There will be a full assessment of the effectiveness of the reorganization of systems, training, manuals, and error rates. The end product will be a program which can be customized for use by agencies within different states, under different managed care models, for a variety of funding requirements as well as helping to streamline case management responsibilities. $ = TOTAL AWARD AMTS & NOT LIMITED TO PORTION OF PROJECT RELATED TO SUBJECT OF SEARCH SUBPROJECT $ = TOTAL AWARD AMOUNT DIVIDED BY NUMBER OF SUBPROJECTS SOURCE: CRISP FORMAT F FY 97 LAST UPDATE 04-07-98 1QUERY 1536 ID SEARCH 06/01/98 PAGE 457 --PROJECT NUMBER......2 R44 MH53691-02 INVESTIGATOR NAME/ADDRESS FY 97 HOLMES, TIMOTHY J IRG/INTRAMURAL UNIT..ZRG2 AUTOQUANT IMAGING INC AWARD AMOUNT......... $366,969 1223 PEOPLES AVE TROY, NY 12180-3590 PERFORMING ORGANIZATION: AUTOQUANT IMAGING, INC. TITLE BRIGHTFIELD MICROSCOPY 3D IMAGE RECONSTRUCTION NO ABSTRACT ON FILE $ = TOTAL AWARD AMTS & NOT LIMITED TO PORTION OF PROJECT RELATED TO SUBJECT OF SEARCH SUBPROJECT $ = TOTAL AWARD AMOUNT DIVIDED BY NUMBER OF SUBPROJECTS SOURCE: CRISP FORMAT F FY 97 LAST UPDATE 04-07-98 1QUERY 1536 ID SEARCH 06/01/98 PAGE 458 --PROJECT NUMBER......2 R44 MH53692-02 INVESTIGATOR NAME/ADDRESS FY 97 HOLMES, TIMOTHY J IRG/INTRAMURAL UNIT..ZRG2 AUTOQUANT IMAGING INC AWARD AMOUNT......... $405,000 1223 PEOPLES AVE TROY, NY 12180-3590 PERFORMING ORGANIZATION: AUTOQUANT IMAGING, INC. TITLE ROBUST 3D FLUORESCENCE CONFOCAL MICROSCOPY DEBLURRING ABSTRACT: The objective of this research is the wide accessibility of 3D deconvolution (computational deblurring and visual clarification of 3D image data) for the neuroscientific community and other life-science communities. Long-term aims are to provide the most reliable, robust and quantitatively accurate system for 3D visualization and morphometry, and to provide neuroscientists and other life-scientists with easy-to-use tools for studying the structure and function of normal and pathologic tissue. The focus of this project is on neuroscientific applications. The commercial objective is a profitable software product, which, owing to innovations, well outperforms competitive products in reliability, robustness, quantitative accuracy, speed and ease of usage. The specific aims of this project are: (1) To develop and test variations of the algorithm for a broader range of widely used confocal microscope types, including the slit-scan and array-detector geomeries; (2) To improve (and test) the robustness of the algorithm under a number of common adverse conditions; (3) To further validate the correctness of the image reconstructions by using fabricated test objects of known geometry; (4) To demonstrate the system on two of the most suitable computer platforms for wide usage. These are the Silicon Graphics unix-based platform and the IBM-PC compatible platform; (5) To beta test prototypes of the product at several potential customer sites. Innovations introduced by us in this research include: (1), a blind deconvolution approach, which obviates the need to measure the point spread function, (2), a maximum likelihood optimization approach, which makes the methodology robust against photodetector noise and other adverse conditions, and (3), a unified underlying mathematical model, which makes the algorithm easily adaptable among confocal geometries.