(1/1272) BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs.
SUMMARY: BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions. AVAILABILITY: From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html (+info)
(2/1272) Waking the health plan giant: Group Health Cooperative stops counting sheep and starts counting key tobacco indicators.
Implementing a comprehensive approach to decreasing tobacco use in a large health plan requires hard work and commitment on the part of many individuals. We found that major organisational change can be accomplished and sustained. Keys to our success included our decision to remove access barriers to our cessation programmes (including cost); obtaining top leadership buy-in; identifying accountable individuals who owned responsibility for change; measuring key processes and outcomes; and finally keeping at it tenaciously through multiple cycles of improvement. (+info)
(3/1272) Patient waiting times in a physician's office.
This observational study measured waiting times, appointment durations, and scheduling variables of a single family practice physician. Waiting time and appointment duration in four sequential groups of sessions were compared using analysis of variance; each group used different scheduling templates. Groups 1 and 2 used a 15-minute base interval; group 3 used a 20-minute base interval. Observations for group 4 were collected at a different health center using a 15-minute base interval. Scheduling variables were correlated with waiting time using correlation coefficients, and data were collected on 1783 appointments. The best waiting time (mean +/- SD) was 17.33 +/- 19.19 minutes. The mean appointment duration for this group was 17.99 +/- 7.97 minutes. The F statistic comparing the four groups of sessions for waiting times was 34.14 and for appointment duration was 37.37, both of which are significant (P < 0.001). The Spearman correlation coefficient for waiting time with queue was 0.2474 (P < 0.001). The Spearman correlation coefficients for mean waiting time and lateness of starting a session (0.4530), patients per hour (0.3461), and patients per session (0.3674) were all significant (P < 0.001). Both scheduling and patient flow affect patient waiting times. The best schedule would consist of shorter sessions that started on time and were extended to accommodate extra patients rather than adding in patients and crowding the schedule. In addition to reducing the actual waiting times, the perception of waiting can be managed to minimize patient dissatisfaction. (+info)
(4/1272) Experience measuring performance improvement in multiphase picture archiving and communications systems implementations.
When planning a picture archiving and communications system (PACS) implementation and determining which equipment will be implemented in earlier and later phases, collection and analysis of selected data will aid in setting implementation priorities. If baseline data are acquired relative to performance objectives, the same information used for implementation planning can be used to measure performance improvement and outcomes. The main categories of data to choose from are: (1) financial data; (2) productivity data; (3) operational parameters; (4) clinical data; and (5) information about customer satisfaction. In the authors' experience, detailed workflow data have not proved valuable in measuring PACS performance and outcomes. Reviewing only one category of data in planning will not provide adequate basis for targeting operational improvements that will lead to the most significant gains. Quality improvement takes into account all factors in production: human capacity, materials, operating capital and assets. Once we have identified key areas of focus for quality improvement in each phase, we can translate objectives into implementation requirements and finally into detailed functional and performance requirements. Here, Integration Resources reports its experience measuring PACS performance relative to phased implementation strategies for three large medical centers. Each medical center had its own objectives for overcoming image management, physical/geographical, and functional/technical barriers. The report outlines (1) principal financial and nonfinancial measures used as performance indicators; (2) implementation strategies chosen by each of the three medical centers; and (3) the results of those strategies as compared with baseline data. (+info)
(5/1272) The philosophy of benchmark testing a standards-based picture archiving and communications system.
The Department of Defense issued its requirements for a Digital Imaging Network-Picture Archiving and Communications System (DIN-PACS) in a Request for Proposals (RFP) to industry in January 1997, with subsequent contracts being awarded in November 1997 to the Agfa Division of Bayer and IBM Global Government Industry. The Government's technical evaluation process consisted of evaluating a written technical proposal as well as conducting a benchmark test of each proposed system at the vendor's test facility. The purpose of benchmark testing was to evaluate the performance of the fully integrated system in a simulated operational environment. The benchmark test procedures and test equipment were developed through a joint effort between the Government, academic institutions, and private consultants. Herein the authors discuss the resources required and the methods used to benchmark test a standards-based PACS. (+info)
(6/1272) Benchmark testing the Digital Imaging Network-Picture Archiving and Communications System proposal of the Department of Defense.
The Department of Defense issued a Request for Proposal (RFP) for its next generation Picture Archiving and Communications System in January of 1997. The RFP was titled Digital Imaging Network-Picture Archiving and Communications System (DIN-PACS). Benchmark testing of the proposed vendors' systems occurred during the summer of 1997. This article highlights the methods for test material and test system organization, the major areas tested, and conduct of actual testing. Department of Defense and contract personnel wrote test procedures for benchmark testing based on the important features of the DIN-PACS Request for Proposal. Identical testing was performed with each vendor's system. The Digital Imaging and Communications in Medicine (DICOM) standard images used for the Benchmark Testing included all modalities. The images were verified as being DICOM standard compliant by the Mallinckrodt Institute of Radiology, Electronic Radiology Laboratory. The Johns Hopkins University Applied Physics Laboratory prepared the Unix-based server for the DICOM images and operated it during testing. The server was loaded with the images and shipped to each vendor's facility for on-site testing. The Defense Supply Center, Philadelphia (DSCP), the Department of Defense agency managing the DIN-PACS contract, provided representatives at each vendor site to ensure all tests were performed equitably and without bias. Each vendor's system was evaluated in the following nine major areas: DICOM Compliance; System Storage and Archive of Images; Network Performance; Workstation Performance; Radiology Information System Performance; Composite Health Care System/Health Level 7 communications standard Interface Performance; Teleradiology Performance; Quality Control; and Failover Functionality. These major sections were subdivided into workable test procedures and were then scored. A combined score for each section was compiled from this data. The names of the involved vendors and the scoring for each is contract sensitive and therefore can not be discussed. All of the vendors that underwent the benchmark testing did well. There was no one vendor that was markedly superior or inferior. There was a typical bell shaped curve of abilities. Each vendor had their own strong points and weaknesses. A standardized benchmark protocol and testing system for PACS architectures would be of great value to all agencies planning to purchase a PACS. This added information would assure the purchased system meets the needed functional requirements as outlined by the purchasers PACS Request for Proposal. (+info)
(7/1272) The development of a quality information system: a case study of Mexico.
One of the primary obstacles in the implementation of continuous quality improvement (CQI) programmes in developing countries is the lack of timely and appropriate information for decentralized decision-making. The integrated quality information system (QIS) described herein demonstrates Mexico's unique effort to package four separate, yet mutually reinforcing, tools for the generation and use of quality-related information at all levels of the Mexican national health care system. The QIS is one element of the continuous quality improvement programme administered by the Secretariat of Health in Mexico. Mexico's QIS was designed to be flexible and capable of adapting to local needs, while at the same time allowing for the standardization of health care quality assurance indicators, and subsequent ability to measure and compare the quality performance of health facilities nationwide. The flexibility of the system extends to permit the optimal use of available data by health care managers at all levels of the health care system, as well as the generation of new information in important areas often neglected in more traditional information systems. Mexico's QIS consists of four integrated components: 1) a set of client and provider surveys, to assess specific issues in the quality of health services delivered; 2) client and provider national satisfaction surveys; 3) a sentinel health events strategy; and 4) a national Comparative Performance Evaluation System, for use by the Secretariate of Health for the quality assessment of state and provincial health care services (internal benchmarking). The QIS represents another step in Mexico's ongoing effort to use data for effective decision-making in the planning, monitoring and evaluation of services delivered by the national health care system. The design and application of Mexico's QIS provides a model for decentralized decision-making that could prove useful for developing countries, where the effective use of quality indicators is often limited. Further, the system could serve as a mechanism for motivating positive change in the way information is collected and used in the process of ensuring high quality health care service delivery. (+info)
(8/1272) Inter-hospital comparison of mortality rates.
OBJECTIVE: To compare crude and adjusted in-hospital mortality rates after prostatectomy between hospitals using routinely collected hospital discharge data and to illustrate the value and limitations of using comparative mortality rates as a surrogate measure of quality of care. METHODS: Mortality rates for non-teaching hospitals (n = 21) were compared to a single notional group of teaching hospitals. Patients age, disease (comorbidity), length of stay, emergency admission, and hospital location were identified using ICD-9-CM coded Victorian hospital morbidity data from public hospitals collected between 1987/88 and 1994/95. Comparisons between hospitals were based on crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) derived using univariate and multivariate logistic regression. Model fit was evaluated using receiver operating characteristic curve i.e. statistic, Somer's D, Tau-a, and R2. RESULTS: The overall crude mortality rates between hospitals achieved borderline significance (alpha2=31.31; d.f.=21; P=0.06); these differences were no longer significant after adjustment (chi2=25.68; P=0.21). On crude analysis of mortality rates, four hospitals were initially identified as 'low' outlier hospitals; after adjustment, none of these remained outside the 95% CI, whereas a new hospital emerged as a 'high' outlier (OR=4.56; P= 0.05). The adjusted ORs between hospitals compared to the reference varied from 0.21 to 5.54, ratio = 26.38. The model provided a good fit to the data (c=0.89; Somer's D= (0.78; Tau-a = 0.013; R2= 0.24). CONCLUSIONS: Regression adjustment of routinely collected data on prostatectomy from the Victorian Inpatient Minimum Database reduced variance associated with age and correlates of illness severity. Reduction of confounding in this way is a move in the direction of exploring differences in quality of care between hospitals. Collection of such information over time, together with refinement of data collection would provide indicators of change in quality of care that could be explored in more detail as appropriate in the clinical setting. (+info)