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This article provides the reader with a basic understanding of the Government Performance and Results Act of 1993. The Act requires federal agencies to institute a planning and reporting management framework to achieve results. It also identifies challenges federal agencies face in implementing a stronger results management approach and promising practices agencies can use in crafting their management approach.
With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, we divide CSPs into two types, namely, permutation CSPs and nonpermutation CSPs. According to their characteristics, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that the multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. Theoretical analyzes show that MAEA-CSPs has a linear space complexity and converges to the global optimum. The first part of the experiments uses 250 benchmark binary CSPs and 79 graph coloring problems from the DIMACS challenge to test the performance of MAEA-CSPs for nonpermutation CSPs. MAEA-CSPs is compared with six well-defined algorithms and the effect of the parameters is analyzed systematically. The second part of the experiments uses a classical CSP, n-queen problems, and a more practical case, job-shop scheduling problems (JSPs), to test the performance of MAEA-CSPs for permutation CSPs. The scalability of MAEA-CSPs along n for n-queen problems is studied with great care. The results show that MAEA-CSPs achieves good performance when n increases from 10(4) to 10(7), and has a linear time complexity. Even for 10(7)-queen problems, MAEA-CSPs finds the solutions by only 150 seconds. For JSPs, 59 benchmark problems are used, and good performance is also obtained.
STUDY OBJECTIVE: Case-mix adjustment is a critical component of quality assessment and benchmarking. The Pediatric Risk of Admission (PRISA) score is composed of descriptive, physiologic, and diagnostic variables that provide a probability of hospital admission as an index of severity. The score was developed and validated in a single tertiary pediatric hospital emergency department (ED) after exclusion of children with minor injuries and illnesses. We provide a multi-institutional recalibration and validation of the PRISA score and test its performance in 4 additional EDs, including patients with minor injuries and illnesses. METHODS: Masked, photocopied, randomly selected medical records of ED patients from 2000 were abstracted and were used to test the performance (discrimination and calibration) of the original PRISA score. This sample differed from the original PRISA sample by including 5 hospitals and including patients with minor injuries and minor illnesses. Independent variables included components of acute and chronic history, physiologic variables, and 3 ED therapies. The dependent variable was hospital admission. PRISA was then recalibrated as needed by using an 80% development sample and a 20% validation sample. Area under the curve and the Hosmer-Lemeshow goodness-of-fit test were used to measure, respectively, discrimination and calibration of the PRISA score after recalibration. We then applied the recalibrated PRISA score to secondary outcomes to test construct validity. We reasoned that a valid measure of ED severity should also be associated with the secondary outcomes of mandatory admissions (admissions using > or =1 inpatient resources) and ICU admissions. RESULTS: The recalibrated PRISA score performed well in all deciles of predicted probability of admission. The area under the curve was 0.81 and the calibration was good (Hosmer-Lemeshow 10.658; df=8; P=.222) for the development sample, and the area under the curve was 0.785 with excellent calibration (Hosmer-Lemeshow 8.341; df=9; P=.500) for the validation sample. The overall development sample had 423.9 admissions predicted and 423 observed; the validation sample had 112.1 predicted and 110 observed. CONCLUSION: The PRISA score has been recalibrated and performs well in EDs of tertiary pediatric hospitals. Comparison with this benchmark may allow individual EDs to improve their performance and may provide insight into best practices.
Performance indicators may provide an indication of insufficient quality of medical care but they do not identify the cause of the problem. The political context for performance indicators is based on market ideology, where quality improvement is the goal and consumer pressure is the means. It is usually difficult to compare performance indicators among hospitals given the differences in definitions, methods of assessment, case mix, preclinical factors and data quality. Performance indicators are risk assessments and therefore subject to chance variation. Conclusions regarding performance indicators cannot be drawn due to the lack of clear, predefined benchmarks. At this time, the presence and degree of consumer pressure is unclear, and the ultimate effects of making performance factors publicly available on the quality of care is unknown. The question is whether mandatory reporting of a set of performance indicators by hospitals can provide sufficient insight into the quality of care; the measure appears to be too rough and too many factors influence the outcome. Procedure assessment is a good alternative to the use of performance indicators.
MOTIVATION: Complex traits and diseases are highly polygenic and understanding the full set of genes involved is a central challenge in biomedicine. However, due to sample size limitations and noise (technical and biological), experimental approaches for disease-gene discovery such as transcriptomics and GWAS result in long, noisy, heterogeneous gene lists, which may be trimmed to a subset of likely relevant genes while leaving several false negatives. Computational gene classification approaches, especially those using genome-scale molecular interaction networks, are promising avenues for complementing such experimental findings by analytically expanding observed gene lists based on the functional relatedness between genes. We previously introduced the network-based gene classification approach, GenePlexus, which was rigorously benchmarked to show state-of-the-art performance, especially for predicting novel genes associated with biological processes and fine-grained phenotypes. Network-based gene classification performance,however, declines for diseases, especially when the inputs are omics and GWAS-based long gene lists. RESULTS: Here, we show that these disease gene lists span multiple biological processes spread across the molecular network, and we propose ModGenePlexus, a new network-based gene classification method that takes a two-stage approach. First, clustering and semi-supervised learning decomposes the input gene list into coherent, denoised network gene modules. Then, ModGenePlexus trains supervised (GenePlexus) classifiers for each module and aggregates predictions to return genome-wide rankings. We benchmarked ModGenePlexus across simulated data, transcriptomic signatures, and GWAS datasets (together spanning hundreds of diseases), showing improved recovery of known disease genes compared to GenePlexus. Beyond improved classification, the results of enrichment analysis of ModGenePlexus outputs are much more interpretable by virtue of revealing nuanced biological processes. Together, these results establish ModGenePlexus as a scalable, interpretable tool for gene classification of GWAS- and omics-derived gene lists across diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: ModGenePlexus is freely available on GitHub at https://github.com/krishnanlab/ModGenePlexus, and the full source code and results supporting this study are available on Zenodo at https://zenodo.org/records/19857910.
The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species.
The benchmarking process has been used extensively to evaluate and improve performance in business and industry. There is currently increasing interest in utilizing this process in the health care field to maximize efficiency and improve patient outcomes. The article defines benchmarking in health care and lists characteristics of the benchmarking process. The process of conducting a clinical benchmarking project aimed at improving outcomes for patients with congestive heart failure is described and determined to be an effective means of reducing costs and improving both patient outcomes and quality of care.
Coronary catheterization laboratories (CCLs) are the cornerstones of the delivery system for many cardiovascular procedures performed in the United States. However, few comprehensive data exist benchmarking physician activities in CCLs. This study benchmarks cost and time data on 82,548 consecutive patient encounters in 53 CCLs for the 18-month period of January 1997 through June 1998. The data are compiled from the OEP program, a relational database developed by Boston Scientific/Scimed (Maple Grove, Minnesota) for use in CCLs. CCL productivity (total time and procedure time) and cost (variable costs and device costs) benchmarks are created for: 1) left heart catheterization; 2) right and left heart catheterization; 3) percutaneous transluminal coronary balloon angioplasty (PTCA); 4) atherectomy; and 5) coronary stents. Results show the variable costs (those costs that vary in direct proportion to changes in CCL activities) for the five procedures are: $308, left heart catheterization; $395, right and left heart catheterization; $841, PTCA; $2,768, atherectomy; and $3,186, coronary stent. These variable costs are lower than the typical average costs reported for these procedures because they do not include hospital, laboratory, and physician costs, only the procedure-specific activity-related costs most directly controlled and/or influenced by CCL physicians or administrators. The total time for the left heart catheterization averaged 64 minutes and 84 minutes for the right and left heart catheterization, respectively, and procedural times averaged 25 and 32 minutes, respectively. For the major interventional procedures N PTCA, atherectomy, and coronary stents, total times averages were 102, 135, and 117 minutes, respectively. Procedural times for these procedures averaged between 60 and 65 percent of the total time. The major implications of these findings are discussed and limitations noted.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables cellular characterization at single-cell resolution. However, its high dimensionality, sparsity, and noise make clustering challenging. Approaches utilizing contrastive learning and data augmentation have been introduced to improve representation quality for scRNA-seq clustering. In particular, dual contrastive frameworks combining instance- and cluster-level objectives can capture both cell-cell similarities and inter-cluster variations. However, existing dual contrastive frameworks focus primarily on discrete cluster boundaries, neglecting the biological continuity inherent in scRNA-seq data. METHODS: We propose scFANCL, a dual contrastive framework designed to capture biological continuity in scRNA data. Rather than treating all non-augmented samples as negatives, scFANCL applies a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, preserving continuous transcriptional relationships among them while maintaining inter-cluster separation. RESULTS: Extensive experiments across seven publicly available scRNA-seq datasets demonstrated that scFANCL achieves competitive clustering performance compared with existing baseline methods, consistently yielding high ARI and NMI scores across datasets of varying size and complexity. Ablation studies further confirmed the contribution of the false negative filtering component, showing measurable improvements over variants without filtering. Downstream analyses further suggest that the learned embeddings may reflect biologically meaningful transcriptional transitions, including continuous differentiation trajectories within related cell types. The source code is available at https://github.com/mjuailab/scFANCL . CONCLUSIONS: scFANCL addresses a key limitation of conventional contrastive learning by applying a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, thereby preserving biological continuity within cell types while maintaining inter-cluster separation. Evaluations across seven benchmark scRNA-seq datasets demonstrate competitive clustering performance, with learned embeddings capturing biologically meaningful transcriptional structure and characteristics of rare cell populations.
OASIS gave Medicare-certified home care providers our first uniform external comparative data to quantify patient outcomes. PPS reimbursement offered further opportunity to use the data to measure operational performance. This article describes the use of external comparative data and PDSA methodology to identify best practices that improved patient outcomes and agency operational performance.
Looking into a future marked by intense competition for talent, growing numbers of employers are striving to create "workplaces of choice." Yet, despite the consensus that health human resources are a vital piece of the healthcare reform puzzle, few health service organizations have developed comprehensive strategies to address work environment issues. The cumulative impact of years of cost-cutting, downsizing and restructuring have left Canada's healthcare workforce demoralized, overworked and coping with working conditions that diminish both the quality of working life and organizational performance.
The gap between top- and bottom-performing plans in NCQA's State of Managed Care Quality report remains enormous. For example, beta-blocker treatment rates range from 52% to 92%. The report includes data on 292 health plans on effectiveness of care and member satisfaction measures. Fewer health plans were willing to release their performance data publicly this year.
Results for disease management [DM] programs have not been as positive as hoped because of clinical issues, lack of access to capital, and administrative issues. The financial experience of DM programs can be quite volatile. Financial projections that are protocol-based, rather than experience-based, may understate the revenue required and the range of possible costs for a DM program by understating the impact of complicating conditions and comorbidities. Actuarial tools (risk analysis and risk projection models) support better understanding of DM contracts. In particular, these models can provide the ability to quantify the impact of the factors that drive costs of a contract and the volatility of those costs. This analysis can assist DM companies in setting appropriate revenue and capital targets. Similar analysis by health plans can identify diseases that are good candidates for DM programs and can provide the basis for performance targets.
Peak-performing hospitals and systems not only provide high-quality care, they were actually able to report rising profit margins over the last year. Learn in detail about the eight characteristics that these peak performers share.
Integration links strategic approach to customer-oriented philosophy. ISO provides essential fundamentals for a business model. Breaking down silo effect key to performance improvement.
Performance data is no longer a closely-held secret, as thousands of hospitals are reporting this information to the CMS and other quality improvement initiatives. The Wisconsin Hospital Association has tapped into this flow of data to build a website that allows hospitals in that state to compare their performance directly to other facilities.
The benchmark approach is gaining attention as an alternative to the No-Observed-Adverse-Effect-Level (NOAEL) approach. However, current guidelines for the design of toxicity tests are based on assessing a NOAEL. It has been suggested that the current study design may not be optimal for assessing a Benchmark Dose (BMD). To further investigate this we performed three simulation studies in which a large number of designs were compared, focusing on continuous endpoints. Four fictitious endpoints were considered, their underlying dose-response curves having a linear, sublinear, supralinear, or sigmoidal shape. In each simulation run the BMD was derived from a model fitted to the generated data, where the selection of the model was based on that particular data set (according to a formal likelihood ratio test procedure). Thus, the model used for deriving the BMD in a single generated data set may not be the same as the one used for generating the data. In this way, model uncertainty is taken into account as well. The results show that the performance of a design is, first of all, determined by the total number of animals used. Distributing them over more dose groups does not result in a poorer performance of the study, despite the smaller number of animals per dose group. Dose placement is another crucial factor, and to minimize the risk of inadequate dose placement, the use of multiple dose studies is favorable. As a concomitant advantage, the use of multiple doses mitigates the disturbing effect of potential systematic errors in single dose groups. However, for endpoints with large residual variation (CV > or = 18%) there is a substantial probability of not detecting the overall dose-response, and this probability increases in designs with increasing number of dose groups. In such situations, six dose groups may be used as a compromise. Designs with high dose levels (i.e., associated with relatively high effects) are helpful in estimating doses with smaller effects (such as the benchmark dose), and it appears bad practice to omit higher dose groups to improve the fit at lower doses. The typical 28-day study design of four dose groups with five animals (per sex) may not be adequate to assess endpoints with large residual variation (CV > or = 18%), both in assessing a benchmark dose and in assessing a NOAEL.
Hospitals continue to be challenged to reduce costs while providing high-quality care. Cost-reduction methods that hospitals can use successfully include cost data, interdisciplinary approaches, benchmarking, clinical pathways, physician profiling, and case management. Cost reduction also can be achieved through performance inprovement. One performance-improvement-strategy is the FOCUS-PDCA model. The letters in the model's name refer to the following steps: Find a process that needs improvement, Organize a team that knows the process, Clarify current knowledge of the process, Understand the process and learn the causes of the variation, Select the improvement opportunities, Plan the change, Do, Check the results, and Act by implementing the change. The FOCUS-PDCA model was used by an East Texas regional hospital to reduce the costs relaed to cholecystectomy surgeries performed there.