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Strategic triggers and metrics help healthcare providers achieve financial success. Metrics help assess progress toward long-term goals. Triggers signal market changes requiring a change in strategy. All metrics may not move in concert. Organizations need to identify indicators, monitor performance.
What does it mean to be a high-performance company? The process of measuring relative performance across industries and eras, declaring top performers, and finding the common drivers of their success is such a difficult one that it might seem a fool's errand to attempt. In fact, no one did for the first thousand or so years of business history. The question didn't even occur to many scholars until Tom Peters and Bob Waterman released In Search of Excellence in 1982. Twenty-three years later, we've witnessed several more attempts--and, just maybe, we're getting closer to answers. In this reported piece, HBR senior editor Julia Kirby explores why it's so difficult to study high performance and how various research efforts--including those from John Kotter and Jim Heskett; Jim Collins and Jerry Porras; Bill Joyce, Nitin Nohria, and Bruce Roberson; and several others outlined in a summary chart-have attacked the problem. The challenge starts with deciding which companies to study closely. Are the stars the ones with the highest market caps, the ones with the greatest sales growth, or simply the ones that remain standing at the end of the game? (And when's the end of the game?) Each major study differs in how it defines success, which companies it therefore declares to be worthy of emulation, and the patterns of activity and attitude it finds in common among them. Yet, Kirby concludes, as each study's method incrementally solves problems others have faced, we are progressing toward a consensus theory of high performance.
Behavioral group practices have agreed on performance indicators and have established national benchmarking standards. Collaborative study, led by the Institute for Behavioral Healthcare's Council of Behavioral Group Practices (CBGP), facilitates practice pattern evaluation, trend analysis, and clinical quality improvement. The study reports the benchmarking standards for quality assessment and improvement standards in behavioral group practices in the comprehensive 1995 and 1996 CBGP Performance Indicator Reports.
OBJECTIVES: To identify foodservice directors' use of performance measures and to determine their current practices of, and attitudes and beliefs about, benchmarking. DESIGN: A survey was conducted using a researcher-developed questionnaire that had been validated in a pilot-test. The questionnaire was mailed to 600 randomly selected foodservice directors; 247 (41%) responses were analyzed. SUBJECTS/SETTING: Subjects were foodservice directors in the United States from 4 categories of foodservice operations: college/university, correctional, health care, and school. STATISTICAL ANALYSES: Results were analyzed using descriptive statistics and chi 2 tests to investigate associations between variables of interest. RESULTS: The most common performance measures used by foodservice directors were food cost percentage, cost per unit or area of service, and meals per labor hour. Internal benchmarking had been used by 71% of the respondents, external benchmarking by 60%, and functional/generic by 25%. Seventy-seven percent of the respondents thought benchmarking had some or great importance in their jobs. Category of foodservice operation was associated with type of benchmarking partner and was related to certain performance measures. Sixty-one percent of respondents reported needing knowledge and skills about benchmarking. APPLICATIONS/CONCLUSIONS: Foodservice directors, regardless of category of foodservice operation, perceive benchmarking as a useful management tool to improve processes, products and services. Foodservice directors can use benchmarking to compare their financial performance with that of other organizations and learn how to improve their facility by examining best-practice processes of successful organizations.
OBJECTIVE: To identify primary care teams (PCT) with the best overall performance and compare these with other PCT with benchmarking methods. DESIGN: Descriptive, cross-sectional study of a set of indictors for the year 2002. SETTING: City of Barcelona (northeastern Spain). PARTICIPANTS: Thirteen seven PCT with more than 2 years' experience, and 771,811 inhabitants in the catchment area. MAIN MEASURES: Indicators were chosen from among those proposed by an advisory group, depending on feasibility of obtaining information. A total of 17 indicators in 4 dimensions were studied: accessibility, clinical effectiveness, case management capacity, and cost-efficiency. Each PCT was scored for each indicator based on the percentile group in the distribution of scores, and for each dimension based on the mean score for all indicators in a given dimension. Overall score for PCT performance was calculated as the weighted sum of the scores for each dimension. As descriptive variables we analyzed time operating under the revised administrative system, patient visits per population served, the population's economic capacity and age of the population. RESULTS. Nine PCT were identified as the benchmark group. Teams in this group had been operating under the revised administrative system for significantly longer than other PCT. In comparison to other PCT, the benchmark group obtained higher scores on all four dimensions, better results on 14 separate indicators, the same results for 1 indicator, and worse results for 2 indicators. CONCLUSIONS. Benchmarking made it possible to identify PCT with the best performance, and to identify areas in need of improvement. This approach is a potentially useful tool for self-evaluation and for stimulating a dynamic for improvement in primary care providers.
INTRODUCTION: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. METHOD: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. RESULTS: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. DISCUSSION: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods.
This paper presents a new method for regression based on the evolution of a type of feed-forward neural networks whose basis function units are products of the inputs raised to real number power. These nodes are usually called product units. The main advantage of product units is their capacity for implementing higher order functions. Nevertheless, the training of product unit based networks poses several problems, since local learning algorithms are not suitable for these networks due to the existence of many local minima on the error surface. Moreover, it is unclear how to establish the structure of the network since, hitherto, all learning methods described in the literature deal only with parameter adjustment. In this paper, we propose a model of evolution of product unit based networks to overcome these difficulties. The proposed model evolves both the weights and the structure of these networks by means of an evolutionary programming algorithm. The performance of the model is evaluated in five widely used benchmark functions and a hard real-world problem of microbial growth modeling. Our evolutionary model is compared to a multistart technique combined with a Levenberg-Marquardt algorithm and shows better overall performance in the benchmark functions as well as the real-world problem.
OBJECTIVES: To answer basic questions, using precise definitions, regarding emergency department (ED) utilization, wait times, services, and attending physician staffing of representative pediatric EDs (PEDs). METHODS: Ten questions with precise definitions were developed and sent to members of the Ambulatory Pediatric Association's PED Directors' Special Interest Group in November of 2001, with two repeated requests 3 and 6 months later. RESULTS: Twenty-one PEDs from 14 states, the District of Columbia, and Canada responded (41%). The average PED has 48,000 patient visits per year (range, 25,000-97,000). Two thirds have urgent care or fast track areas to see nonurgent patients, while only 29% have 23-hour treatment units. The average admission rate is 13.2% (range, 6.8-20.8%). The average rate of patients who leave without being seen is 1.6%. The average patient waits 1 hour to see a physician and spends a total of 3 hours in the PED. The majority of attending staffing is by pediatric emergency medicine (PEM) Board-certified/eligible physicians (73%), although a few PEDs are staffed only by PEM specialists. Attending staffing is 2.8 patients per attending per hour, or 0.36 hours per patient, with more staffing for PEDs with higher admission rates or acuity. The average entry-level base salary for PED physicians in 2000 was 117,250 dollars (range, 98,000 dollars-145,000 dollars). CONCLUSIONS: Benchmarking of PEM staffing and performance indicators by PEM directors yields important administrative data. PEDs have higher census and admission rates compared with information from all EDs, while their attending staffing, wait times, and rate of patients who leave without being seen are comparable to those of general EDs.
The accuracy of dose computation within the lungs depends strongly on the performance of the calculation algorithm in regions of electronic disequilibrium that arise near tissue inhomogeneities with large density variations. There is a lack of data evaluating the performance of highly developed analytical dose calculation algorithms compared to Monte Carlo computations in a clinical setting. We compared full Monte Carlo calculations (performed by our Monte Carlo dose engine MCDE) with two different commercial convolution/superposition (CS) implementations (Pinnacle-CS and Helax-TMS's collapsed cone model Helax-CC) and one pencil beam algorithm (Helax-TMS's pencil beam model Helax-PB) for 10 intensity modulated radiation therapy (IMRT) lung cancer patients. Treatment plans were created for two photon beam qualities (6 and 18 MV). For each dose calculation algorithm, patient, and beam quality, the following set of clinically relevant dose-volume values was reported: (i) minimal, median, and maximal dose (Dmin, D50, and Dmax) for the gross tumor and planning target volumes (GTV and PTV); (ii) the volume of the lungs (excluding the GTV) receiving at least 20 and 30 Gy (V20 and V30) and the mean lung dose; (iii) the 33rd percentile dose (D33) and Dmax delivered to the heart and the expanded esophagus; and (iv) Dmax for the expanded spinal cord. Statistical analysis was performed by means of one-way analysis of variance for repeated measurements and Tukey pairwise comparison of means. Pinnacle-CS showed an excellent agreement with MCDE within the target structures, whereas the best correspondence for the organs at risk (OARs) was found between Helax-CC and MCDE. Results from Helax-PB were unsatisfying for both targets and OARs. Additionally, individual patient results were analyzed. Within the target structures, deviations above 5% were found in one patient for the comparison of MCDE and Helax-CC, while all differences between MCDE and Pinnacle-CS were below 5%. For both Pinnacle-CS and Helax-CC, deviations from MCDE above 5% were found within the OARs: within the lungs for two (6 MV) and six (18 MV) patients for Pinnacle-CS, and within other OARs for two patients for Helax-CC (for Dmax of the heart and D33 of the expanded esophagus) but only for 6 MV. For one patient, all four algorithms were used to recompute the dose after replacing all computed tomography voxels within the patient's skin contour by water. This made all differences above 5% between MCDE and the other dose calculation algorithms disappear. Thus, the observed deviations mainly arose from differences in particle transport modeling within the lungs, and the commissioning of the algorithms was adequately performed (or the commissioning was less important for this type of treatment). In conclusion, not one pair of the dose calculation algorithms we investigated could provide results that were consistent within 5% for all 10 patients for the set of clinically relevant dose-volume indices studied. As the results from both CS algorithms differed significantly, care should be taken when evaluating treatment plans as the choice of dose calculation algorithm may influence clinical results. Full Monte Carlo provides a great benchmarking tool for evaluating the performance of other algorithms for patient dose computations.
Best practice refers to those practices that lead to superior performance in a company or enterprise relative to industry or international leaders. Benchmarking of those activities that are critical to organizational performance is an important part of the identification and implementation of best-practice approaches. This article looks at communication as one aspect in the development of best practice in the management of safety, environment, and quality. A number of barriers to effective communication are identified, and benchmarks for the evaluation of organizational communication are suggested.
The current climate in Canadian healthcare requires that healthcare providers be more accountable to the government and other stakeholders. Using a well-structured performance appraisal system that is based on quantifiable objectives and standards, a high level of accountability can be achieved. The objective of this article is to demonstrate how a sound performance appraisal system can increase accountability and performance of healthcare organizations and their senior management.
This article reports the status of a new cardiovascular fluoroscopy benchmarking phantom. A joint working group of the Society for Cardiac Angiography and Interventions (SCA&I) and the National Electrical Manufacturers Association (NEMA) developed the phantom. The device was adopted as NEMA standard XR 21-2000, "Characteristics of and Test Procedures for a Phantom to Benchmark Cardiac Fluoroscopic and Photographic Performance," in August 2000. The test ensemble includes imaging field geometry, spatial resolution, low-contrast iodine detectability, working thickness range, visibility of moving targets, and phantom entrance dose. The phantom tests systems under conditions simulating normal clinical use for fluoroscopically guided invasive and interventional procedures. Test procedures rely on trained human observers.
BACKGROUND: An operational definition of benchmarking as developed at Xerox is "finding and implementing best practices." Although benchmarking has widely spread throughout industry, it is only just beginning to find application in health care. TYPES OF BENCHMARKING: In internal benchmarking, similar internal functions serve as pilot sites for conducting benchmarking. Competitive benchmarking, the comparison of a work process with that of the best competitor, reveals the performance measure levels to be surpassed. Functional benchmarking compares a work function to that of the functional leader. Generic process benchmarking compares the organization's basic business processes. ADAPTING BENCHMARKING TO HEALTH CARE: Benchmarking can target business, support, and clinical functions. For clinical functions, there are many potential, ready-made networks of people with similar problems and interests. Benchmarking support functions is often difficult because these functions can provide the greatest competitive edge in the purely business sense. A GRASSROOTS BENCHMARKING EXAMPLE: The ten-step Xerox benchmarking model is illustrated with a fictional case study involving improvement in the work processes associated with outpatient and inpatient biopsies. CONCLUSION: The principles of benchmarking are simple, and the benchmarking process is not complicated. Benchmarking is a structured framework for pursuing worthwhile goals in an organized way.
Benchmarking of clinical laboratory activities has become a tool used increasingly to enable administrators and managers to obtain an independent evaluation of the performance of the laboratory and identify opportunities for improvement. Benchmarking is particularly important because of the diversity and complexity of the various sections of the laboratory. The critical component of laboratory benchmarking is peer comparison, as solutions to shortcomings or problems can be titrated and planned through this process. The reliability of benchmarking must be supplemented and modified by the input of the manager's detailed understanding of local circumstances. At this critical moment, the changes in peer review strategies instituted by JCAHO, CAP, CLIA, and individual states create an urgent opportunity to assist medical directors and laboratory managers in maintaining an overview of the performance and quality of laboratory operations. Unannounced site visits will require prompt reports and alerts of undesirable changes in performance. The future goals of benchmarking must expand to include surveys of laboratory test utilization and patient outcomes as ultimate measures of test utility in the clinical process and important assessments of the quality of patient care.
As the pharmaceutical and biotechnology industry enters the 21st century, the pressure on companies to maintain the level of productivity required for consistent year-on-year growth is increasing. Benchmarking has become a tool for obtaining the information needed to support continuous improvement and gain a competitive advantage. During the process of benchmarking, best practices can be identified while giving management the ability to improve on existing performance in an objective, well-informed manner. When used appropriately, benchmarking provides a new perspective on traditional methods while enabling companies to monitor their performance.
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