How do practitioners use conceptual modeling in practice?

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modeling seriously. In addition, what are the most popular techniques and tools used for conceptual modeling? What are the major purposes for which conceptual modeling is used? The study found that the top six most frequently used modeling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modeling, UML, and structured charts. Modeling technique use was found to decrease significantly from smaller to medium-sized organizations, but then to increase significantly in larger organizations (proxying for large, complex projects). Technique use was also found to significantly follow an inverted U-shaped curve, contrary to some prior explanations. Additionally, an important contribution of this study was the identification of the factors that uniquely influence the decision of analysts to continue to use modeling, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models' integration into the business, and tool/software deficiencies. The highest ranked purposes for which modeling was undertaken were database design and management, business process documentation, business process improvement, and software development. (c) 2005 Elsevier B.V. All rights reserved.

Using measurement data in a TSPSM project

One of the challenges for software engineering is collecting meaningful data from industrial projects. Software process improvement depends on measurement to provide baseline status and confirming evidence of the effect of process changes. Without data, any conclusions rely on intuition and guessing. The Team Software ProcessSM (TSPSM) provides a powerful framework for data collection and analysis, in addition to its primary goal as a basis for highly effective software development. In this paper, we describe the experiences of, and benefits realized by, a team using the TSP for the first time. By reviewing how this particular team collected and used data, we show features of the TSP that make it a powerful foundation for software process improvement.

Multisite, quality-improvement collaboration to optimise cardiac care in Queensland public hospitals

Objective: To evaluate changes in quality of in-hospital care of patients with either acute coronary syndromes (ACS) or congestive heart failure (CHF) admitted to hospitals participating in a multisite quality improvement collaboration. Design: Before-and-after study of changes in quality indicators measured on representative patient samples between June 2001 and January 2003. Setting: Nine public hospitals in Queensland. Study populations: Consecutive or randomly selected patients admitted to study hospitals during the baseline period (June 2001 to January 2002; n = 807 for ACS, n = 357 for CHF) and post-intervention period (July 2002 to January 2003; n = 717 for ACS, n = 220 for CHF). Intervention: Provision of comparative baseline feedback at a facilitative workshop combined with hospital-specific quality-improvement interventions supported by on-site quality officers and a central program management group. Main outcome measure: Changes in process-of-care indicators between baseline and post-intervention periods. Results: Compared with baseline, more patients with ACS in the post-intervention period received therapeutic heparin regimens (84% v 72%; P < 0.001), angiotensin-converting enzyme inhibitors (64% v 56%; P = 0.02), lipid-lowering agents (72% v 62%; P < 0.001), early use of coronary angiography (52% v 39%; P < 0.001), in-hospital cardiac counselling (65% v 43%; P < 0.001), and referral to cardiac rehabilitation (15% v 5%; P < 0.001). The numbers of patients with CHF receiving β-blockers also increased (52% v 34%; P < 0.001), with fewer patients receiving deleterious agents (13% v 23%; P = 0.04). Same-cause 30-day readmission rate decreased from 7.2% to 2.4% (P = 0.02) in patients with CHF. Conclusion: Quality-improvement interventions conducted as multisite collaborations may improve in-hospital care of acute cardiac conditions within relatively short time frames.

Using clinical indicators in a quality improvement programme targeting cardiac care

Rationale. The Brisbane Cardiac Consortium, a quality improvement collaboration of clinicians from three hospitals and five divisions of general practice, developed and reported clinical indicators as measures of the quality of care received by patients with acute coronary syndromes or congestive heart failure. Development of indicators. An expert panel derived indicators that measured gaps between evidence and practice. Data collected from hospital records and general practice heart-check forms were used to calculate process and outcome indicators for each condition. Our indicators were reliable (kappa scores 0.7-1.0) and widely accepted by clinicians as having face validity. Independent review of indicator-failed, in-hospital cases revealed that, for 27 of 28 process indicators, clinically legitimate reasons for withholding specific interventions were found in

Predictors of osteoarthritis(OA) improvement in patients treated with Hylan G-F for knee OA.

Aim of study: Different criteria for treatment response were explored to identify predictors of OA improvement. Analyses were based on data from a previously reported 1-year randomized controlled trial of appropriate care with or without hylan G-F 20 in patients with knee OA. Methods: Five definitions of ‘‘patient responder’’ from baseline to month 12 were examined: at least 20% reduction in WOMAC pain score; at least 20% reduction in WOMAC pain score and at least 20% reduction in either the WOMAC stiffness or function score; OARSI responder criteria (Propositions A and B) for intra-articular drugs; and OMERACT-OARSI responder criteria (Proposition D). As an a posteriori analysis, multivariable logistic regression models for each definition of patient responder were developed using a forward selection method. The following variables were defined prior to modeling and considered in the model along with two-way interactions: age (O65 years), BMI, gender, X-ray grade (0, I, II vs III, IV), co-morbidity (1 or 2 conditions vs 3 or more), duration of OA in study knee (years), previous surgery of study knee, hylan G-F 20 injection technique, WOMAC pain, stiffness and function, and treatment group. Results: Hylan G-F 20 was a predictor of improvement for all patient responder definitions P ! 0.001; odds of improvement were 2.7 or higher for patients in the hylan G-F 20 group compared to appropriate care without hylan G-F 20. For three of the five patient responder definitions, X-ray grade was a predictor of improvement (P ! 0.10; lower X-ray grade increased the odds of improvement). For four of the five patient responder definitions, duration of OA was a predictor of improvement (P ! 0.10; shorter duration of OA increased the odds of improvement). Conclusion: Analyses showed that appropriate care with hylan G-F 20 is the dominant predictor of patient improvement. While high grade structural damage does not preclude a response, patients who are targeted early in the disease process when less structural damage has occurred, may have a greater chance of improvement.

Building business cases for maintenance improvement projects

Many maintenance managers find it difficult to justify investments in maintenance improvement initiatives. In part, this is due to a tendency by mine managers to regard maintenance purely as a cost centre, and not as a process able to influence productive capacity and profit. It is also hindered by a lack of alignment between commonly used maintenance performance measures and key business drivers, and the lack of formal business training amongst maintenance professionals. With this in mind, a model to assist maintenance managers in evaluating the benefits of maintenance improvement projects was recently formulated. The model considers four cost saving dimensions. These are: 1. reduction in the cost of unplanned repairs and maintenance, 2. increased or accelerated production and/or sales, 3. spares inventory reduction, and 4. reduction in over-investment in physical assets and operating costs. This paper discusses the application of this model and a number of numerical examples are given to justify investments in maintenance improvement projects having varying objectives.