Transitional Architectures for Enterprise Evolution

To transform your IT architecture, consider implementing discrete plateaus to provide stability and manage change while you move gradually closer to your goal.     

Defining business requirements quickly and accurately

A critical task in an enterprise IT architecture project is to identify and understand key business requirements to ensure that the planned IT systems will fully support and evolve with the business. This article illustrates a more effective approach: leveraging the combined power of value chain, which captures the static business view, and use cases, which animate the business model. In this way, enterprise architects can rapidly define the business's main elements and understand how key systems interact to support business activities.     

QFD in the architecture development process

Quality function deployment ensures that the new design in an enterprise IT architecture project fully implements functional and nonfunctional requirements so that planned IT systems can support the business. In this paper, we outline an approach to designing architectures that fully support requirements, by leveraging a technique called quality function deployment. QFD is a systematic method for tying product and service design decisions directly to customer wants and needs. In the future, we plan to explore issues associated with defining and collecting architecture requirements, designing and collecting architecture requirements, designing efficient and elegant architectures, and creating architecture transition plans.     

First-cycle blood counts and subsequent neutropenia, dose reduction, or delay in early-stage breast cancer therapy

PURPOSE: If patients could be ranked according to their projected need for supportive care therapy, then more efficient and less costly treatment algorithms might be developed. This work reports on the construction of a model of neutropenia, dose reduction, or delay that rank-orders patients according to their need for costly supportive care such as granulocyte growth factors. PATIENTS AND METHODS: A case series and consecutive sample of patients treated for breast cancer were studied. Patients had received standard-dose adjuvant chemotherapy for early-stage nonmetastatic breast cancer and were treated by four medical oncologists. Using 95 patients and validated with 80 additional patients, development models were constructed to predict one or more of the following events: neutropenia (absolute neutrophil count [ANC] < or = 250/microL), dose reduction > or = 15% of that scheduled, or treatment delay > or = 7 days. Two approaches to modeling were attempted. The pretreatment approach used only pretreatment predictors such as chemotherapy regimen and radiation history; the conditional approach included, in addition, blood count information obtained in the first cycle of treatment. RESULTS: The pretreatment model was unsuccessful at predicting neutropenia, dose reduction, or delay (c-statistic = 0.63). Conditional models were good predictors of subsequent events after cycle 1 (c-statistic = 0.87 and 0.78 for development and validation samples, respectively). The depth of the first-cycle ANC was an excellent predictor of events in subsequent cycles (P = .0001 to .004). Chemotherapy plus radiation also increased the risk of subsequent events (P = .0011 to .0901). Decline in hemoglobin (HGB) level during the first cycle of therapy was a significant predictor of events in the development study (P = .0074 and .0015), and although the trend was similar in the validation study, HGB decline failed to reach statistical significance. CONCLUSION: It is possible to rank patients according to their need of supportive care based on blood counts observed in the first cycle of therapy. Such rankings may aid in the choice of appropriate supportive care for patients with early-stage breast cancer.