Requirements engineering in health care: the example of chemotherapy planning in paediatric oncology

Health care is characterized by highly complex processes of patient care that require unusual amount of communication between different health care professionals of different institutions. Sub-optimal processes can significantly impact on the patient’s health, increase the consumption of services and resources and in severe cases can lead to the patient death. For these reasons, requirements engineering for the development of information technology in health care is a complex process as well: without constant and rigorous evaluation, the impact of new systems on the quality of care is unknown and it is possible that badly designed systems significantly harm patients. To overcome these limitations, we present and discuss an approach to requirements engineering that we applied for the development of applications for chemotherapy planning in paediatric oncology. Chemotherapy planning in paediatric oncology is complex and time-consuming and errors must be avoided by all means. In the multi-hospital/multi-trial-centre environment of paediatric oncology, it is especially difficult and time-consuming to analyse requirements. Our approach combines a grounded theory approach with evolutionary prototyping based on the constant development and refinement of a generic domain model, in this case a domain model for chemotherapy planning in paediatric oncology. The prototypes were introduced in medical centres and final results show that the developed generic domain model is adequate.     

Empirical efficiency and volume relationships for HPGe detectors

We have extended the empirical work of Vano et al.[1] relating the slope of the detector efficiency curve to the active volume for Ge detectors. The analysis was carried out using Monte Carlo techniques and covered a wide range of incident energies (200 keV-20 MeV) and active volumes (19.6 cm³–396 cm³). It is shown that the expression of Vano et al.[1] is only valid over the energy range 200 keV-3 MeV for active volumes <50 cm³. The upper bound decreases to 2 MeV for volumes of a few hundred cm³. The usable energy range can, however, be extended to 6 MeV by introducing higher order terms into the polynomial. Above this energy, the shape of the efficiency curve is better described by a non-linear function since linear forms fail simultaneously to fit large active volumes and high energies. We therefore propose a composite function which reduces to the form given in Vano et al. in the low energy/active volume limit. By comparison with the Monte Carlo results, it is estimated that relative efficiencies can be calculated to within 6% over the energy range 200 keV-20 MeV and active volumes 20 cm³–400 cm³. Since the largest errors occur for the smallest volumes, we recommend that for energies <3 MeV a two-fold approach be followed, i.e. using the expression of Vano et al.[1] for active volumes less than 50 cm³ and the proposed non-linear form for larger volumes. For high energy work (E > 3 MeV), we advocate the non-linear form. In this way, average errors can be kept 3%. Finally, we point out that the real power of the expression of Vano et al. lies not in predicting efficiencies, but active volumes.     

A 16-Mb MRAM featuring bootstrapped write drivers

A 16-Mb magnetic random access memory (MRAM) is demonstrated in 0.18-/spl mu/m three-Cu-level CMOS with a three-level MRAM process adder. The chip, the highest density MRAM reported to date, utilizes a 1.42/spl mu/m/sup 2/ 1-transistor 1-magnetic tunnel junction (1T1MTJ) cell, measures 79 mm/sup 2/ and features a /spl times/16 asynchronous SRAM-like interface. The paper describes the cell, architecture, and circuit techniques unique to multi-Mb MRAM design, including a novel bootstrapped write driver circuit. Hardware results are presented.     

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