User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner

Background: The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. Methods: AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Results: Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. Conclusions: AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Factor Analytic Model for evaluating the effects of a human resource profile on organisational productivity: a case study of university academic staff

No Abstract.South African Journal of Higher Education 2005, Vol. 19(3): 527-53

A Framework For The Application Of Knowledge Technology To The Management Of Diseases

Medical diagnosis and therapy constitute a network of inter-related processes. The conventional method of medical diagnosis and therapy of diseases involve the state space search of medical knowledge of diseases and patient history, which could be combinatorial explosive. This paper presents the report of the experimental study of an intelligent, interactive, user friendly knowledge based system, which does a stepwise analysis of the patient's complaints, filtering the cognitive and emotional elements to be able to make inferences. It applies both forward and backward chaining in making inferences concerning the management of the diseases. A case study of the system is carried out using some tropical diseases. It is believed that the system will serve as a good contribution towards the much desired tropical medical informatics. Key Words: Medical Diagnosis, Knowledge Technology, Inference Engine, Tropical Diseases Global Journal of Pure and Applied Sciences Vol.11(1) 2005: 121-12

Do tourism applications' quality and user experience influence its acceptance by tourists?

The aim of the present study is to improve the understanding regarding the acceptance of tourism apps available for the marketing and tourism destination. For this purpose, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was adapted to investigate the determinants of users' behavioral intention for mobile tourism applications according to the quality and user experience of tourism applications. In order to understand whether the design of and user experience with tourist applications influence those apps' use and acceptance, the variable Trust in the Internet operator was added to UTAUT2 as an external variable, as well as App Quality and App User Experience. To investigate the determinants of the users' behavioral intention, a survey of 552 users was performed, and the data were analyzed using the partial least squares path modeling in Spain and Portugal. The results contribute to a deeper understanding of user needs when they decide whether to download a mobile tourism app and whether to use it at their destination.FCT - Foundation for Science and TechnologyPortuguese Foundation for Science and Technology [UIDB/04470/2020 CiTUR]info:eu-repo/semantics/publishedVersio