Project and Preliminary Evaluation of SimHosp, a Tool for Decision Making in Nursing.

Computerized simulators are important tools that support teaching in many areas. The use of these instruments is often described as significant for students and teachers. In this paper, a software piece was developed to aid decision-making in nursing, allowing the simulation of real situations, addressed in the classroom. The simulator architecture corresponds to a multi-agent system supported by a state machine model. To build the knowledge base, a list of contents was selected, including the Nursing Intervention Classification (NIC). An experiment was carried out with the participation of eleven students of the third year of the course. A questionnaire was applied and, as a result, there were more than 90% of acceptance as a relevant educational tool. The simulation, through this tool, contributed to apply theoretical knowledge to the students, besides helping in the development of the nursing decision-making ability.

A computational application for multi-skill nurse staffing in hospital units.

BACKGROUND: Approaches to nurse staffing are commonly concerned with determining the minimum number of care hours according to the illness severity of patients. However, there is a gap in the literature considering multi-skill and multi-shift nurse staffing. This study addresses nurse staffing per skill category, at a strategical decision level, by considering the organization of work in shifts and coping with variability in demand. METHODS: We developed a method to determine the nursing staff levels in a hospital, given the required patient assistance. This method relies on a new mathematical model for complying with the legislation and guidelines while minimizing salary costs. A spreadsheet-based tool was developed to embed the model and to allow simulating different scenarios and evaluating the impact of demand fluctuations, thus supporting decision-making on staff dimensioning. RESULTS: Experiments were carried out considering real data from a Brazilian hospital unit. The results obtained by the model support the current total staff level in the unit under study. However, the distribution of staff among different skill categories revealed that the current real situation can be improved. CONCLUSIONS: The method allows the determining of staff level per shift and skill depending on the mix of patients' illness severity. Hospital management is offered the possibility of optimizing the staff level using a spreadsheet, a tool most managers are familiar with. In addition, it is possible to evaluate the implications of decisions on workforce dimensioning by simulating different demand scenarios. This tool can be easily adapted to other hospitals, using local rules and legislation.

Method for Building a Medical Training Simulator with Bayesian Networks: SimDeCS.

Distance education has grown in importance with the advent of the internet. An adequate evaluation of students in this mode is still difficult. Distance tests or occasional on-site exams do not meet the needs of evaluation of the learning process for distance education. Bayesian networks are adequate for simulating several aspects of clinical reasoning. The possibility of integrating them in distance education student evaluation has not yet been explored much. The present work describes a Simulator based on probabilistic networks built to represent knowledge of clinical practice guidelines in Family and Community Medicine. The Bayesian Network, the basis of the simulator, was modeled to playable by the student, to give immediate feedback according to pedagogical strategies adapted to the student according to past performance, and to give a broad evaluation of performance at the end of the game. Simulators structured by Bayesian Networks may become alternatives in the evaluation of students of Medical Distance Education.