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.

Influence Diagram As a Support Tool for Clinical Decisions In Cardiopulmonary And Metabolic Rehabilitation.

An influence diagram (ID) is a method of graphical representation of uncertain knowledge, which can be employed to support decisions in health care using probabilistic reasoning. We aimed to describe the development of an ID to support the decision-making process in phase II at Cardiopulmonary and Metabolic Rehabilitation Program (CPMR). The development of the ID was carried out through the identification of relevant variables and their possible values, as well as the identification of details of each variable, in order to find a network structure that appropriately connects the nodes that represent the variables, with arcs linking acyclic graphs, and to build the graph using specialized knowledge and the conditional probability table for each node in the graph. In spite of the complexity of the interactions, the model obtained with the ID seems to contribute in the decision-making process in phase II CPMR, providing a second opinion to the health pratictioner and helping in diagnostic, therapeutic and decision-making processes, since it is useful in situations with non-linear modeling or with absent or uncertain information.

Una experiencia en el uso de redes probabilisticas para el diagnostico medico: una experiencia brasilera / An experience in the use of statitics networks for medical diagnosis: a brazilian experience

En este articulo se presenta un sistema de apoyo para las decisiones medicas, basado en la aplicacion de redes probabilisticas (Redes Bayesianas). La tecnologia utilizada es ideal para el manejo de la incertidumbre, muy comun en medicina, ademas de eso, modela el conocimiento del especialista de dominio de una forma intuitiva. Permite realizar cualquiera de los tipos posibles de inferencia probabilistica, o sea, causal, diagnostica, intercausal o mixta. El sistema desarrollado es producto de una alianza entre una institucion privada y una publica. En la primera parte del articulo se realiza una reseña historica de la Inteligencia Artificial, mostrando las ventajas que tiene este nuevo enfoque de las tecnologias de redes probabilisticas. En la segunda parte, se describe el sistema SEAMED V 2.0, usando, como ejemplo, la base de conocimiento en cardiopatias congenitas

Una experiencia en el uso de redes probabilisticas para el diagnostico medico: una experiencia brasilera / An experience in the use of statitics networks for medical diagnosis: a brazilian experience

En este articulo se presenta un sistema de apoyo para las decisiones medicas, basado en la aplicacion de redes probabilisticas (Redes Bayesianas). La tecnologia utilizada es ideal para el manejo de la incertidumbre, muy comun en medicina, ademas de eso, modela el conocimiento del especialista de dominio de una forma intuitiva. Permite realizar cualquiera de los tipos posibles de inferencia probabilistica, o sea, causal, diagnostica, intercausal o mixta. El sistema desarrollado es producto de una alianza entre una institucion privada y una publica. En la primera parte del articulo se realiza una reseña historica de la Inteligencia Artificial, mostrando las ventajas que tiene este nuevo enfoque de las tecnologias de redes probabilisticas. En la segunda parte, se describe el sistema SEAMED V 2.0, usando, como ejemplo, la base de conocimiento en cardiopatias congenitas (AU)