The non-emergency patient transport modelled as a team orienteering problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.

Non emergency patients transport - a mixed integer linear programming

This work presents a model and a heuristic to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving problems with one vehicle was presented, and this heuristic provides good results in terms of accuracy and computation time.

Genetic algorithms for the non emergency patients transport service

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the paper. The authors would like to thank Dr. Elaine DeBock for reviewing the manuscript.

Poly-N-acetyl glucosamine expression in Staphylococcus epidermidis biofilm cells affects the immune response and promotes tissue pathology in infected hosts

Staphylococcus epidermidis is a biofilm - forming bacterium and a leading etiological agent of nosocomial infections. The ability to establish biofilms on indwelling medical devices is a key virulence factor for this bacterium. Still, the influence of poly - N - acetyl glucosamine (PNAG), the major component of the extracellular biofilm matrix, in the host immune response has been scarcely studied. Here, t h is influence was assessed in mice challenged i.p. with PNAG - p roducing (WT) and isogenic - mutant lacking PNAG (M10) bacteria grown in biofilm - inducing conditions. Faster bacterial clearance was observed in the mice infected with WT bacteria than in M10 - infected counterparts , which w as accompanied by earlier neutrophil recruitment and higher IL - 6 production. Interestingly, in the WT - infected mice, but not in those infected with M10 , elevated serum IL - 10 was detected . To further study the effe ct of PNAG in the immune response, mice were primed with WT or M10 biofilm bacteria and subsequently infected with WT biofilm - released cells. WT - primed mice presented a higher frequency of splenic IFN - γ + and IL - 17 + CD4 + T cells, and more severe liver patho logy than M10 - primed counterparts. Nevertheless, T reg cells obtained from the WT - primed mice presented a higher suppressive function than those obtained from M10 - primed mice. This effect was abrogated when IL - 10 - deficient mice were similarly primed and infected indicating that PNAG promotes the differentiati on of highly suppressive T reg cells by a mechanism dependent on IL - 10. Altogether, these results provide evidence help ing explain ing the coexistence of inflammation and bacterial persistence often observed in biofilm - originated S. epidermidis infections

Atuação dos enfermeiros na emergência pré-hospitalar em situações de paragem cárdio-respiratória

Dissertação de mestrado em Enfermagem da Pessoa em Situação Crítica

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.