An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study.

BACKGROUND: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. METHODS: First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. RESULTS: The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 - 96.26%]), whilst the specificity was 85.45% [77.45 - 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 - 95.13%) and 87.85% (80.12 - 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 - 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. CONCLUSIONS: Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress.

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).

Impact of rainfed agriculture on spatio-temporal patterns of water balance and the interaction between groundwater and surface water in sub-humid plains.

The expansion of rainfed agriculture, especially soybean cultivation in sub-humid plains, alters water balance and the exchange between groundwater-surface water (GW-SW). However, to date, there are no studies that analyze how these anthropic disturbances affect hydrological connectivity in these systems, especially the GW-SW interactions. The objective of this study is to analyze how the increase in rainfed agriculture affects the spatio-temporal patterns of the water balance and the GW-SW interaction. For this analysis, a coupled GW-SW flow model was implemented under land use and land cover (LULC) scenarios, to quantify the spatio-temporal dynamics for different components of water balance and GW-SW interactions for the upper creek basin of Del Azul. A simulation was carried out for a period of 13 years (2003-2015) on a daily scale and it was contrasted through three multitemporal LULC maps. The results point that substitution of natural pastures, the reduction of winter crops and the decrease of crop rotation, due to the increase of soybean monoculture in the basin under study, modifies the water balance, especially the annual rates of surface runoff and soil moisture which may increase between 3.5 and 9.4 % and between 1.4 and 4.4 % respectively, thus increasing the annual streamflows between 2.6 and 6.8 % and the groundwater heads between 0.2 and 0.6 m. This leads to changes in the hetereogeneity of the GW-SW interaction, a reduction between 0.3 and 3 % is observed in the discharge from the Pampeano aquifer to the Del Azul stream, while the recharge rates from the Del Azul stream to the Pampeano aquifer increase between 2 and 17.8 %. The application of the SWAT-MODFLOW model under LULC scenarios, improves the prediction of the regional hydrologic connectivity on sub-humid plains, because the hydrological processes occurring in the surface and non-saturated zone are governed by shallow groundwater dynamics.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.

The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients.

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person's intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

Compatibility and correlation of multi-attribute decision making: a case of industrial relocation.

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM.

A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case.

The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.

A Novel Integration of IF-DEMATEL and TOPSIS for the Classifier Selection Problem in Assistive Technology Adoption for People with Dementia.

The classifier selection problem in Assistive Technology Adoption refers to selecting the classification algorithms that have the best performance in predicting the adoption of technology, and is often addressed through measuring different single performance indicators. Satisfactory classifier selection can help in reducing time and costs involved in the technology adoption process. As there are multiple criteria from different domains and several candidate classification algorithms, the classifier selection process is now a problem that can be addressed using Multiple-Criteria Decision-Making (MCDM) methods. This paper proposes a novel approach to address the classifier selection problem by integrating Intuitionistic Fuzzy Sets (IFS), Decision Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The step-by-step procedure behind this application is as follows. First, IF-DEMATEL was used for estimating the criteria and sub-criteria weights considering uncertainty. This method was also employed to evaluate the interrelations among classifier selection criteria. Finally, a modified TOPSIS was applied to generate an overall suitability index per classifier so that the most effective ones can be selected. The proposed approach was validated using a real-world case study concerning the adoption of a mobile-based reminding solution by People with Dementia (PwD). The outputs allow public health managers to accurately identify whether PwD can adopt an assistive technology which results in (i) reduced cost overruns due to wrong classification, (ii) improved quality of life of adopters, and (iii) rapid deployment of intervention alternatives for non-adopters.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review.

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals.

Considering the unexpected emergence of natural and man-made disasters over the world and Turkey, the importance of preparedness of hospitals, which are the first reference points for people to get healthcare services, becomes clear. Determining the level of disaster preparedness of hospitals is an important and necessary issue. This is because identifying hospitals with low level of preparedness is crucial for disaster preparedness planning. In this study, a hybrid fuzzy decision making model was proposed to evaluate the disaster preparedness of hospitals. This model was developed using fuzzy analytic hierarchy process (FAHP)-fuzzy decision making trial and evaluation laboratory (FDEMATEL)-technique for order preference by similarity to ideal solutions (TOPSIS) techniques and aimed to determine a ranking for hospital disaster preparedness. FAHP is used to determine weights of six main criteria (including hospital buildings, equipment, communication, transportation, personnel, flexibility) and a total of thirty-six sub-criteria regarding disaster preparedness. At the same time, FDEMATEL is applied to uncover the interdependence between criteria and sub-criteria. Finally, TOPSIS is used to obtain ranking of hospitals. To provide inputs for TOPSIS implementation, some key performance indicators are established and related data is gathered by the aid of experts from the assessed hospitals. A case study considering 4 hospitals from the Turkish healthcare sector was used to demonstrate the proposed approach. The results evidenced that Personnel is the most important factor (global weight = 0.280) when evaluating the hospital preparedness while Flexibility has the greatest prominence (c + r = 23.09).

An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector.

Emergency Care Networks (ECNs) were created as a response to the increased demand for emergency services and the ever-increasing waiting times experienced by patients in emergency rooms. In this sense, ECNs are called to provide a rapid diagnosis and early intervention so that poor patient outcomes, patient dissatisfaction, and cost overruns can be avoided. Nevertheless, ECNs, as nodal systems, are often inefficient due to the lack of coordination between emergency departments (EDs) and the presence of non-value added activities within each ED. This situation is even more complex in the public healthcare sector of low-income countries where emergency care is provided under constraint resources and limited innovation. Notwithstanding the tremendous efforts made by healthcare clusters and government agencies to tackle this problem, most of ECNs do not yet provide nimble and efficient care to patients. Additionally, little progress has been evidenced regarding the creation of methodological approaches that assist policymakers in solving this problem. In an attempt to address these shortcomings, this paper presents a three-phase methodology based on Discrete-event simulation, payment collateral models, and lean six sigma to support the design of in-time and economically sustainable ECNs. The proposed approach is validated in a public ECN consisting of 2 hospitals and 8 POCs (Point of Care). The results of this study evidenced that the average waiting time in an ECN can be substantially diminished by optimizing the cooperation flows between EDs.

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review.

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

Health need assessment in an indigenous high-altitude population living on an island in Lake Titicaca, Perú.

BACKGROUND: Health needs and access to health care is a huge challenge in developing countries, especially in some isolated indigenous communities. Amantani is an island located at 3854 m above sea level in Lake Titicaca, Peru. There is no official date on key local health needs and determinants, which precludes the prioritization and efficient implementation of health interventions. The objective of this study is to validate a health need assessment tool and ascertain the main health needs of the indigenous high-altitude population living on Amantani. METHODS: We conducted a cross-sectional study to describe the health needs of the indigenous population of Amantani using a questionnaire based on the "Peruvian Demographic and Health Survey". The questionnaire underwent expert and field-work validation. We selected a random sample of the island residents using two-stage cluster sampling. We estimated the prevalence of key health needs and determinants, and evaluated their distribution by age, sex and education through prevalence ratio. All analyses accounted for the complex sampling design. RESULTS: We surveyed 337 individuals (223 adults and 144 children) in 151 houses. The most frequent health needs were: (i) lack of access to medical screening for a)non-communicable diseases (> 63.0%) and b)eye problems (76.5%); and (ii) poor knowledge about communicable diseases (> 54.3%), cancer (71.4%) and contraception (> 32.9%). Smoking and alcohol use was more frequent in males (PR = 4.70 IC95%:1.41-15.63 and PR = 1.69 95% CI:1.27-2.25, respectively). People with higher education had more knowledge about TB/HIV and cancer prevention (p < 0.05). Regarding children's health, > 38% have never undergone eye or dental examination. Corporal punishment and physical bullying at school in the last month were relatively common (23 and 33%, respectively). CONCLUSION: The main health needs in Amantani are related to poor healthcare access and lack of awareness of disease prevention. Our findings can be used to develop and implement efficient health interventions to improve the health and quality of life of indigenous populations living in the islands in Southern Peru/Northern Bolivia.

Epidemiology and Changes in Mortality of Sepsis After the Implementation of Surviving Sepsis Campaign Guidelines.

PURPOSE: To determine the epidemiology and outcome of severe sepsis and septic shock after 9 years of the implementation of the Surviving Sepsis Campaign (SSC) and to build a mortality prediction model. METHODS: This is a prospective, multicenter, observational study performed during a 5-month period in 2011 in a network of 11 intensive care units (ICUs). We compared our findings with those obtained in the same ICUs in a study conducted in 2002. RESULTS: The current cohort included 262 episodes of severe sepsis and/or septic shock, and the 2002 cohort included 324. The prevalence was 14% (95% confidence interval: 12.5-15.7) with no differences to 2002. The population-based incidence was 31 cases/100 000 inhabitants/year. Patients in 2011 had a significantly lower Acute Physiology and Chronic Health Evaluation II (APACHE II; 21.9 ± 6.6 vs 25.5 ± 7.07), Logistic Organ Dysfunction Score (5.6 ± 3.2 vs 6.3 ± 3.6), and Sequential Organ Failure Assessment (SOFA) scores on day 1 (8 ± 3.5 vs 9.6 ± 3.7; P < .01). The main source of infection was intraabdominal (32.5%) although microbiologic isolation was possible in 56.7% of cases. The 2011 cohort had a marked reduction in 48-hour (7% vs 14.8%), ICU (27.2% vs 48.2%), and in-hospital (36.7% vs 54.3%) mortalities. Most relevant factors associated with death were APACHE II score, age, previous immunosuppression and liver insufficiency, alcoholism, nosocomial infection, and Delta SOFA score. CONCLUSION: Although the incidence of sepsis/septic shock remained unchanged during a 10-year period, the implementation of the SSC guidelines resulted in a marked decrease in the overall mortality. The lower severity of patients on ICU admission and the reduced early mortality suggest an improvement in early diagnosis, better initial management, and earlier antibiotic treatment.

Profiling of Egg Layer Flocks Affected with Focal Duodenal Necrosis Based on a Questionnaire Survey.

Focal duodenal necrosis (FDN) is an intestinal disease of egg-laying chickens, characterized by multifocal mucosal erosions in the duodenal loop and proximal jejunum. It is currently considered by the Association of Veterinarians in Egg Production and the United States Animal Health Association as one of the top five disease concerns of the table egg industry in the United States. Previous studies have associated this condition with Clostridium species. The purpose of this study was to investigate the epidemiologic characteristics of table egg layer flocks affected with FDN. An online questionnaire was distributed to commercial layer operations in different states in the United States. Layer farms that had diagnosed FDN within the past 12 mo were surveyed. The questionnaire had 45 questions about management, nutrition, housing, and methods for disease prevention and control. Thirty-seven surveys were sent and 21 were completed, which represents a response rate of 56.7%. The survey results showed the presence of FDN in five egg-layer genetic lines or breed crosses of different ages, with most cases reported between 30-39 wk of age. The pullets were cage-reared in all affected flocks and the majority of flocks in production were housed in traditional cages. Most of the FDN-affected flocks received more than 12 different feed formulations from pre-lay to 60 wk of age. Distiller's dried grain with solubles was a common ingredient added to the feed in the majority of affected flocks, and all flocks were provided with limestone as a calcium source for egg production. Most surveys reported that coccidiosis and roundworm parasitism were not problems in affected flocks in production; however, pests such as flies and rodents were reported as problems in most affected flocks. Additionally, most affected farms never washed feeders, cages, and houses before disinfection, which may not be sufficient to prevent the persistency and transmission of the causative agent of FDN. In conclusion, several management practices that have been associated with enteric disease, including clostridial-associated enteritis, were described by the majority of FDN-affected flocks. Additional studies are needed to determine if management and health practices identified in this survey represent risk factors for FDN.

Abordaje interdisciplinario del tabaquismo en la consulta odontológica: Aplicación de técnicas de intervención psicológicas / Interdisciplinary Approach to Smoking in Dental Practice: Psychological Intervention Techniques Application

Resumen Actualmente, en la comunidad odontológica existe confusión y duda sobre la extensión de la responsabilidad del profesional en la intervención y prevención del tabaquismo, lo cual se podría atribuir, en gran medida, al desconocimiento al respecto. Sin embargo, el odontólogo tiene el compromiso de participar activamente en esta adicción como parte del tratamiento bucodental. Por lo tanto, este artículo presenta una revisión actualizada de la literatura internacional relacionada con el tabaquismo desde la perspectiva de la consulta odontológica, enfocada al empleo de estrategias psicológicas para ofrecer un abordaje interdisciplinario, donde se enmarca el rol del odontólogo y su equipo de trabajo. De esta forma, la psicología de la salud ofrece diversas técnicas de intervención que resultan altamente efectivas para reducir o eliminar el tabaquismo, a saber: entrevista motivacional, exposición con prevención de respuesta, control de estímulos, entrenamiento en solución de problemas, terapia cognitivo-conductual, entrenamiento progresivo en reducción de la ansiedad y el estrés, entrenamiento en habilidades sociales, manejo de la ira y la agresividad, manejo de contingencias, autocontrol, técnicas de estimulación aversiva, técnica de reducción gradual de ingestión de nicotina y alquitrán (RGINA), terapia de pareja y familiar para atender situaciones de conflictos en relaciones interpersonales y terapia grupal.

Abstract Currently, in the dental community there is confusion and doubt about the extent of professional responsibility in the intervention and prevention of smoking, which could be attributed, to a large extent, to lack of knowledge about it. However, the dentist is committed to actively participate in this addiction as part of oral treatment. Therefore, this article presents an updated review of the international literature related to smoking from the perspective of dental consultation, focused on the use of psychological strategies to offer an interdisciplinary approach, which frames the role of the dentist and his team. In this way, health psychology offers several intervention techniques that are highly effective in reducing or eliminating smoking, namely: motivational interviewing, response prevention, stimulus control, problem solving training, cognitive-behavioral, progressive training in anxiety and stress reduction, social skills training, anger management and aggressiveness, contingency management, self-control, aversive stimulation techniques, gradual reduction technique of nicotine and tar consumption (RGINA), couple and family therapy to deal with conflict situations in interpersonal relationships and group therapy.

Efficiency Analysis of Integrated Public Hospital Networks in Outpatient Internal Medicine.

Healthcare systems are evolving towards a complex network of interconnected services due to the increasing costs and the increasing expectations for high service levels. It is evidenced in the literature the importance of implementing management techniques and sophisticated methods to improve the efficiency of healthcare systems, especially in emerging economies. This paper proposes an integrated collaboration model between two public hospitals to reach the reduction of weighted average lead time in outpatient internal medicine department. A strategic framework based on value stream mapping and collaborative practices has been developed in real case study settled in Colombia.

Relationship Between Broiler Body Weights, Eimeria maxima Gross Lesion Scores, and Microscores in Three Anticoccidial Sensitivity Tests.

Anticoccidial sensitivity tests (ASTs) serve to determine the efficacy of anticoccidial drugs against Eimeria field isolates in a controlled laboratory setting. The most commonly measured parameters are body weight gain, feed conversion ratio, gross intestinal lesion scores, and mortality. Due to the difficulty in reliably scoring gross lesion scores of Eimeria maxima , microscopic analysis of intestinal scrapings (microscores) can be used in the field to indicate the presence of this particular Eimeria. The goal of this study was to determine the relationship between E. maxima microscores and broiler body weights and gross E. maxima lesion scores in three ASTs. Day-old broiler chicks were raised for 12 days on a standard corn-soy diet. On Day 12, chicks were placed in Petersime batteries and treatment diets were provided. There were six birds per pen, four pens per treatment, and 12 treatments, for a total of 288 chicks per AST. The treatments were as follows: 1) nonmedicated, noninfected; 2) nonmedicated, infected; 3) lasalocid, infected; 4) salinomycin, infected; 5) diclazuril, infected; 6) monensin, infected; 7) decoquinate, infected; 8) narasin + nicarbazin, infected; 9) narasin, infected; 10) nicarbazin, infected; 11) robenidine, infected; and 12) zoalene, infected. On Day 14, chicks were challenged with an Eimeria field isolate by oral gavage. On Day 20, broilers were weighed, and gross lesion scores and microscores were classified from 0 to 4 depending on the severity of the gross lesion scores and E. maxima microscores. Data from three trials using different field isolates were statistically analyzed using a logarithmic regression model. There was no relationship (P = 0.1224) between microscores and body weight gain. There was a positive relationship between microscores and gross lesion scores (P = 0.004). However, there was also an interaction between isolate and treatment (P < 0.0001). Lastly, the interaction between isolate and gross lesion scores (P = 0.0041) demonstrates that the significance of the relationship between microscores and gross lesion scores may be dependent on pathogenicity of the challenge Eimeria or the amount of E. maxima in the inoculum.

Development of a Dry Medium for Isolation of Histomonas meleagridis in the Field.

Blackhead disease is caused by Histomonas meleagridis, an anaerobic protozoan parasite, and results in mortality rates of up to 100% in turkeys and 30% in chickens. Outbreaks of blackhead disease are unpredictable, and the harvesting of H. meleagridis strains from the field would be a great resource for researchers to study its epidemiology. Therefore, the objective of this study was to develop a dry medium that would allow storage at ambient temperatures until needed. Fifty milliliters of horse serum was dried and then mixed with dry medium M199 with Hanks balanced salts (10.6 g), sodium bicarbonate (0.35 g), and rice powder (0.8 g). To test the ability of reconstituted medium to support growth of H. meleagridis, groups of 10 flasks containing 0.2 g of dry medium were stored for 24 hr at 25 and 60 C before testing. Other groups of flasks containing dry medium were stored at 25, 37, and 42 C for 1, 3, or 6 mo. At each test period, the flasks were reconstituted with 10 ml of water, inoculated with 100 000 H. meleagridis cells, and incubated at 40 C for 48 hr. Fresh liquid medium was used as a control. There were no differences in cell counts in medium stored at 25 or 60 C for 24 hr. After 1 mo, cell counts in reconstituted medium were about half that of fresh liquid medium after 48 hr of incubation. But after 3 and 6 mo, the cell counts were not significantly different in all groups (P < 0.05) after 72 hr of incubation. These results show that dried Dwyer medium can be stored at ambient temperatures for extended times and would be an effective tool for obtaining isolates of H. meleagridis from the field.