Modelación de los flujos de pacientes de alto riesgo con COVID-19 en Matanzas con enfoque Lean

Introducción: En el contexto de la pandemia, la gestión eficiente de los flujos de pacientes con enfoque en su trayectoria es crucial. En este sentido, el enfoque Lean permite aumentar el rendimiento del sistema sanitario, al eliminar actividades que no generan valor al paciente. Objetivo: Realizar un análisis integral de los flujos de pacientes de alto riesgo con COVID-19 en Matanzas, utilizando el enfoque Lean. Materiales y métodos: Se implementó una metodología de tipo cuantitativa, estructurada en cuatro etapas, para la gestión integrada de los flujos de pacientes de alto riesgo con COVID-19, mediante el enfoque Lean. Esta metodología integra herramientas para la selección de expertos, representación de procesos, análisis estructural y mapas de flujos de valor. Se aplicó durante del período de mayor incidencia de la pandemia en Matanzas (1 de mayo al 1 de agosto de 2021). Resultados: Se identificaron deficiencias relacionadas con los flujos de pacientes de alto riesgo con COVID-19 en Matanzas. Se realizó un análisis integral de los flujos, con el fin de realizar una propuesta de mejoras con enfoque Lean. La propuesta garantizó una optimización de 1510 minutos por ciclos de atención, con una eficiencia del 85,86 % del tiempo total de cada ciclo (etapa del tratamiento), y del 59,38 % de los tiempos de espera entre ellos. Conclusiones: Las herramientas Lean permiten realizar un análisis integral de los flujos de pacientes, además de mostrar una vía para su gestión, centrada en la trayectoria y no en la ocupación del recurso.

Introduction: In the context of the pandemic, the efficient management of the patients flow with a focus on their trajectory is crucial. In this sense, the Lean approach allows to increase the performance of the health care system, eliminating activities that do not generate value for the patient. Objective: To carry out an integral analysis of the high risk patients flow with COVID-19 in Matanzas, using the Lean approach. Materials and methods: A quantitative methodology, structured in four stages was implemented for the integrated management of the flow of high risk patients with COVID-19, using the Lean approach. This methodology integrates tools for the selection of experts, process representation, structural analysis and value flow maps. It was applied during the period of highest incidence of the pandemic in Matanzas (May 1st to August 1st 2021). Results: Deficiencies related to the flows of high risks patients with COVID-19 in Matanzas were identified. A comprehensive analysis of the flows was carried out in order to make a proposal for improvements with a Lean approach. The proposal guaranteed an optimization of 1 500 minutes per service cycle, with an efficiency of 85.86% of the total time of each cycle (treatment stage), and 59.38% of the waiting times between them. Conclusions: Lean tools allow a comprehensive analysis of the patients' flow, in addition to showing a route for their management, focused on the trajectory and not on the occupation of the resource.

Main Features and Control Strategies to Reduce Overcrowding in Emergency Departments: A Systematic Review of the Literature.

Purpose: Overcrowding is a problem that affects emergency departments (ED) all over the world; it occurs due to a disproportion between user demand and the physical, human and structural resources available. Essential prerequisites to assessing and managing the phenomenon are its accurate measurement and an understanding of its impact. The objective of this systematic review is to identify the characteristics of the problem, analyzing the proposed strategies aimed at improving patient flow, delay in services provided and overcrowding of emergency departments. Methods: To achieve our objectives, a manual computerized search was performed in the bibliographic databases using as keywords "Emergency Department", "Overcrowding", "Emergency Room", "Emergency Service", "Emergency Unit"",Emergency Ward", "Emergency Outpatient Unit", "Emergency Hospital", "Crowding", "Mass Gathering", "Management" and "Comprehensive Health Care". Two independent reviewers analyzed abstracts, titles and full text articles for admissibility, according to the selected inclusion and exclusion criteria. Results: The process lead to include 19 articles. It was possible to group the solutions proposed in five categories: work organization, investment in primary care, creation of new dedicated professional figures, work and structural modifications and implementation of predictive simulation models using mathematical algorithms. Conclusion: The most effective measures to guarantee an improvement in the flow of patients are represented by both improving the efficiency of human resources and by developing predictive mathematical models, regardless of the type of hospital and its location. Considering the complexity of EDs and the multiple characteristics of overcrowding and that the causes of crowding are different and site-specific, a careful examination of the specifics of each ED is necessary to identify improving fields.

Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study.

BACKGROUND: Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved. OBJECTIVE: In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital. METHODS: Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital. RESULTS: The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient's injury. The analysis of patients' flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found. CONCLUSIONS: Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection-ie, without the need for the involvement of observers, which could compromise the realism of the exercise-of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management.

Does health insurance impact on the hospital seeking behavior among floating population?Evi-dence from the 2014 National Floating Population Dynamic Monitoring Survey / 中国卫生政策研究

Objectives:To analyze the influencing factors and status of hospital seeking behavior among floating population by focusing on the function of health insurance , and provide evidence to comprehensively promote tiered health care system in China .Methods:Using data from 2014 National Floating Population Dynamic Monitoring Survey , multivariate logistic regressions were applied to analyze the influencing factors of hospital seeking behavior for inpatient care .Chi-square tests were used to analyze the different proportion of having basic medical insurance reimbursement and reasons for not having it for different hierarchical inpatient care , and T test were used to analyze the ratio of basic medical insurance reimbursement for hospital costs .Results:The proportion of hospitalization at the county level and below was 56.4%among floating population , the proportion of inflow patients being 51.5%and the rest of the hospital population being counted at a proportion of 37.9%, lower than the corresponding proportion of 72.6%among local res-idents derived from the Fifth National Health Services Survey , but this ratio was 78.9%among registered population . The multivariate logistic regression results indicated that demographic , socio-economic and flow characteristics , hospital location and reasons for hospitalization have significantly affected the migrant inpatients facilities , but health insurance did not significantly influence the hospital seeking behaviors among floating population .Conclusions:The proportion of hospitalization at the county level and below was low among migrants and this proportion presented great regional differ -ences;and medical insurance didn't play a guiding role in the diversion of the internal floating population .