Estrategia de diseño de líneas de espera en la industria de la salud

El desarrollo de la presente investigación tuvo como objetivo contrastar las diversas posturas respecto a las estrategias de diseño de líneas de espera en la industria de la salud, para lo cual se desarrolló un estudio amparado en el enfoque cualitativo con el diseño de la revisión sistemática de literatura gris en bases de datos indexadas y con material proveniente de revistas pertenecientes a los cuartiles Scimago. Tras el desarrollo del proceso de búsqueda se seleccionaron 30 artículos, en los cuales se encontraron semejanzas y discrepancias a nivel de las características de las estrategias de administración de las líneas de espera, los recursos físicos y profesionales con los que se cuenta en el sector salud para la administración de las líneas de espera, y las estrategias de administración de líneas de espera en el sector salud basadas en las condiciones sociodemográficas (sexo, edad, nivel económico, enfermedades, etc.) de los pacientes.  The objective of this research was to contrast the different positions regarding the design strategies of waiting lines in the health industry; for which a study was developed based on the qualitative approach with the design of the systematic review of gray literature in indexed databases and with material from journals belonging to the Scimago quartiles. After the development of the search process, 30 articles were selected, in which similarities and discrepancies were found at the level of the characteristics of the waiting line management strategies, the physical and professional resources available in the health sector for the management of waiting lines, and the waiting line management strategies in the health sector based on the sociodemographic conditions (sex, age, economic level, diseases, etc.) of the patients.  Trabajo de Suficiencia Profesiona

Patient flow model using hybrid discrete event and agent-based simulation in emergency department

The hospital emergency department (ED) is one of the most crucial hospital areas. ED plays a key role in promoting hospitals’ goals of enhancing service efficiency. ED is a complex system due to the stochastic behaviour including the operational patient flow, the unpredictability of the care required by patients, and the department’s complex nature. ED operational patient flow refers to the transferring of patients throughout various locations in specific relation to a healthcare facility. Simulations are effective tools for analysing and optimizing complex ED operational patient flow. Although existing ED operational patient flow simulation models have substantially improved ED operational patient performance in terms of ensuring patient satisfaction and effective treatment, many deficiencies continue to exist in addressing the key challenge in ED, namely, patient throughput issue which is indicated to the long patient throughput time in ED. The patient throughput time issue is affected by causative factors, such as waiting time, length of stay (LoS), and decision-making. This research aims to improve ED operational patient flow by proposing a new ED Operational Patient Flow Simulation Model (SIM-PFED) in order to address the reported key challenge of the patient throughput time. SIM-PFED introduces a new process for patient flow in ED on the basis of the newly proposed operational patient flow by combining discrete event simulation and agent-based simulation and applying a multi attribute decision making method, namely, the technique for order preference by similarity to the ideal solution (TOPSIS). Experiments were performed on four actual hospital ED datasets to assess the effectiveness of SIM-PFED. Experimental results revealed the superiority of SIM-PFED over other alternative models in reducing patient throughput time in ED by consuming less patient waiting time and having a shorter length of stay. The results of the experiments showed the improvement `of percentage in terms of patient throughput time (waiting time and LoS). SIM-PFED's waiting time proficiency is 35.45%, 89.21%, 87.64% and 86.00% advanced than Safety Simulation Model, ABS Model, IS-BDSF and SEDO-UCC correspondingly. In addition, the general average waiting time performance of SIM-PFED against the four models ascertains that the performance of SIM-PFED's is largely improved than that of the Safety Simulation Model, ABS Model, IS-BDSF and SEDO-UCC in regard to the waiting time at a percentage of 74.58%. SIM-PFED's LoS effectiveness is 74.4%, 85%, 91.6% and 87.4% higher than Safety Simulation Model, ABS Model, IS-BDSF and SEDO-UCC correspondingly. The general average LoS performance of SIM-PFED against the four models illustrated that the performance of SIM-PFED's is largely improved than that of the Safety Simulation Model, ABS Model, IS-BDSF and SEDO-UCC in regard to the LoS at a percentage of 85.6%.The findings also demonstrated the effectiveness of SIM-PFED in helping ED decision-makers select the best scenarios to be implemented in ED for ensuring minimal patient throughput time while being cost-effective

Decision support for medical disasters: Evaluation of the IMPRESS system in the live Palermo demo

Abstract Background In medical disasters, coordination, information flows, and decision making are crucial for response and management. Different factors contribute to thwart the response efforts. Some are due to the coordination of the many agencies active in disaster response. Support tools for gathering and analysing data may support task assignment, resource allocation, and acquisition as well as training at different decision levels (in the field and in command-rooms). Validation of Decision Support Systems (DSS) in simulated contexts, simulating real situations, becomes mandatory. In the framework of testing and validation of the IMPRESS project (and of its INCIMOB and INCIMAG tools), one scenario was planned in Palermo, a city of 700,000 inhabitants in the Mediterranean Area of Southern Italy, simulating the sudden liberation of high concentrations of toxic compounds from a fire in Palermo harbor. Emergency Agencies, a real and a simulated Hospital and operators in the field used the system during the response phase. A group of 20 external Observers participated for evaluation purposes. During a joint debriefing session, ad-hoc questionnaires were administered. IMPRESS was useful in improving the execution of important functions during the DEMO; Users agreed about the advantages of the use of IMPRESS tools for conducting crisis activities. INCIMOB use resulted more problematic from an operational point of view. Shortcomings were detected and criticisms were raised due mainly to the lack of training and direct voice communication. Evaluation of DSS in Emergency medicine can benefit from live exercises to highlight weaknesses in both the response system and decision support