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      Modelación de los flujos de pacientes de alto riesgo con COVID-19 en Matanzas con enfoque Lean Translated title: Modeling the flows of high risk patients with COVID-19 in Matanzas with a Lean approach

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          Abstract

          RESUMEN 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.

          Translated abstract

          ABSTRACT 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.

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          Most cited references20

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          Gestión Gubernamental y Ciencia Cubana en el Enfrentamiento a la COVID-19

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            Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS

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              AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units

              Introduction Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. Method Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. Results Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. Conclusions Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation. Mental Health, NHS, AI, Patient Flow, Machine Learning, Natural Language Processing, Digital Phenotyping.
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                Author and article information

                Journal
                rme
                Revista Médica Electrónica
                Rev.Med.Electrón.
                CENTRO PROVINCIAL DE INFORMACIÓN DE CIENCIAS MÉDICAS. MATANZAS (Matanzas, , Cuba )
                1684-1824
                August 2023
                : 45
                : 4
                : 629-643
                Affiliations
                [1] Matanzas orgnameUniversidad de Matanzas Cuba
                [2] Matanzas orgnameUniversidad de Ciencias Médicas de Matanzas Cuba
                [3] Matanzas orgnameEmpresa de Proyectos de Arquitectura e Ingeniería de Matanzas Cuba
                Article
                S1684-18242023000400629 S1684-1824(23)04500400629
                c07073c0-a43c-4ad3-9ba8-0f4ddd882a29

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 13 February 2023
                : 07 June 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 20, Pages: 15
                Product

                SciELO Cuba

                Categories
                ARTÍCULOS ORIGINALES

                integrated management,enfoque Lean.,COVID-19,flujo de pacientes,gestión integrada,Lean approach,patients flow

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