Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla

The health sector is now more than a global trend; It is a topic of global interest because of its direct link with living conditions, welfare and development of persons(Law 1122 of 2007); however, the system has notorious failures in patient monitoring after care;this generated a high rate of reent...

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Autores:
Altamar Maldonado, Zenaida Lucía
Martínez Solano, Cielo Isabel
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2017
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/335
Acceso en línea:
https://hdl.handle.net/11323/335
https://repositorio.cuc.edu.co/
Palabra clave:
Reingreso hospitalario
Caracterización
Calidad de la atención en salud
Paciente
Monitoreo externo
External monitoring
Patient
Quality of health care
Characterization
Hospital re-entry
Rights
openAccess
License
Atribución – No comercial – Compartir igual
id RCUC2_40b0aef41ef2d29dcf9ddb9b8f4b1062
oai_identifier_str oai:repositorio.cuc.edu.co:11323/335
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
title Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
spellingShingle Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
Reingreso hospitalario
Caracterización
Calidad de la atención en salud
Paciente
Monitoreo externo
External monitoring
Patient
Quality of health care
Characterization
Hospital re-entry
title_short Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
title_full Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
title_fullStr Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
title_full_unstemmed Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
title_sort Diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de Barranquilla
dc.creator.fl_str_mv Altamar Maldonado, Zenaida Lucía
Martínez Solano, Cielo Isabel
dc.contributor.advisor.spa.fl_str_mv Ortiz Barrios, Miguel Ángel
Borrero López, Luz Adriana
dc.contributor.author.spa.fl_str_mv Altamar Maldonado, Zenaida Lucía
Martínez Solano, Cielo Isabel
dc.subject.eng.fl_str_mv Reingreso hospitalario
Caracterización
Calidad de la atención en salud
Paciente
Monitoreo externo
topic Reingreso hospitalario
Caracterización
Calidad de la atención en salud
Paciente
Monitoreo externo
External monitoring
Patient
Quality of health care
Characterization
Hospital re-entry
dc.subject.none.fl_str_mv External monitoring
Patient
Quality of health care
Characterization
Hospital re-entry
description The health sector is now more than a global trend; It is a topic of global interest because of its direct link with living conditions, welfare and development of persons(Law 1122 of 2007); however, the system has notorious failures in patient monitoring after care;this generated a high rate of reentry of health care centers. This study was carried out in several stages, where the first time the characterization of the reentry of the patients graduated from the Department of Emergency and hospitalization in the subsector of clinics in the city of Barranquilla through analysis of studies and information on rates of readmissions and their causes; in addition to collecting local information (from staff and patients), studying existing evidence, boosting original research and development; allow in the second stage identify the problems generated by monitoring failures and the logical processes that are executed in order to generate a characterization of the post - care. With the diagnosis of the health system that is currently used for the external monitoring of patients and the characterization of patient readmissions to the aforementioned services, a re-entry risk categorization model will be designed for the graduates of the emergency and hospitalization Department based on the identification of the risk factors that affect their reentry to these services and the correlation risk factors and likelihood of re-entry. The applied methodology consisted in the analysis of the information presented in the SISPRO database (Comprehensive Information System for Social Protection) of the Ministry of Health, followed by the application of a test of randomness in Microsoft Excel to a population of clinics, in order to take a sample for the application of a survey to identify the risk factors that affect in the reentry of patients to the clinics of the city of Barranquilla and for the last time from a clinic in the city. In the findings derived from the results of the surveys, it is evident that operative site infections, as well as those associated with care are factors which increase the likelihood of readmission in the clinics studied. As for the re-entry reduction strategies and the external monitoring of patients by health entities, it is inferred from the results, that these do not perform an optimal follow-up to the evolution of the same after discharge, but are mostly limited to monitoring by telephone contact, thus giving rise to the probability that the patient Reenter the institution. Therefore, there is a need for health entities to implement follow-up processes for the evolution and rehabilitation of patients in a more committed, effective and assertive way to guarantee the continuous care of their health. In this investigation a statistical model was designed to measure the probability of readmissions in the hospitalization departments. The novelty of the research is the proposal of a regression application multivariate logistics to predict re-admissions of 15 days in the hospitalization departments. Our model allows us to classify patients into a category of risk. In this way, prevention plans can be created for each patient in order to reduce the probability of unplanned re-entry.The model provides enough information to analysts who are interested in managing hospital readmissions problem.The model clearly suggests that the simple and accessible parameters are useful for identifying patients at high risk of hospital readmission. Future research should study the behavior of hospital readmission in order to perform comparative analyzes and action under international framework projects.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017-03-24
dc.date.accessioned.none.fl_str_mv 2018-11-03T17:56:25Z
dc.date.available.none.fl_str_mv 2018-11-03T17:56:25Z
dc.type.spa.fl_str_mv Trabajo de grado - Pregrado
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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identifier_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv Martínez, A., B. Llorente, D. M., Echegaray, A., Echezarreta, M. & González, C. (2001). Reingreso hospitalario en medicina interna, 200(8), 27-36. Balla, U., Malnick, S. & Schattner, A. (2008). Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems. Medicine Baltimore, 87(7), 294-300. Barrett, M.L., Wier, L.M., Jiang, H.J. & Steiner, C.A. (2015). All-Cause Readmissions by Payer and Age, 36(10), 100-126. Billings & John. (2006). Case finding for patients at risk of readmission to hospital: development of algorithm identify high risk patients, 70(6), 19-27. Bottle, A., Aylin, P. & Majeed, A. (2006). Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. Journal of the royal society of medicine, 39(4), 406-414. Briceño-Leon, R. (2000). Bienestar, salud pública y promoción de la salud: una aproximación a su desarrollo histórico y social. ciencias de la salud, 3(10), 62-77. Caballero, A., Pinilla, M.I., Mendoza, I.C. & Peña, J.R. (2016). Hospital readmission rate and associated factors among health services enrollees in Colombia. Cad Saude, 32(7), 21. Comas, A. S., Rodado, D. N., & Eras, J. C. (2016). Marcos aplicados a la Gestión de Calidad–Una Revisión Sistemática de la Literatura. Espacios, 37(09) Fingar, K. & Washington, R. (2015). Trends in Hospital Readmissions for Four HighVolume Conditions, 2009–2013. Agency for Healthcare Research and Quality. Feudtner, C. (2009). How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective multi-center study. Pediatrics, 123(1), 286–293. Futoma, J., Morris, J. & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56(5), 229– 238. Gaviria, D., Lemus, M. & Luna, S. (2013). Acciones para controlar el reingreso hospitalario y disminuir el costo por evento de Coomeva eps en el municipio de Tuluá. Golmohammadi, D. & Radnia, N. (2016). Prediction modeling and pattern recognition for patient readmission. International Journal of Production Economics, (171)2, 151–161. Gopegui, P.R. (2014). Mortalidad oculta y reingresos en la UCI de traumatología del Hospital Universitario Miguel Servet de Zaragoza: factores de riesgo e impacto en el resultado hospitalario. Facultad de medicina, Universidad de Zaragoza Hasan, O., Meltzer, D.O., Shaykevich, S.A., Bell, C.M., Kaboli, P.J., Auerbach, A.D., Wetterneck, T.D., Arora, V.M., Zhang, J. & Schnipper, J.L. (2009). Hospital Readmission in General Medicine Patients: A Prediction Model. Society of General Internal Medicine, 33-62. Hasan, O., Meltzer, D. O., Shaykevich, S. A., Bell, C. M., Kaboli, P. J., Auerbach, A. D. & Schnipper, J. L. (2010). Hospital readmission in general medicine patients: a prediction model, Journal of general internal medicine, 25(3), 211-219. Hines, A.L., Barrett, M.L., Jiang, H.J. & Steiner, C.A. (2014). Conditions With the Largest Number of Adult Hospital Readmissions by Payer, 2011. Agency for Healthcare Research and Quality. Jovanovic, M., Radovanovic, S., Vukicevic, M. & Poucke, S.V. (2016). Boris Delibasic Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression, Artificial Intelligence in Medicine, 72, 12-21. Lakshmi C. & Sivakumar Appa Iyer, (2013). Application of queueing theory in health care: A literature review. Operations Research forHealthCare, 2(2), 25–39. Ley N° 1122, el congreso de la república de Colombia, Colombia. 9 de Enero (2007). Ortíz, M.A., Cómbita, J.P., De la Hoz, Á.A., De Felice, F. & Petrillo, A. (2016). An integrated approach of AHP-DEMATEL methods applied for the selection of allied hospitals in outpatient service, International Journal of Medical Engineering and Informatics, 8(2), 87–107. Picker, D., Heard, K., Bailey, T.C., Martin, N.R. & La Rossa, G. (2015). The number of discharge medications predicts thirty-day hospital readmission: A cohort study. BMC Health Services Research, 282(15). Ross, J.S., Mulvey, G.K., Stauffer, B., Patlolla, V, Bernheim, S.M. & Keenan, P.S. (2010). Statistical models and patient predictors of readmission for heart failure: a systematic review. Health Serv Res, 45(6), 1815–35. Silverstein, M.D., Qin, H., Mercer, S.Q., Fong, J. & Haydar, Z. (2008). Risk factors for 30-day hospital readmission in patients ≥65 years of age. Baylor University Medical Center Proceedings, 21(4), 363-372. Smith, H.J, Pasko, D.N., Walters Haygood, C.L., Boone, J.D., Harper, L.M. & Straughn Jr, J.M. (2016). Early warning score: An indicator of adverse outcomes in postoperative patients on a gynecologic oncology service. Gynecologic Oncology, 143(7), 105–108. Yazdan-Ashoori, P., Lee, S., Ibrahim, Q. & Van Spall, H. (2016). Utility of the LACE index at the bedside in predicting 30-day readmission or death in patients hospitalized with heart failure. American Heart Journal, 51-58. Yua, S., Farooq,, F., van Esbroeck, A., Fung, G., Anand, V. & Krishnapuram, B. (2015). Predicting readmission risk with institution specific prediction models. Artificial Intelligence in Medicine, 65(2), 89–96. Zahra Hosseinifard, S., Abbasi, B. & Minas, J.P. (2014). Intensive care unit discharge policies prior to treatment completion. Operations Research for Health Care, 3(3), 168–175. Barrios, M. A., Caballero, J. E. & Sánchez, F. S. (2015). A Methodology for the Creation of Integrated Service Networks in Outpatient Internal Medicine in Ambient Intelligence for Health, 55(20), 247-257. Barrios, M. A. & Jiménez, H. F. (2016). Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department. Journal of medical systems, 40(10), 220. Ministerio de Salud. (2017). Calidad en Salud. Recuperado de http://calidadensalud.minsalud.gov.co/Prestadores/HerramientasparaProfesionales.a spx [Accessed 12 Feb. 2017] Carreño, J. A. (2013). Calidad en la atención en salud en hospitales universitarios. 80(6), 1- 10. Chandrasekaran, A., Anand, G., Sharma, L., Pesavanto, T., Hauenstein, M.L., Nguyen, M., Gadkari, M. & Moffatt-Bruce, S. (2016). Role of in-hospital care quality in reducing anxiety and readmissions of kidney transplant recipients. Journal of surgical research, 252-259. Decreto N° 1011. Ministerio de salud y protección social, Colombia, 3 de Abril. (2006). Informe nacional de calidad de la atención en salud. (2015). Ministerio de Salud Colombia. Pag 110-114. Ministerio de Salud. (2016). Observatorio de Calidad de la Atención en Salud. Recuperado de http://calidadensalud.minsalud.gov.co/SOGC/SistemadeInformaci%C3%B3n/NvP2 14.aspx [Accessed 12 Feb. 2017]. Ministerio de Salud. (2016). Observatorio de Calidad de la Atención en Salud. Recuperado de http://calidadensalud.minsalud.gov.co/SOGC/SistemadeInformaci%C3%B3n/NvP2 13.aspx [Accessed 12 Feb. 2017]. Ministerio de Salud. (2017). Observatorio de Calidad de la Atención en Salud. Recuperado de http://calidadensalud.minsalud.gov.co/Usuarios/Tenerencuenta/Calidad.aspx [Accessed 12 Feb. 2017]. Repositorio Institucional Digital. (2006). Recuperado de https://www.minsalud.gov.co/sites/rid/Paginas/results.aspx?k=((dcispartof:%22Cali dad%20de%20la%20Atenci%C3%B3n%20en%20Salud%22%20AND%20dcispart of:%22SOGC%22%20AND%20dcispartof:%22normas%20habilitaci%C3%B3n%2 0Prestadores%22)) [Accessed 13 Feb. 2017]. Resolución N° 2003. Ministerio de Salud y Protección social, Colombia, 28 de Mayo (2014). Resolución N° 1441. Ministerio de salud y protección social, Colombia, 6 de Mayo (2013)
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spelling Ortiz Barrios, Miguel ÁngelBorrero López, Luz AdrianaAltamar Maldonado, Zenaida LucíaMartínez Solano, Cielo Isabel2018-11-03T17:56:25Z2018-11-03T17:56:25Z2017-03-24https://hdl.handle.net/11323/335Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The health sector is now more than a global trend; It is a topic of global interest because of its direct link with living conditions, welfare and development of persons(Law 1122 of 2007); however, the system has notorious failures in patient monitoring after care;this generated a high rate of reentry of health care centers. This study was carried out in several stages, where the first time the characterization of the reentry of the patients graduated from the Department of Emergency and hospitalization in the subsector of clinics in the city of Barranquilla through analysis of studies and information on rates of readmissions and their causes; in addition to collecting local information (from staff and patients), studying existing evidence, boosting original research and development; allow in the second stage identify the problems generated by monitoring failures and the logical processes that are executed in order to generate a characterization of the post - care. With the diagnosis of the health system that is currently used for the external monitoring of patients and the characterization of patient readmissions to the aforementioned services, a re-entry risk categorization model will be designed for the graduates of the emergency and hospitalization Department based on the identification of the risk factors that affect their reentry to these services and the correlation risk factors and likelihood of re-entry. The applied methodology consisted in the analysis of the information presented in the SISPRO database (Comprehensive Information System for Social Protection) of the Ministry of Health, followed by the application of a test of randomness in Microsoft Excel to a population of clinics, in order to take a sample for the application of a survey to identify the risk factors that affect in the reentry of patients to the clinics of the city of Barranquilla and for the last time from a clinic in the city. In the findings derived from the results of the surveys, it is evident that operative site infections, as well as those associated with care are factors which increase the likelihood of readmission in the clinics studied. As for the re-entry reduction strategies and the external monitoring of patients by health entities, it is inferred from the results, that these do not perform an optimal follow-up to the evolution of the same after discharge, but are mostly limited to monitoring by telephone contact, thus giving rise to the probability that the patient Reenter the institution. Therefore, there is a need for health entities to implement follow-up processes for the evolution and rehabilitation of patients in a more committed, effective and assertive way to guarantee the continuous care of their health. In this investigation a statistical model was designed to measure the probability of readmissions in the hospitalization departments. The novelty of the research is the proposal of a regression application multivariate logistics to predict re-admissions of 15 days in the hospitalization departments. Our model allows us to classify patients into a category of risk. In this way, prevention plans can be created for each patient in order to reduce the probability of unplanned re-entry.The model provides enough information to analysts who are interested in managing hospital readmissions problem.The model clearly suggests that the simple and accessible parameters are useful for identifying patients at high risk of hospital readmission. Future research should study the behavior of hospital readmission in order to perform comparative analyzes and action under international framework projects.El sector salud en la actualidad más que una tendencia mundial; es un tema de interés global debido a su vinculación directa con las condiciones de vida, bienestar y desarrollo de las personas (Ley 1122 de 2007); sin embargo, el sistema tiene falencias notorias en el monitoreo de los pacientes luego de ser atendidos; lo que genera un alto índice de reingreso de los centros asistenciales.Este estudio se realizará en diversas etapas, dónde la primera involucra la caracterización del reingreso de pacientes egresados del Departamento de Urgencias y hospitalización en el subsector de clínicas de la ciudad de Barranquilla a través del análisis de estudios e información sobre tasas de reingresos y sus causas; además de recopilar información local (de personal y pacientes), estudiar evidencias existentes, impulsar la investigación y desarrollo original; que permita en la segunda etapa identificar la problemática generada por fallas en monitoreo y los procesos lógicos que se ejecutan a fin de generar una caracterización de la post – atención.Con el diagnóstico del sistema de salud que actualmente se utiliza para el monitoreo externo de los pacientes y la caracterización de los reingresos de pacientes a los servicios anteriormente mencionados, se procederá al diseño de un modelo de categorización del riesgo de reingreso para pacientes egresados del Departamento de Urgencias y hospitalización a partir de la identificación de los factores de riesgo que inciden en su reingreso a estos servicios y la correlación existente entre los factores de riesgo y la probabilidad de reingreso.La metodología aplicada consisitió en el análisis de la información expuesta en la base de datos SISPRO (sistema Integral de información de la Protección Social) del Ministerio de Salud, seguido de la aplicación de una prueba de aleatoriedad en Microsoft Excel a una población de clinicas, con el fin de tomar una muestra para la aplicación de una encuesta para identificar los factores de riesgo que inciden en el reingreso de pacientes a las clínicas de la ciudad de Barranquilla y por último se extrajo información mas profunda de ciertos pacientes de una clínica de la ciudad. En los hallazgos derivados de los resultados de las encuestas, se evidencia que las infecciones del sitio operatorio, así como las asociadas al cuidado son factores comunes que aumentan la probabilidad de readmisión en las clínicas estudiadas. En cuanto a las estrategias de reducción de reingreso y a la poca frecuencia de monitoreo externo de pacientes por parte de las entidades de salud, se infiere de los resultados, que estas no realizan un óptimo seguimiento a la evolución del mismo después del egreso, si no que se limitan en su mayoría a realizar un monitoreo mediante contacto telefónico, dando así lugar a la probabilidad de que el paciente reingrese a la institución. Por lo tanto existe la necesidad de que las entidades de salud implementen procesos de seguimiento a la evolución y rehabilitación de los pacientes de una forma más comprometida, efectiva y asertiva para garantizar el cuidado continúo de su salud.En esta investigación se diseñó un modelo estadístico para medir la probabilidad de reingresos en los departamentos de hospitalización. La novedad de la investigación es la propuesta de una aplicación de regresión logística multivariada para predecir reingresos de 15 días en los departamentos de hospitalización. Nuestro modelo permite clasificar a los pacientes en una categoría de riesgo. De esta manera se pueden crear planes de prevención para cada paciente con el fin de reducir la probabilidad de reingreso no planificado. El modelo proporciona suficiente información a los analistas que están interesados en la gestión de reingresos hospitalarios.Altamar Maldonado, Zenaida Lucía-e667385d-4438-451a-9dd6-7f4a4aa561b5-0Martínez Solano, Cielo Isabel-b5c370dd-2561-49ff-bf94-63a69048e5cb-300spaCorporación Universidad de la CostaIngeniería IndustrialAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Reingreso hospitalarioCaracterizaciónCalidad de la atención en saludPacienteMonitoreo externoExternal monitoringPatientQuality of health careCharacterizationHospital re-entryDiseño de un modelo de categorización del riesgo de reingreso para pacientes egresados de urgencias y hospitalización en clínicas de BarranquillaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionMartínez, A., B. Llorente, D. M., Echegaray, A., Echezarreta, M. & González, C. (2001). Reingreso hospitalario en medicina interna, 200(8), 27-36. Balla, U., Malnick, S. & Schattner, A. (2008). Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems. Medicine Baltimore, 87(7), 294-300. Barrett, M.L., Wier, L.M., Jiang, H.J. & Steiner, C.A. (2015). All-Cause Readmissions by Payer and Age, 36(10), 100-126. Billings & John. (2006). Case finding for patients at risk of readmission to hospital: development of algorithm identify high risk patients, 70(6), 19-27. Bottle, A., Aylin, P. & Majeed, A. (2006). Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. Journal of the royal society of medicine, 39(4), 406-414. Briceño-Leon, R. (2000). Bienestar, salud pública y promoción de la salud: una aproximación a su desarrollo histórico y social. ciencias de la salud, 3(10), 62-77. Caballero, A., Pinilla, M.I., Mendoza, I.C. & Peña, J.R. (2016). Hospital readmission rate and associated factors among health services enrollees in Colombia. Cad Saude, 32(7), 21. Comas, A. S., Rodado, D. N., & Eras, J. C. (2016). Marcos aplicados a la Gestión de Calidad–Una Revisión Sistemática de la Literatura. Espacios, 37(09) Fingar, K. & Washington, R. (2015). Trends in Hospital Readmissions for Four HighVolume Conditions, 2009–2013. Agency for Healthcare Research and Quality. Feudtner, C. (2009). How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective multi-center study. Pediatrics, 123(1), 286–293. Futoma, J., Morris, J. & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56(5), 229– 238. Gaviria, D., Lemus, M. & Luna, S. (2013). Acciones para controlar el reingreso hospitalario y disminuir el costo por evento de Coomeva eps en el municipio de Tuluá. 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