{"id":20788,"date":"2021-08-30T08:22:00","date_gmt":"2021-08-30T13:22:00","guid":{"rendered":"https:\/\/saluddigital.com\/?p=20788"},"modified":"2025-10-21T12:06:39","modified_gmt":"2025-10-21T18:06:39","slug":"modelo-de-inteligencia-artificial-para-gestionar-las-agendas","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/modelo-de-inteligencia-artificial-para-gestionar-las-agendas\/","title":{"rendered":"Chilean scientists developed an Artificial Intelligence model to manage medical appointment schedules in hospitals"},"content":{"rendered":"
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Researchers from the Center for Mathematical Modeling (CMM) of the University of Chile, created an Artificial Intelligence (AI) model, capable of optimizing the management of the hospital agenda. <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Requesting and scheduling appointments for medical consultations wastes time and resources in hospitals and medical offices when patients do not show up. Faced with this problem, researchers from the CMM of the University of Chile have developed an AI-based system, which aims to increase efficiency in hospitals. They have even tested it for three months in three hospitals in the country.\u00a0<\/p>

The researchers received financial support for the development of the research, through a promotion for scientific development. Jocelyn Dunstan and H\u00e9ctor Ram\u00edrez were in charge of creating software designed to increase and improve efficiency in hospitals.<\/p>

Thanks to machine learning technology, the software seeks to know the probability that patients will not show up at the time of their appointment, this thanks to the data entered on the assistance history of each patient, geographic factors such as the distances between homes and hospital, and other social factors.\u00a0\u00a0\u00a0\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\u201cWe are concerned with making this model try to call as little as possible, but with the highest possible attendance confirmation effectiveness,\u201d explains Ram\u00edrez. Dunstan explained that they require a large amount of historical information from patient data to be able to train these types of models and understand the pattern or profile of the person.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The model was applied in three hospitals: the Luis Calvo Mackenn Pediatric Hospital, the Talca Regional Hospital and the Cordillera Oriente Health Reference Center. The work consisted of intervening in the medical agenda of the hospitals with the intention of testing the predictive model in a real way. This exercise helped the models get more data, improving their efficiency.<\/p>

The scientists explained that the main challenge in the processes of automatic learning and the entrainment of predictive models consists of heterogeneity, since each hospital works differently and its patients have different profiles. \u201cFor this reason, we decided not to make the same model for all hospitals, but to differentiate it by establishment, with its own data. Apart from that the specialties also have different behavior of patients\u201d, explained Ram\u00edrez.<\/p>

It also depends on each patient, the way in which they are alerted about their next visit to the doctor, some prefer it by phone, through WhatsApp, or SMS. And in the same way it depends on each medical specialty. There are differences in the behavior of patients in appointments with an ophthalmologist or a gynecologist, for example.<\/p>

In one of the hospitals, the pilot test of the project managed to reduce the insistence of patients who are reminded of their appointments through telephone calls from 20.3% to 12.5. "The natural thing for us is that for the future this would be something used by most public hospitals, and in some private institutions, where efficiency when contacting patients is very relevant," Ram\u00edrez concluded.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

THE COUNTER<\/strong><\/p>

https:\/\/www.elmostrador.cl\/agenda-pais\/2021\/08\/26\/cientificos-disenan-modelo-para-predecir-conducta-de-pacientes-mediante-inteligencia-artificial\/<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Investigadores del Centro de Modelamiento Matem\u00e1tico (CMM) de la Universidad de Chile, crearon un modelo de Inteligencia Artificial (IA), capaz de optimizar la gesti\u00f3n de la agenda hospitalaria. La solicitud y agenda de citas para consultas m\u00e9dicas, genera p\u00e9rdidas de tiempo y de recursos en hospitales y consultorios m\u00e9dicos, cuando los pacientes no se presentan. Ante esta problem\u00e1tica investigadores del CMM de la Universidad de Chile han desarrollado un sistema basado en IA, que tiene como objetivo aumentar la eficiencia en hospitales.  Incluso lo han probado durante tres meses en tres hospitales del pa\u00eds.  Los investigadores recibieron apoyo econ\u00f3mico para el desarrollo de la investigaci\u00f3n, a trav\u00e9s de un fomento para el desarrollo cient\u00edfico. Jocelyn Dunstan y H\u00e9ctor Ram\u00edrez fueron los encargados de la creaci\u00f3n de un software ideado para aumentar y mejorar la eficiencia en los hospitales. Gracias a tecnolog\u00eda de aprendizaje autom\u00e1tico, el software busca conocer la probabilidad de que los pacientes no se presenten a la hora de su cita, esto gracias a los datos ingresados sobre el historial de asistencia de cada paciente, factores geogr\u00e1ficos como las distancias entre hogares y hospital, y otros factores sociales.     \u201cNos preocupamos de hacer que este modelo trate de llamar lo menos posible, pero con la mayor efectividad posible de confirmaci\u00f3n de asistencia\u201d, explica Ram\u00edrez.  Dunstan explic\u00f3 que requieren una gran cantidad de informaci\u00f3n hist\u00f3rica de los datos de los pacientes para lograr entrenar este tipo de modelos y que comprendan el patr\u00f3n o el perfil de la persona. El modelo fue aplicado en tres hospitales: el Hospital Pedi\u00e1trico Luis Calvo Mackenn, el Hospital Regional de Talca y el Centro de Referencia en Salud Cordillera Oriente. El trabajo consisti\u00f3 en intervenir la agenda m\u00e9dica de los hospitales con intenci\u00f3n de probar el modelo predictivo de forma real. Este ejercicio ayud\u00f3 a que los modelos obtuvieran mayor cantidad de datos, mejorando su eficacia. Los cient\u00edficos explicaron que el principal desaf\u00edo en los procesos de aprendizaje autom\u00e1tico y entra\u00f1amiento de modelos predictivos, consiste en la heterogeneidad, ya que cada hospital funciona de forma distinta y sus pacientes cuentan con perfiles diferentes. \u201cPor ello, decidimos no hacer un mismo modelo para todos los hospitales, sino que diferenciarlo por establecimiento, con sus propios datos. Aparte que las especialidades tambi\u00e9n tienen conducta distinta de pacientes\u201d, explic\u00f3 Ram\u00edrez. Tambi\u00e9n depende de cada paciente, a la forma en que se le alerta sobre su pr\u00f3xima visita al m\u00e9dico, algunos lo prefieren v\u00eda telef\u00f3nica, a trav\u00e9s WhatsApp, o SMS. Y de igual forma depende de cada especialidad m\u00e9dica. Existen diferencias en las conductas de los pacientes en citas con un oftalm\u00f3logo o con un ginec\u00f3logo, por ejemplo. En uno de los hospitales la prueba piloto del proyecto logr\u00f3 reducir de un 20,3% a un 12,5 la insistencia de pacientes que son recordados de sus citas a trav\u00e9s de llamadas telef\u00f3nicas.  \u201cLo natural para nosotros es que para el futuro esto fuese algo utilizado por la mayor parte de hospitales p\u00fablicos, y en algunas instituciones privadas, donde la eficiencia al momento de contactar a los pacientes es muy relevante\u201d, concluy\u00f3 Ram\u00edrez. BIBLIOGRAF\u00cdA EL MOSTRADOR https:\/\/www.elmostrador.cl\/agenda-pais\/2021\/08\/26\/cientificos-disenan-modelo-para-predecir-conducta-de-pacientes-mediante-inteligencia-artificial\/<\/p>","protected":false},"author":1,"featured_media":20789,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-20788","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/20788","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=20788"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/20788\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/20789"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=20788"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=20788"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=20788"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}