El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review

La inteligencia artificial y el aprendizaje automático son tecnologías que ayudan a descubrir patrones en los datos que pueden informar la toma de decisiones clínicas. La Asociación de Enfermeras Registradas de Ontario ha utilizado técnicas de inteligencia artificial para ayudar a comprender las prá...

Full description

Autores:
Singla, Shina
Howitt, Lyndsay
Medeiros, Christina
Grinspun, Doris
Naik, Shanoja
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/26730
Acceso en línea:
http://hdl.handle.net/20.500.12749/26730
https://doi.org/10.29375/01237047.4634
Palabra clave:
Guías de Práctica Clínica como Asunto
Enfermería Basada en la Evidencia
Aprendizaje Automático
Inteligencia Artificial
Sistemas de Información en Salud
Medical sciences
Life sciences
Health sciences
Practice Guidelines as Topic
Evidence-Based Nursing
Machine Learning
Artificial Intelligence
Health Information Systems
Ciências médicas
Ciências da vida
Ciências da saúde
Guias de Prática Clínica como Assunto
Enfermagem Baseada em Evidências
Aprendizado de Máquina
Inteligência Artificial
Sistemas de Informação em Saúde
Ciencias médicas
Ciencias de la vida
Ciencias de la salud
Rights
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id UNAB2_9a1bd9dd047ebc31457b22b3987be60d
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/26730
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
dc.title.translated.eng.fl_str_mv The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review
dc.title.translated.por.fl_str_mv O uso de técnicas de inteligência artificial em sistemas de dados de enfermagem: Scoping Review
title El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
spellingShingle El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
Guías de Práctica Clínica como Asunto
Enfermería Basada en la Evidencia
Aprendizaje Automático
Inteligencia Artificial
Sistemas de Información en Salud
Medical sciences
Life sciences
Health sciences
Practice Guidelines as Topic
Evidence-Based Nursing
Machine Learning
Artificial Intelligence
Health Information Systems
Ciências médicas
Ciências da vida
Ciências da saúde
Guias de Prática Clínica como Assunto
Enfermagem Baseada em Evidências
Aprendizado de Máquina
Inteligência Artificial
Sistemas de Informação em Saúde
Ciencias médicas
Ciencias de la vida
Ciencias de la salud
title_short El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
title_full El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
title_fullStr El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
title_full_unstemmed El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
title_sort El uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping Review
dc.creator.fl_str_mv Singla, Shina
Howitt, Lyndsay
Medeiros, Christina
Grinspun, Doris
Naik, Shanoja
dc.contributor.author.none.fl_str_mv Singla, Shina
Howitt, Lyndsay
Medeiros, Christina
Grinspun, Doris
Naik, Shanoja
dc.contributor.orcid.spa.fl_str_mv Singla, Shina [0000-0003-1341-4395]
Howitt, Lyndsay [0000-0002-6424-2290]
Medeiros, Christina [0000-0002-3956-7472]
Grinspun, Doris [0000-0002-2499-9766]
Naik, Shanoja [0000-0002-5742-6075]
dc.subject.spa.fl_str_mv Guías de Práctica Clínica como Asunto
Enfermería Basada en la Evidencia
Aprendizaje Automático
Inteligencia Artificial
Sistemas de Información en Salud
topic Guías de Práctica Clínica como Asunto
Enfermería Basada en la Evidencia
Aprendizaje Automático
Inteligencia Artificial
Sistemas de Información en Salud
Medical sciences
Life sciences
Health sciences
Practice Guidelines as Topic
Evidence-Based Nursing
Machine Learning
Artificial Intelligence
Health Information Systems
Ciências médicas
Ciências da vida
Ciências da saúde
Guias de Prática Clínica como Assunto
Enfermagem Baseada em Evidências
Aprendizado de Máquina
Inteligência Artificial
Sistemas de Informação em Saúde
Ciencias médicas
Ciencias de la vida
Ciencias de la salud
dc.subject.keywords.eng.fl_str_mv Medical sciences
Life sciences
Health sciences
Practice Guidelines as Topic
Evidence-Based Nursing
Machine Learning
Artificial Intelligence
Health Information Systems
dc.subject.keywords.por.fl_str_mv Ciências médicas
Ciências da vida
Ciências da saúde
Guias de Prática Clínica como Assunto
Enfermagem Baseada em Evidências
Aprendizado de Máquina
Inteligência Artificial
Sistemas de Informação em Saúde
dc.subject.lemb.spa.fl_str_mv Ciencias médicas
Ciencias de la vida
Ciencias de la salud
description La inteligencia artificial y el aprendizaje automático son tecnologías que ayudan a descubrir patrones en los datos que pueden informar la toma de decisiones clínicas. La Asociación de Enfermeras Registradas de Ontario ha utilizado técnicas de inteligencia artificial para ayudar a comprender las prácticas clínicas que generan impacto y las estrategias de implementación. El objetivo de esta revisión es descubrir la adaptación e implementación de diversas técnicas de inteligencia artificial y aprendizaje automático en varios entornos sanitarios, utilizando diferentes sistemas de datos que almacenan datos relacionados con la enfermería. Metodología. En marzo de 2022, se realizó una revisión de alcance para buscar literatura revisada por pares utilizando los siguientes términos: «enfermería», «inteligencia artificial», «sistemas de datos», «estadística» y «datos agregados». Se excluyeron los estudios si no eran relevantes para la enfermería, utilizaban análisis cualitativos o de métodos mixtos, si eran artículos de revisión bibliográfica y no se centraban en la inteligencia artificial o en el uso de datos a nivel de paciente. Resultados. Se recuperó un total de 2,627 artículos, de los cuales 1,518 quedaron tras la eliminación de duplicados. Tras la revisión de títulos y resúmenes, quedaron 1,347 artículos. Posteriormente, con la revisión del texto completo, quedaron 13 estudios. Las técnicas de inteligencia artificial utilizadas por los sistemas de datos sanitarios incluyen, entre otras, la regresión, las redes neuronales, la clasificación y los métodos basados en gráficos. Discusión. Existe un vacío en la aplicación de métodos de inteligencia artificial en los sistemas de datos que evalúan el impacto de la implementación de buenas prácticas en enfermería. Se necesitan más sistemas de datos que empleen técnicas de inteligencia artificial para apoyar la evaluación de buenas prácticas en enfermería y otras profesiones de la salud. Conclusiones. Se recuperaron diversas técnicas de inteligencia artificial en sistemas de datos que almacenan datos relacionados con la enfermería. Sin embargo, se necesitan más sistemas de datos e investigación en este ámbito.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-09-24T15:46:03Z
dc.date.available.none.fl_str_mv 2024-09-24T15:46:03Z
dc.date.issued.none.fl_str_mv 2024-03-31
dc.type.eng.fl_str_mv Article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.local.spa.fl_str_mv Artículo
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.issn.spa.fl_str_mv i-ISSN 0123-7047
e-ISSN 2382-4603
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/26730
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.unab.edu.co
dc.identifier.doi.none.fl_str_mv https://doi.org/10.29375/01237047.4634
identifier_str_mv i-ISSN 0123-7047
e-ISSN 2382-4603
instname:Universidad Autónoma de Bucaramanga UNAB
reponame:Repositorio Institucional UNAB
repourl:https://repository.unab.edu.co
url http://hdl.handle.net/20.500.12749/26730
https://doi.org/10.29375/01237047.4634
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unab.edu.co/index.php/medunab/article/view/4634/4023
dc.relation.uri.spa.fl_str_mv https://revistas.unab.edu.co/index.php/medunab/issue/view/294
dc.relation.references.none.fl_str_mv McCarthy J, Minsky ML, Rocheste, N, Shannon CE. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Mag [Internet]. 2006;27(4):12-14. doi: https://doi.org/10.1609/aimag. v27i4.1904
Fetzer JH. What is Artificial Intelligence? Artificial Intelligence: Its Scope and Limits. Springer Link [Internet]. 1990;4(1):3–27. doi: https://doi. org/10.1007/978-94-009-1900-6
Robert N. How artificial intelligence is changing nursing. Nurs Manag [Internet]. 2019;50(9):30-39. doi: https:// doi.org/10.1097/01.NUMA.0000578988.56622.2
Registered Nurses’ Association of Ontario. Nursing & Compassionate Care in the Age of Artificial Intelligence: Engaging the Emerging Future [Internet]. Canada:RNAO;2020. Available from: https://rnao.ca/ sites/rnao-ca/files/RNAO-AMS_Report-Nursing_and_ Compassionate_Care_in_the_Age_of_AI_Final_For_ Media_Release_10.21.2020.pdf
Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int [Internet]. 2021;21(1):270. doi: https://doi.org/10.1186/s12935-021-01981-1
Malik-Paras A, Pathania M, Vyas-Kumar R. Overview of artificial intelligence in medicine. J Family Med Prim Carec. 2019;8(7):2328-2331. doi: https://doi. org/10.4103/jfmpc.jfmpc_440_19
Ahmad S, Jenkins M. Artificial Intelligence for Nursing Practice and Management: Current and Potential Research and Education. CIN-Comput Inform Nurs [Internet]. 2022;40(3):139-144. doi: https://doi. org/10.1097/CIN.0000000000000871
Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs [Internet]. 2021;77(9):3707-3717. doi: https://doi. org/10.1111/jan.14855
Ackoff RL. From data to wisdom. Journal of applied systems analysis [Internet]. 1989;16:3- 9. Available from: https://scholar.google.com/ scholar?q=Ackoff%20R.L.%2C%20From%20 d a t a % 2 0 t o % 2 0 w i s d o m % 2 C % 2 0 J o u r n a l % 2 0 of%20Applied%20Systems%20Analysis%2C%20 16%2C%201989%3A3-9
Harrison AM, Herasevich V, Gajic O. Automated Sepsis Detection, Alert, and Clinical Decision Support: Act on It or Silence the Alarm? Crit Care Med [Internet]. 2015;43(8):1776-1777. doi: https://doi.org/10.1097/ CCM.0000000000001099
Teng AK, Wilcox AB. A Review of Predictive Analytics Solutions for Sepsis Patients. Appl Clin Inform [Internet]. 2020;11(3):387-398. doi: https://doi. org/10.1055/s-0040-1710525
Cato KD, McGrow K, Rossetti SC. Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nurs Manage [Internet]. 2020;51(11):24-30. doi: https://doi.org/10.1097/01. NUMA.0000719396.83518.d6
Registered Nurses’ Association of Ontario. Best Practice Spotlight Organizations (BPSO). Transforming Nursing Through Knowledge [Internet]. Canada:RNAO;2023. Available from: https://rnao.ca/bpg/bpso
Gómez-Díaz OL, Esparza-Bohórquez M, Jaimes- Valencia ML, Granados-Oliveros LM, Bonilla-Marciales A, Medina-Tarazona C. Experiencia en la implantación y consolidación de las Guías de Buenas Prácticas de la Registered Nurses’ Association of Ontario (RNAO) en el ámbito clínico y académico en Colombia. Enferm Clin [Internet]. 2020;30(3):145-154. doi: https://doi. org/10.1016/j.enfcli.2019.11.013
Moreno-Casbas T, González-María E, Albornos-Muñoz L, Grinspun D. Getting guidelines into practice: lessons learned as Best Practice Spotlight Organization host. Int J Evid Based Healthc [Internet]. 2019;17:S15-S17. doi: https://doi.org/10.1097/XEB.0000000000000178
Higuchi KS, Davies B, Ploeg J. Sustaining guideline implementation: A multisite perspective on activities, challenges and supports. J Clin Nurs [Internet], 2017;26(23-24):4413-4424. doi: https://doi.org/10.1111/ jocn.13770
17. Del Rio-Martínez P, López-García M, Nieto- Martínez C, Cabrera-Cabrera MA, Harillo-Acevedo D, Mengibar-Carrillo A, et al. Aplicación y evaluación de la Guía de buenas prácticas: lactancia materna. Enferm Clin [Internet]. 2020;30(3):168-175. doi: https://doi. org/10.1016/j.enfcli.2020.03.016
Saiz-Vinuesa MD, Albornos-Muñoz L, Fernández-Núñez ML, López-García M, Moreno-Casbas T, González- Sánchez JA. Resultados de la implantación de la Guía de valoración y manejo del dolor en Centros Comprometidos con la Excelencia en Cuidados (CCEC®) en España. Enferm Clin [Internet]. 2020;30(3):212-221. doi: https:// doi.org/10.1016/j.enfcli.2020.04.002
Rolin-Gilman C, Fournier B, Cleverley K. Implementing Best Practice Guidelines in Pain Assessment and Management on a Women’s Psychiatric Inpatient Unit: Exploring Patients’ Perceptions. Pain Manag Nurs [Internet]. 2017;18(3):170-178. doi: https://doi. org/10.1016/j.pmn.2017.03.002
Monsonís-Filella B, Gea-Sánchez M, García-Martínez E, Folgera-Arnau M, Gutiérrez-Vilaplana JM, Blanco- Blanco J. Mejora de la valoración del riesgo y la prevención de las lesiones por presión durante la implantación de una Guía de buenas prácticas clínicas. Enferm Clin [Internet]. 2021;31(2):114-119. doi: https://doi.org/10.1016/j. enfcli.2020.10.027
Campbell KE, Woodbury MG, Houghton PE. Implementation of best practice in the prevention of heel pressure ulcers in the acute orthopedic population. Int Wound J [Internet]. 2010;7(1):28-40. doi: https://doi. org/10.1111/j.1742-481X.2009.00650.x
Singh M, Hynie M, Rivera T, Macisaac L, Glandman A, Cheng A. An evaluation study of the implementation of stroke best practice guidelines using a Knowledge Transfer Team approach. Can J Neurosci Nurs [Internet]. 2015;37(1):24-33. Available from: https://scholar. google.com/scholar_lookup?title=An+evaluation+- study+of+the+implementation+of+stroke+best+practice+ guidelines+using+a+knowledge+transfer+team+approach& author=M+Singh&author=M+Hynie&author= T+Rivera&publication_year=2015&journal=Can+- J+Neurosci+Nurs&pages=24-33&pmid=26152100
Morales-Romero A, González-María E, Ramos-Ramos MA, Hidalgo-López L, Zurita-Muñoz AJ, Quiñoz- Gallardo MD, et al. Implantación de la valoración y el cuidado de los adultos en riesgo de ideación y comportamiento suicida: una Guía de la Registered Nurses’ Association of Ontario (RNAO). Enferm Clin [Internet]. 2020;30(3):155-159. doi: https://doi. org/10.1016/j.enfcli.2019.10.028
Barhorst S, Prior RM, Kanter D. Implementation of a bestpractice guideline: Early enteral nutrition in a neuroscience intensive care unit. J Parenter Enter Nutr [Internet]. 2023;47(1):87-91. doi: https://doi.org/10.1002/jpen.2411
Grinspun, D, Bajnok, I. Transforming nursing through knowledge: Best practices for guideline development, implementation science, and evaluation. [Internet]. Indianapolis (US):Sigma Theta Tau International;2018. Available from: https://scholar.google.com/scholar_ lookup?title=Transforming+nursing+through+knowledge:+ Best+practices+for+guideline+development,+implementation+ science,+and+evaluation&author=I.+Bajnok& author=D.+Grinspun&author=H.+McConnell&author= B.+Davies&publication_year=2018&
Donabedian A. Evaluating the quality of Medical Care. Milbank Q [Internet]. 2005;83(4):691-729. doi: https:// doi.org/10.1111/j.1468-0009.2005.00397.x
Arksey H, O’Malley L. Scoping Studies: Towards a Methodological Framework. Int J Soc Res Methodol [Internet]. 2005;8(1):19-32. doi: https://doi. org/10.1080/1364557032000119616
Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci [Internet]. 2010;5:69. doi: https://doi.org/10.1186/1748-5908-5-69
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med [Internet]. 2018;169(7):467-473. doi: https://doi. org/10.7326/M18-0850
Singla S, Medeiros C, Howitt L, Burt A, Nizum N, Naik S, et al. A Scoping Review Protocol on the Use of Artificial Intelligence Techniques in Nursing Data Systems. Open Science Framework [Internet]. 2023. doi: https://doi. org/10.17605/OSF.IO/YNX76
EndNote [Internet]. India;2023. Available from: https:// endnote.com/
DistillerSR [Internet]. Ontario;2023. Available from: https://www.distillersr.com/
Lee, J. Statistics, descriptive. International encyclopedia of human geography [Internet]. 2020;13-20. doi: https://doi. org/10.1016/b978-0-08-102295-5.10428-7
Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs [Internet]. 2008;62(1):107-115. doi: https:// doi.org/10.1111/j.1365-2648.2007.04569.x
Lowry AW, Futterman CA, Gazit AZ. Acute vital signs changes are underrepresented by a conventional electronic health record when compared with automatically acquired data in a single-center tertiary pediatric cardiac intensive care unit. J Am Med Inf Assoc [Internet]. 2022;29(7):1183- 1190. doi: https://doi.org/10.1093/jamia/ocac033
Huang ZA, Zhu Z, Yau CH, Tan KC. Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network. IEEE Trans Neural Netw Learn Syst [Internet]. 2021;32(7):2847-2861. doi: https://doi. org/10.1109/TNNLS.2020.3007943
Simmons S, Wier G, Pedraza A, Stibich M. Impact of a pulsed xenon disinfection system on hospital onset Clostridioides difficile infections in 48 hospitals over a 5-year period. BMC Infect Dis [Internet]. 2021;21(1):1084. doi: https://doi.org/10.1186/s12879-021-06789-y
Magliano DJ, Chen L, Islam RM, Carstensen B, Gregg WE, Pavkov ME, et al. Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middleincome settings. Lancet Diabetes Endocrinol [Internet]. 2021;9(4):203-211. doi: https://doi.org/10.1016/S2213- 8587(20)30402-2
Ramallo-González AP, González-Vidal A, Skarmeta AF. CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19. Sensors [Internet]. 2021;21(2):484. doi: https://doi.org/10.3390/s21020484
Jung YS, Kim YE, Go DS, Yoon SJ. Projecting the prevalence of obesity in South Korea through 2040: a microsimulation modelling approach. BMJ Open [Internet]. 2020;10(12):e037629. doi: https://doi. org/10.1136/bmjopen-2020-037629
Slijepcevic D, Zeppelzauer M, Schwab C, Raberger AM, Breiteneder C, Horsak B. Input representations and classification strategies for automated human gait analysis. Gait Posture [Internet]. 2020;76:198-203. doi: https://doi. org/10.1016/j.gaitpost.2019.10.021
Ward MA, Stanley A, Deeth LE, Deardon R, Feng Z, Trotz-Williams LA, et al. Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system. BMC Public Health [Internet]. 2019;19(1):1232. doi: https://doi.org/10.1186/s12889- 019-7521-7
Rashmi R, Prasad K, Udupa CBK. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation. Artif Intell Med [Internet]. 2021;121:102191. doi: https://doi.org/10.1016/j. artmed.2021.102191
Shea CM, Weiner BJ, Belden CM. Using Latent Class Analysis to Identify Sophistication Categories of Electronic Medical Record Systems in U.S. Acute Care Hospitals. Soc Sci Comput Rev [Internet]. 2013;31(2):208-20. doi: https://doi.org/10.1177/0894439312448726
Wagenaar BH, Gimbel S, Hoek R, Pfeiffer J, Michel C, Manuel JL, et al. Effects of a health information system data quality intervention on concordance in Mozambique: time-series analyses from 2009-2012. Popul Health Metr [Internet]. 2015;13(1):9. doi: https://doi.org/10.1186/ s12963-015-0043-3
Solimini AG, D’Addario M, Villari P. Ecological correlation between diabetes hospitalizations and fine particulate matter in Italian provinces. BMC Public Health [Internet]. 2015;15(1):708. doi: https://doi.org/10.1186/ s12889-015-2018-5
Sanchez D, Dubay D, Prabhakar B, Taber DJ. Evolving Trends in Racial Disparities for Peri-Operative Outcomes with the New Kidney Allocation System (KAS) Implementation. J Racial Ethn Health Disparities [Internet]. 2018;5(6):1171-1179. doi: https://doi.org/10.1007/ s40615-018-0464-3
Atzori L, Iera A, Morabito G. The Internet of Things: A survey. Comput Netw [Internet]. 2010;54(15):2787-2805. doi: https://doi.org/10.1016/j.comnet.2010.05.010
von Gerich H, Moen H, Block LJ, Chu CH, DeForest H, Hobensack M, et al. Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. Int J Nurs Stud [Internet]. 2022;127:104153. doi: https://doi.org/10.1016/j.ijnurstu.2021.104153
Rubin D, White E, Bailer A, Gregory EF. Roles of Registered Nurses in Pediatric Preventive Care Delivery: A Pilot Study on Between-office Variation and Withinoffice Role Overlap. J Pediatr Nurs [Internet]. 2020;52:5-9. doi: https://doi.org/10.1016/j.pedn.2020.01.012
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rights.local.spa.fl_str_mv Abierto (Texto Completo)
dc.rights.creativecommons.*.fl_str_mv Atribución-NoComercial-SinDerivadas 2.5 Colombia
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
Abierto (Texto Completo)
Atribución-NoComercial-SinDerivadas 2.5 Colombia
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.grantor.spa.fl_str_mv Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv Facultad Ciencias de la Salud
dc.source.spa.fl_str_mv Vol. 26 Núm. 3 (2023): diciembre 2023 - marzo 2024: Personal de Salud; Conocimiento; Inteligencia Artificial; 512-521
institution Universidad Autónoma de Bucaramanga - UNAB
bitstream.url.fl_str_mv https://repository.unab.edu.co/bitstream/20.500.12749/26730/1/Art%c3%adculo.pdf
https://repository.unab.edu.co/bitstream/20.500.12749/26730/2/license.txt
https://repository.unab.edu.co/bitstream/20.500.12749/26730/3/Art%c3%adculo.pdf.jpg
bitstream.checksum.fl_str_mv 00d8fe8f940402768b9c734750271931
737346e09d47a3db691f1370de49426a
73391ebea6cbed51c98738fd2d971cfb
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional | Universidad Autónoma de Bucaramanga - UNAB
repository.mail.fl_str_mv repositorio@unab.edu.co
_version_ 1812205472947634176
spelling Singla, Shina4144a500-1e2d-47f3-b1b8-2d13e9a2f626Howitt, Lyndsaybd41bb35-cb25-4692-bb5d-83a6f983a2d8Medeiros, Christina6267901b-7d54-400c-8620-c0a733e66babGrinspun, Dorisad11f0ae-1742-4fdb-b464-e5e187677619Naik, Shanojacee03666-2c5f-4ccb-8042-bd3bf2d5437fSingla, Shina [0000-0003-1341-4395]Howitt, Lyndsay [0000-0002-6424-2290]Medeiros, Christina [0000-0002-3956-7472]Grinspun, Doris [0000-0002-2499-9766]Naik, Shanoja [0000-0002-5742-6075]2024-09-24T15:46:03Z2024-09-24T15:46:03Z2024-03-31i-ISSN 0123-7047e-ISSN 2382-4603http://hdl.handle.net/20.500.12749/26730instname:Universidad Autónoma de Bucaramanga UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/01237047.4634La inteligencia artificial y el aprendizaje automático son tecnologías que ayudan a descubrir patrones en los datos que pueden informar la toma de decisiones clínicas. La Asociación de Enfermeras Registradas de Ontario ha utilizado técnicas de inteligencia artificial para ayudar a comprender las prácticas clínicas que generan impacto y las estrategias de implementación. El objetivo de esta revisión es descubrir la adaptación e implementación de diversas técnicas de inteligencia artificial y aprendizaje automático en varios entornos sanitarios, utilizando diferentes sistemas de datos que almacenan datos relacionados con la enfermería. Metodología. En marzo de 2022, se realizó una revisión de alcance para buscar literatura revisada por pares utilizando los siguientes términos: «enfermería», «inteligencia artificial», «sistemas de datos», «estadística» y «datos agregados». Se excluyeron los estudios si no eran relevantes para la enfermería, utilizaban análisis cualitativos o de métodos mixtos, si eran artículos de revisión bibliográfica y no se centraban en la inteligencia artificial o en el uso de datos a nivel de paciente. Resultados. Se recuperó un total de 2,627 artículos, de los cuales 1,518 quedaron tras la eliminación de duplicados. Tras la revisión de títulos y resúmenes, quedaron 1,347 artículos. Posteriormente, con la revisión del texto completo, quedaron 13 estudios. Las técnicas de inteligencia artificial utilizadas por los sistemas de datos sanitarios incluyen, entre otras, la regresión, las redes neuronales, la clasificación y los métodos basados en gráficos. Discusión. Existe un vacío en la aplicación de métodos de inteligencia artificial en los sistemas de datos que evalúan el impacto de la implementación de buenas prácticas en enfermería. Se necesitan más sistemas de datos que empleen técnicas de inteligencia artificial para apoyar la evaluación de buenas prácticas en enfermería y otras profesiones de la salud. Conclusiones. Se recuperaron diversas técnicas de inteligencia artificial en sistemas de datos que almacenan datos relacionados con la enfermería. Sin embargo, se necesitan más sistemas de datos e investigación en este ámbito.Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area.A inteligência artificial e o aprendizado de máquina são tecnologias que ajudam a descobrir padrões em dados que podem informar a tomada de decisões clínicas. A Associação de Enfermeiras Registradas de Ontário vem utilizando técnicas de inteligência artificial para ajudar a entender as práticas clínicas que geram impacto e as estratégias de implementação. O objetivo desta revisão é descobrir a adaptação e implementação de diversas técnicas de inteligência artificial e aprendizado de máquina em diversos ambientes de saúde, utilizando diferentes sistemas de dados que armazenam dados relacionados à enfermagem. Metodologia. Em março de 2022, foi realizada uma revisão de escopo para pesquisar literatura revisada por pares usando os seguintes termos: «enfermagem», «inteligência artificial», «sistemas de dados», «estatísticas» e «dados agregados». Foram excluídos os estudos que não se mostravam relevantes para a enfermagem, utilizavam análises qualitativas ou de métodos mistos, se eram de artigos de revisão de literatura e não focavam na inteligência artificial ou no uso de dados no nível do paciente. Resultados. Foram recuperados 2,627 artigos no total, dos quais 1,518 permaneceram após a eliminação das duplicatas. Após a revisão de títulos e resumos, restaram 1,347 artigos. Posteriormente, com a revisão do texto completo, restaram 13 estudos. As técnicas de inteligência artificial usadas pelos sistemas de dados de saúde incluem, entre outras, regressão, redes neurais, classificação e métodos baseados em gráficos. Discussão. Existe uma lacuna na aplicação de métodos de inteligência artificial em sistemas de dados que avaliam o impacto da implementação de boas práticas de enfermagem. São necessários mais sistemas de dados que implementem técnicas de inteligência artificial para apoiar a avaliação de boas práticas em enfermagem e outras profissões de saúde. Conclusões. Diversas técnicas de inteligência artificial foram recuperadas em sistemas de dados que armazenam dados relacionados à enfermagem. No entanto, são necessários mais sistemas de dados e investigação nesta área.application/pdfspahttps://revistas.unab.edu.co/index.php/medunab/article/view/4634/4023https://revistas.unab.edu.co/index.php/medunab/issue/view/294McCarthy J, Minsky ML, Rocheste, N, Shannon CE. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Mag [Internet]. 2006;27(4):12-14. doi: https://doi.org/10.1609/aimag. v27i4.1904Fetzer JH. What is Artificial Intelligence? Artificial Intelligence: Its Scope and Limits. Springer Link [Internet]. 1990;4(1):3–27. doi: https://doi. org/10.1007/978-94-009-1900-6Robert N. How artificial intelligence is changing nursing. Nurs Manag [Internet]. 2019;50(9):30-39. doi: https:// doi.org/10.1097/01.NUMA.0000578988.56622.2Registered Nurses’ Association of Ontario. Nursing & Compassionate Care in the Age of Artificial Intelligence: Engaging the Emerging Future [Internet]. Canada:RNAO;2020. Available from: https://rnao.ca/ sites/rnao-ca/files/RNAO-AMS_Report-Nursing_and_ Compassionate_Care_in_the_Age_of_AI_Final_For_ Media_Release_10.21.2020.pdfIqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int [Internet]. 2021;21(1):270. doi: https://doi.org/10.1186/s12935-021-01981-1Malik-Paras A, Pathania M, Vyas-Kumar R. Overview of artificial intelligence in medicine. J Family Med Prim Carec. 2019;8(7):2328-2331. doi: https://doi. org/10.4103/jfmpc.jfmpc_440_19Ahmad S, Jenkins M. Artificial Intelligence for Nursing Practice and Management: Current and Potential Research and Education. CIN-Comput Inform Nurs [Internet]. 2022;40(3):139-144. doi: https://doi. org/10.1097/CIN.0000000000000871Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs [Internet]. 2021;77(9):3707-3717. doi: https://doi. org/10.1111/jan.14855Ackoff RL. From data to wisdom. Journal of applied systems analysis [Internet]. 1989;16:3- 9. Available from: https://scholar.google.com/ scholar?q=Ackoff%20R.L.%2C%20From%20 d a t a % 2 0 t o % 2 0 w i s d o m % 2 C % 2 0 J o u r n a l % 2 0 of%20Applied%20Systems%20Analysis%2C%20 16%2C%201989%3A3-9Harrison AM, Herasevich V, Gajic O. Automated Sepsis Detection, Alert, and Clinical Decision Support: Act on It or Silence the Alarm? Crit Care Med [Internet]. 2015;43(8):1776-1777. doi: https://doi.org/10.1097/ CCM.0000000000001099Teng AK, Wilcox AB. A Review of Predictive Analytics Solutions for Sepsis Patients. Appl Clin Inform [Internet]. 2020;11(3):387-398. doi: https://doi. org/10.1055/s-0040-1710525Cato KD, McGrow K, Rossetti SC. Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nurs Manage [Internet]. 2020;51(11):24-30. doi: https://doi.org/10.1097/01. NUMA.0000719396.83518.d6Registered Nurses’ Association of Ontario. Best Practice Spotlight Organizations (BPSO). Transforming Nursing Through Knowledge [Internet]. Canada:RNAO;2023. Available from: https://rnao.ca/bpg/bpsoGómez-Díaz OL, Esparza-Bohórquez M, Jaimes- Valencia ML, Granados-Oliveros LM, Bonilla-Marciales A, Medina-Tarazona C. Experiencia en la implantación y consolidación de las Guías de Buenas Prácticas de la Registered Nurses’ Association of Ontario (RNAO) en el ámbito clínico y académico en Colombia. Enferm Clin [Internet]. 2020;30(3):145-154. doi: https://doi. org/10.1016/j.enfcli.2019.11.013Moreno-Casbas T, González-María E, Albornos-Muñoz L, Grinspun D. Getting guidelines into practice: lessons learned as Best Practice Spotlight Organization host. Int J Evid Based Healthc [Internet]. 2019;17:S15-S17. doi: https://doi.org/10.1097/XEB.0000000000000178Higuchi KS, Davies B, Ploeg J. Sustaining guideline implementation: A multisite perspective on activities, challenges and supports. J Clin Nurs [Internet], 2017;26(23-24):4413-4424. doi: https://doi.org/10.1111/ jocn.1377017. Del Rio-Martínez P, López-García M, Nieto- Martínez C, Cabrera-Cabrera MA, Harillo-Acevedo D, Mengibar-Carrillo A, et al. Aplicación y evaluación de la Guía de buenas prácticas: lactancia materna. Enferm Clin [Internet]. 2020;30(3):168-175. doi: https://doi. org/10.1016/j.enfcli.2020.03.016Saiz-Vinuesa MD, Albornos-Muñoz L, Fernández-Núñez ML, López-García M, Moreno-Casbas T, González- Sánchez JA. Resultados de la implantación de la Guía de valoración y manejo del dolor en Centros Comprometidos con la Excelencia en Cuidados (CCEC®) en España. Enferm Clin [Internet]. 2020;30(3):212-221. doi: https:// doi.org/10.1016/j.enfcli.2020.04.002Rolin-Gilman C, Fournier B, Cleverley K. Implementing Best Practice Guidelines in Pain Assessment and Management on a Women’s Psychiatric Inpatient Unit: Exploring Patients’ Perceptions. Pain Manag Nurs [Internet]. 2017;18(3):170-178. doi: https://doi. org/10.1016/j.pmn.2017.03.002Monsonís-Filella B, Gea-Sánchez M, García-Martínez E, Folgera-Arnau M, Gutiérrez-Vilaplana JM, Blanco- Blanco J. Mejora de la valoración del riesgo y la prevención de las lesiones por presión durante la implantación de una Guía de buenas prácticas clínicas. Enferm Clin [Internet]. 2021;31(2):114-119. doi: https://doi.org/10.1016/j. enfcli.2020.10.027Campbell KE, Woodbury MG, Houghton PE. Implementation of best practice in the prevention of heel pressure ulcers in the acute orthopedic population. Int Wound J [Internet]. 2010;7(1):28-40. doi: https://doi. org/10.1111/j.1742-481X.2009.00650.xSingh M, Hynie M, Rivera T, Macisaac L, Glandman A, Cheng A. An evaluation study of the implementation of stroke best practice guidelines using a Knowledge Transfer Team approach. Can J Neurosci Nurs [Internet]. 2015;37(1):24-33. Available from: https://scholar. google.com/scholar_lookup?title=An+evaluation+- study+of+the+implementation+of+stroke+best+practice+ guidelines+using+a+knowledge+transfer+team+approach& author=M+Singh&author=M+Hynie&author= T+Rivera&publication_year=2015&journal=Can+- J+Neurosci+Nurs&pages=24-33&pmid=26152100Morales-Romero A, González-María E, Ramos-Ramos MA, Hidalgo-López L, Zurita-Muñoz AJ, Quiñoz- Gallardo MD, et al. Implantación de la valoración y el cuidado de los adultos en riesgo de ideación y comportamiento suicida: una Guía de la Registered Nurses’ Association of Ontario (RNAO). Enferm Clin [Internet]. 2020;30(3):155-159. doi: https://doi. org/10.1016/j.enfcli.2019.10.028Barhorst S, Prior RM, Kanter D. Implementation of a bestpractice guideline: Early enteral nutrition in a neuroscience intensive care unit. J Parenter Enter Nutr [Internet]. 2023;47(1):87-91. doi: https://doi.org/10.1002/jpen.2411Grinspun, D, Bajnok, I. Transforming nursing through knowledge: Best practices for guideline development, implementation science, and evaluation. [Internet]. Indianapolis (US):Sigma Theta Tau International;2018. Available from: https://scholar.google.com/scholar_ lookup?title=Transforming+nursing+through+knowledge:+ Best+practices+for+guideline+development,+implementation+ science,+and+evaluation&author=I.+Bajnok& author=D.+Grinspun&author=H.+McConnell&author= B.+Davies&publication_year=2018&Donabedian A. Evaluating the quality of Medical Care. Milbank Q [Internet]. 2005;83(4):691-729. doi: https:// doi.org/10.1111/j.1468-0009.2005.00397.xArksey H, O’Malley L. Scoping Studies: Towards a Methodological Framework. Int J Soc Res Methodol [Internet]. 2005;8(1):19-32. doi: https://doi. org/10.1080/1364557032000119616Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci [Internet]. 2010;5:69. doi: https://doi.org/10.1186/1748-5908-5-69Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med [Internet]. 2018;169(7):467-473. doi: https://doi. org/10.7326/M18-0850Singla S, Medeiros C, Howitt L, Burt A, Nizum N, Naik S, et al. A Scoping Review Protocol on the Use of Artificial Intelligence Techniques in Nursing Data Systems. Open Science Framework [Internet]. 2023. doi: https://doi. org/10.17605/OSF.IO/YNX76EndNote [Internet]. India;2023. Available from: https:// endnote.com/DistillerSR [Internet]. Ontario;2023. Available from: https://www.distillersr.com/Lee, J. Statistics, descriptive. International encyclopedia of human geography [Internet]. 2020;13-20. doi: https://doi. org/10.1016/b978-0-08-102295-5.10428-7Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs [Internet]. 2008;62(1):107-115. doi: https:// doi.org/10.1111/j.1365-2648.2007.04569.xLowry AW, Futterman CA, Gazit AZ. Acute vital signs changes are underrepresented by a conventional electronic health record when compared with automatically acquired data in a single-center tertiary pediatric cardiac intensive care unit. J Am Med Inf Assoc [Internet]. 2022;29(7):1183- 1190. doi: https://doi.org/10.1093/jamia/ocac033Huang ZA, Zhu Z, Yau CH, Tan KC. Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network. IEEE Trans Neural Netw Learn Syst [Internet]. 2021;32(7):2847-2861. doi: https://doi. org/10.1109/TNNLS.2020.3007943Simmons S, Wier G, Pedraza A, Stibich M. Impact of a pulsed xenon disinfection system on hospital onset Clostridioides difficile infections in 48 hospitals over a 5-year period. BMC Infect Dis [Internet]. 2021;21(1):1084. doi: https://doi.org/10.1186/s12879-021-06789-yMagliano DJ, Chen L, Islam RM, Carstensen B, Gregg WE, Pavkov ME, et al. Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middleincome settings. Lancet Diabetes Endocrinol [Internet]. 2021;9(4):203-211. doi: https://doi.org/10.1016/S2213- 8587(20)30402-2Ramallo-González AP, González-Vidal A, Skarmeta AF. CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19. Sensors [Internet]. 2021;21(2):484. doi: https://doi.org/10.3390/s21020484Jung YS, Kim YE, Go DS, Yoon SJ. Projecting the prevalence of obesity in South Korea through 2040: a microsimulation modelling approach. BMJ Open [Internet]. 2020;10(12):e037629. doi: https://doi. org/10.1136/bmjopen-2020-037629Slijepcevic D, Zeppelzauer M, Schwab C, Raberger AM, Breiteneder C, Horsak B. Input representations and classification strategies for automated human gait analysis. Gait Posture [Internet]. 2020;76:198-203. doi: https://doi. org/10.1016/j.gaitpost.2019.10.021Ward MA, Stanley A, Deeth LE, Deardon R, Feng Z, Trotz-Williams LA, et al. Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system. BMC Public Health [Internet]. 2019;19(1):1232. doi: https://doi.org/10.1186/s12889- 019-7521-7Rashmi R, Prasad K, Udupa CBK. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation. Artif Intell Med [Internet]. 2021;121:102191. doi: https://doi.org/10.1016/j. artmed.2021.102191Shea CM, Weiner BJ, Belden CM. Using Latent Class Analysis to Identify Sophistication Categories of Electronic Medical Record Systems in U.S. Acute Care Hospitals. Soc Sci Comput Rev [Internet]. 2013;31(2):208-20. doi: https://doi.org/10.1177/0894439312448726Wagenaar BH, Gimbel S, Hoek R, Pfeiffer J, Michel C, Manuel JL, et al. Effects of a health information system data quality intervention on concordance in Mozambique: time-series analyses from 2009-2012. Popul Health Metr [Internet]. 2015;13(1):9. doi: https://doi.org/10.1186/ s12963-015-0043-3Solimini AG, D’Addario M, Villari P. Ecological correlation between diabetes hospitalizations and fine particulate matter in Italian provinces. BMC Public Health [Internet]. 2015;15(1):708. doi: https://doi.org/10.1186/ s12889-015-2018-5Sanchez D, Dubay D, Prabhakar B, Taber DJ. Evolving Trends in Racial Disparities for Peri-Operative Outcomes with the New Kidney Allocation System (KAS) Implementation. J Racial Ethn Health Disparities [Internet]. 2018;5(6):1171-1179. doi: https://doi.org/10.1007/ s40615-018-0464-3Atzori L, Iera A, Morabito G. The Internet of Things: A survey. Comput Netw [Internet]. 2010;54(15):2787-2805. doi: https://doi.org/10.1016/j.comnet.2010.05.010von Gerich H, Moen H, Block LJ, Chu CH, DeForest H, Hobensack M, et al. Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. Int J Nurs Stud [Internet]. 2022;127:104153. doi: https://doi.org/10.1016/j.ijnurstu.2021.104153Rubin D, White E, Bailer A, Gregory EF. Roles of Registered Nurses in Pediatric Preventive Care Delivery: A Pilot Study on Between-office Variation and Withinoffice Role Overlap. J Pediatr Nurs [Internet]. 2020;52:5-9. doi: https://doi.org/10.1016/j.pedn.2020.01.012http://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Vol. 26 Núm. 3 (2023): diciembre 2023 - marzo 2024: Personal de Salud; Conocimiento; Inteligencia Artificial; 512-521Guías de Práctica Clínica como AsuntoEnfermería Basada en la EvidenciaAprendizaje AutomáticoInteligencia ArtificialSistemas de Información en SaludMedical sciencesLife sciencesHealth sciencesPractice Guidelines as TopicEvidence-Based NursingMachine LearningArtificial IntelligenceHealth Information SystemsCiências médicasCiências da vidaCiências da saúdeGuias de Prática Clínica como AssuntoEnfermagem Baseada em EvidênciasAprendizado de MáquinaInteligência ArtificialSistemas de Informação em SaúdeCiencias médicasCiencias de la vidaCiencias de la saludEl uso de técnicas de inteligencia artificial en los sistemas de datos de enfermería: Scoping ReviewThe Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping ReviewO uso de técnicas de inteligência artificial em sistemas de dados de enfermagem: Scoping ReviewArticleinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_6501http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Universidad Autónoma de Bucaramanga UNABFacultad Ciencias de la SaludORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf740194https://repository.unab.edu.co/bitstream/20.500.12749/26730/1/Art%c3%adculo.pdf00d8fe8f940402768b9c734750271931MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8183https://repository.unab.edu.co/bitstream/20.500.12749/26730/2/license.txt737346e09d47a3db691f1370de49426aMD52open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg11427https://repository.unab.edu.co/bitstream/20.500.12749/26730/3/Art%c3%adculo.pdf.jpg73391ebea6cbed51c98738fd2d971cfbMD53open access20.500.12749/26730oai:repository.unab.edu.co:20.500.12749/267302024-09-24 22:00:52.154open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGFzIHB1YmxpY2FjaW9uZXMgZGUgbGEgcmV2aXN0YSBNZWRVTkFCIGVzdMOhbiBiYWpvIHVuYSBMaWNlbmNpYSBkZSBBdHJpYnVjacOzbiBkZSBCaWVuZXMgQ29tdW5lcyBDcmVhdGl2b3MgKENyZWF0aXZlIENvbW1vbnMsIENDKSB0aXBvIDQuMCwgY29uIGRlcmVjaG9zIGRlIGF0cmlidWNpw7NuIHkgbm8gY29tZXJjaWFs