Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/17156 |
Resumo: | Introduction: Information on potential drug interactions (PDI) are obtained from databases available on the web or through mobile healthcare applications (mHealth), and can prevent unfavorable clinical outcomes for patients. This study compared PDI information available in Micromedex® drug interaction checker, its web version and its mHealth app. Method: A cross-sectional study realized based on a retrospective review of drug prescriptions in a reference hospital in infectology in the Midwest Region of Brazil, 2018. We selected all prescriptions containing two or more drugs. Drugs were classified according to the first level of the Anatomical Therapeutic Chemical (ATC) classification, according to the route of administration and the number of drugs prescribed. PDIs were classified according to the severity system and four-level evidence classification system. Results: This study selected 72 patients, predominantly male, median age of 38 years, average length of stay of 15.8 days, and most diagnosed with HIV/AIDS. The most frequently prescribed anatomical groups according to ATC were digestive system and metabolism (22.1%) and general anti-infectives for systemic use (21.6%). The average number of drugs per prescription was 10.8 (SD±6.7). The Micromedex® mHealth app found 381 PDIs while its web version detected 502 PDIs, with an average of 5.3 and 7.0 and frequency of 61.1% and 72.2%, respectively. According to the severity classification in mHealth and web versions, the following stood out, respectively: 221 and 321 severe; 139 and 149 moderate. The majority (>65%) of identified PDIs had their documentation classified as reasonable. Conclusion: Digital tools although they aid decision-making, are not unanimous and consistent in detecting such interactions. |
id |
UNIFEI_038a517dc81419b249842d15091816ad |
---|---|
oai_identifier_str |
oai:ojs.pkp.sfu.ca:article/17156 |
network_acronym_str |
UNIFEI |
network_name_str |
Research, Society and Development |
repository_id_str |
|
spelling |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital settingDivergencias entre las bases en la era de la mHealth: investigación de las interacciones farmacológicas en un hospital de enfermedades infecciosasDivergências intrabases na era mHealth: investigação de interações medicamentosas em um hospital de doenças infecciosas Drug prescriptionsInfectologyMobile deviceDrug interactionMobile healthSmartphone.Prescrições de medicamentosInfectologiaDispositivo móvelInteração medicamentosaSaúde móvelSmartphone.Prescripciones de medicamentosInfectologíaDispositivo móvilLa interacción de drogasSalud móvilSmartphone.Introduction: Information on potential drug interactions (PDI) are obtained from databases available on the web or through mobile healthcare applications (mHealth), and can prevent unfavorable clinical outcomes for patients. This study compared PDI information available in Micromedex® drug interaction checker, its web version and its mHealth app. Method: A cross-sectional study realized based on a retrospective review of drug prescriptions in a reference hospital in infectology in the Midwest Region of Brazil, 2018. We selected all prescriptions containing two or more drugs. Drugs were classified according to the first level of the Anatomical Therapeutic Chemical (ATC) classification, according to the route of administration and the number of drugs prescribed. PDIs were classified according to the severity system and four-level evidence classification system. Results: This study selected 72 patients, predominantly male, median age of 38 years, average length of stay of 15.8 days, and most diagnosed with HIV/AIDS. The most frequently prescribed anatomical groups according to ATC were digestive system and metabolism (22.1%) and general anti-infectives for systemic use (21.6%). The average number of drugs per prescription was 10.8 (SD±6.7). The Micromedex® mHealth app found 381 PDIs while its web version detected 502 PDIs, with an average of 5.3 and 7.0 and frequency of 61.1% and 72.2%, respectively. According to the severity classification in mHealth and web versions, the following stood out, respectively: 221 and 321 severe; 139 and 149 moderate. The majority (>65%) of identified PDIs had their documentation classified as reasonable. Conclusion: Digital tools although they aid decision-making, are not unanimous and consistent in detecting such interactions.Introducción: La información sobre posibles interacciones medicamentosas (PIF) se puede obtener de las bases de datos disponibles en la web o mediante aplicaciones de atención médica móviles, y puede evitar resultados clínicos desfavorables para los pacientes. Este estudio comparó la información de IMP disponible en el verificador de interacción de medicamentos Micromedex®, su versión web y su aplicación mHealth. Metodos: Se realizó un estudio transversal basado en una revisión retrospectiva de prescripciones de medicamentos en un hospital de referencia en enfermedades infecciosas en la Región Medio Oeste de Brasil, 2018. Seleccionamos todas las prescripciones que contienen dos o más medicamentos. Los fármacos se clasificaron según el primer nivel de la clasificación Anatómico Terapéutico Químico (ATC), según la vía de administración y el número de fármacos prescritos. Los PIF se clasificaron según el sistema de gravedad y el sistema de clasificación de evidencia de cuatro niveles. Resultados: Se seleccionaron 72 pacientes, predominantemente varones, mediana de edad de 38 años, estancia media de 15,8 días y la mayoría diagnosticados de VIH / SIDA. Los grupos anatómicos prescritos con mayor frecuencia según ATC fueron aparato digestivo y metabolismo (22,1%) y antiinfecciosos generales de uso sistémico (21,6%). El número medio de medicamentos por prescripción fue de 10,8 (DE ± 6,7). Se encontraron 381 IMP en Micromedex® mHealth mientras que su versión web detectó 502 PIF, con un promedio de 5.3 y 7.0 y una frecuencia de 61.1% y 72.2%, respectivamente. Según la clasificación de gravedad en las versiones mHealth y web, se destacaron, respectivamente: 221 y 321 graves; 139 y 149 moderado. La documentación de la mayoría (> 65%) de los IMP identificados se clasificó como razonable. Conclusión: Las herramientas digitales, aunque ayudan en la toma de decisiones, no son unánimes y coincidentes en la detección de Según la clasificación de gravedad en las versiones mHealth y web, se destacaron, respectivamente: 221 y 321 graves; 139 y 149 moderado. La documentación de la mayoría (> 65%) de los IMP identificados se clasificó como razonable. Conclusión: Las herramientas digitales, aunque ayudan en la toma de decisiones, no son unánimes y coincidentes en la detección de PIF.Introdução: Informações sobre interações medicamentosas potenciais (IMP) podem ser obtidas em bases de dados disponíveis na web ou por meio de aplicativos móveis em saúde (mHealth), e podem evitar desfechos clínicos desfavoráveis aos pacientes. Este estudo comparou as informações de IMP disponíveis no verificador de interações medicamentosas Micromedex®, em sua versão disponível na web e seu aplicativo mHealth. Métodos: Realizou-se estudo transversal com base em revisão retrospectiva de prescrições de medicamentos em hospital de referência em infectologia da Região Centro-Oeste do Brasil, 2018. Selecionamos todas as prescrições contendo dois ou mais medicamentos. Os medicamentos foram classificados de acordo com o primeiro nível da classificação Anatomical Therapeutic Chemical (ATC), de acordo com a via de administração e o número de medicamentos prescritos. As IMP foram classificados de acordo com o sistema de gravidade e sistema de classificação de evidências em quatro níveis. Resultados: Foram selecionados 72 pacientes, com predomínio do sexo masculino, mediana de idade de 38 anos, tempo médio de internação de 15,8 dias, e maioria com diagnóstico de HIV/Aids. Os grupos anatômicos mais frequentemente prescritos segundo ATC foram aparelho digestivo e metabolismo (22,1%) e anti-infecciosos gerais para uso sistémico (21,6%). O número médio de fármacos por prescrição foi de 10,8 (DP±6,7). Encontrou-se 381 IMP em Micromedex® mHealth enquanto sua versão web detectou 502 IMP, com média de 5,3 e 7,0 e frequência de 61,1% e 72,2%, respectivamente. Segundo a classificação de gravidade na versão mHealth e web, destacaram-se respectivamente: 221 e 321 graves; 139 e 149 moderadas. A maioria (>65%) das IMP identificadas tiveram a sua documentação classificada como razoável. Conclusão: As ferramentas digitais, embora auxiliem na tomada de decisão, não são unânimes e concordantes na detecção de IMP.Research, Society and Development2021-11-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1715610.33448/rsd-v10i14.17156Research, Society and Development; Vol. 10 No. 14; e559101417156Research, Society and Development; Vol. 10 Núm. 14; e559101417156Research, Society and Development; v. 10 n. 14; e5591014171562525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/17156/19893Copyright (c) 2021 Roberta Souza; Pedro Ivo da Silva; Paulo César Cascao; Clarissa Alencar Sousa; Angela Ferreira Lopeshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSouza, Roberta Silva, Pedro Ivo da Cascao, Paulo César Sousa, Clarissa AlencarLopes, Angela Ferreira 2021-12-04T11:48:39Zoai:ojs.pkp.sfu.ca:article/17156Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:37:33.121621Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting Divergencias entre las bases en la era de la mHealth: investigación de las interacciones farmacológicas en un hospital de enfermedades infecciosas Divergências intrabases na era mHealth: investigação de interações medicamentosas em um hospital de doenças infecciosas |
title |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
spellingShingle |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting Souza, Roberta Drug prescriptions Infectology Mobile device Drug interaction Mobile health Smartphone. Prescrições de medicamentos Infectologia Dispositivo móvel Interação medicamentosa Saúde móvel Smartphone. Prescripciones de medicamentos Infectología Dispositivo móvil La interacción de drogas Salud móvil Smartphone. |
title_short |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
title_full |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
title_fullStr |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
title_full_unstemmed |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
title_sort |
Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting |
author |
Souza, Roberta |
author_facet |
Souza, Roberta Silva, Pedro Ivo da Cascao, Paulo César Sousa, Clarissa Alencar Lopes, Angela Ferreira |
author_role |
author |
author2 |
Silva, Pedro Ivo da Cascao, Paulo César Sousa, Clarissa Alencar Lopes, Angela Ferreira |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Souza, Roberta Silva, Pedro Ivo da Cascao, Paulo César Sousa, Clarissa Alencar Lopes, Angela Ferreira |
dc.subject.por.fl_str_mv |
Drug prescriptions Infectology Mobile device Drug interaction Mobile health Smartphone. Prescrições de medicamentos Infectologia Dispositivo móvel Interação medicamentosa Saúde móvel Smartphone. Prescripciones de medicamentos Infectología Dispositivo móvil La interacción de drogas Salud móvil Smartphone. |
topic |
Drug prescriptions Infectology Mobile device Drug interaction Mobile health Smartphone. Prescrições de medicamentos Infectologia Dispositivo móvel Interação medicamentosa Saúde móvel Smartphone. Prescripciones de medicamentos Infectología Dispositivo móvil La interacción de drogas Salud móvil Smartphone. |
description |
Introduction: Information on potential drug interactions (PDI) are obtained from databases available on the web or through mobile healthcare applications (mHealth), and can prevent unfavorable clinical outcomes for patients. This study compared PDI information available in Micromedex® drug interaction checker, its web version and its mHealth app. Method: A cross-sectional study realized based on a retrospective review of drug prescriptions in a reference hospital in infectology in the Midwest Region of Brazil, 2018. We selected all prescriptions containing two or more drugs. Drugs were classified according to the first level of the Anatomical Therapeutic Chemical (ATC) classification, according to the route of administration and the number of drugs prescribed. PDIs were classified according to the severity system and four-level evidence classification system. Results: This study selected 72 patients, predominantly male, median age of 38 years, average length of stay of 15.8 days, and most diagnosed with HIV/AIDS. The most frequently prescribed anatomical groups according to ATC were digestive system and metabolism (22.1%) and general anti-infectives for systemic use (21.6%). The average number of drugs per prescription was 10.8 (SD±6.7). The Micromedex® mHealth app found 381 PDIs while its web version detected 502 PDIs, with an average of 5.3 and 7.0 and frequency of 61.1% and 72.2%, respectively. According to the severity classification in mHealth and web versions, the following stood out, respectively: 221 and 321 severe; 139 and 149 moderate. The majority (>65%) of identified PDIs had their documentation classified as reasonable. Conclusion: Digital tools although they aid decision-making, are not unanimous and consistent in detecting such interactions. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-14 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/17156 10.33448/rsd-v10i14.17156 |
url |
https://rsdjournal.org/index.php/rsd/article/view/17156 |
identifier_str_mv |
10.33448/rsd-v10i14.17156 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/17156/19893 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 14; e559101417156 Research, Society and Development; Vol. 10 Núm. 14; e559101417156 Research, Society and Development; v. 10 n. 14; e559101417156 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
_version_ |
1797052681695526912 |