Internet de las Cosas como apoyo para reducir los errores hospitalarios relacionados con la administración de medicamentos

Autores/as

DOI:

https://doi.org/10.33448/rsd-v12i3.40425

Palabras clave:

Errores de medicación; Internet de las Cosas; Seguridad del Paciente; Gestión de ciencia, tecnología e innovación en salud.

Resumen

Objetivos: Esta investigación muestra una visión sobre el potencial de las tecnologías digitales para reducir los errores de medicación en pacientes hospitalizados o bajo atención hospitalaria. Este estudio expone los tipos de errores más comunes en el manejo de medicamentos y sus posibles causas. Además, investiga soluciones utilizando las técnicas de Internet de las Cosas para reducir los errores de medicación, presentando un ejemplo práctico de uso. Métodos: Se aplicó una revisión rápida, utilizando artículos de dos bases de datos. El análisis se limitó al período de 2017 a enero de 2022, resultando en 147 artículos del Portal Regional de la BVS – Biblioteca Virtual en Salud y 257 artículos de la base de datos PubMed. Resultados: Al final, se analizaron 40 estudios. Se realizó un mapeo de errores relacionados con el tema. Además, se identificó la aplicación de la tecnología y su efectividad reportada en los estudios. Según la investigación, la desatención o distracción por exceso de jornada laboral fue identificada como principal motivo que conduce a errores de medicación con los sistemas utilizados en los hospitales. Conclusión: Existen varias oportunidades para mejorar los procedimientos hospitalarios con nuevos enfoques tecnológicos, como la Internet de las Cosas. Mediante la implementación de tecnologías innovadoras, los errores de medicación hospitalaria se pueden gestionar de manera más eficiente, brindando una mejor atención y ahorro de costos mediante el uso eficiente de la tecnología. Además de asegurar la salud de los pacientes, las tecnologías pueden ayudar a reducir los costes que resultan de la aplicación equivocada de medicamentos a pacientes, así como permite reducir costos de internación, mejorar la tasa de ocupación y tiempo de alta Hospitalar, mejorar la productividad de los empleados del hospital, así como evitar procesos judiciales consecuentes.

Biografía del autor/a

Luis Fernando Espinosa Cocian, FTEC Faculdades

Luis Fernando Espinosa Cocian graduou-se em Engenharia Elétrica na Universidade Federal do Rio Grande do Sul e obteve o título de Mestre em Engenharia de Instrumentação Eletroeletrônica na mesma universidade. Concluiu o curso de Análise e Desenvolvimento de Sistemas e se especializou em Engenharia Biomédica Clínica da Universidade Estácio de Sá, RJ. Recebeu o título de Engenheiro de Segurança do Trabalho da Universidade Luterana do Brasil. Atuou em pesquisas do programa de doutorado em Engenharia Metalúrgica e dos Materiais da Universidade Federal do Rio Grande do Sul, tendo desenvolvido novos sistemas computacionais para simulação de processos de solidificação de metais. É autor de livros e publicações nas áreas de Engenharia, Computação e Automação e foi coordenador e professor dos cursos de Automação Industrial e de Engenharia Elétrica da Universidade Luterana do Brasil, assim como dos cursos de Engenharia Elétrica e Engenharia de Telecomunicações da Universidade La Salle. Atualmente é estudante do Curso Superior de Formação de Professores para Educação Profissional no Institudo Federal Farroupilha IFFAR, atua como professor do Curso de Engenharia Elétrica na Universidade LaSalle e na FTec Faculdades. As suas principais áreas de interesse e atuação são: concepção de novos sistemas de medição eletroeletrônicos para automação industrial e hospitalar, técnicas de geração não-convencionais de energia, inovações em automação, IOT, sistemas integrados adaptáveis de computação, sistemas de segurança modernos, Indústria 4.0 e sobre a computação aplicada aos processos de Engenharia. Nos tempos livres gosta de viajar, ler e desenvolver materiais didáticos de Engenharia para ajudar na formação das novas gerações de Engenheiros. As suas atribuições profissionais estão definidas pela Resolução 218/73 ARTº 8 (Geração, Transmissão, Distribuição e utilização da Energia Elétrica; Equipamentos, Materiais e Máquinas Elétricas; Sistemas de Medição e Controle Elétricos), Resolução 218/73 ART. 9º (Materiais e Equipamentos Eletrônicos; Sistemas de Comunicação e Telecomunicações; Sistemas de Medição e Controle Elétrico e Eletrônico, Engenharia de Computação e Engenharia Biomédica), Resolução 1103/2018 (Engenheiro Biomédico), assim como pela Resolução 359/91 ART. 4º e Resolução 437/99 ART. 4º (Engenharia de Segurança do Trabalho) do sistema CONFEA - CREARS e CREASC sob os registros RS088866-D e SC094001-5.

Citas

Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2019). The application of internet of things in healthcare: a systematic literature review and classification. In Universal Access in the Information Society (Vol. 18, Issue 4). Springer Berlin Heidelberg. https://doi.org/10.1007/s10209-018-0618-4

Al-Turjman, F., Nawaz, M. H., & Ulusar, U. D. (2020). Intelligence in the Internet of Medical Things era: A systematic review of current and future trends. Computer Communications, 150(December 2019), 644–660. https://doi.org/10.1016/j.comcom.2019.12.030

Amato, M. G., Salazar, A., Hickman, T. T. T., Quist, A. J. L., Volk, L. A., Wright, A., McEvoy, D., Galanter, W. L., Koppel, R., Loudin, B., Adelman, J., McGreevey, J. D., Smith, D. H., Bates, D. W., & Schiff, G. D. (2017). Computerized prescriber order entry-related patient safety reports: Analysis of 2522 medication errors. Journal of the American Medical Informatics Association, 24(2), 316–322. https://doi.org/10.1093/jamia/ocw125

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

Barakat, S., & Franklin, B. D. (2020). An Evaluation of the Impact of Barcode Patient and Medication Scanning on Nursing Workflow at a UK Teaching Hospital. Pharmacy (Basel, Switzerland), 8(3), 148. https://doi.org/10.3390/pharmacy8030148

Ben Souissi, S., Abed, M., El Hiki, L., Fortemps, P., & Pirlot, M. (2019). PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions. Journal of Biomedical Informatics, 99(July), 103304. https://doi.org/10.1016/j.jbi.2019.103304

Biltoft, J., & Finneman, L. (2018). Clinical and financial effects of smart pump-electronic medical record interoperability at a hospital in a regional health system. Am J Health Syst Pharm, 75(14), 1064–1068. https://doi.org/10.2146/ajhp161058

Burkoski, V., Yoon, J., Solomon, S., Hall, T. N. T., Karas, A. B., Jarrett, S. R., & Collins, B. E. (2019). Closed-Loop Medication System: Leveraging Technology to Elevate Safety. Nurs Leadersh (Tor Ont), 32(SP), 16–28. https://doi.org/10.12927/cjnl.2019.25817

Burlea-Schiopoiu, A., & Ferhati, K. (2020). The Managerial Implications of the Key Performance Indicators in Healthcare Sector: A Cluster Analysis. Healthcare (Basel, Switzerland), 9(1). https://doi.org/10.3390/healthcare9010019

Cabilan, C. J., Hughes, J. A., & Shannon, C. (2017). The use of a contextual, modal and psychological classification of medication errors in the emergency department: a retrospective descriptive study. J Clin Nurs, 26(23–24), 4335–4343. https://doi.org/10.1111/jocn.13760

Chien, S. C., Chin, Y. P. H., Yoon, C. H., Islam, M. M., Jian, W. S., Hsu, C. K., Chen, C. Y., Chien, P. H., & Li, Y. C. J. (2021). A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis. PLoS ONE, 16(2 February). https://doi.org/10.1371/journal.pone.0246597

Dalmolin, G. R. dos S. (2012). Erros de medicação no ambiente hospitalar : uma abordagem através da bioética complexa [Universidade Federal do Rio Grande do Sul]. https://lume.ufrgs.br/handle/10183/60813

Debono, D., Taylor, N., Lipworth, W., Greenfield, D., Travaglia, J., Black, D., & Braithwaite, J. (2017). Applying the Theoretical Domains Framework to identify barriers and targeted interventions to enhance nurses’ use of electronic medication management systems in two Australian hospitals. Implementation Science, 12(1). https://doi.org/10.1186/s13012-017-0572-1

Ehrler, F., & Siebert, J. N. (2020). PedAMINES: A disruptive mHealth app to tackle paediatric medication errors. Swiss Medical Weekly, 150(35–36), 1–10. https://doi.org/10.4414/smw.2020.20335

Firouzi, F., Farahani, B., Ibrahim, M., & Chakrabarty, K. (2018). Keynote paper: From EDA to IoT eHealth: Promises, challenges, and solutions. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(12), 2965–2978. https://doi.org/10.1109/TCAD.2018.2801227

Froese, L., Dian, J., Batson, C., Gomez, A., Sainbhi, A. S., Unger, B., & Zeiler, F. A. (2021). Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research. Frontiers in Big Data, 4(August), 1–9. https://doi.org/10.3389/fdata.2021.689358

González-Bueno, J., Sevilla-Sánchez, D., Puigoriol-Juvanteny, E., Molist-Brunet, N., Codina-Jané, C., & Espaulella-Panicot, J. (2021). Factors associated with medication non-adherence among patients with multimorbidity and polypharmacy admitted to an intermediate care center. International Journal of Environmental Research and Public Health, 18(18). https://doi.org/10.3390/ijerph18189606

Griffon, N., Schuers, M., Joulakian, M., Bubenheim, M., Leroy, J.-P. P., & Darmoni, S. J. (2017). Physician satisfaction with transition from CPOE to paper-based prescription. International Journal of Medical Informatics, 103(November 2016), 42–48. https://doi.org/10.1016/j.ijmedinf.2017.04.007

Habibzadeh, H., Dinesh, K., Rajabi Shishvan, O., Boggio-Dandry, A., Sharma, G., & Soyata, T. (2020). A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective. IEEE Internet of Things Journal, 7(1), 53–71. https://doi.org/10.1109/JIOT.2019.2946359

Henry, T. A. (2014). Focusing on new hospital technology. J Med Assoc Ga, 103(3), 8–10. https://pesquisa.bvsalud.org/portal/resource/pt/mdl-25665338

Hsieh, M.-C. C., Chiang, P.-Y. Y., Lee, Y.-C. C., Wang, E. M.-Y. Y., Kung, W.-C. C., Hu, Y.-T. T., Huang, M.-S. S., & Hsieh, H.-C. C. (2021). An investigation of human errors in medication adverse event improvement priority using a hybrid approach. Healthcare (Switzerland), 9(4), 1–16. https://doi.org/10.3390/healthcare9040442

Hsieh, M. C., Chiang, P. Y., Lee, Y. C., Wang, E. M. Y., Kung, W. C., Hu, Y. T., Huang, M. S., & Hsieh, H. C. (2021). An investigation of human errors in medication adverse event improvement priority using a hybrid approach. Healthcare (Switzerland), 9(4), 1–16. https://doi.org/10.3390/healthcare9040442

Jurado, C., Calmels, V., Lobinet, E., Divol, E., Hanaire, H., Metsu, D., & Sallerin, B. (2018). The Electronic Pharmaceutical Record: A new method for medication reconciliation. Journal of Evaluation in Clinical Practice, 24(4), 681–687. https://doi.org/10.1111/jep.12942

Kennedy, A. R., & Massey, L. R. (2019). Pediatric medication safety considerations for pharmacists in an adult hospital setting. American Journal of Health-System Pharmacy, 76(19), 1481–1491. https://doi.org/10.1093/ajhp/zxz168

Keyworth, C., Hart, J., Thoong, H., Ferguson, J., & Tully, M. (2017). A technological innovation to reduce prescribing errors based on implementation intentions: The acceptability and feasibility of myprescribe. JMIR Human Factors, 4(3). https://doi.org/10.2196/humanfactors.7153

Kim, M. S., Seok, J. H., & Kim, B. M. (2020). Mediating role of the perceived benefits of using a medication safety system in the relationship between transformational leadership and the medication-error management climate. Journal of Research in Nursing : JRN, 25(1), 22–34. https://doi.org/10.1177/1744987118824621

King, V. J., Stevens, A., Nussbaumer-Streit, B., Kamel, C., & Garritty, C. (2022). Paper 2: Performing rapid reviews. Systematic Reviews, 11(1), 1–10. https://doi.org/10.1186/s13643-022-02011-5

Kirkendall, E., Huth, H., Rauenbuehler, B., Moses, A., Melton, K., & Ni, Y. (2020). The generalizability of a medication administration discrepancy detection system: Quantitative comparative analysis. JMIR Medical Informatics, 8(12), 1–14. https://doi.org/10.2196/22031

Küng, K., Aeschbacher, K., Rütsche, A., Goette, J., Zürcher, S., Schmidli, J., & Schwendimann, R. (2021). Effect of barcode technology on medication preparation safety: A quasi-experimental study. International Journal for Quality in Health Care, 33(1), 1–8. https://doi.org/10.1093/intqhc/mzab043

Lawal, B. K., Aliyu, A. A., Ibrahim, U. I., Maiha, B. B., & Mohammed, S. (2020). Medication safety practices in healthcare facilities in Kaduna State, Nigeria: a study protocol. Therapeutic Advances in Drug Safety, 11, 2042098620927574. https://doi.org/10.1177/2042098620927574

Liao, C. Y., Wu, M. F., Poon, S. K., Liu, Y. M., Chen, H. C., Wu, C. L., Sheu, W. H. H., & Liou, W. S. (2019). Improving medication safety by cloud technology: Progression and value-added applications in Taiwan. International Journal of Medical Informatics, 126(May 2018), 65–71. https://doi.org/10.1016/j.ijmedinf.2019.03.012

Lichtner, V., Westbrook, J. I., & Franklin, B. D. (2018). Pharmacy Interweaving Safety Within Hospital Health Information Technology. Stud Health Technol Inform, 252, 105–111. https://pesquisa.bvsalud.org/portal/resource/pt/mdl-30040691

Lu, Y. H., Lee, L. Y., Chen, Y. L., Cheng, H. I., Tsai, W. T., Kuo, C. C., Chen, C. Y., & Huang, Y. Bin. (2017). Developing an App by Exploiting Web-Based Mobile Technology to Inspect Controlled Substances in Patient Care Units. BioMed Research International, 2017. https://doi.org/10.1155/2017/3195369

Lupia, T., Scabini, S., Mornese Pinna, S., Di Perri, G., De Rosa, F. G., & Corcione, S. (2020). 2019 novel coronavirus (2019-nCoV) outbreak: A new challenge. Journal of Global Antimicrobial Resistance, 21, 22–27. https://doi.org/10.1016/j.jgar.2020.02.021

Ma, X., Yao, T., Hu, M., Dong, Y., Liu, W., Wang, F., & Liu, J. (2019). A Survey on Deep Learning Empowered IoT Applications. IEEE Access, 7, 181721–181732. https://doi.org/10.1109/ACCESS.2019.2958962

Minh Dang, L., Piran, M. J., Han, D., Min, K., & Moon, H. (2019). A survey on internet of things and cloud computing for healthcare. Electronics (Switzerland), 8(7), 1–49. https://doi.org/10.3390/electronics8070768

Mohan, A., Manikandan, S., Ravikumar, T. S., & Batmanabane, G. (2019). Decreasing medication errors in four intensive care units of a tertiary care teaching hospital in India using a sensitization programme. Natl Med J India, 32(4), 207–212. https://doi.org/10.4103/0970-258X.291294

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7). https://doi.org/10.1371/journal.pmed.1000097

Morales, A. S., Ourique, F. de O., & Cazella, S. C. (2021). A Comprehensive Review on the Challenges for Intelligent Systems Related with Internet of Things for Medical Decision. Studies in Fuzziness and Soft Computing, 410, 221–240. https://doi.org/10.1007/978-3-030-70111-6_11

Mulac, A., Mathiesen, L., Taxis, K., & Gerd Granås, A. (2021). Barcode medication administration technology use in hospital practice: a mixed-methods observational study of policy deviations. BMJ Qual Saf, 30(12), 1021–1030. https://doi.org/10.1136/bmjqs-2021-013223

Oliveros, N. V., Caro, T. G., Menendez-Conde, C. P., Álvarez-Díaz, A. M., Álvarez, S. M.-A., Vicedo, T. B., & Silveira, E. D. (2017). Effect of an electronic medication administration record application on patient safety. J Eval Clin Pract, 23(4), 888–894. https://doi.org/10.1111/jep.12753

Pontefract, S. K., Hodson, J., Slee, A., Shah, S., Girling, A. J., Williams, R., Sheikh, A., & Coleman, J. J. (2018). Impact of a commercial order entry system on prescribing errors amenable to computerised decision support in the hospital setting: A prospective pre-post study. BMJ Quality and Safety, 27(9), 725–736. https://doi.org/10.1136/bmjqs-2017-007135

Prado, R., & Vilela, B. (2019). Implantação de tecnologias para prevenção de erros de medicação em hospital de alta complexidade : análise de custos e resultados. 17(4), 1–7. https://doi.org/10.31744/einstein

Prieto-Avalos, G., Cruz-Ramos, N. A., Alor-Hernández, G., Luis Sánchez-Cervantes, J., Rodríguez-Mazahua, L., & Guarneros-Nolasco, L. R. (2022). Wearable Devices for Physical Monitoring of Heart: A Review. https://doi.org/10.3390/bios12050292

Qadri, Y. A., Nauman, A., Zikria, Y. Bin, Vasilakos, A. V., & Kim, S. W. (2020). The Future of Healthcare Internet of Things: A Survey of Emerging Technologies. IEEE Communications Surveys & Tutorials, c, 1–1. https://doi.org/10.1109/comst.2020.2973314

Rabelo Néri, E. D., Chaves Gadêlha, P. G., Maia, S. G., da Silva Pereira, A. G., de Almeida, P. C., Martins Rodrigues, C. R., Portela, M. P., & de França Fonteles, M. M. (2011). Erros de prescrição de medicamentos em um hospital brasileiro. Revista Da Associação Médica Brasileira, 57(3), 306–314. https://doi.org/10.1590/s0104-42302011000300013

Reinhardt, H., Otte, P., Eggleton, A. G., Ruch, M., Wöhrl, S., Ajayi, S., Duyster, J., Jung, M., Hug, M. J., & Engelhardt, M. (2019). Avoiding chemotherapy prescribing errors: Analysis and innovative strategies. Cancer, 125(9), 1547–1557. https://doi.org/10.1002/cncr.31950

Risør, B. W., Lisby, M., & Sørensen, J. (2017). Cost-Effectiveness Analysis of an Automated Medication System Implemented in a Danish Hospital Setting. Value in Health, 20(7), 886–893. https://doi.org/10.1016/j.jval.2017.03.001

Roh, H., Shin, S., Han, J., & Lim, S. (2021). A deep learning-based medication behavior monitoring system. Mathematical Biosciences and Engineering, 18(2), 1513–1528. https://doi.org/10.3934/MBE.2021078

Rosa, M. B., & Perini, E. (2003). Erros de medicação: quem foi? Revista Da Associação Médica Brasileira, 49(3), 335–341. https://doi.org/10.1590/s0104-42302003000300041

Salazar de Pablo, G., Vaquerizo-Serrano, J., Catalan, A., Arango, C., Moreno, C., Ferre, F., Shin, J. Il, Sullivan, S., Brondino, N., Solmi, M., & Fusar-Poli, P. (2020). Impact of coronavirus syndromes on physical and mental health of health care workers: Systematic review and meta-analysis. Journal of Affective Disorders, 275(May), 48–57. https://doi.org/10.1016/j.jad.2020.06.022

Scarpato, N., Pieroni, A., Di Nunzio, L., & Fallucchi, F. (2017). E-health-IoT universe: A review. International Journal on Advanced Science, Engineering and Information Technology, 7(6), 2328–2336. https://doi.org/10.18517/ijaseit.7.6.4467

Schwendimann, R., Blatter, C., Dhaini, S., Simon, M., & Ausserhofer, D. (2018). The occurrence, types, consequences and preventability of in-hospital adverse events - A scoping review. BMC Health Services Research, 18(1), 1–13. https://doi.org/10.1186/s12913-018-3335-z

Sessions, L. C., Nemeth, L. S., Catchpole, K., & Kelechi, T. J. (2019). Nurses’ perceptions of high-alert medication administration safety: A qualitative descriptive study. J Adv Nurs, 75(12), 3654–3667. https://doi.org/10.1111/jan.14173

Shah, S. N., Seger, D. L., Fiskio, J. M., Horn, J. R., & Bates, D. W. (2021). Comparison of Medication Alerts from Two Commercial Applications in the USA. Drug Safety, 44(6), 661–668. https://doi.org/10.1007/s40264-021-01048-0

Siebert, J. N., Ehrler, F., Lovis, C., Combescure, C., Haddad, K., Gervaix, A., & Manzano, S. (2017). A mobile device app to reduce medication errors and time to drug delivery during pediatric cardiopulmonary resuscitation: Study protocol of a multicenter randomized controlled crossover trial. JMIR Research Protocols, 6(8), e167. https://doi.org/10.2196/resprot.7901

Spat, S., Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Augustin, T., Chiarugi, F., Lichtenegger, K. M., Plank, J., & Pieber, T. R. (2017). A Mobile Computerized Decision Support System to Prevent Hypoglycemia in Hospitalized Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 11(1), 20–28. https://doi.org/10.1177/1932296816676501

Ting, H. W., Chung, S. L., Chen, C. F., Chiu, H. Y., & Hsieh, Y. W. (2020). A drug identification model developed using deep learning technologies: Experience of a medical center in Taiwan. BMC Health Services Research, 20(1), 1–9. https://doi.org/10.1186/s12913-020-05166-w

Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13(1). https://doi.org/10.1186/s12916-015-0465-6

Van Der Veen, W., Van Den Bemt, P. M., Bijlsma, M., De Gier, H. J., & Taxis, K. (2018). Association between workarounds and medication administration errors in bar code-assisted medication administration: Protocol of a multicenter study. Journal of the American Medical Informatics Association, 25(4), 385–392. https://doi.org/10.2196/resprot.7060

Vélez-Díaz-Pallarés, M., Álvarez Díaz, A. M., Gramage Caro, T., Vicente Oliveros, N., Delgado-Silveira, E., Muñoz García, M., Cruz-Jentoft, A. J., & Bermejo-Vicedo, T. (2017). Technology-induced errors associated with computerized provider order entry software for older patients. International Journal of Clinical Pharmacy, 39(4), 729–742. https://doi.org/10.1007/s11096-017-0474-y

Vicente Oliveros, N., Gramage Caro, T., Pérez Menendez-Conde, C., Álvarez-Díaz, A. M., Martín-Aragón Álvarez, S., Bermejo Vicedo, T., & Delgado Silveira, E. (2017). Effect of an electronic medication administration record application on patient safety. Journal of Evaluation in Clinical Practice, 23(4), 888–894. https://doi.org/10.1111/jep.12753

Vilela, R. P. B., Castilho, V., Jericó, M. de C., & Faria, J. I. L. (2017). Educação permanente: tecnologia para a prevenção do erro de medicação. CuidArte, Enferm, 11(2), 203–208. http://www.webfipa.net/facfipa/ner/sumarios/cuidarte/2017v2/203.pdf

Wang, G., Zhou, S., Rezaei, S., Liu, X., & Huang, A. (2019). An ambulatory blood pressure monitor mobile health system for early warning for stroke risk: Longitudinal observational study. JMIR MHealth and UHealth, 7(10), 1–13. https://doi.org/10.2196/14926

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22/02/2023

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COCIAN, . L. F. E. .; MORALES, A. S. .; SCHNEIDER, I. J. C. . Internet de las Cosas como apoyo para reducir los errores hospitalarios relacionados con la administración de medicamentos. Research, Society and Development, [S. l.], v. 12, n. 3, p. e6312340425, 2023. DOI: 10.33448/rsd-v12i3.40425. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/40425. Acesso em: 22 dic. 2024.

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