ABC | Volume 113, Nº5, Novembro 2019

Diretrizes Diretriz da Sociedade Brasileira de Cardiologia sobre Telemedicina na Cardiologia – 2019 Arq Bras Cardiol. 2019; 113(5):1006-1056 Internet.Acessoem06 jun2019.Disponí velemhttp://www.portalmedico. org.br/resolucoes/cfm/2002/1643_2002.htm Lopes MA, Oliveira GM, Amaral Junior A, Pereira ES. Window to the Future or Door to Chaos? Arq Bras Cardiol. 2019;112(4):461-5 38. Lopes MA, Oliveira GM, Amaral Jr A, Pereira ES. Janela para o Futuro ou Porta para o Caos? Arq Bras Cardiol. 2019;112(4):461-5 39. Davoudi S, Stead D. Urban-rural relationships: An introduction and brief history. Built Environment. 2002;28(4):268-77 40. Rogers H, Castree N, Kitchen R. A Dictionary of Human Geography. Oxford:OxfordUniversityPress;2013.Internet.[Accessed in2019Jun06]. Available from: https://doi.org/10.1093/acref/9780199599868.001.0001 41. Brezzi M, Dijkstra L, Ruiz V. OECD Extended Regional Typology: The Economic Performance of Remote Rural Regions. In: OECD Regional Development Working Papers. Internet. [Accessed in 2018 Nov 10] Available from :https://doi.org/dx.doi.org/10.1787/5kg6z83tw7f4-en 42. Instituto Brasileiro de Geografia e Estatística. (IBGE). Classificação e caracterização dos espaços rurais e urbanos do Brasil; uma primeira aproximação. Rio de Janeiro; 2017. (Estudos e Pesquisas – Informação Geográfica. N.11) 43. Instituto Brasileiro de Geografia e Estatística. (IBGE). Sistema de mapeamento para base territorial- SISMAI- Manual do Usuário. Rio de Janeiro; 2010 44. Instituto Brasileiro de Geografia e Estatística. (IBGE). Censo IBGE 2010. Internet. [Acesso em 06 jun 2019]. Disponível em: ps://censo2010. ibge.gov.br 45. Scheffer M, Cassenote A, Guilloux AG, Miotto B, Mainardi GM, Matijasevich A, et al. Demografia Médica no Brasil 2018. Sao Paulo: FMUSP.2018:1-286. Available from: https://jornal.usp.br/wp-content/ uploads/DemografiaMedica2018.pdf 46. Brasil. Ministério da Saúde. Cadastro Nacional de Estabelecimentos de Saude. Internet. [Acesso em10março 2019]. Disponível em: http://cnes. datasus.gov.br/ 47. InternationalTelecommunicationUnion. (ITU). Internet.Accessed in2019 Feb 02]. Available from: https://www.itu.int/en/ITU-T/e-health/Pages/ default.aspx 48. Pan American Health Organization. (PAHO). eHealth in the Region of the Americas: breaking down the barriers to implementation. Results of the World Health Organization’s Third Global Survey on eHealth. Washington (DC); 2016.Habicht T, Reinap M, Kasekamp K, Sikkut R, Aaben, L, van Ginneken E. Estonia: Health System Review. Health Syst Transit. 2018;20(1):1-189 49. Sanyal C, Stolee, P, Juzwishin, D, Husereau D. Economic evaluations of eHealth technologies: A systematic review. PLoS One. 2018;13(6):e0198112 50. National Agency for Telecommunication. (ANATEL). Internet. [Acccessed in 2019 March 04]. Available from: www.anatel.gov.br 51. TELECO Blog. Celulares por Região SMP/SMC/Estado. Internet. [Acesso em 13 Mar 2019]. Disponível em: http://www.teleco.com.br/nceluf.asp 52. Topol Review: Preparing the healthcare workforce to deliver the digital future, as parto f the draft health and careworkforce strategy for England to 2027- facing the facts, shaping the future. Secretary of State for Health and Social Care. Internet. [Accessed in 2019 Feb 02]. Available from: https:// topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf 53. Park SH, Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018;286(3):800–9 54. Ribeiro AL, Oliveira GM. Rumo a uma Cardiologia Centrada no Paciente e Guiada por Dados. Arq Bras Cardiol. 2019;112(4):371-3 55. Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. ArtificialIntelligenceinCardiology.JAmCollCardiol.2018;71(23):2668–79 56. Dawes TJW, deMarvao A, ShiW, Fletcher T,WatsonGMJ,Wharton J, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283(2):381–90 57. Oskouie SK, Prenner SB, Shah SJ, Sauer AJ. Differences in repolarization heterogeneity among heart failure with preserved ejection fraction phenotypic subgroups. Am J Cardiol. 2017;120(4):601–6. 58. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944 59. Goto S, Kimura M, Katsumata Y, Goto S, Kamatani T, Ichihara G, et al. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS One. 2019;14(1):e0210103 60. Seetharam K, Kagiyama N, Sengupta PP. Application of mobile health, telemedicine and artificial intelligence to echocardiography. Echo Res Pract. 2019 Feb 1. pii: ERP-18-0081.R1. [Epub ahead of print] 61. Ribeiro AH, RibeiroMH, PaixãoG, Oliveira D, Gomes P. Canazart D, et al. Automaticdiagnosisoftheshort-duration12-leadECGusingadeepneural network the CODE study. Internet. [Accessed in 2019 Jun 06]. Available from: https:arxiv.org/abs/1904.01949 62. Triantafyllidis AK, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. J Med Internet Res. 2019;21(4):e12286 63. Topol EJ. The Topol Review. An independent report on behalf of the Secretary of State for Health and Social Care. Internet. [Accessed in 2019 Feb 02]. Available from: https://topol.hee.nhs.uk/ 64. EuropeanCommission-Futurium- Independenthigh-levelexpertgroupon Artificial Intelligence. Ethics guidelines for trustworthy. Internet. [Accessed in 2019 Feb 03]. Available from: https://ec.europa -eu/digital/-single- market/in/high-level-expert-group-artificial-intelligence 65. Granja C, JanssenW, JohansenMA. Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature. J Med Internet Res. 2018;20(5):e10235 66. Bashshur RL, Shannon G, Krupinski EA, Grigsby J. Sustaining and realizing the promise of telemedicine. Telemed J E Health. 2013;19(5):339-45 67. Campos FE, Haddad AE, Wen CL, Alkmim MB. Telessaúde em Apoio à Atenção Primária à Saúde no Brasil. In: Santos AF, et al (Org.). Telessaúde Um Instrumento de Suporte Assistencial e Educação Permanente. Belo Horizonte: UFMG. 2006; p.59-74 68. Brasil. Ministério da Saúde.Internet. [Acesso em 04 abr 2019]. Disponível em: http://portalms.saude.gov.br/sistema-unico-de-saude/ princípios-do-sus. 69. Pan American Health Organization. (PAHO). World Health Day: PAHO calls for equitable access to health care. Internet. [Accessed in 2019 Apr 09]. Available from: https://www.paho.org/ 70. HjelmNM. Benefits and drawbacks of telemedicine. J Telemed Telecare. 2005;11(2):60-70 71. Bashshur RL, Howell JD, Krupinski EA, Harms KM, Bashshur N, Doarn CR. The Empirical Foundations of Telemedicine Interventions in Primary Care. Telemed J E Health. 2016;22(5):342-75 72. Nerlich M, Balas EA, Schall T, Stieglitz SP, Filzmaier R, Asbach P, et al. Teleconsultation practice guidelines: report from G8 Global Health Applications Subproject 4. Telemed J E Health. 2002;8(4):411-8 73. Ohinmaa A, Hailey D, Roine R. Elements for assessment of telemedicine applications. Int J Technol Assess Health Care. 2001;17(2):190-202 74. Alkmim MB, Figueira RM, Marcolino MS, Cardoso CS, Pena de Abreu M, Cunha LR, et al. Improving patient access to specialized health care: the Telehealth Network of Minas Gerais, Brazil. Bull World Health Organ 2012;90(5):373-8 1049

RkJQdWJsaXNoZXIy MjM4Mjg=