Semi-automated data collection from electronic health records in a stroke unit in Brazil

ABSTRACT

Background: There is a high demand for stroke patient data in the public health systems of middle and low-income countries. Objective: To develop a stroke databank for integrating clinical or functional data and benchmarks from stroke patients. Methods: This was an observational, cross-sectional, prospective study. A tool was developed to collect all clinical data during hospitalizations due to stroke, using an electronic editor of structured forms that was integrated with electronic medical records. Validation of fields in the electronic editor was programmed using a structured query language (SQL). To store the results from SQL, a virtual table was created and programmed to update daily. To develop an interface between the data and user, the Embarcadero Delphi software and the DevExpress component were used to generate the information displayed on the screen. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, including patients who were admitted to the stroke unit. Results: The database was created and integrated with the hospital electronic system, thus allowing daily data collection. Quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers, which resulted in improved processes and patient care after a stroke. An intelligent portal was created, in which the information referring to the patients was accessible. Conclusions: Based on semi-automated data collection, it was possible to create a dynamic and optimized Brazilian stroke databank.

RESUMO

Antecedentes: Há alta demanda de dados de pacientes com acidente vascular cerebral (AVC) nos sistemas de saúde de países de baixa e média renda. Objetivo: Desenvolver um banco de dados de AVC para integrar dados clínicos ou funcionais e indicadores de qualidade de pacientes com AVC. Métodos: Estudo observacional, transversal e prospectivo. Foi desenvolvida uma ferramenta para coletar dados clínicos durante as internações por AVC por meio de um editor eletrônico de formulários estruturados integrado ao prontuário eletrônico. A validação dos campos no editor eletrônico foi programada em linguagem de consulta estruturada (SQL). Para armazenar os resultados da SQL, uma tabela virtual foi criada e programada para atualização diária. Para desenvolver interface entre os dados e o usuário, foram utilizados o software Embarcadero Delphi e o componente DevExpress para gerar informações apresentadas na tela. Os dados foram extraídos dos campos do formulário e também do cruzamento de outras informações do sistema informatizado, incluindo pacientes internados na unidade de AVC. Resultados: O banco de dados foi criado e integrado ao sistema eletrônico do hospital, permitindo coleta diária de dados. Indicadores de qualidade foram criados no banco de dados para que o sistema acompanhasse e realizasse a tomada de decisão com os gestores dos serviços de saúde, resultando em melhoria no processo e no atendimento ao paciente após AVC. Foi criado um portal inteligente, no qual eram registradas as informações referentes aos pacientes. Conclusões: Com a coleta de dados semiautomática, foi possível criar um banco de dados de AVC dinâmico e otimizado em unidade de AVC no Brasil.

Keywords:

Stroke - Benchmarking - Artificial Intelligence - Supervised Machine Learning - Emergency Service - Hospital

Palavras-chave:

Acidente Vascular Cerebral - Benchmarking - Inteligência Artificial - Aprendizado de Máquina Supervisionado - Serviço Hospitalar de Emergência

Authors’ contributions:

GJL, CCMF, RCO, RB: conceptualization, data curation, formal analysis, writing – original draft preparation, project administration, supervision, writing – review & editing; RFZV, JTS, FCW, GPM, NCF: investigation, methodology; RB, SGZB, MCL, SARP: supervision, validation, visualization, roles/writing – original draft, writing – review.

Publication History

Received: 22 December 2020

Accepted: 21 February 2021

Article published online:
30 January 2023

• References

• 1 Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden and mortality: estimates from monitoring, surveillance, and modelling. Lancet Neurol 2009; 04 8 (04) 345-354 https://orcid.org/10.1016/S1474-4422(09)70023-7
• 2 Grysiewicz RA, Thomas K, Pandey DK. Epidemiology of ischemic and hemorrhagic stroke: incidence, prevalence, mortality, and risk factors. Neurol Clin 2008; 11 26 (04) 871-895 https://orcid.org/10.1016/j.ncl.2008.07.003
• 3 Feigin VL, Lawes CM, Bennett DA, Barker-Collo SL, Parag V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol 2009; 04 8 (04) 355-369 https://orcid.org/10.1016/S1474-4422(09)70025-0
• 4 Bronstein K, Murray P, Licata-Gehr E, Banko MA, Kelly-Hayes M, Fast S. et al. The Stroke Data Bank project: implications for nursing research. J Neurosci Nurs 1986; 06 18 (03) 132-134 https://orcid.org/10.1097/01376517-198606000-00005
• 5 Kunitz SC, Gross CR, Heyman A, Kase CS, Mohr JP, Price TR. et al. The Pilot stroke data bank: definition, design, and data. Stroke Jul-Aug 1984; 15 (04) 740-746 https://orcid.org/10.1161/01.str.15.4.740
• 6 Foulkes MA, Wolf PA, Price TR, Mohr JP, Hier DB. The Stroke Data Bank: design, methods, and baseline characteristics. Stroke 1988; 05 19 (05) 547-554 https://orcid.org/10.1161/01.str.19.5.547
• 7 Ahmed N, Wahlgren N, Grond M, Hennerici M, Lees KR, Mikulik R. et al. Implementation and outcome of thrombolysis with alteplase 3–4.5 h after an acute stroke: an updated analysis from SITS-ISTR. Lancet Neurol 2010; 09 9 (09) 866-874 https://orcid.org/10.1016/S1474-4422(10)70165-4
• 8 Wahlgren N, Ahmed N, Davalos A, Ford GA, Grond M, Hacke W. et al. Thrombolysis with alteplase for acute ischaemic stroke in the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST): an observational study. Lancet 01/2007; 369 9558 275-282 https://orcid.org/10.1016/S0140-6736(07)60149-4
• 9 Hinchey JA, Shephard T, Tonn ST, Ruthazer R, Selker HP, Kent DM. Benchmarks and determinants of adherence to stroke performance measures. Stroke 2008; 05 39 (05) 1619-1620 https://orcid.org/10.1161/STROKEAHA.107.496570
• 10 Rossaneis MA, Gabriel CS, Haddad MCFL, Melo MRAC, Bernardes A. Knowledge on health indicators by nurses in hospitalization unities. Rev Eletr Enf 2014; 12 16 (04) 769-776 http://doi.org/10.5216/ree.v16i4.22956
• 11 Cavalcante RB, Cunha SGS, Bernardes MFVG, Gontijo TL, Guimarães EAA, Oliveira VC. Hospital Information System: use in decision making. Journal of Health Informatics 2012; 4: 73-79
• 12 Patrício CM, Maia MM, Machiavelli JL, Navaes AN. O prontuário eletrônico do paciente no sistema de saúde brasileiro: uma realidade para os médicos. Sci Med 2011; 21 (03) 121-131
• 13 Brazil - Ministério da Saúde Portaria nº665. 12 04 2012 accessed on Nov 10, 2020 Available at: https://bvsms.saude.gov.br/bvs/saudelegis/gm/2012/PRT0665_12_04_2012.html
• 14 Nemati HR, Steiger DM, Iyer LS, Herschel RT. Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis Support Syst 2002; 06 33 (02) 14361-14361 https://doi.org/10.1016/S0167-9236(01)00141-5
• 15 Uraikul VU, Chan CW, Tontiwachwuthikul P. Artificial intelligence for monitoring and supervisory control of process systems. Eng Appl Artif Intell 2007; 03 20 (02) 115-131 https://doi.org/10.1016/j.engappai.2006.07.002
• 16 Project Management Institute. Learn about PMI. accessed on June 06, 2019 Available at: https://www.pmi.org/about/learn-about-pmi
• 17 Project Management Institute. Guia do Conhecimento em Gerenciamento de Projetos Guia PMBOK. 6th ed.. Newtown Square, PA: Project Management Institute; 2017
• 18 Araújo CC, Oliveira CA, Cruz C, Daniel D, Souza J, Caserta M. Gerenciamento de Banco de Dados: Análise Comparativa de SGBD’S. accessed on Aug 15, 2019 Available at: https://www.devmedia.com.br/gerenciamento-de-banco-de-dados-analise-comparativa-de-sgbd-s/30788
• 19 Rolim CLRC, Martins M. Quality of care for ischemic stroke in the Brazilian Unified National Health System. Cad Saúde Pública 2011; 11 27 (11) 2106-2116 https://doi.org/10.1590/S0102-311X2011001100004
• 20 Rolim CLRC, Martins M. Computerized tomography utilization for stroke inpatients in the Brazilian Health System. Rev Bras Epidemiol 2012; 03 15 (01) 179-187 https://doi.org/10.1590/S1415-790X2012000100016
• 21 Araújo JP, Darcis JVV, Tomas ACV, Mello WA. Mortality trend due to cerebrovascular accident in the City of Maringá, Paraná between the years of 2005 to 2015. Int J Cardiovasc Sci 2018; 02 3 (01) 56-62 https://doi.org/10.5935/2359-4802.20170097
• 22 Zétola VHF, Nóvak EM, Camargo CHF, Júnior HC, Coral P, Muzzio JA. et al. Acidente vascular cerebral em pacientes jovens: análise de 164 casos. Arq Neuro-Psiquiatr 2001; 09 59 3B 740-745 https://doi.org/10.1590/S0004-282X2001000500017
• 23 Martins SCO, Sacks C, Hacke W, Brainin M, Figueiredo FA, Pontes Neto OM. et al. Priorities to reduce the burden of stroke in Latin American countries. Lancet Neurol 2019; 07 18 (07) 674-683 https://doi.org/10.1016/S1474-4422(19)30068-7
• 24 Cabral NL, Freire AT, Conforto AB, Dos Santos N, Reis FI, Nagel V. et al. Increase of stroke incidence in young adults in a middle-income country: a 10-year population-based study. Stroke 2017; 11 48 (11) 2925-2930 https://doi.org/10.1161/STROKEAHA.117.018531
• 25 Rede Brasil AVC. Rede Brasil AVC. accessed on Jan 25, 2020 Available at: http://www.redebrasilavc.org.br/para-profissionais-de-saude/o-que-e-registro-sits/
• 26 Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2014; 02 2: 3-3 https://doi.org/10.1186/2047-2501-2-3
• 27 Wang L, Alexander CA. Stroke care and the role of big data in healthcare and stroke. Rehabil Sci 2016; 11 1 (01) 16-24 https://doi.org/10.11648/j.rs.20160101.13