Application of Cause-Consequence associative models of occupational accidents in safety in anesthesia

Bona-Gracia, C.; Rubio-García, B.; García-Foncillas-López, R.

doi:10.1016/j.redare.2018.01.011

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"apellidos" => "García-Foncillas-López" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Hospital Universitario Miguel Servet, Zaragoza, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Facultad de Ciencias Sociales y del Trabajo, Universidad de Zaragoza, Zaragoza, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Aplicación de modelos asociativos Causa-Consecuencia de accidentalidad laboral en el estudio de la seguridad en anestesia" ] ] "textoCompleto" => "Patient safety is now the cornerstone of quality of care and encompasses actions aimed at reducing adverse events arising from medical interventions.Although anaesthesiology is regarded as a safe speciality, anaesthesiologist are under no obligation in Spain to report work-related incidents, and no objective, non-biased analyses are carried out. This contrasts with other fields, such as occupational medicine and heath and safety, and prevents us from obtaining an accurate picture of patient safety in our field. Using “big data” analysis techniques, the copious amounts of data from the many thousands of patients treated in Spain can help us detect factors that can contribute towards patient safety.Several safety analysis systems are available in Spain, including the Patient Safety Reporting and Learning System (SiNASP, in its Spanish acronym), developed by the Ministry of Health, Social Services and Equality, and SENSAR, a safety reporting system developed specifically for anaesthesiology-related incidents. The results obtained from the analyses performed by SENSAR are used to create alerts and action protocols to reduce the number and frequency of practice-related incidents. Adverse events are analysed using Root Cause Analysis (ACR) methodology. Although ACR is a highly scientific approach, it focusses more on qualitative analysis. Our system, on the other hand, is based on quantitative analysis.In view of the extensive research into Cause-Consequence associative models in occupational accidents, we hypothesised that these can be used to objectively analyse the risk factors that lead to anaesthesia-related complications. Therefore, we are currently developing a model for the objective Cause-Consequence analysis of safety-related data from anaesthesia procedures based on ACSOM, a mathematical model<a class="elsevierStyleCrossRef" href="#bib0030">1</a> used for the analysis and control of occupational accidents.To validate the mathematical model, we performed an indirect analysis using data collected from the medical records of surgical patients treated at the Miguel Servet University Hospital of Zaragoza, in order to identify Risk Factor (RF) and Complications (C) binomials.The ANESTHSOM methodology is divided into 2 parts; first, we use the same statistical method described in ACSOM, registered as OMEGA®,<a class="elsevierStyleCrossRefs" href="#bib0030">1–4</a> to test these data. This includes different multivariate statistical techniques, such as a preliminary exploratory analysis using Habertman standardised residuals, correspondence analysis, cluster analysis, factorial analysis of major components, etc., and detects associations between risk factors and complications, as well as associations between different risk factors and between different complications. This enables us to identify the priority RF-C associations. In the second part, comparative statistical tests are performed using two hypothesis testing methods (translocation and equality of proportions). Comparing “the pattern” with the data from each centre, both in terms of order and proportion, allows us to identify which RF-C associations occur most frequently and in what order they should be addressed, in other words, the safety weak points, the order of priority, and where more resources are needed to control and reduce these incidents.We are currently developing a user-friendly graphic interface that we call BIOIN that will perform simple, automated comparisons of input data. This graphic interface is designed to test a list of 50 possible causes and 20 consequences, and has been programmed so to perform simple, automated comparisons using data from existing databases and can be used online.<a class="elsevierStyleCrossRef" href="#bib0050">5</a>We are aware that our mathematical model requires external validity. Our experience with the validation of ACSOM, which required data from the International Labor Organization, taught us the importance of using large quantities of data and variables for this purpose. It would be interesting to apply our method to a database large enough to yield relevant results and conclusions, allowing us to analyse accidents within a hospital, a specialty or a specific region. Once validated, we believe that our mathematical model can help identify areas of improvement in patient safety and efficiently allocate economic resources to those with top priority." "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Bona-Gracia C, Rubio-García B, García-Foncillas-López R. Aplicación de modelos asociativos Causa-Consecuencia de accidentalidad laboral en el estudio de la seguridad en anestesia. Rev Esp Anestesiol Reanim. 2018;65:173–174." ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:5 [ 0 => array:3 [ "identificador" => "bib0030" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:3 [ "comentario" => "ISBN: 978-3-659-02770-3" "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "ACSOM. Herramienta para el análisis de la accidentalidad laboral" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "E. Rubio Calvo" 1 => "A.I. García Felipe" 2 => "J.C. Conte" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2012" "editorial" => "Editorial Académica Española" "editorialLocalizacion" => "Saarbrücken, Alemania" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0035" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Análisis de complicaciones en anestesiología y reanimación: ANESTHSOM" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "B. Rubio García" 1 => "A.I. García" 2 => "L. Diaz" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2014" "editorial" => "Editorial Académica Española" "editorialLocalizacion" => "Saarbrücken, Alemania" ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0040" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "OMEGA. Metodología para la previsión del accidente en poblaciones laborales. Aplicación a microempresas" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "J.C. Conte" 1 => "E. Rubio" 2 => "A.I. García" 3 => "A.I. Domínguez" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Med Segur Trab" "fecha" => "2006" "volumen" => "52" "paginaInicial" => "27" "paginaFinal" => "37" ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0045" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "OMEGA. Metodología para la previsión del accidente en poblaciones laborales. Aplicación a pequeñas y grandes empresas" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "J.C. Conte" 1 => "E. Rubio" 2 => "A.I. García" 3 => "A.I. Domínguez" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Md Segur Trab" "fecha" => "2006" "volumen" => "52" "paginaInicial" => "39" "paginaFinal" => "49" ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0050" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:3 [ "comentario" => "[No. asiento registral: 10/2015/228]" "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "BIOIN Delta. Registro de propiedad intelectual" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "E. Rubio" 1 => "A.I. García" 2 => "E. Gracia" 3 => "R. García-Foncillas" 4 => "B. Rubio" 5 => "E.J. Rubio" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:1 [ "fecha" => "2015" ] ] ] ] ] ] ] ] ] ] "agradecimientos" => array:1 [ 0 => array:4 [ "identificador" => "xack340175" "titulo" => "Acknowledgement" "texto" => "We would like to thank Dr. Emilio Ángel Rubio Calvo for his important contributions to this study." 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Revista Española de Anestesiología y Reanimación (REDAR) -the Spanish Journal of Anesthesiology and Resuscitation- is the scientific body of the Spanish Society of Anesthesiology, Resuscitation and Pain Therapy. This is a monthly journal (10 issues) that publishes scientific articles of all areas within its specialty: clinical anesthesia, resuscitation-intensive medicine and critical care, diagnosis and treatment of acute and chronic pain, urgent care and emergencies and studies on basic science and related fields. The journal accepts work in both Spanish and English.

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Vol. 65. Issue 3.

Pages 173-174 (March 2018)

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