Letter to the Editor
Can we use national registries to predict discharge reports?: An example with the ENVIN and RETRAUCI registries
¿Podemos utilizar los registros nacionales para predecir el informe de alta?: ejemplo con los registros ENVIN y RETRAUCI
Marcos Valiente Fernández
, Amanda Lesmes González de Aledo, Isaías Martín Badía, Francisco de Paula Delgado Moya
Autor para correspondencia
Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, Spain
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Therefore, it is essential to invest time in the best possible way. In modern medicine, with a heavy care burden, we spend a significant part of our time writing clinical reports and filling in databases.</p><p id="par0015" class="elsevierStylePara elsevierViewall">However, we know that clinical reports per se do not harbor much value, and our ‘administrative’ work is filled with inefficiencies by entering the same parameters multiple times (e.g., patient admission dates in their medical history, RETRAUCI,<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> and ENVIN<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a>). However, records generate value since we manage to group the information, facilitate statistical analysis, and consequently generate knowledge.</p><p id="par0020" class="elsevierStylePara elsevierViewall">If the previous observation is correct, we should spend more time (being a scarce and finite good) on what brings more value, the registries. However, this would leave us with no time for clinical reports.</p><p id="par0025" class="elsevierStylePara elsevierViewall">Currently, there are natural language models based on artificial intelligence (MLN). These models could help us save time in writing discharge reports, allowing us to spend time creating quality records in registries. Medical knowledge plays a significant role in the quality of the records as it serves as a verifier of the information entered in difficult-to-classify patients.</p><p id="par0030" class="elsevierStylePara elsevierViewall">The fundamental idea is to introduce copious and quality information into the national registries, export a structured report from national registries, and from there ‘predict’ the discharge report through artificial intelligence using MLN.</p><p id="par0035" class="elsevierStylePara elsevierViewall">What is proposed here is an inversion of medical activity. We propose that medical information shift its priorities and gradually prioritize filling out records - since this is where we condense the value - and from these records, we export a structured text on which we can apply natural language models to 'predict' the clinical report.</p><p id="par0040" class="elsevierStylePara elsevierViewall">MLNs work with input information (structured report generated from registries), a determined algorithm, ‘prompt’ (which somehow establishes the prediction orders, exposed in supplementary data), to finally obtain a result, 'output' (in our case, the clinical report).</p><p id="par0045" class="elsevierStylePara elsevierViewall">To optimize this process, we should work on the following aspects:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">•</span><p id="par0050" class="elsevierStylePara elsevierViewall">A relatively stable and quality ‘input’: requires structured texts derived from registries. The quality would come from verifying the medical data entered and more granular records that better capture the medical reality. In this regard, work should be done on the ‘integration’ of large national records to enhance efficiency in data entry that allows a better linkage of data to the patient. Interoperability between registries would be a desirable goal.</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">•</span><p id="par0055" class="elsevierStylePara elsevierViewall">If we have a stable structured text, we can iteratively improve our ‘prompt’ (algorithm), so we would get an ‘output’ that was increasingly similar to the sought clinical report.</p></li></ul></p><p id="par0060" class="elsevierStylePara elsevierViewall">In this regard, we propose different examples in the supplementary data hybridizing information from RETRAUCI and ENVIN (supplementary data). This approach currently presents limitations that we present in supplementary data.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financing</span><p id="par0065" class="elsevierStylePara elsevierViewall">None.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Conflicts of interest</span><p id="par0070" class="elsevierStylePara elsevierViewall">The authors declare that none have conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Financing" ] 1 => array:2 [ "identificador" => "sec0010" "titulo" => "Conflicts of interest" ] 2 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Valiente Fernández M, Lesmes González de Aledo A, Martín Badía I, de Paula Delgado Moya F. ¿Podemos utilizar los registros nacionales para predecir el informe de alta?: ejemplo con los registros ENVIN y RETRAUCI. 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Registry of trauma in the ICU (RETRAUCI). Pilot phase. Epidemiología del trauma grave en España. REgistro de TRAuma en UCI (RETRAUCI). Fase piloto" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M. Chico-Fernández" 1 => "J.A. Llompart-Pou" 2 => "F. Guerrero-López" 3 => "M. Sánchez-Casado" 4 => "I. García-Sáez" 5 => "M.D. Mayor-García" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medin.2015.07.011" "Revista" => array:7 [ "tituloSerie" => "Med Intensiva." "fecha" => "2016" "volumen" => "40" "numero" => "6" "paginaInicial" => "327" "paginaFinal" => "347" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/26440993" "web" => "Medline" ] ] ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Characteristics and outcomes of patients admitted to Spanish ICU: a prospective observational study from the ENVIN-HELICS registry (2006-2011)" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "P.M. Olaechea" 1 => "F. Álvarez-Lerma" 2 => "M. Palomar" 3 => "R. Gimeno" 4 => "M.P. Gracia" 5 => "N. Mas" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medin.2015.07.003" "Revista" => array:7 [ "tituloSerie" => "Med Intensiva." 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