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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">The fundamental idea of this paper is to provide an alternative for reducing the time dedicated to the tedious process of writing clinical reports while simultaneously generating added value&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Time is the most valuable asset we have&#44; as it cannot be recovered&#46; Therefore&#44; it is essential to invest time in the best possible way&#46; In modern medicine&#44; with a heavy care burden&#44; we spend a significant part of our time writing clinical reports and filling in databases&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">However&#44; we know that clinical reports per se do not harbor much value&#44; and our &#8216;administrative&#8217; work is filled with inefficiencies by entering the same parameters multiple times &#40;e&#46;g&#46;&#44; patient admission dates in their medical history&#44; RETRAUCI&#44;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> and ENVIN<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a>&#41;&#46; However&#44; records generate value since we manage to group the information&#44; facilitate statistical analysis&#44; and consequently generate knowledge&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">If the previous observation is correct&#44; we should spend more time &#40;being a scarce and finite good&#41; on what brings more value&#44; the registries&#46; However&#44; this would leave us with no time for clinical reports&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Currently&#44; there are natural language models based on artificial intelligence &#40;MLN&#41;&#46; These models could help us save time in writing discharge reports&#44; allowing us to spend time creating quality records in registries&#46; 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&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">The fundamental idea is to introduce copious and quality information into the national registries&#44; export a structured report from national registries&#44; and from there &#8216;predict&#8217; the discharge report through artificial intelligence using MLN&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">What is proposed here is an inversion of medical activity&#46; 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&#44; we export a structured text on which we can apply natural language models to &#39;predict&#39; the clinical report&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">MLNs work with input information &#40;structured report generated from registries&#41;&#44; a determined algorithm&#44; &#8216;prompt&#8217; &#40;which somehow establishes the prediction orders&#44; exposed in supplementary data&#41;&#44; to finally obtain a result&#44; &#39;output&#39; &#40;in our case&#44; the clinical report&#41;&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">To optimize this process&#44; we should work on the following aspects&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">&#8226;</span><p id="par0050" class="elsevierStylePara elsevierViewall">A relatively stable and quality &#8216;input&#8217;&#58; requires structured texts derived from registries&#46; The quality would come from verifying the medical data entered and more granular records that better capture the medical reality&#46; In this regard&#44; work should be done on the &#8216;integration&#8217; of large national records to enhance efficiency in data entry that allows a better linkage of data to the patient&#46; Interoperability between registries would be a desirable goal&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">&#8226;</span><p id="par0055" class="elsevierStylePara elsevierViewall">If we have a stable structured text&#44; we can iteratively improve our &#8216;prompt&#8217; &#40;algorithm&#41;&#44; so we would get an &#8216;output&#8217; that was increasingly similar to the sought clinical report&#46;</p></li></ul></p><p id="par0060" class="elsevierStylePara elsevierViewall">In this regard&#44; we propose different examples in the supplementary data hybridizing information from RETRAUCI and ENVIN &#40;supplementary data&#41;&#46; This approach currently presents limitations that we present in supplementary data&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financing</span><p id="par0065" class="elsevierStylePara elsevierViewall">None&#46;</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&#46;</p></span></span>"
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