Journal Information
Vol. 47. Issue 11.
Pages 675-676 (November 2023)
Share
Share
Download PDF
More article options
Vol. 47. Issue 11.
Pages 675-676 (November 2023)
Letter to the Editor
Full text access
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
Visits
546
Marcos Valiente Fernández
Corresponding author
mvalientefernandez@gmail.com

Corresponding author.
, Amanda Lesmes González de Aledo, Isaías Martín Badía, Francisco de Paula Delgado Moya
Servicio de Medicina Intensiva, Hospital Universitario 12 de Octubre, Madrid, Spain
This item has received
Article information
Full Text
Bibliography
Download PDF
Statistics
Additional material (3)
Full Text

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.

Time is the most valuable asset we have, as it cannot be recovered. 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.

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,1 and ENVIN2). However, records generate value since we manage to group the information, facilitate statistical analysis, and consequently generate knowledge.

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.

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.

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.

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.

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).

To optimize this process, we should work on the following aspects:

  • 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.

  • 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.

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.

Financing

None.

Conflicts of interest

The authors declare that none have conflicts of interest.

Appendix A
Supplementary data

The following are Supplementary data to this article:

References
[1]
M. Chico-Fernández, J.A. Llompart-Pou, F. Guerrero-López, M. Sánchez-Casado, I. García-Sáez, M.D. Mayor-García, et al.
Epidemiology of severe trauma in Spain. Registry of trauma in the ICU (RETRAUCI). Pilot phase. Epidemiología del trauma grave en España. REgistro de TRAuma en UCI (RETRAUCI). Fase piloto.
Med Intensiva., 40 (2016), pp. 327-347
[2]
P.M. Olaechea, F. Álvarez-Lerma, M. Palomar, R. Gimeno, M.P. Gracia, N. Mas, et al.
Characteristics and outcomes of patients admitted to Spanish ICU: a prospective observational study from the ENVIN-HELICS registry (2006-2011).
Med Intensiva., 40 (2016), pp. 216-229
Copyright © 2023. Elsevier España, S.L.U. and SEMICYUC
Idiomas
Medicina Intensiva (English Edition)
Article options
Tools
Supplemental materials
es en

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?