A Technological Framework to Support Asthma Patient Adherence Using Pictograms
2024
Artículo
en
ISSN-2076-3417
Notas
Indexación: Scopus.
Resumen
Background: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health.
Citación
Applied Sciences (Switzerland), Volume 14, Issue 15, August 2024, Article number 6410
Editor
Multidisciplinary Digital Publishing Institute (MDPI)
Mas detalles del registro
Agradecimientos
This research was funded by Universidad de Concepción grant number 219.092.053-M, FONDEF, ANID (National Research and Development Agency, Chile, Funder URL https://anid.cl/, accessed on 17 July 2024), grant number ID19I10120 (RLFI, CT, ML, CR, JG, EP, JN, JP, FM, GN), the National Center on Health Information Systems (CTI230006 CENS), and the Millennium Nucleus on Sociomedicine, grant ANID–MILENIO–NCS2021_013 (CT, RLFI, ML). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments: The authors would like to thank the Universidad de Concepción, FONDEF, the National Center on Health Information Systems, and the Millennium Nucleus on Sociomedicine for supporting the authors of this work. The authors would like to thank the health management boards and family health centers participating in this study in Valparaíso, San Antonio, Hualpén y Talcahuano. This recognition extends particularly to the health teams dealing with asthma respiratory units (Salas ERA)
Folio/s de proyecto/s
FONDEF ANID grant number (19I10120)
Millennium Nucleus on Sociomedicine, grant ANID–MILENIO (NCS2021_013)
Millennium Nucleus on Sociomedicine, grant ANID–MILENIO (NCS2021_013)
Licencia CC
CC BY 4.0 Attribution 4.0 International
Licencia CC
https://creativecommons.org/licenses/by/4.0/