# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 1 Introduction
Glyph employs a database of medical terms where each pictogram and its corresponding label are linked to a graphical (atomic or composite) or semantic (disease, device, etc.) category. To accurately identify information related to medicines, Glyph utilizes a database available in DailyMed. Plain-text indications are segmented into semantic units for pictogram classification and labeled in categories that facilitate the concatenation of images (weight, composite or atomic units, frequency of medicine doses, negation, daytime of the dose). To compose the image, the authors use a rule engine based on grammatical patterns obtained from clinical indications of a cardiology unit. Finally, Glyph's performance was evaluated in a study by [PERSON] et al., revealing that participants who received discharge instructions with Glyph-generated pictograms recalled 35% more of their discharge instructions at discharge than those who received standard discharge instructions. Additionally, participants who received discharge instructions with pictograms reported higher satisfaction with their understanding of the instructions one week after discharge compared to those who received standard discharge instructions [18]. In the article by [PERSON] et al., a study is described to test physicians' perception of the worthiness of developing a system for complementing medical indications with pictograms. The authors applied an expert judgment survey to medical professionals in Concepcion, Chile, consisting of 66 questions seeking opinions on whether the medical instruction pictogram pairs were \"unnecessary\", \"useful\", or \"essential\". Results showed that 22 of the pairs of indications-pictograms were considered \"essential\", 11 were considered \"useful\", and none were considered \"unnecessary\" [19]. In [PERSON] 2019 study, the effectiveness of using pictograms to complement medical indications in primary healthcare facilities in Cape Town, South Africa, was examined. The author found that the use of pictograms was beneficial in aiding the understanding of clinical information in low-literate patient populations and across languages. Specifically, the author noted that a large font size, bold text, and white space positively impacted the recognition of important text in the indication. Furthermore, the understanding of medical indications was enhanced with a verbal explanation and when pictograms were designed in collaboration with the target population [20]. On the other hand, [PERSON] et al. present a randomized controlled study conducted in chronic patients in a primary health care center in Thailand, indicating that medication adherence with the use of pictograms was significantly higher than in the control group [21]. In their article, [PERSON] et al. present the findings of a randomized clinical trial conducted in patients with a chronic disease. The study revealed that 77% of participants reported improved understanding of medication instructions with the use of pictograms, 67% preferred pictograms over written instructions for receiving their medication instructions, and 93% felt that pictograms should be utilized on medication labels [22]. In a recent systematic review of the effectiveness of pictograms in healthcare settings, [PERSON] et al. found that all included studies concluded that pictograms had a positive impact on enhancing patient adherence to medical treatment and helped decrease dosing errors. They further found that pictograms had a positive impact on adherence to treatments in patients with low health literacy levels compared to written or oral interventions [23]. In this study, we propose a technological framework to support adherence in asthma patients using pictograms. This framework is called SIMAP (automatic system to complement medical indications with pictograms). SIMAP used a user-centered methodology for the construction and validation of each of its components.