Pre-systematic review on software tools to evaluate

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CENTERIS - International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies, CENTERIS - International Conference on ENTERprise CENTERIS/ProjMAN/HCist 2018 Information Systems / ProjMAN International Conference on Project MANagement / HCist - International CENTERIS - International Conference on ENTERprise Information Systems / Conference on Health and Social Care Information Systems and Technologies, ProjMAN International Conference on Project MANagement / HCist International Pre-systematic reviewCENTERIS/ProjMAN/HCist on software tools to2018 evaluate package inserts Conference on Health and Social Care Information Systems and Technologies, of medicines as prescription information CENTERIS/ProjMAN/HCist 2018

a

Pre-systematic review on software tools to evaluate package inserts a, b Carla Pires *, Afonso Pre-systematicofreview on software toolsCavaco toinformation evaluate package inserts medicines as prescription CBIOS - Universidade Lusófona's Research Center for Biosciences and Health Technologies, Campo Grande, 376, 1749-024, Lisboa, Portugal of medicines as prescription information Faculty of Pharmacy, University of Lisbon, Lisboa, Portugal a, Av. Prof. Gama Pinto, 1649-003 b b

a

Carla Pires *, Afonso Cavaco b Carla Piresa,*,andAfonso CavacoCampo CBIOS - Universidade Lusófona's Research Center for Biosciences Health Technologies, Grande, 376, 1749-024, Lisboa, Portugal

Abstract

b Faculty of Pharmacy, University of Lisbon, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal CBIOS - Universidade Lusófona's Research Center for Biosciences and Health Technologies, Campo Grande, 376, 1749-024, Lisboa, Portugal b of Pharmacy, of Lisbon, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal Information on medicalFaculty prescription, suchUniversity as the one included in package leaflets of medicines (PLs) should benefit from a

Information Technology (IT) management resources. Objectives: to identify and evaluate studies describing prescription Abstract information, and software capable of analyzing, processing, extracting, or simplifying the information included in PLs. Methods: Abstract PRISMA checklist was prescription, applied, using (“package “package leaflets” (PLs) or “summary of product Information on medical suchas askeywords: the one included in inserts” package or leaflets of medicines should benefit from characteristics” or “bulas” or “folhetos” or “prescription information”) and “software”; bulas/folhetos are the Portuguese Information Technology (IT) management resources. Objectives: to identify and evaluate studies describing prescription Information medical prescription, such asinthe one included in Library, package leaflets of information medicines (PLs) should benefit from designations of PLs. Keywords were PubMed, Cochrane and SciELO without time restrictions in June 2018. information, on and software capable of searched analyzing, processing, extracting, or simplifying the included in PLs. Methods: Information Technology (IT) management resources. Objectives: to identify and evaluate studies describing prescription A complementary carried out as in Google Scholar (2016-2018). 219 studies were or identified and of 12 product met the PRISMA checklistsearch was was applied, using keywords: (“package inserts”Results: or “package leaflets” “summary information, andor software of analyzing, extracting, simplifying the information included PLs.Portuguese Methods: inclusion criteria. From thecapable 12 studies, 3processing, studies were related toorsoftware applications that evaluate text and the characteristics” “bulas” or selected “folhetos” or “prescription information”) and “software”; bulas/folhetos areinfeatures the PRISMA checklist was applied, using as keywords: (“package inserts” or “package leaflets” or “summary of product remaining 9 studies were related to other topics, such as extraction and collection of information. Conclusion: software that designations of PLs. Keywords were searched in PubMed, Cochrane Library, and SciELO without time restrictions in Juneextract 2018. characteristics” or search “bulas” or carried “folhetos” “prescription information”) andspecific “software”; bulas/folhetos are development the Portuguese and analyze information ofwas PLs, or thatout evaluate and simplify is limited and certain languages. The of A complementary inorGoogle ScholarPLs (2016-2018). Results: 219tostudies were identified and 12 met the designations of Keywords were searched in3PubMed, Cochrane and SciELO without restrictions in Juneand 2018. new IT tools in PLs. thisFrom field may relevant, especially for local cultures and contexts. inclusion criteria. the 12beselected studies, studies were relatedLibrary, to software applications thattime evaluate text features the A complementary was carried outtopics, in Google Scholar (2016-2018). Results: 219 studiesConclusion: were identified and that 12 met the remaining 9 studiessearch were related to other such as extraction and collection of information. software extract © 2018 The Authors. Published by Elsevier Ltd.3 studies were related to software applications that evaluate text features and the inclusion criteria. From the 12 selected studies, Keywords: Package leafleats of medicines; Package inserts; Software tools; Bulas; Folhetos de medicamentos and information of PLs, or that and simplify PLs is limited and specific to certain languages. The development of Thisanalyze is an open access article under the evaluate CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) remaining 9 studies were related other topics, such for as extraction and and collection of information. Conclusion: software that extract new IT tools inpeer-review this field may be to relevant, especially local cultures contexts. Selection and under responsibility of the scientific the CENTERIS - International Conference on and analyze information of PLs, or that evaluate and simplify PLs iscommittee limited andofspecific to certain languages. The development of ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference new IT tools in this field may be relevant, especially for local cultures and contexts. Keywords: leafleats medicines; Package inserts; Software tools; Bulas; Folhetos de medicamentos on Health Package and Social CareofInformation Systems and Technologies. Keywords: Package leafleats of medicines; Package inserts; Software tools; Bulas; Folhetos de medicamentos

* Corresponding author. Tel.: 00351 21 751 5500; fax: 00351 21 757 7006. E-mail address: [email protected] 1877-0509 © 2018 author. The Authors. Published by5500; Elsevier * Corresponding Tel.: 00351 21 751 fax:Ltd. 00351 21 757 7006. ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) address: [email protected] Selection and peer-review responsibility of thefax: scientific of the CENTERIS - International Conference on ENTERprise * Corresponding author. under Tel.: 00351 21 751 5500; 00351committee 21 757 7006. Information [email protected] / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social E-mail address: 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. Care Information Systems and Technologies. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © © 2018The TheAuthors. Authors. Published by Elsevier 1877-0509 Published by Elsevier Ltd. Ltd. Selection and 2018 peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access/ ProjMAN article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Information Systems on Project MANagement HCist - International Conference on Health Social Selection and peer-review under- International responsibilityConference of the scientific committee of the /CENTERIS - International Conference onand ENTERprise Selection and peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise Care Information Systems and Technologies. Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Information / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Social CareSystems Information Systems and Technologies. Care Information Systems and Technologies. 10.1016/j.procs.2018.10.025

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Carla Pires et al. / Procedia Computer Science 138 (2018) 177–184 Caarla Pires et al. / Procedia Computer Science 00 (2018) 000–000

1. Introduction In the European Union (EU), package leaflets (PLs) of medicines follow a specific format, namely containing information on the indication and use of a certain medicine [1]. PLs are available for millions of patients, since they are mandatory inside all the packages of medicines in the EU [2]. PLs are described as long and complex prescription-related documents [3-5]. In accordance to recent studies, PLs comprise many abbreviations and technical words, and are heterogeneously organized, regarding typographical features [6-7]. For instance, in a Portuguese study, involving 503 subjects from two Portuguese regions, only 41 (8%) participants declared to never read PLs. Importantly, study participants have revealed significant comprehension constrains on the understanding of a set of PLs, including the more literate ones in some cases [8, 9]. The most significant predictors for improper subjects' understanding were low level of education and literacy [8-10]. Considering the results of the last Portuguese Census (2011), from the Portuguese resident population, only 12% have frequented a higher education institution [11]. Also, in 2012, limited health literacy was found in 72.9% of 1544 Portuguese-speaking residents, with the older and less educated participants significantly showing limited health literacy, consequently confirming subjects’ difficulty in understanding health-related documents [12]. Text simplifications of prescription-related documents, such as PLs are especially noteworthy to assure the comprehension of these documents by low literate and educated subjects [4, 8, 9, 13]. Interestingly, studies describing the use of software tools to simplify PLs are scarce [4, 6]. PLs comprise relevant and extensive prescription information for patients, such as information on medicine dosage, contraindications, adverse drug reactions, etc. Thus, IT applications should be used in the management of healthrelated content, knowing software ability to automatically analyze and process large amounts of information [1,2,6]. For instance, automatic evaluations of prescription information from the national health systems, including hospitals, would be valuable to assure and monitor patients' safety and therapy efficacy [14-16]. The development of software tools to consult, analyse, simplify, or display information is especially pertinent, since most users of medicines have an easy access to this complex information in internet and m-health solutions. On the other hand, subjects' comprehension of PLs may depend on diverse factors, such as content, layout, design of PLs or the displaying method of information [14, 15]. Consequently, the development of software tools may be important to handle and analyze PLs from the large quantities of medicines in the market. The development of suitable software and data banks has shown their usefulness; for instance, computerized methods have significantly contributed to prevent risk and to assure patient safety [16]. Healthcare practitioners have considered medicine leaflets as an important source of information. These professionals have indicated that PLs should comprise hyperlinks functions, be automatically updated and integrated in software to support daily practice [10, 17]. There are some public initiatives to assure the electronic publication of PLs in apps or portals, for instance from the Portuguese and European Medicines agencies. In opposition, it was not found divulged information by medicine agencies on the automatic management or simplification of PLs to facilitate readers’ consultation or understanding of these documents [18-20]. In this context, the goals of the present review were to identify and analyze available studies related to any type of software or program application capable of analyzing, simplifying, processing, or extracting the information from PLs of medicines, including prescription information. 2. Methods The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) describes a minimum set of items for reporting systematic reviews.† The PRISMA Checklist was applied to confirm the correctness of the structure of our initial or pre-systematic review.‡

† ‡

The PRISMA website is available at: http://www.prisma-statement.org/. The PRISMA Checklist may be accessed at: http://prismastatement.org/PRISMAStatement/Checklist.aspx.

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Inclusion criteria All the studies describing originally developed software tools to manage prescription information, namely to analyze, simplify, process, and/or extract the information from PLs were selected. The remaining studies were excluded. Databases The studies fulfilling the inclusion criteria were identified in PubMed, Cochrane Library, and SciELO without time restrictions. A complementary search was carried out in Academic Google; only studies published between 2016 and 23-6-2018were identified due time constrains and the limited number of words for publication. PubMed database was selected to carry out this initial search, since it includes more than 28 million references, including MEDLINE and online books in the topic area of drugs and medicines. § The National Center for Biotechnology Information (NCBI) is responsible for developing and maintaining PubMed. The Cochrane Library was selected because it is composed of a collection of databases in the health care domain, namely Cochrane Reviews, which is a database of systemic review and meta-analyses. These collections were considered especially essential to identify revisions on the same topics of the present work. The electronic library SciELO, covering a selected collection of more than three hundred Brazilian scientific journals, was included to screen other works in Portuguese. Search terms In PubMed, Cochrane Library, and SciELO the searched terms were “package inserts and software”, “package leaflets and software”, “summary of product characteristics and software”, and “prescription information and software”. Considering the number of documents in Google Scholar could be too vast, the searched terms were “package inserts and software tools”, “package leaflets and software tools”, “summary of product characteristics and software tools”, and “prescription information and software tools” to obtain intelligible search criteria and limit the number of records extracted. The terms “bulas and software” and “folhetos and software” were screened in all selected databases to assure the inclusion of Portuguese research, despite keywords in English are usually applied; bulas or folhetos are the Portuguese designation for PLs. The term “prescription information” was selected because this type of information contributes to assure patients' safety and therapy efficacy [14-16]. The terms “package inserts” and “package leaflets” were selected since they are widely used by the European Medicines Agency and the Food and Drug Administration to nominate medicine information.** The term “software” was selected, since this is a worldwide designation of a program application, or "the programs used to direct the operation of a computer, as well as documentation giving instructions on how to use them". †† The term “summary of product characteristics” (SPC) was selected, because SPCs provide medicine information for health care professionals; SPCs are more complete informative sources than PLs; and PLs are developed based on the information of SPCs. In this sense, it was hypothesized that software applications to process or analyze SPCs are likely to be applied to other type of documents, such as PLs [21]. The search was carried out during June 2018, and the last results collected in 23-06-2018. The reference management software EndNote Web from Clarivate Analytics was used to identify repeated studies. ‡‡ 3. Results In PubMed the search retrieved (number of results per keywords): 111 “package inserts and software”, 11 “package leaflets and software”; 16 “summary of product characteristics and software”, 2 “bulas and software”, and 0 “folhetos and software” (i.e. a total of 140 records). Only three studies were repeated, 130 were excluded because they did not

PubMed database may be accessed at: https://www.ncbi.nlm.nih.gov/pubmed/. Examples of these documents may be accessed at, respectively: http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/landing/epar_search.jsp&mid=WC0b01ac058001d124, and https://www.accessdata.fda.gov/scripts/cder/daf/ †† http://www.dictionary.com/browse/software ‡‡ Endnote Web is available at: http://www.myendnoteweb.com/EndNoteWeb.html. §

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comply with the inclusion criteria, and 7 were selected. In Cochrane Library the search retrieved a total of 11 results (number of results per keywords): 6 “Package insert and software”, 2 “Package leaflet and software”, 3 “summary of product characteristics and software”, 0 “bulas and software”, and 0 “folhetos and software” (none result repeated). In SciELO the search retrieved a total of 3 results (number of results per keywords): 1 “package insert and software”, 0 “package leaflet and software”, 0 “summary of product characteristics and software”, 1 “bulas and software”, and 1 “folhetos and software” (one result repeated). Regarding the term “prescription information and software” 0 results were retrieved in PubMed, ScieELO, and Cochrane Library. In these 3 databases all studies have been identified without time restrictions. All the studies identified in Cochrane Library and SciELO were excluded, because they did not comply with the inclusion criteria. In Google Scholar search retrieved (number of results per keywords): 18 “package inserts and software tools” (2 selected studies); 5 “package leaflets and software tools” (0 selected); 14 “summary of product characteristics and software tools” (1 selected study), 7 "bulas/folhetos and software tools" (0 selected), and 21 "prescription information and software tools" (2 selected studies) Overall, 65 studies were identified in Google Scholar: 1 repeated, 5 selected, and 59 excluded; studies published between 2016 to 23 June 2018. The global results are presented in Figure 1 using the recommended PRISMA 2009 flow diagram. In Table 1, the 7 selected studies (journal articles) are qualitatively described, such as objectives or functions of the software. All selected articles were written in English. Thus, the translation of the papers was not required. It was not possible to locate other types of publications in PubMed, such as books or abstracts.

Records identified through database searching (n = 140+11+3+65=219)

Records after duplicates removed (n =3+1+1=5)

Records screened (n = 214)

Records excluded (n = 130+11+2+59=202)

Full-text articles assessed for eligibility (n = 12)

Studies included in qualitative synthesis (n = 12)

Fig. 1. PRISMA 2009 flow diagram to illustrate the identification of studies describing any type of software to evaluate PLs


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Table 1. Selected studies: objectives, functions of the software and main conclusions Authors

Year

Objectives

Software functions

Main conclusions

Poleksic, Turner, Dalal, et al [22]

2017

A computational procedure for extracting information about drug adverse reactions

To extract drug adverse reaction frequency from reports of the Federal Administration database

Data are susceptible of being organized by population (e.g. gender) or therapy features (e.g. dosage)

Banpatte, Shinde, Patil, et al [23]

2017

To develop a Drug Discover system (a database application)

To inform of adverse drug reactions and suggest medicines by types of diseases

The complexity of the database was simplified by combining 5 repositories into 2 main databases

Novais [24]

2017

To predict Adverse Effects of Drugs

Two experiments applying data mining techniques and algorithms

Data Mining tools may be useful to predict drug adverse reactions

Mukherjee [25]

2017

To propose an Android application

To send medication reminders (e.g. scheduled), provide information on medicines or alerts if medications have been exposed to high temperatures, and check adherence

An android application with multiple applications have been purposed.

Hadle, Kessler, Litzenberg, et al [26]

2016

To assure a proper quality assurance of the radiotherapy process

Automation of data transfer, and automatic checking, validating, and selecting of data

The automatic workflow of quality assurance tasks was facilitated

Pfistermeister, Sedlmayr, Patapovas, et al [27]

2016

To develop a new standardized rating tool of system-generated drug alerts; the prototype have used summary of product characteristics for alert generation

To develop and to apply a standardized tool that allows its users to evaluate the quality of system-generated drug alerts

Physicians have considered most alerts formally correct but clinically irrelevant; this methodology may contribute to increase the specificity of clinical decision support systems

Lamy, Ugon, Berthelot [28]

2016

Automatic extraction of adverse events from summary of product characteristics; this methodology may be useful to build drug databases

Natural language processing and table parsing methods were setup for extracting adverse events in 10 summaries of product characteristics

Tables are preferred than text to present adverse events in summaries of product characteristics, regarding the extraction of adverse events

Iordatii, Venot, Duclos [15]

2015

To design a interface, which allows physicians to develop a rapid understanding of the value of a new drug for their current activities; analyze of information on 5 medicines

Tables were used to compare properties of the new pharmaceutical product with the existing ones, for instance efficacy, safety and ease of use by indication

The interface provides structured information to physicians

Renahy, Vuitton, Rath B., et al*[29]

2015

To analyze documents relating to vaccination, including PLs of an anti-H1N1 influenza vaccine

To support the writing of documents about vaccines using controlled language

Language issues were identified in PLs such as, technical terms, redundancy, incorrect use of passive voice, and ambiguous synonyms

Pires, Cavaco, Martins, and Vigário* [6]

2015

To quantify the average number of words and the most frequent abbreviations in a large set of package inserts; longer PLs and abbreviations are likely to reduce the readability of PL

To automatically count the number of words and abbreviations in each sampled PLs

Software tools may be used to improve the readability of PLs

Elkin, Carter, Nabar, et al.* [30]

2011

Codified data was computationally extracted

To create semantic triplets (or codification of semantic data using three identities)

The text density of medicine labels is substantial, and it possible to create simpler and accessible information to


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SNOMED CT™ §§ from the sections of drug labels. Duclos, Cartolano, Ghez, et al. [31]

2004

To automatically construct an Antibiotic spectra and resistance informative base concerning prevalence are displayed using the pharmacodynamic colour codes to identify properties of antibiotics and a susceptibility differences visualization tool * Studies specifically describing software tools for analyzing and optimizing text features of PLs.

computer programs (e.g. to support improved care) An overview of the prevalence of resistance as expressed in summary of product characteristics is presented

4. Discussion The first identified study was published in 2004, followed by an augmented number of publications after 2014, which may reflect an increasing demand for informatics tools to manage PLs information. From the twelve identified studies in the present review [6, 15, 27-31], software tools for analysing and/or optimizing text features of PLs written in English are specifically described in three studies [6, 29, 30]. The conclusions of these studies pointed PLs as complex and long documents due the prevalence of too many specialized terms, abbreviations and redundancies (repeated information), as well grammar issues. Overall, these software tools seemed to be appropriate to support the writing and optimization of PLs, helping the development of more readable and comprehensible documents. Naturally, they may be applied to support the intelligible development of other health-related written documents. The remaining nine studies comprised software applications with other purposes, such as: to rate the quality of information [27]; to extract adverse events with the aim of building drug databases or to predict adverse effects of drugs (e.g. using data mining techniques and algorithms) [22-24, 28]; to suggest medicines for treating certain diseases [23]; to purpose an android application [25]; to assure quality assurance in a radiotherapy process [26]; to design interfaces with the objective of quickly understanding new information on medicines, as well to compare the identified information with the existing one [15]; or to display information of antibiotic spectra and resistance prevalence with colour codes with the aim of classifying susceptibility differences [30]. Moreover, software tools to specifically carry out risk analyses based on specific algorithms and patients’ health data, the possibility of automatically improve the lay-out, figures or tables of PLs, or the use of these types of software are not described by medicine agencies [1, 4, 7, 16]. Besides the initiatives taken by academies or the private sector, medicine agencies should be particularly aware of the importance of developing software tools to evaluate and process large amounts of health-related information, instead of only providing portals or other resources (e.g. apps) for online presentation of PLs [18-20]. Additional computer-based resources e.g. dedicated search algorithms could be a good opportunity to extract and compare contents, to detect errors, or to improve patients' comprehension. Generally, the identified number of software tools to analyse or process PLs was scarce and specific of certain languages, such as English. Thus, the development of new tools in this field is required to increase the intelligibility and consultation of PLs by patients, providers, and healthcare professionals, since they may not have a good English proficiency. The application of software tools to evaluate, process or optimize PLs may be of especial importance at a National level, given Portuguese population still has limited education and literacy [11, 12]. It seems the screened databases were appropriately selected: PubMed is a large database comprising diverse specialized journals in software and informatics, which turns present results in an indicator of scarce research in this area. Also, Cochrane Library, SciELO, and Google Scholar comprise diverse collections and journals, thus constituting good options to identify key information on the searched topic. Google Scholar was useful to find nonindexed publications, such as thesis, although only two were identified in the present study [24, 25].

SNOMED CT™ is computer collection of medical terms, which may be processed. Information available at: https://www.snomed.org/snomedct/what-is-snomed-ct/history-of-snomed-ct.

§§


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4.1 Indications for future research Specific informatics tools are necessary, since the linguistic features of texts depend on the type of language. For instance, linguistically there are many other aspects that need to be evaluated in texts, namely text of PLs, such as phonological, morphologic, grammatical, or semantic issues [32]. Studies involving artificial intelligence, patients’ friendly interfaces, hyperlinked information between PLs and internet relevant sites, and apps to dynamically consult PLs are lacking. The automatic standardization of layout, graphic aspect and pictures are necessary to assure the intelligibility of PLs. Software applications that specifically analyse precautions/contraindications of medicines or cautions in especial groups (e.g. paediatric, pregnancy, or geriatric) are needed. Also, software tools to manage healthrelated information and information flow process in pharmacies, hospitals, or nurseries are lacking. Finally, usability studies enrolling health professional, or patients in the development of informatic tools are recommended. 4.2 Limitations The number of selected studies may have been reduced due a limited number of searched databases and terms. Only a limited interval of time was considered in Google Scholar due time constrains, as well as, the limited number of words for publication. A future review on the present topic may include more search terms, such as “consumer medicine information”, “medication guides”, “instructions for use”, “European public assessment reports” ***, and “program applications”, and the search of these terms in additional databases, such as “PsycINFO”, “Embase”, “CINAHL”, “Web of Science”, and grey literature. Even knowing that some articles would be repeated, this additional search could find further relevant studies. Specific linguistic/language software to evaluate text features was not searched, but these tools may also be suitable to evaluate written information from a health domain. The inclusion of software engineers is recommended to provide a deeper and complete informatics analysis. 5. Conclusion Software tools to specifically evaluate prescription information, including software capable of analyzing, processing, extracting, or simplifying the information from PLs seem to be limited. The development of new computer-based tools may be relevant in this field, knowing the continuously augmentation in number and complexity of healthcare contexts and information requirements, as well as the simultaneous languages variety and cultural backgrounds worldwide.

Acknowledgements FCT - Fundação para a Ciência e Tecnologia; Grants: SFRH/BD/76531/2011, PEst-OE/LIN/UI0214/2013, and UID/DTP/04567/2016. References [1] European Medicine Agency. (2016), Product-information templates: Quality Review of Documents human product-information annotated template (English) version10 for centralised procedures and version 4 for MRP, DCP and referral, published 02/2016http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000134.jsp. 2016. [2]

European Commission. Directive 2001/83/EC Community code on medicinal products. Available at: http://ec.europa.eu/health//sites/health/files/files/eudr alex/vol1/dir_2001_83_consol_2012/dir_2001_83_cons_2012_en.pdf (accessed 04.03.18).

[3] Fuchs, J. (2010). “The Way Forward in Package Insert User Tests From a CRO’s Perspective.” Drug Information Journal 44: 119–129. [4] Pires, C., Vigário, M., Cavaco, A. (2015). “Readability of medicinal package leaflets: a systematic review.” Rev Saúde Pública 49:4

European public assessment reports (EPAR) are published for all human or veterinary medicine applications, which marketing authorisations are granted or refused. EPARs are referred in Article 13(3) of Regulation (EC) No 726/2004, and are more broad documents than PLs or SPCs.

***

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[5] Pires, C., Vigário, M., and Cavaco, A. (2015). “Problems identified in the package leaflets of the Portuguese non-generic medicines.” Acta Med Port. 28(1):21-8. [6] Pires, C., Cavaco, A., Martins, F., and Vigário, M. (2015). “Using an Automatic Tool to Identify Potential Readability Issues in a Large Sample of Medicinal Package Inserts.” Methods of Information in Medicine 54(4):379-81. [7] Pires, C, Vigário, M., and Cavaco, A. (2016). “Graphical content of medicinal package inserts: an exploratory study to evaluate potential legibility issues.” Health Info Libr J. 33(2):121-39. [8] Pires, C., Vigário, M., and Cavaco, A. (2016). Factors influencing subjects' comprehension of a set of medicine package inserts. International journal of clinical pharmacy 38(4):888-98. [9] Pires, C., Vigário, M., and Cavaco, A. (2017). “How sociodemographic features impact subjects' opinion on packages leaflets of medicines?” Australasian Medical Journal 10(9):774-784. [10] Pires, C., and Cavaco, A. (2014). “Exploring the perspectives of potential consumers and healthcare professionals on the readability of a package insert: a case study of an over-the-counter medicine.” Eur J Clin Pharmacol 70(5):583-8. [11] Instituto Nacional de Estatística. (2011). Census 2011. INE, ISSN 2182-4215. [12] Paiva, D., Silva, S., Sevro, M., Moura-Ferreira, P., Lunet, N., and Azevedo, A. (2017). “Limited Health Literacy in Portugal Assessed with the Newest Vital Sign.” Acta Med Port 30(12):861-869. [13] Veiga, S., and Serrão, C. (2016). “Health literacy of a sample of Portuguese elderly.” Applied Research In Health And Social Sciences: Interface And Interaction 13(1): 14-26. [14] Raynor, D.K., Blenkinsopp, A., Knapp, P., et al. (2007). “A systematic review of quantitative and qualitative research on the role and effectiveness of written information available to patients about individual medicines.” Health Technol Assess 11(5):1-160. [15] Iordatii, M., Venot, A., and Duclos, C. (2015). “Design and evaluation of a software for the objective and easy-to-read presentation of new drug properties to physicians.” BMC medical informatics and decision making 15:42. [16] Bouvenot, G.. (2001). “How to prevent drug therapy risk.” Rev Med Interne 22(12):1237-43. [17] Vromans L, Doyle G, Petak-Opel S, Rödiger A, Röttgermann M, Schlüssel E, and Stetter E. (2013). “Shaping medicinal product information: a before and after study exploring physicians' perspectives on the summary of product characteristics.” BMJ Open 3(8): e003033. [18] INFARMED, I.P. (2016). “Poupe na receita.” Available at: http://www.infarmed.pt/web/infarmed/poupe-na-receita (accessed 28.03.18). [19] European Medicine Agency. (2011). “Expression of interest for software development companies.” EMA/425283/2011. [20] Maxwell, S., Eichle, H., Bucsics, A., et al. (2011). “e-SPC – delivering drug information in the 21st century: developing new approaches to deliver drug information to prescribers”. Br J Clin Pharmacol 73(1): 12–15[21] Rubrichi, S., and Quaglini, S. (2012). “Summary of Product Characteristics content extraction for a safe drugs usage.” Journal of Biomedical Informatics 45: 231–239. [22] Poleksic, A., Turner, C., Dalal, R., et al. (2017). "Mining FDA resources to compute population-specific frequencies of adverse drug reactions." IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, USA, 1809-1814. [23] Banpatte, S., Shinde, U., Patil, R., et al. (2017). "Drug Discovery Based on Model Driven Architecture." IRJET 4(4): 886-893. [24] Novais, S. (2017). "Predicting Adverse Effects of Drugs." Master Thesis to obtain the Master degree in Medical engineering, FEUP.http://hdl.handle.net/10216/105434. [25] Mukherjee, A. (2017). "iMediCap App: An Android Application for Medication Assistance." Report to obtain the Degree of Master of Science in Engineering, University of Texas. [26] Hadley, S., Kessler, M., Litzenberg, D., et al. (2016). "SafetyNet: streamlining and automating QA in radiotherapy." Journal of Applied Clinical Medical Physics 17(1): 387-395. [27] Pfistermeister, B., Sedlmayr, B., Patapovas, A., et al. (2016). “Development of a Standardized Rating Tool for Drug Alerts to Reduce Information Overload.” Methods of Information in Medicine 55(6):507-515 [28] Lamy, J.B., Ugon, A., and Berthelot, H. (2016). “Automatic Extraction of Drug Adverse Effects from Product Characteristics (SPCs): A Text Versus Table Comparison.” Studies in Health Technology and Informatics 228:339-43. [29] Renahy, J., Vuitton, D., Rath B., Thomas, I., de Grivel V., and Cardey S. (2015). “Controlled language and information on vaccines: application to package inserts.” Current Drug Safety 10(1):41-8. [30] Elkin, P.L., Carter, J.S., Nabar, M., Tuttle, M., Lincoln, M., and Brown, S.H. (2011). “Drug knowledge expressed as computable semantic triples.” Studies in health technology and informatics 166: 38-47. [31] Duclos, C., Cartolano, G.L., Ghez, M., and Venot, A. (2004). “Structured representation of the pharmacodynamics section of the summary of product characteristics for antibiotics: application for automated extraction and visualization of their antimicrobial activity spectra.” Journal of the American Medical Informatics Association: JAMIA 11(4):285-93.