Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review.

BACKGROUND: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard. OBJECTIVE: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies. METHODS: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard. RESULTS: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability. CONCLUSIONS: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

Sizing Prenatal mPHRs using COSMIC Measurement Method.

This paper presents an empirical evaluation of the COSMIC Function Points method (e.g., ISO 19761) through measuring the functional size of 33 prenatal mobile Personal Health Records (mPHRs) apps. This evaluation compares the functional size of each mobile app measured using the COSMIC method to the score of the app obtained in a previous evaluation that relied on functions extraction using a quality assessment questionnaire. It includes as well an investigation of the relationships between the functional sizes of these apps, their ratings in the apps stores, as well as the number of installs. As results, it was noticed that there is a considerable shift between the rankings of the functional sizes and the functionality scores obtained in the opinion-based questionnaire, for most of the apps assessed. Moreover, the study of the relationship between the functional sizes and the ratings, as well as the number of installs indicated that these variables are not linked, since they are impacted by external factors. The findings support the use of the COSMIC method for these apps in regard to measuring the functional size for further updates or improvements, which can also help developers to have an overview about the existing apps on the market and compare between them. Moreover, COSMIC is more effective since it covers all the features and functionalities of prenatal mPHRs.

Functional size measurement of postnatal care apps: Morocco case study.

This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco "Mamma&Baby". This evaluation is based on a comparison of the COSMIC _ISO 19,761 functional size of these apps with the score obtained in a previous evaluation based on functions extraction through a quality assessment questionnaire. This comparison allows to discuss the relationship between the functional size of the 18 apps, their users' ratings in the Play Store as well as the number of downloads. While for most of the assessed apps, there is only a small shift between the rankings of the two evaluations, for some apps, the shift is huge due to the number of features added and not covered by the score previously obtained. This study illustrates the use of COSMIC as an effective method for corrective or evolutionary updates since it takes into account all the functions and features of postnatal apps. For the "Mamma&Baby" app, efforts are required to boost the number of downloads, optimize its visibility, and attract the highest number of users.

Software Development Effort Estimation Using Regression Fuzzy Models.

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call "regression fuzzy logic." State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size, and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

Usability evaluation of mobile applications using ISO 9241 and ISO 25062 standards.

This paper presents an empirical study based on a set of measures to evaluate the usability of mobile applications running on different mobile operating systems, including Android, iOS and Symbian. The aim is to evaluate empirically a framework that we have developed on the use of the Software Quality Standard ISO 9126 in mobile environments, especially the usability characteristic. To do that, 32 users had participated in the experiment and we have used ISO 25062 and ISO 9241 standards for objective measures by working with two widely used mobile applications: Google Apps and Google Maps. The QUIS 7.0 questionnaire have been used to collect measures assessing the users' level of satisfaction when using these two mobile applications. By analyzing the results we highlighted a set of mobile usability issues that are related to the hardware as well as to the software and that need to be taken into account by designers and developers in order to improve the usability of mobile applications.