Development Of Healthcare Applications Research Articles

From Code to Care: The Impact of Software Engineering and QA on Healthcare Delivery Systems

The rapid digitalization of healthcare has led to an unprecedented reliance on software systems to improve patient care, streamline operations, and ensure regulatory compliance. This article presents a comprehensive analysis of the critical role that software engineering and quality assurance (QA) play in three key areas of modern healthcare: Electronic Health Records (EHRs), telemedicine platforms, and medical device software. Through an examination of current practices, challenges, and case studies, we demonstrate how robust software engineering methodologies and rigorous QA processes contribute to the development of reliable, secure, and efficient healthcare applications. Our findings highlight the importance of data integrity, interoperability, user experience, and regulatory compliance in healthcare software development. Furthermore, we explore emerging trends such as artificial intelligence, blockchain, and the Internet of Medical Things (IoMT), discussing their implications for future software engineering and QA practices in healthcare. This article provides valuable insights for healthcare organizations, software developers, and policymakers, offering recommendations to enhance the quality and safety of software-driven healthcare solutions.

Community-engaged artificial intelligence research: A scoping review.

The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount and dimensionality caused challenges in many disciplines, with the primary issues being high computing costs, memory costs, and low accuracy performance. These issues will arise since Machine Learning (ML) classifiers are mostly used in these fields. However, noisy and irrelevant features have an impact on ML accuracy. Therefore, to choose the best subset of features and decrease the dimensionality of the data, Metaheuristics (MHs) optimization algorithms are applied to Feature Selection (FS) using various modalities of medical imaging or disease datasets with different dimensions. The review starts by giving a general overview of the many approaches to AI algorithms, followed by a general overview of the various MH algorithms for healthcare applications, an analysis of MHs boosted AI for healthcare applications, and using a wide range of research databases as a data source for access to numerous field publications. The final section of this review discusses the problems and challenges facing healthcare application development.

THE EFFECT OF THE USE OF GAMIFICATION STRATEGY IN MOBILE HEALTH APPLICATIONS ON CUSTOMER LOYALTY

The introduction of mobile technologies’s included users in the mobile world with new features. The progress of mobile marketing has affected the development of the mobile application market. Mobile applications, which developed in parallel with the development of mobile marketing, helped the development of new applications in the health sector. Developing mobile technologies’re becoming widespread in the health sector. With the mobile health applications developed, different opportunities can be offered to patients or users who want a healthier lifestyle, and the applications can have the power to increase the quality of life of people. One of the rising values in mobile health applications’s gamification. With gamification, health services can be offered to the user in a mobile environment. It aims to monitor their health by rewarding its users because it’s motivational on the basis of gamification. The gaming experience leads to the formation of loyalty towards the mobile application with the value created by the user by connecting to the mobile application. In this research, the effect of game experiences on customer loyalty as a result of gamified content in mobile health applications was investigated. As a result of the research, it’s been shown that the game experience dimensions in mobile health applications’ve a positive effect on customer loyalty and suggested that healthcare companies or application developers can apply gamification strategies in their mobile applications to increase the loyalty of their customers.

CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization.

Healthcare has always been a strategic domain in which innovative technologies can be applied to increase the effectiveness of services and patient care quality. Recent advancements have been made in the adoption of Digital Twins (DTs) and Personal Knowledge Graphs (PKGs) in this field. Despite this, their introduction has been hindered by the complex nature of the context itself which leads to many challenges both technical and organizational. In this article, we reviewed the literature about these technologies and their integrations, identifying the most critical requirements for clinical platforms. These latter have been used to design CONNECTED (COmpreheNsive and staNdardized hEalth-Care plaTforms to collEct and harmonize clinical Data), a conceptual framework aimed at defining guidelines to overcome the crucial issues related to the development of healthcare applications. It is structured in a multi-layer shape, in which heterogeneous data sources are first integrated, then standardized, and finally used to realize general-purpose DTs of patients backed by PKGs and accessible through dedicated APIs. These DTs will be the foundation on which smart applications can be built.

Usability of a mobile application for health professionals in home care services: a user-centered approach

The use of mobile devices by healthcare professionals has led to rapid growth in the development of mobile healthcare applications designed to improve healthcare services. This study was conducted to assess the acceptability and usability of a mobile application for health professionals in relation to their work in hospitalization at home. A mixed methods approach was used. Acceptance, included the satisfaction of the professionals, attitudes toward using the application, and intention or willingness to continue using the application. Usability tests were performed in laboratory analyzing five controlled clinical tasks, and the interaction of the participants with the mobile application was based on the six basic facial expressions published by the American Psychological Association. Perceived satisfaction was assessed using the computer system usability questionnaire. Thirty-two participants completed the task scenarios and questionnaire. More than 90 per cent of participants were able to complete the tasks with only some difficult with vital signs. Satisfaction had a score of 6.18/7 (SD: 0.76), and recommendation of the mobile application had a score of 6.21/7 (SD: 0.81). This study showed a significant usability and acceptability of this mobile application, in terms of effectiveness, efficiency, and satisfaction.

Factors Influencing the Aged in the Use of Mobile Healthcare Applications: An Empirical Study in China.

Mobile healthcare applications are of significant potential value in the development of the aged-care industry due to their great convenience, high efficiency, and low cost. Since the cognition and utilization rates of mobile healthcare applications for the elderly are still low, this study explored the factors that affect the elderly's adoption of mobile healthcare applications. This study conducted a questionnaire survey on the elderly in China and received 365 valuable responses. This study combined the technology acceptance model, protection motivation theory, and perceived risk theory to build a research model of factors affecting the use of mobile healthcare applications by the elderly. The data were analyzed using a structural equation model. The results were as follows: according to the empirical research, (1) perceived usefulness and perceived ease of use positively affect the use attitude of the elderly; perceived usefulness and user attitude positively affect the behavior intention of the elderly; perceived ease of use positively affects perceived usefulness; (2) perceived severity has a significant positive correlation with use attitude; perceived susceptibility and attitude to use have no significant impact; (3) perceived risk is negatively correlated with the use attitude and behavioral intention. The above-mentioned factors should be taken into consideration during the development of mobile healthcare applications for the aged to upgrade the overall service quality of mobile healthcare applications, thus enhancing the operational level of mobile healthcare applications and the health literacy of the aged.

MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format.

Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR. FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide. The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915000 resources. The full dataset contained 315000 patients covering approximately 5840000 resources. The final datasets in NDJSON format are accessible on PhysioNet. Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work. The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.

Smart Healthcare Applications over 5G Networks: A Systematic Review

Provisioning of health services such as care, monitoring, and remote surgery is being improved thanks to fifth-generation cellular technology (5G). As 5G expands globally, more smart healthcare applications have been developed due to its extensive eMBB (Enhanced Mobile Broadband) and URLLC (Ultra-Reliable Low Latency Communications) features that can be used to generate healthcare systems that allow minimizing the face-to-face assistance of patients at hospital centers. This powerful network provides high transmission speeds, ultra-low latency, and a network capacity greater than that of 4G. Fifth-generation cellular technology is expected to be a means to provide excellent quality of medical care, through its technological provision to the use of IoMT (Internet of Medical Things) devices. Due to the numerous contributions in research on this topic, it is necessary to develop a review that provides an orderly perspective on research trends and niches for researchers to use as a starting point for their work. In this context, this article presents a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), with article selection based on inclusion and exclusion criteria that avoid bias. This research was based on research questions that were answered from the included works. These questions focus on technical characteristics, health benefits, and security protocols necessary for the development of smart healthcare applications. We have identified that a high percentage of existing works in the literature are proposals (56.81%, n = 25) and theoretical studies (22.73%, n = 10); few implementations (15.91%, n = 7) and prototypes (4.55%, n = 2) exist, due to the limited global deployment of 5G. However, the panorama looks promising based on proposals and future work that these technological systems allow, all based on improving healthcare for people.

Routing Protocol for Healthcare Applications Data Over the 6LoWPAN-based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have increasingly been adopted as a viable solution in the healthcare industry for many years. One novel application of WSNs in healthcare is real-time health data collection and tele-healthcare service delivery via the Internet. With the increasing number of applications of Wireless Sensor Networks (WSNs) in healthcare has emerged during the past decades, especially during the COVID-19 pandemic which has affected millions of people, particularly the elderly, the advantages and benefits of applying WSNs to monitor patients and improve the quality of healthcare have become clearer. Today's sensing and communications technologies have reached a point where these WSNs can be readily implemented and deployed, albeit with some limitations and security concerns.Collecting patients' health data or vital signs from WSNs deployed in hospitals or elderly nursing homes has been one of the most challenging tasks in the healthcare industry. In this paper, our main focus is on the routing protocol for human psychological data generated by healthcare application over the 6LoWPAN-based Wireless Sensor Networks (WSNs) as well as the development of affordable healthcare applications by leveraging novel WSN technology. Firstly, we present our latest achievements in implementing healthcare systems using the framework proposed in [10]. Additionally, We employ this framework to simulate 6LoWPAN-based Wireless Sensor Networks (WSNs) in two different healthcare settings using the network simulator NS-3. Finally, we provide preliminary results from the application of the developed application in on-site hospitals and nursing homes.

Recent progress of dielectric polymer composites for bionics

Natural structures, natural phenomena, animal and plant forms, and human behavior always provide us with inspiration and stimulate more creativity. The design and application of dielectric polymer composites that can be utilized to fabricate artificial muscles show great potential in the field of bionics. However, great challenges still exist, which severely prevent the development of dielectric polymer composites. In this review, we systematically surveyed recent advancements in nature-inspired monolayer and multilayer dielectric polymer composites with excellent properties, as well as the description of their structures and characteristics. In addition, we further discussed the applications of such composites in integrated systems for the development of bionic robotics, healthcare and biomedical applications. Moreover, an in-depth analysis of challenges and prospects in this field was also provided. Mimicking nature has become essential for the design and application of dielectric polymer composites, and we believe this approach will drive continued advances in the intelligent and medical industries.

932 The Use of Medical & Dental Clinical Healthcare Apps by Dental Core Trainee Groups in Scotland

Abstract Introduction The development of healthcare applications and their use has increased significantly in clinical practice. A variety of different applications are available covering clinical guidelines, prescribing advice and productivity. These applications may aid clinical decision making and improve patient care Aim This survey aimed to determine the use of the medical & dental healthcare applications within the Dental Core Trainee group (DCT) and describe the demographics of the population using them. Method An electronic questionnaire was distributed to DCT groups in Scotland between May and July 2018. The questionnaire collected demographic data and type of application used. Trainees also provided comments on their experiences. Results 49 responses were received with 14 different apps described. SDCEP(89%) and BNF(63%) apps were used most commonly. 22% of respondents describe using apps daily with 73% reporting at least monthly. The average app use was 2 per person. SDCEP(89%) and BNF(63%) apps were most common. DCTs in OMFS (36%) use more apps (4) and do so more frequently. This may reflect the nature of the work undertaken. 66% were using Apple i0S devices. 51 comments were obtained about the perceived advantages and disadvantages of apps. Respondents praised the benefits of app updates, but report concerns about the reliability and quality assurance. The use of mobile phones in clinical settings also generated contrasting responses. App use does not appear age or sex linked. Conclusion Applications are used regularly within the DCT group but questions about their quality and appropriateness in the clinical setting remain.

Social Modeling of Virtual Healthy Food Intake

People tend to adapt the amount of their food intake to that of others around them. This so-called social modeling of eating has been extensively studied over the past decades. The current study complements these experiments and aims to investigate social modeling of healthy food intake using a video paradigm in which a virtual model consumed either a small or a large portion of apple. In addition, it was tested whether modeling effects of a virtual female confederate were equally strong in male and female participants. Thirty participants (13 female, 17 male) completed the low norm condition in which a virtual model consumed 30 g of apple. Another 30 participants (17 female, 13 male) were allocated to the high norm condition in which a virtual model consumed 100 g of apple. Participants completed an irrelevant task, after which their own intake of apple was measured. Average intake in the low norm condition was 3 g, average intake in the high norm condition was 26 g (p < 0.001). In conclusion, participants adapted their intake to that of the virtual model. This effect was found irrespective of gender; female and male participants equally adapted their intake to that of a female virtual model. Stimulating food intake via a virtual model in people who have difficulty to meet dietary requirements or inhibiting food intake in people who tend to consume too much would be interesting new steps in the development of healthcare applications.

Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using an Improved CNN

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Development and Application of Digital Therapeutics to Improve Outcomes in Managing Chronic Conditions

Widespread development of new healthcare applications in digital therapeutics (DTx), specifically, is eminent, backed by a recent wave of regulatory support promoting innovation. As industry stakeholders explore the range of DTx technologies on the market today, these offerings can be understood better as digital extenders of the clinical care team as opposed to doctor replacements. With DTx as extenders, the healthcare industry will experience a revolution in the way chronic conditions and diseases are managed via technology, uprooting traditional methods of treatment.

Introducing Cloud-Assisted Micro-Service-Based Software Development Framework for Healthcare Systems

In healthcare services, application development is considered the most complex and time-consuming phase. As it is difficult to plan and time-intense, it requires high maintenance. Healthcare applications need strict compliance and the scope of application is immense along with associates, classes in services, and classified system. Application designing in healthcare with the help of traditional approaches such as monolithic and service-oriented architecture (SOA) generate problems in different areas like service availability, remote access to services, service provisioning, scalability, healthcare systems integration with each other. That is why there is a need for less sophisticated and user-friendly healthcare systems, which are easy to plan and develop, inexpensive requirement maintenance, and agile testing. To overcome the aforesaid issues in the domain of healthcare application development, this paper develops a framework of micro services for the development of healthcare services using cloud computing infrastructure. Micro-service-based techniques provide lightly coupled and fine-grained methodology. With the use of micro services technique presented in this work, the efficiency, scalability, and performance are improved. In this research, an approach for development and deployment properly in the cloud for healthcare applications is developed. Thus, it contributes to the system design approach and system analysis. Quantitative and qualitative results are reported showing the advantages of micro services approach used.

5G‐based smart healthcare system designing and field trial in hospitals

With the 5G worldwide deployment, the scale of vertical applications is innovated benefit from 5G technologies including MEC (Multi-access Edge Computing), network slicing, etc. Especially for healthcare, 5G had been used for COVID-19 protection and intelligent medical processing. However, limited by the hospital's traditional information infrastructures, those 5G-based healthcare applications are hard to be deployed and most only for demonstration, also isolated from the existing medical systems. So what is the next generation of smart healthcare information infrastructures is the key issue for the long-term development of 5G healthcare applications. Even though the standardized 5G MEC framework has been widely used in many vertical scenarios, it is also hard to satisfy hospital-specific requirements such as hospital-dedicated deployment, medical data security, and various network connections, etc. This paper proposes a 5G-based architecture for smart healthcare information infrastructure, a new network element iGW (industry gateway) is defined, and the smart healthcare dedicated cloud platform iMEP (industry multi-access edge platform) is also introduced here, making it possible to satisfy both the hospital-specific requirements and the long-term evolution. Meanwhile, the implementation methodology and the corresponding field test results are presented, which show the significant network performance gain achieved by the proposed new system structure.

The Use of Metamodel-based Approach for Designing Healthcare Applications

Recently, the use of Model-Driven Engineering (MDE) via metamodeling approach is gaining more attention for software applications development. The community from the healthcare domain also attempts to employ the metamodel approach for producing quality healthcare applications. Healthcare applications have become an imperative in every attempt to improve healthcare management. Numerous studies reported that the healthcare domain is seen as a complex and unique domain, which involves dynamic characteristics. In addition, it is widely recognized that the increase of information exchange in the healthcare domain is caused by the diversity of healthcare data. This has led to the increase use of information technologies in the healthcare industry so as to enhance the healthcare delivery process via healthcare applications. However, the complexity of healthcare information leads to ineffective models and design of healthcare applications. Modeling the healthcare processes and developing healthcare applications are challenging tasks. Hence, the advances of MDE have influenced the use of the metamodeling technique in the development of healthcare applications. Various metamodels are developed as a solution to provide a clear healthcare process model and a correct healthcare application. The aim of this paper is to analyse the use of the metamodel-based approach in designing healthcare applications. We believe that the metamodel-based approach would improve the development of healthcare applications.

IPA 분석을 통한 모바일 헬스케어 애플리케이션 평가 : 고령자의 사용성을 중심으로

The purpose of this study is to identify problems with the healthcare application currently developed and commercialized from the viewpoint of the usability of the elderly and to suggest improvement directions. To this end, the importance and satisfaction of healthcare application usability were compared and analyzed for 199 people over the age of 60. As a result of performing the paired t-test and IPA analysis based on a total of 13 usability items, the usability attributes related to Contents and System showed relatively high satisfaction, while the usability attributes related to Design and Process showed lower satisfaction than the importance. Through the above results, academic and practical implications for the development and service direction of healthcare applications for the elderly were provided.