Search Results (25)

Healthcare Information Systems (HISs) are essential for improving care quality, managing chronic diseases, and supporting clinical decision-making. Despite significant investments, HIS implementations often fail due to the complexity of healthcare environments. Maturity Models (MMs) have emerged as tools to guide organizational improvement by assessing readiness, process efficiency, technology adoption, and interoperability. This study presents a comprehensive literature review identifying 45 Maturity Models used across various healthcare domains, including telemedicine, analytics, business intelligence, and electronic medical records. These models, often based on Capability Maturity Model Integration (CMMI), vary in structure, scope, and maturity stages. The findings demonstrate that structured maturity assessments help healthcare organizations plan, implement, and optimize HIS more effectively, leading to enhanced clinical and operational performance. This review contributes to an understanding of how different MMs can support healthcare digital transformation and provides a resource for selecting appropriate models based on specific organizational goals and technological contexts. Full article

Background: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. Objective: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. Methods: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. Results: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. Conclusions: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare. Full article

(1) Background: In the UK, mental health needs for children and young people (CYP) are rising, whilst access to care is declining, particularly in the North of England and post-COVID-19. However, Health Information Technologies (HITs) can simplify access to Child and Adolescent Mental Health Services (CAMHS), reduce waiting times, and provide anonymous support and reliable information. (2) Methods: A single-centre prospective observational study examined the impact of “CYP as One”—a digital referral point to CAMHS—on waiting times and referral rejection rates. (3) Results: Waiting times during the first 12 months of “CYP as One” implementation were compared to the 12 months prior using non-parametric tests. “CYP as One” demonstrated an increase of 1314 referrals, with self-referrals rising by 71%. Initial implementation showed an increase in waiting times by 16.13 days (53.89 days) compared to pre-implementation (37.76 days) (p < 0.001). However, months 10 (M = 16.18, p < 0.001), 11 (M = 17.45, p < 0.001), and 12 (M = 31.45, p < 0.001) implementation showed reduced waiting times. Rejection rates rose due to a 108% increase in referral volume. “CYP as One” improved access and reduced waiting times after the initial phase. (4) Conclusions: Further research is needed to assess its long-term impact and cost-effectiveness, particularly regarding specific mental health conditions and staff time. Full article

Background: The ecological validity associated with usability testing of health information technologies (HITs) can affect test results and the predictability of real-world performance. It is, therefore, necessary to identify conditions with the greatest effect on validity. Method: We conducted a comparative analysis of two usability testing conditions. We tested a HIT designed for anesthesiologists to detect pain signals and compared two fidelity levels of ecological validity. We measured the difference in the number and type of use errors identified between high and low-fidelity experimental conditions. Results: We identified the same error types in both test conditions, although the number of errors varied as a function of the condition. The difference in total error counts was relatively modest and not consistent across levels of severity. Conclusions: Increasing ecological validity does not invariably increase the ability to detect use errors. Our findings suggest that low-fidelity tests are an efficient way to identify and mitigate usability issues affecting ease of use, effectiveness, and safety. We believe early low-fidelity testing is an efficient but underused way to maximize the value of usability testing. Full article

Team-based primary care has been shown to be an important initiative for transforming primary care to achieve whole-person care, enhance health equity, and reduce provider burnout. Organizational approaches have been explored to better implement team-based care but a thorough understanding of the role of system functions is lacking. We aimed to identify the combinations of system functionalities in primary care practices that most enable effective teamwork. We used a novel method, qualitative comparative analysis (QCA), to identify cross-case patterns in 19 primary care practices in the Harvard Academic Innovations Collaborative (AIC), an initiative for transforming primary care practices by establishing teams and implementing team-based care. QCA findings identified that primary care practices with strong team dynamics exhibited strengths in three operational care process functionalities, including management of abnormal test results, cancer screening and medication management for high-priority patients, care transitions, and in health information technology (HIT) functionality. HIT functionality alone was not sufficient to achieve the desired outcomes. System functionalities in a primary care practice that support physicians and their teams in identifying patients with urgent and complex acute illnesses requiring immediate response and care and overcoming barriers to collaboration within and across institutional settings, may be essential for sustaining strong team-based primary care. Full article

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs. Full article

The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges. Full article

Background: Like other chronic viral illnesses, HIV infection necessitates consistent self-management and adherence to care and treatment, which in turn relies on optimal collaboration between patients and healthcare professionals (HCPs), including physicians, nurses, pharmacists, and clinical care coordinators. By providing people living with HIV (PLHIV) with access to their personal health information, educational material, and a communication channel with HCPs, a tailored patient portal could support their engagement in care. Our team intends to implement a patient portal in HIV-specialized clinics in Canada and France. We sought to understand the perceived risks and benefits among PLHIV and HCPs of patient portal use in HIV clinical care. Methods: This qualitative study recruited PLHIV and HIV-specialized HCPs, through maximum variation sampling and purposeful sampling, respectively. Semi-structured focus group discussions (FGDs) were held separately with PLHIV and HCPs between August 2019 and January 2020. FGDs were recorded, transcribed, coded using NVivo 12 software, and analyzed using content analysis. Results: A total of twenty-eight PLHIV participated in four FGDs, and thirty-one HCPs participated in six FGDs. PLHIV included eighteen men, nine women, and one person identifying as other; while, HCPs included ten men, twenty women, and one person identifying as other. A multi-disciplinary team of HCPs were included, involving physicians, nurses, pharmacists, social workers, and clinical coordinators. Participants identified five potential risks: (1) breach of confidentiality, (2) stress or uncertainty, (3) contribution to the digital divide, (4) dehumanization of care, and (5) increase in HCPs’ workload. They also highlighted four main benefits of using a patient portal: (1) improvement in HIV self-management, (2) facilitation of patient visits, (3) responsiveness to patient preferences, and (4) fulfillment of current or evolving patient needs. Conclusion: PLHIV and HCPs identified both risks and benefits of using a patient portal in HIV care. By engaging stakeholders and understanding their perspectives, the configuration of a patient portal can be optimized for end-users and concerns may be mitigated during its implementation. Full article

Healthcare providers’ burnout may potentially have a negative impact on patient care. The use of the electronic health record (EHR) increases the burden for healthcare providers (HCPs), particularly during the coronavirus disease-2019 (COVID-19) pandemic. This study assessed the stress and burnout related to the use of EHRs and health information technology (HIT) tools among HCPs during COVID-19 in Saudi Arabia. We used a self-developed survey tool. It consisted of five sections; demographics and professional data, experience using EHR, effects of EHR use, use of EHR and technology tools during COVID-19, and health and wellbeing. The survey link was emailed to a random sample of HCPs registered with a national scientific regulatory body. Univariate, bivariate, and multivariate analyses were performed to measure the association between burnout and study variables. A total of 182 participants completed the survey. 50.5% of participants reported a presence of HIT-related stress, and 40.1% reported a presence of burnout. The variables independently associated with burnout were providing tertiary level of care, working with COVID-19 suspected cases, dissatisfaction with EHRs, and agreement with the statement that using EHRs added frustration to the workday. Further research that explores possible solutions is warranted to minimize burnout among HCPs, especially during infectious outbreaks. Full article

Breast and prostate cancer patients may experience physical and psychological distress, and a possible decrease in sleep quality. Subjective and objective methods measure different aspects of sleep quality. Our study attempted to determine differences between objective and subjective measurements of sleep quality using bivariate and Pearson’s correlation data analysis. Forty breast (n = 20) and prostate (n = 20) cancer patients were recruited in this observational study. Participants were given an actigraphy device (ACT) and asked to continuously wear it for seven consecutive days, for objective data collection. Following this period, they filled out the Pittsburgh Sleep Quality Index Questionnaire (PSQI) to collect subjective data on sleep quality. The correlation results showed that, for breast cancer patients, PSQI sleep duration was moderately correlated with ACT total sleeping time (TST) (r = −0.534, p < 0.05), and PSQI daytime dysfunction was related to ACT efficiency (r = 0.521, p < 0.05). For prostate cancer patients, PSQI sleep disturbances were related to ACT TST (r = 0.626, p < 0.05). Both objective and subjective measurements are important in validating and determining details of sleep quality, with combined results being more insightful, and can also help in personalized care to further improve quality of life among cancer patients. Full article

Several countries nowadays are facing a tough social challenge caused by the aging population. This public health issue continues to impose strain on clinical healthcare, such as the need to prevent terminal patients’ pressure ulcers. Provocative approaches to resolve this issue include health information technology (HIT). In this regard, this paper explores one technological solution based on a smart medical bed (SMB). By integrating a convolutional neural network (CNN) and long-short term memory (LSTM) model, we found this model enhanced performance compared to prior solutions. Further, we provide a fuzzy inferred solution that can control our proposed proprietary automated SMB layout to optimize patients’ posture and mitigate pressure ulcers. Therefore, our proposed SMB can allow autonomous care to be given, helping prevent medical complications when lying down for a long time. Our proposed SMB also helps reduce the burden on primary caregivers in fighting against staff shortages due to public health issues such as the increasing aging population. Full article

The COVID-19 pandemic literally stopped most human movement and activities as it initially spread, which included dental practices and dental education. This defined the need for significative changes in teaching and learning with the use of “e-learning” methods, also for traineeships. This study was designed to determine the undergraduate student perception of these new methods as part of their education. This involved 353 students attending the Dental School of the G. D’Annunzio University of Chieti–Pescara, from the first to the sixth years. A questionnaire in Italian and was set-up using “Google Forms” and sent by email to the students. The questionnaire was divided into three parts: the first part included questions for general information, including age, sex and year of course; the second part had multiple choice questions related to their evaluation of the e-learning teaching, using a scale of opinion in the replies to each question (e.g., “scarce”, “fair”, “satisfying”, “very good” and “excellent”); and the third part included two open questions to indicate the strengths and limitations of these new teaching and learning approaches. The categorical variables in the first and second parts of the questionnaire were evaluated using Chi squared tests, setting significance at p < 0.05, while the comments were evaluated qualitatively. The student feedback showed significant appreciation (p < 0.05) of the new methods and the efforts that the lecturers put in to provide lectures of as high a quality as possible. However, a lack of practical training was significantly perceived as an important problem in the structure of their new curriculum (p < 0.05). COVID-19 has been an epic tragedy that has hit the human population not only in terms of health and healthcare, but also quality of life. This includes the quality of dental education within universities. However, the pandemic can be seen to also represent motivation to invest in the necessary technological innovation to deliver the best possible education to our future dentists. Full article

Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one. Full article

In this paper, we introduce a new kind of Service Level Agreement(SLA) Template to better control dynamically quality of medical monitoring platform service. Our approach is based on Health care system and Health Information Technology (HIT) research area, specifically the field of telemonitoring system for patients who suffer from chronic obstructive pulmonary disease (COPD). According to WHO statistics, COPD is the third leading cause of death worldwide. To this end, several solutions or platforms exist today to monitor COPD. Most of these platforms manage large volume of patient data. This can bring about quality and lost data problems. To address these issues, control mechanisms must be proposed and designed to improve the quality of service (QoS) on these platforms. A platform with continuously monitored QoS can save patients’ lives and reduce data quality risk. In this article, we propose an ontology that uses SLAs data from COPD monitoring platforms with dynamic data from a patient context. We dynamically calculate the number of patient data incidents and the number of service request incidents from two dynamic contexts: SLA and the patient context. If the number of incidents is higher than what is expected in the SLA, then alerts are sent to the interface parties in real time. Finally, the contribution of this article is the proposed virtual SLA template to better control SLA violation and improve quality of medical monitoring platforms services. Full article