Healthcare Tools Research Articles

Transformación de la educación universitaria en salud a través de la inteligencia artificial

Artificial Intelligence is presented as a tool to improve medical care and reduce the workload of health professionals. Although it offers faster diagnoses and personalized treatments, its rapid evolution creates ethical and educational challenges in medical training. The work highlights the importance of structured education and interdisciplinary collaboration to successfully integrate AI into the training of healthcare professionals, recognizing this transition as a crucial opportunity to improve higher education in this ever-changing field. The project focuses on a detailed review of the literature on changes in university health education through information and communication technologies. It uses key ideas from relevant authors and recent studies for comprehensive analysis. AI stands out as a transformative tool in healthcare, promising improvements in clinical outcomes and efficiency, alleviating workload. Its integration into medical education offers opportunities for personalization of learning, practical skills, and access to quality educational content, although it poses ethical and professional challenges, such as information accuracy and data privacy. Addressing these challenges is crucial to ensure ethical and responsible use of AI in medical training, maximizing its benefits and minimizing the risks.

The Use of AI in Predicting Patient Outcomes

Artificial intelligence (AI) has emerged as a revolutionary tool in healthcare, significantly improving the ability to predict patient outcomes. From chronic disease management to personalized treatments, AI can analyze vast datasets and provide timely, accurate predictions that guide clinical decision-making. Traditional statistical models have been useful but often limited in handling complex data relationships. AI, through machine learning (ML) and deep learning (DL), transcends these limitations, offering improved predictions across diseases like cancer, tuberculosis, and HIV/AIDS. This paper investigates current AI methods, advantages, challenges, and ethical considerations associated with its use in healthcare, highlighting the transformative potential of AI in outcome prediction while addressing concerns around data privacy, regulatory constraints, and model transparency. Keywords: Artificial Intelligence, Machine Learning, Patient Outcomes, Clinical Decision Support Systems, Predictive Analytics.

Implementing the Debriefing Assessment for Simulation in Healthcare (DASH) Tool for Training Medical Faculty.

Introduction Despite simulation being widely recognized as an essential component of medical education, there remains a dearth of literature on its implementation and effectiveness in pre-clerkship medical education. In order to effectively encompass healthcare simulations within medical instruction, it is imperative to first address the often neglected gap in faculty development and training. With this in mind, we aim to present our experience in implementing the Debriefing Assessment for Simulation in Healthcare (DASH) tool in training faculty at Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA. Methods Twenty participating faculty practiced instructing a simulation session and completed the DASH tool. Quantitative and qualitative approaches were used to report the findings. Results The Survey Rater and Student DASH version results were analyzed using the Mann-Whitney U test to see if there was a difference between the two groups for each question. Conclusions All faculty participating in healthcare simulations should be trained in order to deliver a structured debriefing experience. In healthcare medical simulation, the identification of faculty training is imperative to address many of the challenges of healthcare today, including patient safety issues and limitations of medical training in hospital settings. Implications The study aims to serve as a template that researchers can use in identifying potential faculty training development, as the benefits of having faculty trained in simulation methodology are successful in higher educational settings. Further studies in simulation-based education faculty training are recommended.

The Role of Artificial Intelligence in Clinical Decision Support Systems

Clinical Decision Support Systems (CDSS) are integral tools in modern healthcare, designed to assist clinicians by providing patient-specific recommendations based on vast medical data and knowledge. The advent of Artificial Intelligence (AI) has significantly enhanced CDSS, enabling sophisticated predictive analytics, early detection of complications, and personalized interventions. AI techniques like machine learning, natural language processing, and deep learning play crucial roles in refining CDSS functionality. However, challenges such as data quality, AI transparency, and clinician trust hinder widespread adoption. Future trends focus on improving AI integration in CDSS through better data representation, automation, and ethical considerations. This paper investigates the fundamental aspects of CDSS, the applications of AI in healthcare, and the challenges and future directions for AI-driven CDSS. Keywords: Clinical Decision Support Systems (CDSS), Artificial Intelligence (AI), Machine Learning, Healthcare Technology, Predictive Analytics.

Utilization of telemedicine services of institute of national importance in the western region of INDIA: A mixed-method study.

Telemedicine has emerged as a crucial healthcare tool, especially during the COVID-19 pandemic, offering risk-free contactless communication between physicians and patients. In India, the introduction of official guidelines in 2019 and initiatives like the "e-Sanjeevani" national teleconsultation portal have accelerated telemedicine's adoption. However, the utilization and satisfaction levels of telemedicine services in Institutes of National Importance (INI) remain underexplored. This mixed-method study aims to describe the utilization of telemedicine services at the All India Institute of Medical Sciences (AIIMS), Rajkot, an INI in western India, and assess patients' satisfaction with these services. Data was collected from patients availing telemedicine services through the e-Sanjeevani portal at AIIMS Rajkot between February and December 2022. Quantitative analysis included patient demographics, specialty attended, location, call duration, and wrong call rates. Qualitative analysis involved interviews with patients to assess their experiences and satisfaction levels. Statistical analyses were performed using SPSS for quantitative data and Atlas Ti for qualitative data. A total of 12,442 doctor-to-doctor and 7,876 patient-to-doctor consultations were recorded, with a diverse age distribution (21-40 years, 45.4%). Most patients were from medical specialties, with general medicine, Pulmonary Medicine, and Dermatology being the highest. The data revealed a wide geographic distribution, with over 70% of cases from more than 200 kilometers away. Despite some challenges, including a 38.2% wrong call rate, the telemedicine services were well-received, with 96% of respondents reporting overall satisfaction. Telemedicine services at AIIMS Rajkot have shown high user satisfaction and accessibility, highlighting their potential in improving healthcare delivery and access, particularly in remote areas. However, efforts to enhance digital literacy and address challenges are necessary for further optimization of telemedicine services.

Effectiveness of ChatGPT 4.0 in Telemedicine-Based Management of Metastatic Prostate Carcinoma.

The recent rise in telemedicine, notably during the COVID-19 pandemic, highlights the potential of integrating artificial intelligence tools in healthcare. This study assessed the effectiveness of ChatGPT versus medical oncologists in the telemedicine-based management of metastatic prostate cancer. In this retrospective study, 102 patients who met inclusion criteria were analyzed to compare the competencies of ChatGPT and oncologists in telemedicine consultations. ChatGPT's role in pre-charting and determining the need for in-person consultations was evaluated. The primary outcome was the concordance between ChatGPT and oncologists in treatment decisions. Results showed a moderate concordance (Cohen's Kappa = 0.43, p < 0.001). The number of diagnoses made by both parties was not significantly different (median number of diagnoses: 5 vs. 5, p = 0.12). In conclusion, ChatGPT exhibited moderate agreement with oncologists in management via telemedicine, indicating the need for further research to explore its healthcare applications.

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics and neonatology, owing to the lack of suitable dosage forms for these patients, often rendering them "therapeutic orphans". The development and application of pediatric drug formulations encounter numerous challenges, including physiological heterogeneity within age groups, limited profitability for the pharmaceutical industry, and ethical and clinical constraints. Many drugs are used unlicensed or off-label, posing a high risk of toxicity and reduced efficacy. Despite these circumstances, some regulatory changes are being performed, thus thrusting research innovation in this field. Up-to-date peer-reviewed journal articles, books, government and institutional reports, data repositories and databases were used as main data sources. Among the main strategies proposed to address the current pediatric care situation, nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive, versatile, tunable, site-specific drug release. Tissue engineering is in the spotlight as strategy to address pediatric cardiac diseases, together with theragnostic systems. The integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches, ushering in an era of innovative and personalized drug delivery for pediatric patients. Finally, the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great potential. This promises not only to enhance efficiency in drug development in general, but also in the pediatric field, hopefully boosting clinical trials for this population. Despite the long road ahead, the deepening of nanotechnology, the evolution of tissue engineering, and the combination of traditional techniques with artificial intelligence are the most recently reported strategies in the specific field of pediatric therapeutics.

Markers of healthcare: the scientific and legal environment of a medical university

The article notes and examines in detail the currently important healthcare tool that provides training for highly qualified personnel — research work carried out at a medical university. In May 2024, the decree of the President of the Russian Federation defines strategic goals for the development of the social sphere facing domestic healthcare and relating, in particular, to strengthening human health, increasing healthy life expectancy, timely disease prevention, and increasing the level of satisfaction of participants in a special military operation with conditions for medical rehabilitation. The analysis of the features of legal support of scientific personnel of medical universities involved in the training of highly qualified doctors who are called upon to directly solve the above tasks, possessing not only theoretical knowledge, but also having passed scientific and practical training.

Evaluating ChatGPT 4.0's User Satisfaction Among Doctors Across Different Medical Departments.

In an era increasingly focused on integrating Artificial Intelligence (AI) into healthcare, the utility and user satisfaction of AI applications like ChatGPT have become pivotal research areas. This study, conducted in Greece, engaged 193 doctors from various medical departments who interacted with ChatGPT 4.0 through a custom web application. The participants, representing a diverse range of medical specialties, received responses from the specific chatbot tailored to their specific departmental inquiries. Their satisfaction was gauged using a validated form featuring a 1-to-5 rating scale. The results highlighted a possible correlation between the doctors' medical departments and their satisfaction levels with ChatGPT 4.0. Significantly, doctors from certain departments (like General Surgery and Cardiology) reported lower satisfaction scores, ranging from 2.73 to 2.80 out of 5, in contrast to their colleagues from departments like Biopathology and Orthopedics, who scored between 4.00 and 4.46 out of 5. This variation in satisfaction levels underscores the diverse needs within different medical specialties and illuminates both the potential of ChatGPT and the areas needing improvement, especially in delivering department-specific medical information. Despite its limitations, ChatGPT version 4.0 is emerging as a valuable tool in the medical community, indicating potential future advancements and more extensive integration into healthcare practices. The study's findings are crucial in understanding the distinct preferences and requirements of healthcare professionals across various medical departments, thereby guiding the future development of AI tools in healthcare.

Association between liver fibrosis scores and short-term clinical outcomes in hospitalized chronic kidney disease patients: a prospective observational study.

In resource-constrained countries, inadequate access to healthcare and prognostic tools can be the Achilles' heel in effectively managing chronic kidney disease (CKD). There is a significant similarity in the pathogenesis of CKD and liver fibrosis. The role of liver fibrosis (LF) scores in predicting short-term clinical outcomes in hospitalized patients with CKD is unknown. Our study aimed at calculating LF scores and studying the association of liver fibrosis with short-term mortality and morbidity in CKD patients. Patients aged above 15 years diagnosed with CKD as per the KDIGO criteria were enrolled. LF scores, namely, NFS, GPRI, and FIB-4 scores were calculated. Patients were followed up for a period of 28 days for good and poor composite outcomes, namely, the requirement of hemodialysis, non-invasive ventilation, prolonged hospital stay, and neurological and cardiovascular outcomes including death. Among 163 patients, 70.5% were below 60 years of age, 82.2% were male and 35% were diabetic. At 28-day follow up, 52.1% had poor composite outcome. The AUROC for GPRI and FIB-4 in predicting poor outcomes was 0.783 (95% CI: 0.71-0.855) (p < 0.001) and 0.62 (95% CI: 0.534-0.706) (p = 0.008), respectively. The AUROC for GPRI and NFS in predicting all-cause mortality was 0.735 (95% CI: 0.627-0.843) (p = 0.001) and 0.876 (95% CI, 0.8-0.952) (p < 0.001), respectively. We found a positive association between LF scores and CKD outcomes in hospitalized patients. The LF scores significantly predicted poor outcomes in patients with CKD. Among the scores, GPRI was found to be a stronger predictor in predicting outcomes in both diabetic and non-diabetic patients with CKD. A high GPRI score was also associated with poor outcomes and increased mortality in both diabetics and non-diabetics.

Patient's perspectives of epilepsy care by specialists and generalists: qualitative evidence synthesis.

In the UK, epilepsy care involves both specialists (eg, neurologists) and generalists (eg, general practitioners). Policymakers typically consider that epilepsy care should be integrated and involve both specialists and generalists. However, few understand exactly how patients view and compare specialist and generalist care. This systematic review aimed to explore patient perspectives of specialist care and generalist care for epilepsy in a qualitative evidence synthesis. Systematic Review, qualitative evidence synthesis using an identified framework. Systematic searches in 5 databases retrieved 17 eligible studies. Data was extracted and synthesised using framework analysis informed by the 'United Model of Generalism'. Three themes were developed: 'Epilepsy care can be burdensome' (eg, through care fragmentation); 'Patient's experience is that care is not always accessible' (eg, lack of a continuum between standardised and interpretive care); 'How care could change for people with epilepsy' (eg, clinicians currently have insufficient time to deviate from protocol-driven care to address psychosocial needs). People with epilepsy frequently observe that generalists lack expertise in epilepsy management. This synthesis of patient experiences indicates recommendations should focus on improving communication and integration between specialists and generalists for epilepsy care. Patient experiences indicate specialist care risks being burdensome and generalist knowledge insufficient, requiring enhanced primary care clinician skills and improved awareness of patient psychosocial needs. The findings argue in favour of healthcare policies, materials and tools to continually support patient perspectives in developing epilepsy services.

Effective Patient Engagement in Private Healthcare Institutions in Riyadh: An Analysis of Management Strategies

The healthcare sector in Saudi Arabia is undergoing a remarkable transformation driven by factors such as population growth and rising disposable income. This has led to a significant surge in demand for private healthcare services, particularly in major cities like Riyadh. While catering to the needs of a growing patient base, there is an urgent need to ensure patient-centered care to enhance service quality and satisfaction. Multiple research studies have affirmed the positive impact of patient engagement. Studies consistently demonstrate that patients actively involved in their healthcare decisions experience improved quality of life, better adherence to treatment, and reduced hospital readmission rates. This ultimately leads to improved health outcomes and reduced overall healthcare costs. Despite these well-established benefits, many private healthcare institutions in Riyadh struggle to effectively integrate patient engagement strategies into their practices. This implementation gap necessitates a deeper understanding of the factors influencing these efforts. This study aims to address this pressing need by investigating the factors affecting patient engagement initiatives within private healthcare institutions in Riyadh. The study will explore the influence of three key domains: Digital Technology: The increasing reliance on digital tools in healthcare holds the potential to revolutionize patient engagement. This study will explore how technology platforms, telemedicine applications, and patient portals can facilitate patient engagement. Organizational Leadership: Leadership plays a pivotal role in fostering a patient-centered care culture. This research will examine how leadership styles, commitment to patient engagement, and resource allocation impact patient engagement initiatives. Organizational Culture: The overall culture of a healthcare institution significantly influences patient interactions. This study will explore how factors such as communication practices, staff training, and patient empowerment within the organizational culture shape patient engagement. By examining these three domains, the study aims to provide valuable insights into the factors that influence patient engagement in private healthcare settings in Riyadh. The findings will contribute to the development of effective strategies for enhancing patient engagement and improving overall healthcare quality.

Knowledge, attitude, and practice of artificial intelligence applications in medicine among physicians in Sudan: a national cross-sectional survey.

Artificial intelligence (AI) has emerged as a rapidly developing tool within the medical landscape, globally aiding in diagnosis and healthcare management. However, its integration within healthcare systems remains varied across different regions. In Sudan, there exists a burgeoning interest in AI potential applications within medicine. This study aims to evaluate the knowledge, attitudes, and practices of AI applications in medicine among physicians in Sudan. The authors conducted a web-based survey cross-sectional analytical study using an online questionnaire-based survey regarding demographic details, knowledge, attitudes, and practice of AI distributing through various e-mail listings and social media platforms. A sample of 825 Physicians including doctors in Sudan with different ranks and specialties were selected using the convenient non-probability sampling technique. Out of 825 Physicians, 666 (80.7%) of Physicians have previous knowledge about AI. However, only a small number 123 (14.9%) were taught about AI during their time in medical school, even fewer, just 120 (14.5%) had AI-related lessons in their training program. Regarding attitude, 675 (81.8%) agree that AI is very important in medicine, almost the same number, 681 (82.6%) support the idea of teaching AI in medical schools. Practically, 535 (64.8%) of doctors, think that should get special training in using AI tools in healthcare. Excitingly 651 (78.9%) of physicians are interested in working with AI in future. Based on different ranks of doctors toward AI; Medical Officers exhibited the highest proportion at (32.7%) of knowledge and understanding of AI concepts, followed by House Officers at (16.7%) (p=0.076); regarding attitude, Medical Officers demonstrated the highest (31.6%) favorable attitude, followed by House Officers at (17.5%) (p=0.229); In practice also, Medical Officer showed the highest portion (28.0%) among participants (p=0.129). While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.

Perceptions of the Impact of Artificial Intelligence among Internal Medicine Physicians as a Step in Social Responsibility Implementation: A Cross-Sectional Study.

Artificial Intelligence (AI) has emerged as an essential tool in healthcare for optimizing healthcare delivery and improving patient outcomes. This study is motivated by using AI in healthcare as a step for social responsibility implementation. The research aimed to investigate the attitudes of healthcare professionals on this issue, and it assessed physicians' opinions regarding their perceptions of AI and their intention to use and implement AI tools in their activity. An electronic survey was proposed during February-June 2024 to a sample of healthcare professionals (309 were admitted into the study, 62 males and 247 females, with a mean age of 42). The results of the survey highlighted both groups' excellent perceptions of AI and the low perceived knowledge of AI, which arises from more technical questions. The use of AI in healthcare represents a step for social responsibility implementation; it is an unstoppable process, and stakeholders should take into consideration investing more in monitoring and training activities.

Optimising the structure of outpatient clinic letters: insights from a survey of general practitioners and hospital doctors.

Outpatient letters are an essential communication tool in healthcare. Yet doctors receive little training on letter writing and what details recipients consider important. We surveyed 106 hospital doctors and 63 general practitioners (GPs), identifying differences in each group's preferences; GPs preferred more structured, detailed letters. Opportunities for feedback, formal templates and advanced software systems can improve communication in outpatient clinics.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Shaping the future of healthcare in British Columbia: Establishing provincial clinical governance for responsible deployment of artificial intelligence tools.

As healthcare embraces the transformative potential of Artificial Intelligence (AI), it is imperative to safeguard patient and provider safety, equity, and trust in the healthcare system. This article outlines the approach taken by the British Columbia (BC) Provincial Health Services Authority (PHSA) to establish clinical governance for the responsible deployment of AI tools in healthcare. Leveraging its province-wide mandate and expertise, PHSA establishes the infrastructure and processes to proactively and systematically intake, assess, prioritize, and evaluate AI tools. PHSA proposes a coordinated approach in AI tool deployment in collaboration with regional health authorities to prevent duplication of efforts and ensure equitable access to existing and emerging AI tools across the province of BC, incorporating principles of anti-Indigenous racism, cultural safety, and humility. The proposed governance structure underscores the identification of clinical needs, proactive ethics review, rigorous risk assessment, data validation, transparent communication, provider training, and ongoing evaluation to ensure success.

Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services

The aging population is placing pressure on the healthcare system, and the private sector is innovating healthcare through digitalization. Mobile applications for medical escort services (MA-MES) could become a sustainable healthcare tool, assisting adult children in scheduling escorts to accompany their elderly parents to medical appointments. This creates new collaborative methods and service processes for healthcare services. This research applies the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model to identify the intentions of adult children to use MA-MES for their elderly parents. Data were gathered from 350 individuals in the age group of 20 to 59 years and analyzed using structural equation modeling (SEM). The findings indicated that the performance expectancy (PE), effort expectancy (EE), social influence (SI), price value (PV), and perceived trust (PT) significantly increased behavioral intentions (BI). Perceived risk (PR) mediated the relationship between PT and BI, while age moderated the effects of PT on BI. Cohabitation with elderly parents moderated the effects of SI and PT on BI. This research proposes a unique model to predict the intentions of adult children to use MA-MES for their aging parents. It provides insights enabling managers to carry out continuous innovation in MA-MES.

Barriers and facilitators of improved nutritional support for patients newly diagnosed with cancer: a pre-implementation study

BackgroundDisease-related malnutrition affects a significant number of patients with cancer and poses a major social problem worldwide. Despite both global and national guidelines to prevent and treat malnutrition, the prevalence is high, ranging from 20 to 70% in all patients with cancer. This study aimed to explore the current practice of nutritional support for patients with cancer at a large university hospital in Norway and to explore potential barriers and facilitators of the intervention in the Green Approach to Improved Nutritional support for patients with cancer (GAIN), prior to implementation in a clinical setting.MethodsThe study used individual interviews and a focus group discussion to collect data. Study participants included different healthcare professionals and patients with cancer treated at a nutrition outpatient clinic. The Consolidated Framework for Implementation Research (CFIR) was used to guide the thematic data analysis.ResultsBarriers connected to the current nutritional support were limited resources and undefined roles concerning responsibility for providing nutritional support among healthcare professionals. Facilitators included a desire for change regarding the current nutritional practice. The GAIN intervention was perceived as feasible for patients and healthcare professionals. Potential barriers included limited knowledge of technology, lack of motivation among patients, and a potential added burden experienced by the participating patients.ConclusionsThe identification of the potential barriers and facilitators of the current nutritional support to patients with cancer will be used to plan the implementation of improved nutritional support in a randomized controlled trial for patients with cancer prior to clinical implementation. The current findings may be of value to others trying to implement either or both nutritional support and digital application tools in a clinical healthcare setting.Trial registrationThe study was registered in the National Institutes of Health Clinical trials 08/09/22. The identification code is NCT05544318.

The utility of website-based quality improvement tools for health professionals: a systematic review.

As technology continues to advance, it is important to understand how website-based tools can support quality improvement. Website-based tools refer to resources such as toolkits that users can access and use autonomously through a dedicated website. This review examined how website-based tools can support healthcare professionals with quality improvement, including the optimal processes used to develop tools and the elements of an effective tool. A systematic search of seven databases was conducted to include articles published between January 2012 and January 2024. Articles were included if they were peer reviewed, written in English, based in health settings, and reported the development or evaluation of a quality improvement website-based tool for professionals. A narrative synthesis was conducted using NVivo. Risk of bias was assessed using the Mixed Methods Appraisal Tool. All papers were independently screened and coded by two authors using a six-phase conceptual framework by Braun and Clarke. Eighteen studies met the inclusion criteria. Themes identified were tool development processes, quality improvement mechanisms and barriers and facilitators to tool usage. Digitalizing existing quality improvement processes (n = 7), identifying gaps in practice (n = 6), and contributing to professional development (n = 3) were common quality improvement aims. Tools were associated with the reported enhancement of accuracy and efficiency in clinical tasks, improvement in adherence to guidelines, facilitation of reflective practice, and provision of tailored feedback for continuous quality improvement. Common features were educational resources (n = 7) and assisting the user to assess current practices against standards/recommendations (n = 6), which supported professionals in achieving better clinical outcomes, increased professional satisfaction and streamlined workflow in various settings. Studies reported facilitators to tool usage including relevance to practice, accessibility, and facilitating multidisciplinary action, making these tools practical and time-efficient for healthcare. However, barriers such as being time consuming, irrelevant to practice, difficult to use, and lack of organizational engagement were reported. Almost all tools were co-developed with stakeholders. The co-design approaches varied, reflecting different levels of stakeholder engagement and adoption of co-design methodologies. It is noted that the quality of included studies was low. These findings offer valuable insights for future development of quality improvement website-based tools in healthcare. Recommendations include ensuring tools are co-developed with healthcare professionals, focusing on practical usability and addressing common barriers to enhance engagement and effectiveness in improving healthcare quality. Randomized controlled trials are warranted to provide objective evidence of tool efficacy.