Barriers Preventing Medical Trainees from Active Participation in Research Activities.

Background: Research has increasingly become important to career progression and a compulsory component in most medical programs. While medical trainees are consistently urged to undertake research endeavors, they frequently encounter obstacles at both personal and organizational levels that impede the pursuit of high-quality research. This study aims to identify the barriers and recommend successful interventions to increase research productivity amongst medical trainees. Methods: A descriptive cross-sectional survey was carried out among interns, residents, and fellows within a single hospital located in the emirate of Abu Dhabi, UAE. The survey included inquiries regarding perceived obstacles hindering engagement in research activities, factors driving motivation for research involvement, and the assessment of how research participation relates to their job in terms of relevance. Results: Fifty-seven medical trainees participated in the survey, reflecting a response rate of 53%. The survey highlighted common obstacles, notably including time constraints, insufficient statistical and methodology training, the weight of other educational commitments, as well as inadequate incentives and rewards. While a majority of participants expressed interest in engaging in research activities, the consensus was that more incentives and increased funding opportunities would significantly encourage their involvement. Conclusion: Implementing successful interventions such as allocating dedicated time for research, facilitating access to research mentors, and organizing training sessions have the potential to be effective strategies in fostering a thriving research culture and subsequently elevating research productivity of medical trainees.

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model.

Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

Evaluation of point-of-care ultrasound training among healthcare providers: a pilot study.

BACKGROUND: The use of Point-of-Care Ultrasound (POCUS) has become prevalent across a variety of clinical settings. Many healthcare professionals have started getting hands-on training. To evaluate the effectiveness of such training programs, this study aimed to assess a 4 day POCUS training course on healthcare providers' skills and knowledge acquisition. A secondary objective of this study is to gain valuable insights into the degree of perception, attitude, interest levels and perceived barriers of medical providers performing POCUS. METHODS: This is a prospective cohort study performed on healthcare providers in an integrated healthcare facility in Abu Dhabi undergoing the POCUS training course in February 2022. Course participants took a pre-course survey to evaluate their baseline knowledge, skills, confidence, perception, and interest in POCUS. The same survey was repeated immediately post-course. In total, seven healthcare professionals responded to the survey with a response rate of 53.8%. All data and information gathered were used to understand the effectiveness of POCUS training and gain insights into the degree of perception, interest and preparedness of POCUS among healthcare professionals in practice. RESULTS: Our results demonstrated that the brief POCUS course was effective in improving POCUS skills, knowledge and confidence amongst in-practice healthcare providers from varying medical specialties. The median skill score increased from 25% pre-course to 50% post-course. There is a notable increase in all skills scores after the POCUS training course with the greatest change in scores seen for adjusting 'gain and depth of image (54.84%), assessing VeXUS score (52.38%) and evaluating lung congestion (50%). The study also provided valuable insights into the perception, attitude, interest and potential barriers of POCUS implementation. Although significant barriers to POCUS are present including the lack of POCUS curriculum, what is challenging is lack of expertise and skills to perform POCUS. Therefore, medical providers must acquire prespecified skills to fully utilize POCUS effectively. CONCLUSION: The study confirmed the effectiveness of short POCUS training in improving the skills, knowledge and confidence of medical providers in practice. Healthcare professionals can master POCUS skills and techniques and gain confidence through brief training courses.

Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review.

BACKGROUND: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE: This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS: A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS: The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS: There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

Evaluating Drivers of the Patient Experience Triangle: Stress, Anxiety, and Frustration.

Patient experience is a widely used indicator for assessing the quality-of-care process during a patient's journey in hospital. However, the literature rarely discusses three components: patient stress, anxiety, and frustration. Furthermore, little is known about what drives each component during hospital visits. In order to explore this, we utilized data from a patient experience survey, including patient- and provider-related determinants, that was administered at a local hospital in Abu Dhabi, UAE. A machine-learning-based random forest (RF) algorithm, along with its embedded importance analysis function feature, was used to explore and rank the drivers of patient stress, anxiety, and frustration throughout two stages of the patient journey: registration and consultation. The attribute 'age' was identified as the primary patient-related determinant driving patient stress, anxiety, and frustration throughout the registration and consultation stages. In the registration stage, 'total time taken for registration' was the key driver of patient stress, whereas 'courtesy demonstrated by the registration staff in meeting your needs' was the key driver of anxiety and frustration. In the consultation step, 'waiting time to see the doctor/physician' was the key driver of both patient stress and frustration, whereas 'the doctor/physician was able to explain your symptoms using language that was easy to understand' was the main driver of anxiety. The RF algorithm provided valuable insights, showing the relative importance of factors affecting patient stress, anxiety, and frustration throughout the registration and consultation stages. Healthcare managers can utilize and allocate resources to improve the overall patient experience during hospital visits based on the importance of patient- and provider-related determinants.

A Systematic Literature Review of Safety Culture in Hemodialysis Settings.

Background: Safety culture is an important aspect of quality in healthcare settings. There are many risks that patients can encounter in hemodialysis settings one of which is the infection risks due to the regular need to access bloodstreams using catheters and needles. Implementation of prevention guidelines, protocols and strategies that reinforce safety culture excellence are essential to mitigate risks. The objective of this study was to identify and characterize the main strategies that enhance and improve patient safety culture in hemodialysis settings. Methods: Medline (via PubMed) and Scopus were searched from 2010 to 2020 in English. Terms defining safety culture, patient safety were combined with the term hemodialysis during the search. The studies were chosen based on inclusion criteria. Results: A total of 17 articles reporting on six countries were identified that met inclusion criteria following the PRISMA statement. From the 17 papers, practices that were successfully applied to improve safety culture in hemodialysis settings included (i) training of nurses on the technologies used in hemodialysis treatment, (ii) proactive risk identification tools to prevent infections (iii) root cause analysis in evaluating the errors, (iv) hemodialysis checklist to be used by the dialysis nurses to reduce the adverse events, and (v) effective communication and mutual trust between the employee and leadership to support no-blame environment, and improve the safety culture. Conclusion: This systematic review provided significant insights on the strategies that healthcare safety managers and policy makers can implement to enhance safety culture in hemodialysis settings.

COVID-19, jobs and skills-Exploratory analysis of the job postings in the US and UK healthcare job market.

The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner.

Nexus between drivers of COVID-19 and country risks.

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.

Identifying Patient Satisfaction Determinants in Hemodialysis Settings: A Systematic Review.

Purpose: Patient satisfaction is a measure of care quality that assists providers in determining the effectiveness of their services while meeting patients' expectations. This study aimed to review existing studies that have focused on patients' satisfaction determinants in Hemodialysis (HD) settings. Methods: Electronic databases (PubMed, ScienceDirect, Scopus, and Google Scholar) were searched from 2000 onwards to identify studies using search terms related to patient satisfaction and hemodialysis centers. Article review was limited to studies written in English. A total of 19 articles were included by following the PRISMA statement. Data were extracted using a structured form and summarized in a tabular format to identify different determinants that showed a relationship with patient satisfaction. Determinants were classified into provider-related determinants and patient-related characteristics. Results: Provider-related determinants of patient satisfaction in HD centers include staff, facility, service, and treatment. Patient-related characteristics associated with satisfaction include demographics and health status history. Based on this systematic review, key correlates of patient satisfaction in hemodialysis centers include: staff, facility, service, treatment, patient's demographics, and health status. Conclusion: The findings of this study can help healthcare facilities in taking measures in line with the specified determinants to enhance patient satisfaction and improve the organizational performance of the healthcare centers. It is important to constantly study and improve these determinants based on patient feedback to improve patient satisfaction and quality of care.

Burnout and Cardiovascular Risk in Healthcare Professionals During the COVID-19 Pandemic.

Introduction: The objective of this study was to investigate the psychosocial and cardiovascular markers in healthcare professionals during the COVID-19 pandemic. Methods: This was a STROBE compliant, blended exploratory study. Residents, staff physicians, nurses, and auxiliary healthcare professionals from both inpatient and outpatient medicine services were recruited using a planned random probability sample. The Maslach Burnout Inventory (MBI), Fuster-BEWAT score (FBS), and socio-demographic factors, as well as sleep quality, were studied. The correlations between burnout severity and cardiovascular risk were examined using multivariable linear regression models adjusted for confounding variables, such as sociodemographic and anthropometric characteristics. Results: The regression analysis with FBS as the outcome showed a negative association between cardiovascular health and emotional exhaustion [Coef.(95%CI): -0.029 (-0.048, -0.01), p = 0.002]. The higher the emotional exhaustion the lower the cardiovascular health. Further, the model showed a positive association between personal accomplishment and cardiovascular health [Coef.(95%CI): 0.045 (0.007, 0.082), p = 0.02]. Emotional exhaustion was significantly positive correlated with REM sleep and light average (Spearman's rank correlation: 0.37 and 0.35, respectively, with P < 0.05). Conclusion: The data from this study show that healthcare practitioners who are with burnout and emotional exhaustion have an elevated cardiovascular risk, however, causality cannot be determined. As an adaptive response to stressful situations, REM sleep increases. The findings of this study may be relevant in creating preventive strategies for burnout and cardiovascular risk reduction or prevention. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT04422418].

The integration of mHealth technologies in telemedicine during the COVID-19 era: A cross-sectional study.

Telemedicine is a rapidly expanding field of medicine and an alternative method for delivering quality medical care to patients' fingertips. With the COVID-19 pandemic, there has been an increase in the use of telemedicine to connect patients and healthcare providers, which has been made possible by mobile health (mHealth) applications. The goal of this study was to compare the satisfaction of patients with telemedicine among mHealth users and non-users. This was a survey-based study that included outpatients from Abu Dhabi. The association between patient satisfaction with telemedicine and use of mHealth technologies was described using regression models. This study included a total of 515 completed responses. The use of mHealth application was significantly associated with ease of booking telemedicine appointments (OR 2.61, 95% CI 1.63-4.18; P < .001), perception of similarity of quality of care between telemedicine consultations and in-person visits (OR 1.81, 95% CI 1.26-2.61; P = .001), and preference for using telemedicine applications over in-person visits during the COVID-19 pandemic (OR 1.74, 95% CI 1.12-2.72; P = .015). Our study results support that the use of mHealth applications is associated with increased patient satisfaction with telemedicine appointments.

Exploring Quality Differences in Telemedicine Between Hospital Outpatient Departments and Community Clinics: Cross-sectional Study.

BACKGROUND: Telemedicine is a care delivery modality that has the potential to broaden the reach and flexibility of health care services. In the United Arab Emirates, telemedicine services are mainly delivered through either integrated hospital outpatient department (OPDs) or community clinics. However, it is unknown if patients' perceptions of, and satisfaction with, telemedicine services differ between these two types of health care systems during the COVID-19 pandemic. OBJECTIVE: We aimed to explore the differences in patients' perceptions of, and satisfaction with, telemedicine between hospital OPDs and community clinics during the COVID-19 pandemic. We also aimed to identify patient- or visit-related characteristics contributing to patient satisfaction with telemedicine. METHODS: In this cross-sectional study that was conducted at Abu Dhabi health care centers, we invited outpatients aged 18 years or over, who completed a telemedicine visit during the COVID-19 pandemic, to participate in our study. Patients' perceptions of, and satisfaction with, telemedicine regarding the two system types (ie, hospital OPDs and community clinics) were assessed using an online survey that was sent as a link through the SMS system. Regression models were used to describe the association between patient- and visit-related characteristics, as well as the perception of, and satisfaction with, telemedicine services. RESULTS: A total of 515 patients participated in this survey. Patients' satisfaction with telemedicine services was equally high among the settings, with no statistically significant difference between the two setting types (hospital OPDs: 253/343, 73.8%; community clinics: 114/172, 66.3%; P=.19). Video consultation was significantly associated with increased patient satisfaction (odds ratio [OR] 2.57, 95% CI 1.04-6.33; P=.04) and patients' support of the transition to telemedicine use during and after the pandemic (OR 2.88, 95% CI 1.18-7.07; P=.02). Patients who used video consultations were more likely to report that telemedicine improved access to health care services (OR 3.06, 95% CI 1.71-8.03; P=.02), reduced waiting times and travel costs (OR 4.94, 95% CI 1.15-21.19; P=.03), addressed patients' needs (OR 2.63, 95% CI 1.13-6.11; P=.03), and eased expression of patients' medical concerns during the COVID-19 pandemic (OR 2.19, 95% CI 0.89-5.38; P=.09). Surprisingly, middle-aged patients were two times more likely to be satisfied with telemedicine services (OR 2.12, 95% CI 1.09-4.14; P=.03), as compared to any other age group in this study. CONCLUSIONS: These findings suggest that patient satisfaction was unaffected by the health system setting in which patients received the teleconsultations, whether they were at hospitals or community clinics. Video consultation was associated with increased patient satisfaction with telemedicine services. Efforts should be focused on strategic planning for enhanced telemedicine services, video consultation in particular, for both emergent circumstances, such as the COVID-19 pandemic, and day-to-day health care delivery.

Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety.

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

Prioritizing Multidimensional Interdependent Factors Influencing COVID-19 Risk.

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting.

Variation in hospital performance measures from the Turkey Ministry of Health.

BACKGROUND: The Turkish healthcare system has seen broad population-based improvements in expanded health insurance coverage and access to healthcare services. Hospital performance in this national system is understudied. We aimed to identify trends in hospital performance over time following implementation of the Health Transformation Program and describe how regional outcomes correlate with regional vital statistics. OBJECTIVE: We examine hospital performance data collected by the PHA from 2013 to 2015. We aim to identify the temporal variation in hospital performance for nearly 30 individual measures and to describe the relationship between hospital-level performance measures and regional vital statistics. METHODS: We conducted a retrospective cohort study of 674 public hospitals in Turkey using baseline data from 2013 and follow-up data from 2014-15 collected by the Turkish Statistical Institution and the Public Hospital Agency. We report demographic and socioeconomic data across 12 geographic regions and analyze 29 hospital-level performance measures across four domains: (i) health services; (ii) administrative services; (iii) financial services and (iv) quality measures. We examine temporal variation, and study correlation between performance measures and regional vital statistics. We fit mixed-effects linear regression models to estimate linear trend over time accounting for within-hospital residual correlation. We prepared our manuscript in accordance with guidelines set by the STROBE statement for cohort studies. RESULTS: During the 3 years of study period, 21 of 29 measures improved and 8 measures worsened. All but three measures demonstrated significant differences across regions of the country. Several measures, including inpatient efficiency, patient satisfaction and audit score, are associated with regional infant mortality and life expectancy. CONCLUSIONS: Evidence of temporal improvement in hospital-level performance may suggest some positive changes within the Turkish national healthcare system. Correlation of some measures with regional level health outcomes suggests a quality measurement strategy to monitor performance changes in the future. Although hospital-level functions have improved performance, the results of our study may help achieve further improvement for the health of the country's citizens.

Exploring drivers of patient satisfaction using a random forest algorithm.

BACKGROUND: Patient satisfaction is a multi-dimensional concept that provides insights into various quality aspects in healthcare. Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear. METHODS: We used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions-demographics, time, behaviour, and procedure-influence patient satisfaction. RESULTS: Our results showed that the 'age' attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. 'Total time taken for registration' and 'attentiveness and knowledge of the doctor/physician while listening to your queries' are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that 'demographics' are the most influential type in the registration stage, whereas 'behaviour' is the most influential in the consultation stage. CONCLUSIONS: Generating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern.

Physicians' Attitudes Toward Telemedicine Consultations During the COVID-19 Pandemic: Cross-sectional Study.

BACKGROUND: To mitigate the effect of the COVID-19 pandemic, health care systems worldwide have implemented telemedicine technologies to respond to the growing need for health care services during these unprecedented times. In the United Arab Emirates, video and audio consultations have been implemented to deliver health services during the pandemic. OBJECTIVE: This study aimed to evaluate whether differences exist in physicians' attitudes and perceptions of video and audio consultations when delivering telemedicine services during the COVID-19 pandemic. METHODS: This survey was conducted on a cohort of 880 physicians from outpatient facilities in Abu Dhabi, which delivered telemedicine services during the COVID-19 pandemic between November and December 2020. In total, 623 physicians responded (response rate=70.8%). The survey included a 5-point Likert scale to measure physician's attitudes and perceptions of video and audio consultations with reference to the quality of the clinical consultation and the professional productivity. Descriptive statistics were used to describe physicians' sociodemographic characteristics (age, sex, designation, clinical specialty, duration of practice, and previous experience with telemedicine) and telemedicine modality (video vs audio consultations). Regression models were used to assess the association between telemedicine modality and physicians' characteristics with the perceived outcomes of the web-based consultation. RESULTS: Compared to audio consultations, video consultations were significantly associated with physicians' confidence toward managing acute consultations (odds ratio [OR] 1.62, 95% CI 1.2-2.21; P=.002) and an increased ability to provide patient education during the web-based consultation (OR 2.21, 95% CI 1.04-4.33; P=.04). There was no significant difference in physicians' confidence toward managing long-term and follow-up consultations through video or audio consultations (OR 1.35, 95% CI 0.88-2.08; P=.17). Video consultations were less likely to be associated with a reduced overall consultation time (OR 0.69, 95% CI 0.51-0.93; P=.02) and reduced time for patient note-taking compared to face-to-face visits (OR 0.48, 95% CI 0.36-0.65; P<.001). Previous experience with telemedicine was significantly associated with a lower perceived risk of misdiagnosis (OR 0.46, 95% CI 0.3-0.71; P<.001) and an enhanced physician-patient rapport (OR 2.49, 95% CI 1.26-4.9; P=.008). CONCLUSIONS: These results indicate that video consultations should be adopted frequently in the new remote clinical consultations. Previous experience with telemedicine was associated with a 2-fold confidence in treating acute conditions, less than a half of the perceived risk of misdiagnosis, and an increased ability to provide patients with health education and enhance the physician-patient rapport. Additionally, these results show that audio consultations are equivalent to video consultations in providing remote follow-up care to patients with chronic conditions. These findings may be beneficial to policymakers of e-health programs in low- and middle-income countries, where audio consultations may significantly increase access to geographically remote health services.

Ensuring protocol compliance and data transparency in clinical trials using Blockchain smart contracts.

BACKGROUND: Clinical Trials (CTs) help in testing and validating the safety and efficacy of newly discovered drugs on specific patient population cohorts. However, these trials usually experience many challenges, such as extensive time frames, high financial cost, regulatory and administrative barriers, and insufficient workforce. In addition, CTs face several data management challenges pertaining to protocol compliance, patient enrollment, transparency, traceability, data integrity, and selective reporting. Blockchain can potentially address such challenges because of its intrinsic features and properties. Although existing literature broadly discusses the applicability of blockchain-based solutions for CTs, only a few studies present their working proof-of-concept. METHODS: We propose a blockchain-based framework for CT data management, using Ethereum smart contracts, which employs IPFS as the file storage system to automate processes and information exchange among CT stakeholders. CT documents stored in the IPFS are difficult to tamper with as they are given unique cryptographic hashes. We present algorithms that capture various stages of CT data management. We develop the Ethereum smart contract using Remix IDE that is validated under different scenarios. RESULTS: The proposed framework results are advantageous to all stakeholders ensuring transparency, data integrity, and protocol compliance. Although the proposed solution is tested on the Ethereum blockchain platform, it can be deployed in private blockchain networks using their native smart contract technologies. We make our smart contract code publicly available on Github. CONCLUSIONS: We conclude that the proposed framework can be highly effective in ensuring that the trial abides by the protocol and the functions are executed only by the stakeholders who are given permission. It also assures data integrity and promotes transparency and traceability of information among stakeholders.

Evaluating the Impact of Patient No-Shows on Service Quality.

PURPOSE: Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand. METHODS: In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region. RESULTS: Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients' financial situation, environmental factors and scheduling policy. CONCLUSION: In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications.

PURPOSE: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes. METHODS: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi. RESULTS: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste. CONCLUSION: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.