Patient-centered Healthcare System Research Articles

Exploring healthcare workers’ experience and barriers in disclosing patient safety-related incidents: A qualitative study in Indonesian hospitals

ABSTRACT Background Open disclosure of patient safety incidents is a strategy to enhance patient safety by preventing future occurrences. This study aimed to identify factors that facilitate or hinder the implementation of open disclosure in Indonesian hospitals. Methods Using a qualitative descriptive research method, 12 healthcare professionals with at least one year of experience and prior incident disclosure experience participated in online interviews. Thematic analysis revealed that open disclosure practices are uncommon in Indonesian hospitals. Results The findings suggest that open disclosure practices are not common practice in Indonesian hospitals. Key issues include unclear responsibility for disclosure, determining which incidents should be disclosed, understanding patient and family responses, implementing follow-up procedures and addressing barriers such as cultural factors, fear of disclosure, lack of protection and insufficient awareness and training among healthcare professionals. Conclusion Promoting open disclosure can improve patient-provider relationships and patient safety. Therefore, healthcare organizations, policymakers and educational institutions should collaborate to foster open disclosure, safeguard patients and educate healthcare professionals, leading to a more secure, transparent and patient-centric healthcare system.

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant. Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models. Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain. Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.

'Two heads are better than one'-exploring the experiences of Ghanaian communities on the role of patient and public participation in health system improvement.

Patient and Public Participation (PPP) is key to improving health systems. Yet, studies have shown that PPP implementations across many countries have been largely tokenistic. Particularly, in Ghana, whilst PPP is prioritized in national health policies and legislation, there appears to be little research focused on understanding PPP's role in health system improvement. The aim of this study, therefore, is to examine how PPP is working across the Ghanaian health system levels, as well as to understand the perspectives and experiences of participants on how PPP contributes to health system improvement. The qualitative study was undertaken in six communities in three districts in the Ashanti region of Ghana. Data were collected from semi-structured individual interviews. The selection of participants was purposive, based on their PPP-related roles. As a result, findings of this study may not reflect the experiences of others who are not directly involved in PPP initiatives. Thirty-five participants, mainly health service users and health professionals, were interviewed. Data were transcribed and analysed descriptively using Braun and Clarke's (2006) thematic analysis approach. Overall, participants noted PPP implementation was largely limited at higher health system levels (i.e. national, regional and district levels), but was functioning at the community level. PPP also improved access to health services, responsiveness to patient needs, community-health worker relationships, health-seeking behaviours, empowered healthcare users and improved health outcomes. The study, therefore, recommended the need to undertake PPP across all levels of the health system to maximize PPP's role in health system improvement. Finally, the study suggested prioritizing PPP, especially for resource-poor countries, to complement government's efforts in improving accessibility of healthcare services to many communities and also provide a more patient-centred healthcare system responsive to patients' and public needs.

A review of emerging trends in telemedicine: Healthcare delivery transformations

Telemedicine, characterized by the remote delivery of healthcare services using technology, has undergone remarkable transformations in recent years. This review explores the emerging trends in telemedicine and their profound impact on healthcare delivery. The paper begins by providing a background on telemedicine, tracing its evolution, and emphasizing its growing significance in the healthcare landscape. The primary objective is to shed light on the current state of telemedicine, identify key trends, and analyze their transformative effects on healthcare delivery. The technological landscape of telemedicine is examined, focusing on innovations in video conferencing, virtual consultations, and the integration of electronic health records. Furthermore, the role of artificial intelligence (AI) is explored, encompassing diagnostic assistance through medical imaging algorithms and the integration of chatbots and virtual health assistants. Specialized areas of telemedicine, such as mental health services and chronic disease management, are discussed in detail to highlight specific transformations in these domains. The critical aspect of patient and healthcare provider adoption is scrutinized, emphasizing patient engagement, overcoming technological barriers, and addressing the challenges faced by healthcare professionals in virtual settings. Anticipating the future of telemedicine, the review outlines upcoming innovations such as virtual reality applications and the impact of 5G technology. Simultaneously, it addresses persisting challenges, including health inequalities, ensuring quality of care, and ethical considerations. This review synthesizes the current state of telemedicine, identifies transformative trends, and provides insights into the future of healthcare delivery. It calls for continued research, policy development, and strategic implementation to maximize the potential of telemedicine in creating a more accessible, efficient, and patient-centric healthcare system.

Navigating the Ethical Landscape: Implementing Machine Learning in Smart Healthcare Informatics

The integration of Machine Learning (ML) into healthcare informatics holds immense promise, revolutionizing patient care and treatment strategies. However, as this technology advances, it brings forth ethical challenges crucial for careful navigation. ML offers unprecedented abilities to analyze vast healthcare data, leading to personalized medicine and improved outcomes. Yet, ethical concerns emerge, notably in privacy protection, algorithm bias, transparency, informed consent, and data quality. Transparency, explainability, and patient autonomy in decision-making processes are crucial to foster trust and accountability. Striking a balance between innovation and compliance, ensuring data quality, and promoting human-AI collaboration are essential. Addressing these challenges demands adherence to ethical frameworks, continuous monitoring, multidisciplinary governance, education, and regulatory compliance. To fully harness ML's potential in healthcare while upholding ethical standards, collaboration among stakeholders is imperative, ensuring patient welfare remains central amid technological advancements. Ethical considerations must be embedded at every stage of ML implementation to maintain an ethical, equitable, and patient-centered healthcare system.

Readiness for Inter-Professional Learning among Undergraduate Allied Health Science Students in Hayatabad Peshawar

Background: Inter-professional learning (IPL) is increasingly recognized as a vital component of healthcare education, fostering collaboration, enhancing teamwork skills, and preparing students for a patient-centered healthcare system. Despite its growing importance, the readiness and attitudes towards IPL among healthcare students vary across disciplines and regions, necessitating detailed exploration. Objective: This study aimed to assess the attitudes towards and readiness for IPL among undergraduate allied health science students at Khyber Medical University, Peshawar, and to explore the implications of these attitudes for healthcare education and practice. Methods: A cross-sectional survey was conducted over six months, utilizing convenience sampling to recruit 376 undergraduate students from the departments of Physical Therapy, Occupational Therapy, Speech and Language Pathology, and Nursing. The Readiness for Inter-professional Learning Scale (RIPLS) questionnaire was distributed through WhatsApp, Twitter, and email, covering aspects such as teamwork and collaboration, professional identity, and roles and responsibilities. Data were analyzed using SPSS version 25 to calculate means, standard deviations, and to conduct Independent t-tests. Results: The study found that a significant majority of students (72.1%) expressed strong agreement with the importance of IPL in their curriculum. The mean scores for the RIPLS sub-scales were: Teamwork and Collaboration (13.76 ± 2.609), Negative Professional Identity (10.57 ± 1.972), Positive Professional Identity (6.40 ± 1.359), and Role and Responsibility (7.50 ± 1.267). Independent t-tests revealed significant gender differences in attitudes towards teamwork and collaboration (Male: 13.42 ± 2.017, Female: 14.07 ± 3.034, p=0.015) and roles and responsibility (Male: 7.3187 ± 1.16940, Female: 7.6701 ± 1.33290, p=0.007), but not in professional identities. Conclusion: The study underscores a generally positive attitude towards IPL among healthcare students, with notable readiness for teamwork and understanding of roles and responsibilities. These findings highlight the necessity of integrating IPL into healthcare education curricula to enhance collaborative skills and prepare students for the complexities of modern healthcare delivery.

Challenges and strategies for conducting research in primary health care practice: an integrative review

BackgroundProviding accessible and high-quality patient-centered healthcare remains a challenge in many countries, despite global efforts to strengthen primary health care (PHC). Research and knowledge management are integral to enhancing PHC, facilitating the implementation of successful strategies, and promoting the use of evidence-based practices. Practice-based research in primary care (PC-PBR) has emerged as a valuable approach, with its external validity to diverse PHC settings, making it an effective means of translating research findings into professional practice.ObjectiveTo identify challenges and strategies for conducting practice-based research in primary health care services.MethodAn integrative literature review was conducted by searching the PubMed, Embase, Scopus, Web of Science, and Lilacs databases. The research question, guided by the PICo framework, directed the execution of study selection and data extraction. Data analysis followed the RAdAR method's three phases: pre-analysis, data analysis, and interpretation of results.ResultsOut of 440 initially identified articles, 26 met the inclusion criteria. Most studies were conducted in high-income countries, primarily the United States. The challenges and strategies for PC-PBR were categorized into six themes: research planning, infrastructure, engagement of healthcare professionals, knowledge translation, the relationship between universities and health services, and international collaboration. Notable challenges included research planning complexities, lack of infrastructure, difficulties in engaging healthcare professionals, and barriers to knowledge translation. Strategies underscore the importance of adapting research agendas to local contexts, providing research training, fostering stakeholder engagement, and establishing practice-based research networks.ConclusionThe challenges encountered in PC-PBR are consistent across various contexts, highlighting the need for systematic, long-term actions involving health managers, decision-makers, academics, diverse healthcare professionals, and patients. This approach is essential to transform primary care, especially in low- and middle-income countries, into an innovative, comprehensive, patient-centered, and accessible healthcare system. By addressing these challenges and implementing the strategies, PC-PBR can play a pivotal role in bridging the gap between research and practice, ultimately improving patient care and population health.

Implementation of Telemedicine in Medical Practice: Opportunities and Barriers

The integration of telemedicine into medical practice represents a transformative shift in healthcare delivery, offering both unprecedented opportunities and encountering significant barriers. This journal article explores the implementation of telemedicine, examining the multifaceted landscape of benefits and challenges associated with its adoption in medical settings.
 The opportunities presented by telemedicine include enhanced accessibility to healthcare services, especially in remote or underserved areas, improved patient outcomes through timely interventions, and increased efficiency in healthcare delivery. Telemedicine also facilitates remote patient monitoring, teleconsultations, and the exchange of medical information, contributing to a more patient-centric and adaptable healthcare system.
 However, the implementation of telemedicine faces several barriers, including regulatory complexities, concerns about data security and privacy, technological infrastructure limitations, and the need for healthcare professionals to adapt to new modes of communication. Cultural and organizational resistance further complicates the seamless integration of telemedicine into established medical practices.
 The article critically analyzes case studies and global implementations of telemedicine, highlighting successful models and lessons learned. It emphasizes the importance of policy frameworks, standardization, and ongoing training for healthcare providers to optimize the benefits of telemedicine while mitigating potential risks.

A STUDY ON MACHINE LEARNING IN ENHANCING CLINICAL DECISION SUPPORT SYSTEMS

Abstract—In recent years, the integration of machine learning into clinical decision support systems has revolutionized healthcare. This groundbreaking technology has empowered healthcare professionals by enhancing their ability to make more accurate, timely, and personalized decisions for patients. Through the power of machine learning, healthcare providers can now tap into vast amounts of patient data to improve diagnoses, treatment recommendations, and overall patient care. This transformation not only saves lives but also contributes to a more efficient and patient-centered healthcare system. Keywords—Clinical decision support systems, Healthcare, Patient data, Evidence-based recommendations, Patient-centered healthcare system

Editor’s Welcome

Tēnā koutou katoa and welcome to Issue 36 of Te Hautaka o ngā Akongā Rongoā, the New Zealand Medical Student Journal (NZMSJ). The theme of this issue is “Advocacy in Health,” which is crucial in the betterment of the healthcare system for both patients and health professionals. It is our privilege to share with our readers a wide range of insightful and high-quality articles by health care professional students, clinicians, and academics from Aotearoa. Advocacy plays a core role in the realm of healthcare, serving as a powerful catalyst for positive change and improved outcomes on both individual and systemic levels. Healthcare advocacy for the individual involves standing up for the rights, needs, and well-being of patients, ensuring that their voices are heard and their concerns addressed. On a broader scale, advocacy in healthcare contributes to the refinement of healthcare policies, regulations, and services. As providers working on the front lines, health professionals often better identify areas of necessary improvement than policy-makers. Advocacy campaigns and initiatives can shed light on systemic issues, drive policy changes, and influence healthcare regulations that ultimately shape the healthcare experience for everyone. We believe that by holding stakeholders accountable and pushing for transparency, advocacy serves as a counterbalance to potential shortcomings in the healthcare sector. Overall, the significance of ad- vocacy in healthcare extends beyond individual cases, contributing to a more inclusive, patient-centric, and effective healthcare system for entire communities and populations.

Data-Driven Healthcare: Evaluating the Effectiveness of the Patient Record Management System at RHU-Del Carmen

This paper delves into the transformative potential of data-driven approaches in healthcare and evaluates the implementation and impact of the Patient Record Management System (PRMS) at RHU-Del Carmen. The study aims to streamline healthcare operations, enhance data accessibility, and promote evidence-based decision-making. Through a comprehensive evaluation with an overall score of 4.4 out of 5, the study highlights the system's effectiveness in improving patient care, optimizing care coordination, and providing valuable clinical insights. The findings affirm the significance of data-driven healthcare practices and emphasize the importance of leveraging patient data for enhanced healthcare outcomes, ultimately driving towards a patient-centric and data-enabled healthcare system

Toward Patient-Centric Healthcare Systems: Key Requirements and Framework for Personal Health Records Based on Blockchain Technology

Healthcare data are considered sensitive and confidential, and storing these sensitive data in traditional (i.e., centralized) databases may expose risks, such as penetration or data leaks. Furthermore, patients may have incomplete health records since they visit various healthcare centers and leave their data scattered in different places. One solution to resolve these problems and permit patients to own their records is a decentralized personal health record (PHR); this can be achieved through decentralization and distribution systems, which are fundamental attributes of blockchain technology. Additionally, the requirements for this solution should be identified to provide practical solutions for stakeholders. This study aims to identify the key requirements for PHRs. A design science methodology was utilized to meet the study objectives, and thirteen healthcare experts were interviewed to elicit the requirements and the previous studies. Thirty-three requirements are defined, and based on these, high- and low-level architectures are developed and explained. The result illustrates that the developed solution-based Hyperledger Fabric framework is a promising method for the achievement of PHRs that guarantee security aspects, such as integrity, confidentiality, privacy, traceability, and access control.

Preferences for labor and childbirth, expressed orally or as a written birth plan: Prevalence and determinants from a nationwide population-based study.

Shared decision-making is an important component of a patient-centered healthcare system. We assessed the prevalence of parturients with preferences for their labor and childbirth, expressed verbally in the birthing room or as a written birth plan, and studied maternal, obstetric, and organizational factors associated with their expression. Data came from the 2016 National Perinatal Survey, a cross-sectional nationwide population-based survey conducted in France. Preferences for labor and childbirth were studied in three categories: expressed verbally, in writing (birth plan), or unexpressed or nonexistent. Analyses used multinomial multilevel logistic regression. The analysis included 11,633 parturients: 3.7% had written a birth plan, 17.3% expressed their preferences verbally, and 79.0% either did not have or did not express any preferences. Compared with the latter group, written or verbal preferences were both significantly associated with prenatal care by independent midwives (respectively, adjusted odds ratio (aOR) 2.19; 95% confidence interval (CI), [1.59-3.03], and aOR 1.43; 95% CI [1.19-1.71]) and with attendance at childbirth education classes (respectively, aOR 4.99; 95% CI [3.49-7.15], and aOR 2.27; 95% CI [1.98-2.62]). As years in traditional schooling increased, so did its association with preferences. Conversely, parturients from African countries were significantly less likely than French mothers to express preferences. A written birth plan was also associated with characteristics of maternity unit organization. Only one in five parturients reported having expressed preferences for labor and childbirth to healthcare professionals in the birthing room. This expression of preferences was associated with maternal characteristics and the organization of care.

A Patient-Centered Model of Fast-Track Lung Cancer Diagnosis

IntroductionDespite the increasing importance of digital resources in modern life over the past decades, little is known about the impact of internet-based solutions on patient's health. We aimed to study the potential benefit of a digital platform helping patients to deal with abnormal chest CT scan revealing possible lung cancer. MethodsWe set up a fast-track lung cancer diagnosis pathway through a secure online platform. Patient-generated information combined with online review of their imaging enables preplanning of further investigations ahead of clinical assessment. We compared outcomes of “self-referred” patients (patient group), who directly fill out the online questionnaire, to general practitioner-driven patients (GP group), who were referred by their GP. ResultsFrom June 2021 to June 2022, we included 125 patients (61% males, median age 67 years, IQR 56.9-72.5): 41% in the patient group and 59% in the GP group. No difference was found between groups in terms of time from contact to first appointment (median 5 days in both groups, P = .6), percentage of pathways including prebooked tests (94% vs. 92%, P = .6), number of scheduled invasive procedures (median 1, IQR 1-2 vs. 2, IQR 1-2, P = .4) and in final cancer diagnosis (76% vs. 78%, P = .4). ConclusionA lung cancer diagnosis pathway directly accessible by patients through a secure online platform was feasible and as efficient as the usual general practitioner pathway. It demonstrated the benefit of leaning on new digital tools in order to answer to the new challenges of a patient-centered health care system.

How to treat with gynecological cancers during COVID-19 pandemic: A review

The COVID-19 pandemic has created difficulties in managing gynecological malignancies, leading to limitations on routine visits, reduced face-to-face appointments, postponements of surgeries and interventions, and necessary adjustments to treatment strategies. Hospitals have been overwhelmed with COVID-19 cases, making resource allocation a focal point. The pandemic significantly impacted the mental well-being of women with gynecological cancers, leading to heightened anxiety, worries about treatment delays, and increased stress, depression, and fear. Strategies to support mental health, such as telehealth-based mental health services offering online therapy and support groups, have proven beneficial. Additionally, incorporating mindfulness techniques and meditation practices into daily routines could assist patients in coping with stress and anxiety, ultimately fostering emotional resilience. The COVID-19 pandemic has complicated the traditional approach to prioritizing cancer treatment based on cancer stage. Certain gynecological cancers and their treatments can weaken patients' immune systems, making them more susceptible to severe COVID-19 complications. Advanced-stage and aggressively growing cancers pose a particular risk, as postponing treatment due to COVID-19 concerns could lead to disease progression and impact treatment success and patient survival. The urgency of therapeutic decisions during the pandemic is influenced by cancer stage and aggressiveness, with advanced and rapidly progressing cancers requiring prompt intervention. Patient age and overall health status also play crucial roles, as younger patients with strong immune systems may tolerate treatment delays better than older patients with underlying health conditions. COVID-19 outbreak has undeniably disrupted traditional approaches to managing gynecological malignancies, but it has also spurred creativity and adaptation. Treatment decisions for gynecological cancer patients must consider age, overall health, and cancer stage. By embracing telemedicine, using risk assessment models, and prioritizing collaborative patient management, a more resilient and patient-centered healthcare system can be developed to address current and future pandemic challenges.

Inpatient Satisfaction with Nursing Care and Its Impact Factors in Chinese Tertiary Hospitals: A Cross-Sectional Study.

To evaluate the level of patient satisfaction with nursing care in China's major tertiary hospitals, and to explore patient and hospital level factors associated with patient satisfaction, in order to provide policy implications for the evaluation and improvement of nursing care, toward a patient-centered healthcare system. Patient satisfaction with nursing care has been designated as a key measure of the quality of nursing care and is one of the main focuses of the current healthcare improvement campaign in China. We participated in the 2021 China National Patient Survey and designed and administered a survey instrument with five domains, to evaluate patient satisfaction with nursing care among 24,971 inpatients from 143 tertiary hospitals across 31 provinces in China. Descriptive analysis and binary logistic regressions were utilized to assess the level of satisfaction and identify key factors related to nursing satisfaction. The overall satisfaction level is high, but satisfaction with health guidance is relatively low. Patients' sociodemographic characteristics, including age, income, and education, are significantly associated with their satisfaction. Hospital characteristics, such as geographic location, type, and nurse-to-doctor ratio, significantly impact inpatient satisfaction with nursing care. Patients of hospitals in the eastern region, general hospitals, and hospitals with higher nurse-to-doctor ratios are more satisfied with nursing care. More attention should be paid to equitably allocating healthcare resources among different regions, improving the management and competence of non-general hospitals, and strengthening the recruitment and retention of the nursing workforce. Moreover, patient characteristics should be considered when evaluating patient satisfaction across hospitals. Patient and Public Contribution: These findings will help us better understand patients' satisfaction regarding nursing care in Chinese tertiary hospitals and develop targeted interventions, to establish a patient-centered healthcare system.

Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs)

One of the next frontiers in medical research, particularly in orthopaedic surgery, is personalized treatment outcome prediction. In personalized medicine, treatment choices are adjusted for the patient based on the individual's and their disease's distinct features. A high-value and patient-centered health care system requires evaluating results that integrate the patient's viewpoint. Patient-reported outcome measures (PROMs) are widely used to shed light on patients' perceptions of their health status after an intervention by using validated questionnaires. The aim of this study is to examine whether meteorological or light (night vs. day) conditions affect PROM scores and hence indirectly affect health-related outcomes. We collected scores for PROMs from questionnaires completed by patients (N = 2326) who had undergone hip and knee interventions between June 2017 and May 2020 at the IRCCS Orthopaedic Institute Galeazzi (IOG), Milan, Italy. Nearest neighbour propensity score (PS) matching was applied to ensure the similarity of the groups tested under the different weather-related conditions. The exposure PS was derived through logistic regression. The data were analysed using statistical tests (Student's t-test and Mann-Whitney U test). According to Cohen's effect size, weather conditions may affect the scores for PROMs and, indirectly, health-related outcomes via influencing the relative humidity and weather-related conditions. The findings suggest avoiding PROMs' collection in certain conditions if the odds of outcome-based underperformance are to be minimized. This would ensure a balance between costs for PROMs' collection and data availability.

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models.

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

Tackling disease burden through primary care

<p>Lack of appropriate knowledge and awareness among the providers and community about the risk factors and essential life style changes is the leading cause of increasing morbidity and hospital admissions due to non-communicable diseases in our country. The foundations of a patient-centred healthcare system in Pakistan needs to be laid down as a priority. Strengthening of capacity building of the primary healthcare teams across all provinces and building an early primary care physician contact must be ensured at all levels.  </p>

Evaluating a Smart Healthcare System Design Through Participatory Approach.

Advances in computer communication technology have enabled the rapid growth of e-health services for delivering healthcare, such as facilitating online consent and data sharing between patients and health professionals. Developing a patient-centric healthcare system is challenging because by necessity, it should be secure, reliable, and resilient to cyber threats, whilst remaining user-friendly. Key to any development aiming for a refined proof-of-concept (PoC) system is the pursuit of comprehensive public system testing and evaluation. This paper focuses on the methodology and results obtained from the participatory approach adopted by the EU H2020 project Serums to evaluate and demonstrate the effectiveness of a smart healthcare system based on emergent technologies like blockchain, data lake, and multi-factor authentication. We discuss the challenges faced by remote PoC system evaluations with end-users as a consequence of the Covid-19 pandemic.