Performance Of Health Care System Research Articles

Characterising performance information use in the primary healthcare systems of El Salvador, Lebanon and Malawi: multiple qualitative case study protocol

IntroductionGovernments in low-income and middle-income countries (LMICs) and official development assistance agencies use a variety of performance measurement and management approaches to improve the performance of healthcare systems. The effectiveness...

Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis

A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems.

4.Q. Workshop: Showcasing PHIRI use case results measuring the impact of COVID-19 on population health

Abstract The COVID-19 pandemic has clearly demonstrated the urgent need for a cross-border and structured European mechanism to exchange, organise and access reliable health information between countries, especially in the area of population health. Population health information, defined by data on health status, health determinants and healthcare systems performance, allows for oriented research to increase the knowledge base in Europe and underpin political decision-making. Its exchange requires timely and topical provision of high-quality health information. There are many indirect effects of the COVID-19 pandemic that affect health through various pathways including secondary consequences on health and wellbeing due to delayed prevention, diagnosis and medical treatment. Within the Population Health Information Research Infrastructure (PHIRI) we look at four use cases measuring the impact of COVID-19 on population health and demonstrating how a broad variety of routine data can be pooled and/or used for secondary analysis in a distributed way across Europe aiming to facilitate research by making scalable, reproducible methods available. These use cases represent pilot activities for the benefits and added value of an infrastructure supporting federated analysis by bringing together data from different European countries and feeding the results into the federated research infrastructure. In over 20 data hubs, data is mobilised and ready to be analysed in a distributed manner. The use case outputs will be processed in an interoperable way by formalising data models, data management processes and analytical pipelines, all of which are part of the client-server PHIRI federated infrastructure implemented as here 10.5281/zenodo.6483177. The workshop aims to ensure a better understanding of COVID-19 impacts in specific subgroups and risk settings by conducting research through real-life use cases of immediate relevance. The FAIRified use cases analysis results focusing on comparisons between countries are presented and provide actionable outcomes to guide policy makers in preparedness and response scenarios. Knowledge and expertise developed across Europe is shared in this workshop. The four presentations will focus on selected aspects of COVID-19 impacts on population health. The first presentation will be on direct and indirect determinants of COVID-19 infection and outcomes in vulnerable population groups with reference to inequalities. This will be followed by a contribution of COVID-19 related delayed care in breast cancer patients. The third presentation looks at the impacts of COVID-19 on perinatal health inequalities followed by the fourth on insights in COVID-19 related changes in population mental health. Exchange with the audience will facilitate knowledge and opinion exchange through an interactive Mentimeter poll during the session. Key messages • The results will support the exchange of knowledge and expertise by facilitating insights in the impacts of COVID-19 in specific subgroups and risk settings compared across European countries. • Actionable outcomes to guide political decision-making in preparedness and response scenarios will be provided.

Development and Validation of Models for Preoperative Risk of Postoperative Infections Using Electronic Health Records Data

Introduction: The American College of Surgeons (ACS) NSQIP and the National Health and Safety Network (NHSN) provide risk-adjusted postoperative infection rates for hundreds of hospitals across the US. However, manual review of electronic health records (EHR) is burdensome, expensive, and inefficient. The purpose of this study was to develop and validate preoperative prediction models for infectious adverse events using machine learning techniques which does not require manual chart review. Methods: We developed models for postoperative infection surveillance using EHR data linked to ACS NSQIP outcomes data. Combined with the preoperative risk models presented here, we will estimate risk-adjusted postoperative infection rates. EHR predictors included laboratory data, procedures, diagnosis phenotypes, wound class, surgeon specialties, comorbidities, and other operative variables. We used the knockoff filter to select predictors for the following outcomes: surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia. Predictors were first selected by the knockoff filter in a training dataset, then logistic regression models were fit using the selected predictors. Performance was evaluated in the test dataset. Results: Each model contained between 6-12 predictors, and achieved >66% sensitivity, >75% specificity, and 0.75-0.9 area under the curve in the test dataset. This is similar performance compared with the ACS NSQIP risk calculator and other previously published preoperative risk calculators that require user manual input. Conclusion: Preoperative risk was estimated with good performance in a large healthcare system using EHR data. These models can be combined with postoperative surveillance models to estimate risk-adjusted rates of postoperative infections at the surgeon and subspecialty levels for all operations.

Modeling the impact of mitigation policies in a pandemic: A system dynamics approach

In the absence of effective and adequate vaccines, healthcare decision-makers must rely on non-pharmaceutical interventions (NPIs), such as lockdown, testing, hospital capacity building, and increasing the number of medical staff to control the outbreak of an infectious disease like COVID-19. This manuscript presents a System Dynamics (SD) model to analyze the healthcare system performance under various NPIs during a pandemic. The proposed model, which extends the commonly-used Susceptible-Exposed-Infectious-Recovered (SEIR) model, comprises four sub-models: outbreak, hospital performance, medicine supply, and staff functionality. These sub-models work in harmony to stimulate the impact of NPIs on the disease outbreak pattern and the healthcare system's response to demand surge. The proposed model considers the uncertainty about the nature of the disease, the public's behavior, medicine availability, and medical staff efficiency. The proposed model was applied for the ex-ante evaluation of candidate NPIs adoptable against the COVID-19 outbreak in Iran. Consistent with the reported statistics, the results show that the peak demand can significantly exceed the healthcare system's initial capacity if no action is taken. If simultaneously implemented, lockdown and testing can considerably delay the peak of infections, reduce its magnitude, dampen the hospital demand, and decrease mortality. The proposed model is unique as it determines the extent to which system components (e.g., community, healthcare system, and medicine supply chain) impact the observed outcomes (e.g., morbidity and mortality rates). Its structure is generic and flexible, which facilitates the extension and application of the model to evaluate candidate mitigation policies in various geographical contexts.

IoT Technology Applications and Healthcare System's Performance in the era of 4IR: Evidence from Bangladesh

IoT Technology Applications and Healthcare System's Performance in the era of 4IR: Evidence from Bangladesh

Using the quadruple aim to understand the impact of virtual delivery of care within Ontario community health centres: a qualitative study.

The onset of the COVID-19 pandemic and introduction of various restrictions resulted in drastic changes to 'traditional' primary healthcare service delivery modalities. To understand the impact of virtual care on healthcare system performance within the context of Ontario community health centres (CHCs). Thematic analysis of qualitative interviews with 36 providers and 31 patients. Semi-structured phone interviews were conducted in the autumn of 2020. Interviews were recorded, transcribed verbatim, and analysed thematically using the quadruple aim framework. The transition to virtual delivery of services has had both positive and negative impacts on care. Virtual care removed certain barriers to care such as access. However, patients and providers found that phone visits impacted quality of care owing to the inability to read body language and having to rely solely on patient descriptions. Video visits allowed for a similar experience to in-person visits but technical issues constrained this interaction. Depending on the service provided, some providers felt they were not providing the same quality of care. However, providers reported reductions in no-show rates and highlighted the efficiency of virtual appointments. Providers also found they were able to collaborate at a similar level before the pandemic and saw improvements in work-life balance. Overall, patients and providers alike preferred virtual visits with those known to them, and for less complex or transactional aspects of care. The study described positive and negative impacts on patient care, population health, health system costs, and provider experience. These results will be useful for primary care organisations in post-pandemic planning; however, future research is needed for a deeper exploration of the impact on quality of care specifically for more complex health concerns.

Utilization of an Internal Medicine Triaging Resident in the Emergency Department Reduces Preventable Admissions and Improves Trainee Experiences.

Increasing patient care requirements and suboptimal communication between emergency department (ED) and Internal Medicine (IM) services may lead to inefficient hospital utilization, lapses in transitions of care, and reduced trainee satisfaction in the inpatient setting. Furthermore, a lack of triaging roles for IM trainees has been a common limitation in graduate medical education. We aimed to demonstrate that the addition of an IM triaging resident (TR) in the ED may represent an innovative solution to these problems. A single-center pilot study was performed. An IM trainee served as the TR at a tertiary Veterans Affairs hospital for 2 weeks. The TR evaluated medical patients in a parallel manner with ED physicians and assisted in the initial management, disposition, and transitions of care under the supervision of an IM attending physician. Hospital utilization and patient safety were tracked using electronic records, and trainee satisfaction was measured using daily surveys administered to IM resident teams. Of the 62 cases evaluated by the TR for medical admission, 26 (42%) represented preventable admissions; 12 (46%) of those patients were discharged from the ED, representing a 19% overall reduction. There were statistically significant improvements in trainee experiences relating to patient flow (P < 0.01) and initial patient management (P < 0.02), and our intervention did not have a negative impact on ED performance metrics or patient safety. Expansion of this model in select integrated health systems may improve graduate medical education and healthcare system performance. Future iterations of this study can aim to improve transitions of care between ambulatory and inpatient providers and limit the overuse of antimicrobial agents, radiography, and consultative services.

Do Hospitals Achieving "Leader in LGBTQ Healthcare Equality" Maintain Higher Patient Scores Compared To Nonleaders?

The lesbian, gay, bisexual, transgender, and queer (LGBTQ) population deals with complex medical and behavioral issues coupled with barriers in accessing both healthcare and health insurance leading to poorer health outcomes as compared with the general population. Because this community is often overlooked in efforts to improve minority health disparities, in 2007, the Human Rights Campaign Foundation introduced the Healthcare Equality Index (HEI) for evaluating healthcare facilities' policies and practices related to the equity and inclusion of their LGBTQ patients, visitors, and employees. This study's aim was to determine whether the LGBTQ Healthcare Equality Leader institution designation advantage found by DiLeo et al (2020) has a continuing positive effect on Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) performance. We found that patient scores for both Overall Rating and Willingness to Recommend were higher with noted improvement for HEI Leader institutions as compared with non-HEI Leader institutions over this study's 3-year period. Based on the findings of this study, it can be implied that hospitals do receive a return on their investment for achieving or maintaining the HEI Leader designation and this investment benefits all populations served by the organization inferred by their maintained higher patient experience scores.

Impact of Learning Organization’s Dimensions on Saudi Nurses’ Performance, A Cross-Sectional Study

Background: As nurses are the forces that implement changes to meet organizational missions and objectives, the impact of learning organization on their performance indicates the effect of implementing a learning organization on the healthcare system performance. This study aims to assess the impact of learning organization dimensions on nurses’ performance. Method: The study uses a cross-sectional, correlational design. Results: The results revealed a significant relationship between learning organization dimensions and nurses’ contextual performance (r = 0.278, p &lt; 0.01). Conclusion: The findings revealed positive significant relationships between the learning organization’s dimensions and the nursing staff’s performance. The greatest correlation value is continuous learning at the task performance and contextual performance levels.

The epidemiological characteristic and trends of burns globally

BackgroundBurns is a type of injury, caused by unintentional exposure to substances of high temperature, including hot liquid, solid, and objects radiating heat energy, placing a high burden not only on patients’ families but also on national healthcare systems globally. It is difficult for policymakers and clinicians to formulate targeted management strategies for burns because data on current epidemiological patterns worldwide are lacking.MethodsData on burns were obtained from the Global Burden of Disease (GBD) 2019 Study. The incidence, disability-adjusted life years (DALYs), and deaths of burns in 204 countries and regions from 1990 to 2019 were calculated and stratified by sex, age, geographical location, and sociodemographic index (SDI). The estimated annual percentage change (EAPC) of incidence, DALYs, and deaths was calculated to evaluate the temporal trends. All analyses were performed using R software, version 4.1.1, with 2-sided P-values < .05 indicating a statistically significant difference.ResultsA total of 8,378,122 new cases (95% UI, 6,531,887–10,363,109cases) of burns were identified globally in 2019, which is almost evenly split between men and women, and most of the new cases were concentrated in the 10–19-year age group. Besides, burns account for 111,292 deaths (95% UI, 132,392–88,188) globally in 2019, most of which were concentrated in those aged 1–4 years. The burden of burns measured in DALYs was 7,460,448.65 (95% UI, 5,794,505.89–9,478,717.81) in 2019, of which 67% and 33% could be attributed to YLLs and YLDs, respectively. The EAPC of incidence, DALYs, and deaths were negative, the age-standardized rate (ASR) of incidence, DALYs, and deaths were considered to be decreasing in most of the regions, and the EAPCs were negatively correlated with SDI levels, universal health coverage (UHC), and gross domestic product (GDP).ConclusionGlobally, the age-standardized rates of burn incidence, DALYs, and mortality, as well as the number of burn DALYs and death cases will continuously decrease, but the number of new burn cases has an increasing tendency globally. In addition, the EAPCs of burns in incidence, DALYs, and deaths indicated that the burden of burns was considered to be decreasing in most of the regions. And from the relationship of EAPCs with SDI, UHC index, and GDP, indicate that prevention burns not only depend on health spending per capita but also depend on the education level per capita and healthcare system performance, but it does not mean higher health spending corresponds to higher UHC index, which needs high efficiency of translating health spending into individuals health gains.

Protocol for a proof-of-concept study evaluating systematic quality improvement with Realtime event support (SQUIRES)

Introduction: Quality improvement (QI) in healthcare results in better patient outcomes, healthcare system performance, and professional development. One target of QI initiatives in the perioperative period is surgical site infections (SSI), for which several risk factors have been identified. Reliable administration of indicated surgical antibiotic prophylaxis is a modifiable factor of particular relevance. We hypothesize that a novel telemedicine-augmented quality improvement program will improve administration of surgical antibiotic prophylaxis. Objectives: The objective of this QI study is to evaluate the utility of a telemedicine-augmented QI initiative on administration of timely surgical antibiotic prophylaxis. The incidence of SSI will also be reported for multiple surgical services. Methods: This will be a multi-center prospective before-and-after proof-of-concept study. Patients undergoing a surgical procedure across seven operating room facilities at four hospitals in the BJC Healthcare System will be included. Approximately 40,000 patients over an eight-month period will be enrolled. This eight-month period will include a baseline observational phase, an education intervention phase, an intervention phase employing real-time event detection with associated guidance from a remote telemedicine center, and a subsequent observational phase. The primary outcome will be administration of on-time surgical antibiotic prophylaxis throughout the trial. Other outcomes will include incidence of SSIs. Registration Information: This trial is registered on clinicaltrials.gov, NCT04983329 (30th July 2021).

Socioeconomic inequality in public satisfaction with the healthcare system in China: a quantile regression analysis

BackgroundAs China pursues better social equality and improvement in public services (healthcare), public satisfaction has been considered as a key performance indicator. There is a great need to better understand the disparities and inequalities in the public satisfaction with its healthcare system.MethodsBased on Chinese General Social Survey (CGSS) 2015 ( a set of nationally representative survey data, the most recent wave containing information about public satisfaction with the healthcare system), this study utilizes the quantile regression method to analyze how the public satisfaction at high or low quantile of the score distribution varies according to the socio-economic status and healthcare system performance indicators, especially in rural areas.ResultsThis study found that, at the highest percentile, better Self-Reported-Health (SRH) is associated significantly with a lower satisfaction score (coefficient -4.10, P < 0.01). High socioeconomic status (especially "above average" group) has higher satisfaction scores at both mean (coefficient 3.74, P<0.01) and median (coefficient 3.83, P<0.01). This effect is also significant across the lower quantiles of the satisfaction levels. West and Middle region (the less developed regions) tended to be more satisfied, whereas those in Northeast reported a large negative effect (coefficient -7.07, P < 0.01) at the median. While rural residents generally reported higher levels of satisfaction, rural residents’ preference regarding hospital beds and primary care access seems generally to be opposite to that of urban residents.ConclusionOur findings suggest that the ongoing healthcare reform needs to integrate more preventive care to meet the healthy residents’ expectation and demands. More attention should be guided to the vulnerable healthcare system in the Northeast region, which has a stagnant local economy. Outcome-based quality care is especially preferred in rural healthcare, in addition to improvement of utilization and access. In addition, the “pro-rich” inequality is an ongoing concern about the system.

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

Measuring Organizational Culture in Ethiopia's Primary Care System: Validation of a Practical Survey Tool for Managers.

Organizational culture has been widely recognized as predictive of health system performance and improved outcomes across various healthcare settings. Research on organizational culture in healthcare has been largely conducted in high-income settings, and validated scales to measure this concept in primary healthcare systems in low- and middle-income country (LMIC) settings are lacking. Our study aimed to validate a tool to measure organizational culture in the context of the Ethiopian Primary Healthcare Transformation Initiative (PTI), a collaborative of the Federal Ministry of Health (FMoH) and the Yale Global Health Leadership Initiative to strengthen primary healthcare system performance in Ethiopia. Following established survey development and adaptation guidelines, we adapted a 31-item US-based organizational culture scale using (1) cognitive interviewing, (2) testing with 1176 district and zonal health officials from four regions in Ethiopia, and (3) exploratory factor analysis (EFA). Based on the results of cognitive interviewing, an adapted 30-item survey was piloted. The factor analyses of 1034 complete surveys (88% complete responses) identified five constructs of the scale which demonstrated strong validity and internal consistency: learning and problem solving, psychological safety, resistance to change, time for improvement, and commitment to the organization. Of the 30 a priori items, 26 items loaded well on the five constructs (loading values 0.40-0.86), and 4 items failed to load. Cronbach alpha coefficients were 0.86 for the scale as a whole and ranged from 0.65 to 0.90 for the subscales. The five-factor solution accounted for 62% of total variance in culture scores across respondents. Through validation and factor analyses, we generated a 26-item scale for measuring organizational culture in public primary healthcare systems in LMIC settings. This validated tool can be useful for managers, implementers, policy-makers, and researchers to assess and improve organizational culture in support of improved primary healthcare system performance.

Activating people to partner in health and self-care: use of the Patient Activation Measure.

Summary Patient activation is a behavioural concept and is at the heart of personalised care. It is defined as an individual’s knowledge, skill and confidence for managing their health and health care.Evidence indicates that patient activation scores can predict health behaviour and are closely linked to various clinical outcomes: reduced unnecessary emergency department visits, hospital admissions and re‐admissions. Patients with lower activation levels (25–40% of the population) are less likely to adopt healthy behaviour, and more likely to have poorer clinical outcomes and higher rates of hospitalisation.Effective interventions can improve a patient’s activation level, and positive change in activation equates to positive change in self‐care behaviour. But to improve patient activation, we must first measure it using a robust evidence‐based tool such as the Patient Activation Measure (PAM) survey.Armed with the patient’s PAM score, providers can tailor their care and help patients achieve better self‐care, which can improve outcomes of care and reduce unnecessary health care utilisation.The PAM is also useful for population segmentation and risk stratification — to target interventions and health strategies to meet the needs of patients who are at different points along the activation continuum, to measure the performance of health care systems, and to evaluate the effectiveness of health care interventions.The role of patient activation requires further serious consideration if we are to improve the long‐term health and wellbeing of all Australians. The PAM tool is a feasible and cost‐effective solution for achieving the Quadruple Aim — improving population health, the cost‐efficiency of the health system, and patient and provider experience.

What predicts the technical efficiency in healthcare systems of OECD countries? A two-stage DEA approach

ABSTRACT Background: Waste in healthcare systems can only be revealed by performance measurements. Aims: This study revealed the technical efficiency of healthcare systems and their determinants in the Organization for Economic Co-operation and Development countries. Methods: We developed a data envelopment analysis (DEA) model and a panel Tobit regression model. Gross domestic product per capita, the economic growth rate, Gini Coefficient, the unemployment rate, 2008 global financial crisis, alcohol consumption, obesity and smoking rates, the ratio of dependent populations, and the ratio of university graduates were regressors in the panel Tobit. We employed a Simar and Wilson (2007) regression to check the robustness of Tobit results. Results: Gross domestic product per capita and the ratio of university graduates significantly and negatively affected inefficiency scores (p < 0.05). The unemployment rate, Gini Coefficient, 2008 global financial crisis, alcohol consumption, obesity and smoking rates, and the ratio of dependent populations significantly and positively affected inefficiency scores (p < 0.05). The economic growth rate also positively affected inefficiency scores but it was not found to be significant (p > 0.05). Simar and Wilson (2007) results were found to be consistent with Tobit results. Conclusions: Decision-makers should try to reveal the factors affecting the performance of healthcare systems.

Slovenian health care system performance in the light of Euro Health Consumer Index 2012

Health care systems are complex entities with several stakeholders that include patients, health care providers, payers, regulators and government. For lay and professional public, the methodology of health care system performance analysis, result interpretation and translation to policy making is not well understood. Slovenian health care system performance results from a Swedish health care analyst (Euro Health Consumer Index 2012) and opportunities for improvement are discussed. We specifically addressed methodological challenges of international health care system performance comparisons.

How can the healthcare system deliver sustainable performance? A scoping review

BackgroundIncreasing health costs, demand and patient multimorbidity challenge the sustainability of healthcare systems. These challenges persist and have been amplified by the global pandemic.ObjectivesWe aimed to develop an understanding of...

A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed.

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.