BACKGROUND: Kenya’s public tertiary healthcare is facing persistent quality of healthcare challenges characterized by acute shortage of healthcare workers, frequent industrial unrest, broken-down healthcare facilities, and erratic supply of essential commodities. To address these systemic challenges the government introduced the asset lease financing (ALF) mechanism aimed at strengthen tertiary hospitals through modern medical equipment and technologies. However, the effect of ALF on quality remains highly debated and controversial. This study examined the effect and constraints of ALF in improving quality of healthcare within Kenya’s tertiary hospitals. METHODS: A convergent parallel mixed-methods design was employed with quantitative data collected from 145 hospital managers, staff and patients. Descriptive statistics were used to summarize participants characteristics and indicators of study variables. Ordinary least square regression was then used to estimate the effect of ALF on quality of tertiary healthcare, controlling for existing traditional funding. Complementary qualitative insights were gathered from 26 policymakers, hospital managers, and health financing experts through semi-structured interviews and analyzed using thematic analysis to identify patterns in strengths and constraints. Integration of findings happened through triangulation to enhance interpretation and understanding. RESULTS: Analysis showed that asset lease financing had a significant positive effect on quality of tertiary healthcare (β = 0.587, p < 0.01), explaining 26% of the variance. When traditional funding was controlled, ALF remained significant (β = 0.495, p < 0.01), with the model explaining 33% of the variance. Respondents attributed this to improved access to advanced diagnostic and therapeutic equipment, as well as expanded service capacity. However, descriptive summaries and qualitative perspectives revealed several constraints limiting ALF optimal effect in improving tertiary healthcare quality in Kenya. Stakeholders noted high recurrent costs, under-utilized assets, weak contract negotiation, and top-down procurement processes that limited hospital autonomy and contribution. Operational gaps, including inadequate training and delayed maintenance, further constrained ALF effect on quality. CONCLUSIONS: ALF has the potential to enhance quality of healthcare and technological capacity in Kenya’s tertiary hospitals, but its effects are contingent on robust governance, effective contract design, and alignment with institutional capacity which seem lacking in the Kenyan context. Without these safeguards, current leasing arrangements risk becoming fiscally unsustainable with little quality enhancement. Policymakers should strengthen transparency, decentralize decision-making, and incorporate performance-based provisions into leasing contracts to maximize ALF effect in enhancing quality of care.
Background The medical device sector, valued at $569 billion, faces persistent financing challenges. Around 78% of startups fail because of capital shortages, not due to lacking technical quality. Blockchain-based tokenization emerges as a way to broaden access, yet success relies on economic factors of platforms and clear regulations. Methods Transaction cost data from Bitcoin, Ethereum, and XRP Ledger covered 540 days from January 2024 to June 2025, providing 3,240 observations per network. Experts, numbering 12, participated in a modified Delphi method to form a framework tailored to healthcare. Project outcomes came from Monte Carlo simulations running 10,000 iterations, checked by a triple control-loop system, and compared against two real-world examples. Volumes of transactions drew from stochastic models involving monthly, quarterly, and annual elements, mixing fixed regulatory needs with variable market influences. Results Layer-1 (L1) fees differ by orders of magnitude; representative 2025 snapshots show BTC and ETH L1 far above XRPL and major ETH L2s. XRPL fees are typically a tiny fraction of a cent; the base cost is 10 drops (0.00001 XRP) and is dynamically adjusted by network load. Probabilities of success varied from 10.1% to 12.3% on Bitcoin, 31.4%–48.3% on Ethereum based on Layer-2 adoption, and 71.6%–73.2% on XRP Ledger. Investor involvement correlated negatively with logarithms of costs, showing Spearman <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow></mml:math> of −0.91. Differences in success exceeded 60 percentage points across platforms. Examples illustrated how elevated expenses reduce engagement in VitaDAO on Ethereum, whereas low-cost systems like XRP Healthcare support ongoing involvement. Conclusion Choosing a blockchain platform critically influences viability in tokenizing medical devices. Layer-2 options reduce cost gaps but add complexities in bridging and use. Platforms offering stability, minimal fees, and regulatory alignment promote wider inclusion and reliable funding. Technical features, steady costs, and readiness for compliance together shape whether tokenization boosts innovation in healthcare or maintains barriers.
Open access
Quality and Safety in Healthcare
Neuroethics, Human Enhancement, Biomedical Innovations
Angela Hemesath, William M. Tian, Bryce W. Polascik, Suzanna Joseph · 10 authors
Purpose:. To combine the perspectives of health and commercialization experts on the ethical and regulatory needs for non-fungible token (NFT) implementation in healthcare.Design:. PerspectiveMethods:. For a multidisciplinary perspective by an interdisciplinary group, current event articles and research articles were interpreted and assessed.Results:. Health data has become fragmented and disorganized, resulting in poor accessibility, increased administrative costs, and integrity vulnerability. Healthcare is uniquely suited to adopt blockchain and NFT technology as potential solutions. The incorporation of blockchain technology may offer multiple improvements in data-sharing through consensus, tokenization, and decentralization. However, the current regulatory infrastructure to support blockchain is poorly defined.Conclusions:. Healthcare NFTs would revolutionize patient control over their health data and promote more ethical transparency of data ownership while also reducing administrative security costs. However, blockchain poses unprecedented requirements of healthcare regulation within the unique realms of patient privacy and data ownership. Large-scale implementation of blockchain cannot be achieved without regulatory collaboration.
Open access
Pharmaceutical Economics and Policy
Health Systems, Economic Evaluations, Quality of Life
Mehrad Sarvi, Fereshteh Azadi Parand, Ali Tavakoli Golpaygani
In today's supply chains, ensuring the authenticity of goods, particularly in healthcare, is crucial for the community's health. Medical implants face significant challenges, including counterfeit issues deteriorated by inadequate supervision and transparency. These challenges have led to problems with tracking and authenticity verification due to a lack of transparency in Iran's current system. Blockchain technology offers promise with its transparent and distributed ledger system. The objective of this study is to propose and evaluate a blockchain-based prototype for tracking medical implants considering Iran's regulatory rules. This study aims to enhance supply chain efficiency and authenticity verification. Due to the exploratory nature of this study, it is focusing on evaluating the feasibility solution based on blockchain technology instead of numerical results.
Don Husereau, Terrence Sullivan, Harriet Feilotter, Marcio M. Gomes · 9 authors
AIMS: The Canadian province of Ontario provides full coverage for its residents (pop.14.8 M) for hospital-based diagnostic testing. Historical governance of the healthcare system and a legacy scheme of health technology assessment (HTA) and financing has led to a suboptimal approach of adopting advanced diagnostic technology (i.e. protein expression, cytogenetic, and molecular/genetic) for guiding therapeutic decisions. The aim of this research is to explore systemic barriers and provide guidance to improve patient and care provider experiences by reducing delays and inequity of access to testing, while benefitting laboratory innovators and maximizing system efficiency. MATERIALS AND METHODS: = 2). The forum considered evidence of good practices in adoption, implementation, and financing laboratory services and identified barriers as well as feasible options for improving advanced diagnostic testing in Ontario. RESULTS: Overarching challenges identified included: barriers to define what is needed; need for a clear approach to adoption; and the need for more oversight and coordination. Recommendations to address these included a shift to an anticipatory system of test adoption, creating a fit-for-purpose system of health technology management that consolidates existing evaluation processes, and modernizing the governance and financing of testing so that it is managed at a care-delivery level. CONCLUSIONS: The proposals for change in Ontario highlight the role that HTA, governance, and financing of health technology play along the continuum of a health technology life cycle within a healthcare system where decision-making is highly decentralized. Resource availability and capacity were not a concern - instead, solutions require higher levels of coordination and system integration along with innovative approaches to HTA.
Open access
Health Systems, Economic Evaluations, Quality of Life
Introduction: The main challenge of modern hospitals is purchasing medical technologies. Hospital-based health technology assessments (HB-HTAs) are used in healthcare facilities around the world to support management boards in providing relevant technologies for patients. Aim: This study was undertaken to update the existing body of knowledge on the characteristics of HB-HTA systems/models in the selected European countries. Insights gained from this study were used to provide an optimal approach for implementing HB-HTA in Poland. Materials and methods: Firstly, we carried out a systematic review in PubMed and embase. Secondly, we searched for gray literature via the AdHopHTA online handbook and the design book of the AdHopHTA project, as well as literature describing healthcare systems provided by the WHO. Then, we conducted in-depth interviews with HB-HTA experts from four countries. Finally, we selected ten countries from Europe and prepared frameworks for data collection and analyses. Results: The selected countries (Switzerland, Spain, France, Italy, Denmark, Finland, Sweden, the Netherlands, and Austria) are examples of decentralized or deconcentrated healthcare systems. In terms of HB-HTA, differences in organisational models (independent group, stand-alone, integrated-essential, integrated-specialised), type of financing (internally vs. externally), collaboration with an HTA National Agency and other stakeholders (e.g., Patients’ Associations) were identified. HB-HTA engages multi-skilled staff with various academic backgrounds and operates mainly on a voluntary basis. Conclusion: Strengths and weaknesses associated with various organisational models must be carefully considered in the context of support for decentralized or centralized models of implementation while embarking on HTA activities in Polish hospitals.
Open access
Health Systems, Economic Evaluations, Quality of Life
INTRODUCTION: The amount of mandatory data that needs to be analyzed as part of a medical device postmarket surveillance (PMS) system has grown exponentially in recent times. This is a consequence of increasingly demanding and complex regulatory requirements from Health Authorities, aimed at a better understanding of the medical device safety evaluation. Proactive approaches to PMS processes are becoming more necessary as regulators increase the scrutiny of device safety. New technologies have been explored to address some of the challenges associated with this changing regulatory environment. AREAS COVERED: This paper focuses on the different technical aspects of blockchain and how this new technology has the potential to support the ongoing efforts to improve the PMS system for medical devices. EXPERT OPINION: To address these challenges, we suggest to generate a private PMS data permissioned blockchain with a proof-of-authority consensus mechanism, to which only a restricted number of designated and audited participants have authorization to validate transactions and add them to the PMS data blockchain ledger. Blockchain has the potential to support a more efficient approach, which could offer many advantages to the different stakeholders involved in the PMS process, such as supporting with new regulatory initiatives.
Managing medical device monitoring processes is challenging and lacks a realtime, life cycle tracking strategy to reduce adverse medical events and revision costs for hospital administrators, physicians, and patients. Understanding the malfunctions of medical devices for cardiac and orthopedic patients could save lives and reduce hospital liability. Grounded in the business process reengineering conceptual framework, the purpose of this single qualitative case study was to explore strategies hospital managers used to redesign the implant recall surveillance process at one hospital in Pennsylvania. The 5 participants selected successfully implemented a medical device surveillance process that reduced adverse medical events and revision costs. Data were collected using semistructured interviews and a review of relevant medical device surveillance workflow documents. The 4 themes that emerged from a thematic analysis were effective data communication process, central data repository integration, continuous process improvement, and end-to-end surveillance process. A key recommendation for hospital administrators, physicians, and managers is to use blockchain distributed ledger technology to assess device identification challenges as part of the surveillance process to reduce health risks. The implication for positive social change includes the potential to improve the quality of life for medical device recipients who may spend less on healthcare services.
The Internet of Things is a novel paradigm which involves the increasing prevalence of objects and entities supported with identifiers and the ability to exchange the data over a network. However, with all these advantages the risk comes, as the huge number of connected devices gives hackers more entry points. Distributed Ledger Technology (DLT) can stave off security threats to Internet enabled devices by providing a distributed ledger for their functioning, thereby eliminating the central node that networks usually depend on for management by their users. Internet of Things and Application (IOTA) is a new technology designed specifically for the Internet of Things (IoT) industry which depends on the distributed ledger for storing transactions. The main contribution of this thesis is to study and run a set of test cases in healthcare industry to prove the effectiveness and viability of using IOTA in many healthcare applications using data, images or even videos. We will also do a comparative analysis with Blockchain to prove that IOTA technology could stand all odds in terms of feasibility, reliability and robust data security.
We are pleased to publish the second issue of the Global Journal on Quality and Safety in Healthcare (JQSH). In this issue, we would like to discuss the similarities and differences between research and quality improvement (QI) projects in health care. Imagine you are working in a hospital or a department within a hospital and you want to improve an aspect of health-care quality and safety by focusing on the issue of medication errors. Given that situation, you decide to implement a “zero harm” rule because of medication errors. The question is will this be a QI or a research project? In another example, you are a resident working in an oncology department and you noticed that most patients receiving certain chemotherapeutic agents had neuropathy complications, so you decided to collaborate with the physical therapist on a project to compare patients who received chemotherapy drugs and exercise with those who did not exercise. Again, the question is will this be a research project or a QI project? Regardless of the answer, it is important to implement the project systematically. If your project is focused on QI, then you should consult the QI specialists in your hospital who can help you to use the appropriate QI methodology, which includes Plan, Do, Study, Act (PDSA) cycles. If your project qualifies as research, then you should consult a research methodologist and biostatistician regarding study design, sample size, and others and work with the institutional review board (IRB) to provide guidance and templates.Many health professionals do not know how a research project differs from a QI project and when they complement each other.[1–3] Our traditional thinking is that quality and safety improvement in health care as well as the effectiveness of an intervention can only be studied in the form of a traditional scientific research project, as it has its own well-established rigorous approach. We may be ignorant or unaware of how to use the QI scientific approach to study the performance of a health-care system.[4,5] The problem lies within our frame of thinking because we are prioritizing the proof of effectiveness over bringing about and sustaining improvement. We use the results of pre-assessment and post-assessment research as the gold standard for evidence-based policy and practice, whereas in reality, sustaining the improvement is continuous and more dynamic.[1,6]Research projects are question-driven and focus on providing proof of effectiveness. The main purpose of research is to generate new generalizable knowledge about a particular subject to a study population, where the study results often end up published in academic journals. In this case, researchers must follow a strict study protocol approved by the IRB, including obtaining the consent from study participants before starting the project and report any deviation from the protocol to the IRB, if needed.[7–9] However, QI projects are data-driven and focus on showing sustained improvement to a specific process and system or outcomes within a health-care organization using, if possible, the research evidence generated as the basis for developing the improvement interventions.[10] A QI project does not aim to generate new knowledge as a research project does, rather, it generates several learning lessons as to what actually works and does not work and why. A QI project produces empirical evidence to benefit other organizations within a similar context and setting, which are interested in replicating the change to improve a process or system using the rapid PDSA cycle approach.[11] Through cycles of testing, we learn what is going to improve and why, without the need to generalize the results to another context, as research projects usually aim to do. Also in QI projects, the measurement framework is not about pre and post. It is about continually measuring the metric of interest that you want to improve and coming up with not just one intervention but multiple interventions based on learning from prior PDSA cycles. At the end, you reach the point of realizing sustained improvement through a series of interventions that were informed by testing in the actual system that you want to improve. The PDSA cycle is repeated, and new changes are made to continue to improve a process and, ultimately, the outcome. The essential measurements included in a QI project are process measures, outcomes measures, and balancing measures, which are used to show that the improvement occurs over time. Data from QI activities are usually aggregated and presented in run/control charts, histograms, and line graphs, whereas data from research are analyzed using statistical tests such as t-test, chi-square test, and regression analysis, and then aggregated and presented in appropriate tables and/or graphs.Typically, QI results are shared within the organization and might be implemented in other departments. The lessons learned from QI activities can be published; however, it must be clear to the readers that the project was for QI, not traditional research. Although a QI project does not require IRB approval, some organizations have QI committees that approve and coordinate QI project activities, and some organizations require articles to be approved before submitting for publication.In summary, the sustained improvement realized in a QI project can be complemented and validated with a thorough research-based assessment of effectiveness.[12] We should not consider the proof of effectiveness the same as the proof of sustained improvement, but they both are very important. I would like to emphasize that both research and QI projects use scientific and systematic approaches, albeit different, but both methods are scientific and rigorous in their own ways. The aims, methods, and outcomes in research and QI projects are quite different. Hence, understanding the differences and similarities between research and QI projects will help to determine the right approach when designing and implementing the right project for the right purpose using the right method. Table 1 is a snapshot comparison between QI and research with more focus on the project's aim and method aspects.In research projects, we can be guided by asking the following: Do we have a clear question to be investigated and answered?What do we hope to accomplish by answering the question?What is currently known about the topic?What are the risks and benefits for patients involved with the study of this topic?What type of study design will be used (observational vs. experimental)?How will the data be analyzed and presented (statistical tests, P-values, etc.)?In QI projects, we can ask the following: What is the magnitude of the quality problem based on available data?What types of quality tools have been used to measure and assess the problem?What is the measurement plan to be used during implementation of the project?What types of changes/interventions will be tested during the PDSA cycles?Has the proposed change/intervention been used in other health-care settings or reported in the literature?Will the results of this project directly improve patient-care outcomes or processes?Is the organization's management supportive of the project and willing to dedicate employee's time and supplies to do the project?What is the sustainability plan for the results?
Open access
Health Systems, Economic Evaluations, Quality of Life
Zoltán Balogh, Emil Gatial, José Barbosa, Paulo Leitão · 5 authors
Maintenance is a key factor to ensure the production efficiency, since the occurrence of unexpected failures leads to a degradation of the system performance, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. The article aims to propose a reference architecture which will improve the way maintenance is considered in the current manufacturing world, by enabling an overall increase of production rates, while increasing the operational equipment effectiveness and decreasing the impact of maintenance needs. This objective would be accomplished by establishing an IoT infrastructure for the collection of the huge amount of available shop floor data, which can be analyzed, considering data analytics algorithms, predictive maintenance models and forecasting techniques, to perform the machine/system health assessment and prediction of maintenance needs, e.g. by detecting earlier the occurrence of possible failures and consequently the need to implement maintenance interventions. The scheduling of predictive maintenance needs will be integrated with the existing maintenance planning tools, and especially synchronized with the production planning tools to achieve a nondisruptive maintenance impact in the production system. A cloud-based collaborative maintenance services platform allows the secure collection, aggregation and analysis of a large amount of shared data from numerous manufacturers that use the same or similar machinery, and acts as an open market where companies can contract specialized maintenance services. This reference architecture aims to provide replicable architecture to be broadly applicable in a variety of industries, capable to improve the production efficiency through a real-time health monitoring and early detection of failures and outages, to speed up the maintenance delivery, and consequently mitigate their impact.
The Failure Mode and Effect Analysis (FMEA) is a one of the requirements which was required by the Automotive Industries Action Group (AIAG) to all the automotive suppliers and manufacturers worldwide through the TS16949 Quality System. There were a lot of dicrepencies detected on implementing the FMEA which directly related to the user experinces and knowledge. The descrepencies cause the FMEA not meeting the objectives of it. Conceptually, Poka Yoke is able to fit into the Process FMEA. Failure Mode and Effect Analysis (FMEA) helps predict and prevent problems through proper control or detection methods. Mistake proofing emphasizes detection and correction of mistakes before they become defects. Poka Yoke helps people and processes work correctly the first time. It refers to techniques that make mistakes impossible to commit. These techniques eliminate defects from products and processes as well as substantially improve their quality and reliability. Poka Yoke can be considered an extension of FMEA. The use of simple Poka Yoke ideas and methods in product and process design eliminates both human and mechanical errors. Ultimately, both FMEA and Poka Yoke methodologies result in zero defects and benefit either the end or the next-in-line customer. The first concept of Poka Yoke emphasizes elimination of the cause or occurrence of the error that creates the defects by concentrating on the cause of the error in the process. The defect is prevented by stopping the line or the machine when the root cause of the defect is triggered or detected. The second concept of Poka Yoke focuses on the effectiveness of the detection system. The foolproof detection system eliminates the defect or detects the error that causes defects. The implementation of the Poka Yoke concept in a foolproof detection system eliminates the possibility that error or defects will slip through the process and reach the customer.