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
Chigozie Athanasius Nnadiekwe, Ikechi Saviour Igboanusi, Jae‐Min Lee, Dong-Seong Kim
The proposed framework leverages Non-Fungible Tokens (NFTs) to revolutionize supply chain management by ensuring product authenticity. Each product document stored in a repository is minted as an NFT. The NFT is a digital twin of the product's certification, containing metadata for tracking its origin, authenticity, and key details. When a buyer verifies the product's authenticity, the NFT is burned as a final step, ensuring that the product cannot be re-certified without a new issuance. This framework creates a transparent, tamper-proof, and decentralized system to enhance trust and accountability in the supply chain. By integrating the Ethereum-based Pure Chain network and smart contracts, this solution benefits from high availability, immutability, and the ability to automate key processes. The system verified and retrieved the base URI of a designated product identity (ID) whose NFT was stored in a GitHub repository. A vulnerability check of the smart contract codes with solidcheck.io shows that the smart contracts are 84 % secured with no serious security threats, as all considerations were made in the contract code design.
The management of medical devices throughout their lifecycle is a complex and critical process in healthcare, traditionally handled through centralized systems that often suffer from significant challenges such as data security vulnerabilities, inefficiency, and lack of transparency. This research paper introduces a novel approach to Medical Device Lifecycle Management (MDLCM) by leveraging blockchain technology to decentralize the process, aiming to enhance security, efficiency, and stakeholder trust. We identify the inherent problems in current centralized systems, including data tampering risks and operational inefficiencies, which can compromise patient safety and impede the seamless management of medical devices. To address these issues, our study proposes a blockchain-based decentralized system, utilizing Ethereum smart contracts to automate and secure the MDLCM process. The methodology encompasses the design and implementation of a smart contract framework to facilitate transparent and secure interactions among all stakeholders involved in the lifecycle of medical devices, from manufacturing to post-market surveillance. Our results demonstrate the potential of blockchain technology to provide a robust solution for the challenges faced by traditional MDLCM systems, offering improved data integrity, traceability, and automation of compliance checks. The implications of this research are significant, suggesting a transformative shift towards more secure, efficient, and transparent management of medical devices within the healthcare sector. However, limitations such as blockchain scalability and the need for a supportive regulatory framework are acknowledged. Future work will focus on enhancing the scalability of the blockchain solution, exploring integration with other technologies like IoT and AI, and fostering collaboration among stakeholders to facilitate widespread adoption.
The dramatic development of technology in recent years has affected most organizations and companies. Artificial intelligence (AI) and Blockchain can be mentioned as the most important technologies that are developing rapidly. In Industry 4.0, complex data are produced in high volume, which makes implementing improvement projects with traditional methods lose their effectiveness. Six Sigma is one of the most prominent improvement methods organizations and companies use to identify and solve problems. Therefore, for implementing Six Sigma projects in Industry 4.0, the need to develop the traditional Six Sigma toolbox is felt. AI and Blockchain can be suitable tools for developing and improving Six Sigma for Industry 4.0. A systematic review identified 58 articles in this article that presented solutions for integrating AI or Blockchain in Six Sigma. Some articles have evaluated the performance of their proposed method by implementing the proposed models. The most widely used machine learning and deep learning algorithms in Six Sigma have been identified. Also, Six Sigma approaches that mostly use AI or Blockchain have been identified by the analysis of articles. Decision tree algorithms and artificial neural networks are used in most Six Sigma define–measure–analyze–improve–control (DMAIC) projects. Therefore, by reviewing the articles, it was found that AI and Blockchain are mainly used as efficient tools in DMAIC and the Design for Six Sigma models to implement Six Sigma projects. In this article, 28 main gaps are presented as future works for future research.
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
Timothy Bechtel, Lorenzo Capineri, Gennadiy Pochanin, F. Crawford · 6 authors
The modern world is characterized by the pervasive presence of electronic sensors, microprocessors, robotics, and wireless connectivity. Powerful computers can perform virtual simulations that would previously have required physical models or experiments. Conversely, these same machines can control robotic devices that take virtual models and produce physical objects (e.g. 3-D printing). In manufacturing, this represents a new industrial revolution, dubbed “Industry 4.0.” In brief, the recognized industrial revolutions are: Industry 1.0. The First Industrial Revolution (in the late 18th and early 19th centuries) involved the transition from an agrarian economy to industrial production (e.g. weaving looms and other mechanical devices driven by water wheels and steam), and advances in metallurgy. Industry 2.0. The Second Industrial Revolution (spanning the late 19th and early 20th centuries) brought the widespread use of electric power, mass production on assembly lines, and division of labor (e.g., the Chicago and Cincinnati meat packing plants, the Ford Motor Company) – all providing greatly increased productivity. Industry 3.0. The Third Industrial Revolution involved the integration of electronics and information systems into production, providing intensive automation and application of mechanical/robotic manipulation in production processes. In the closing decades of the 20th century, the proliferation of electronic devices (such as transistors and later integrated circuits) allowed more complete automation of individual machines, supplementing or replacing human operators. This period also spanned the full development of software systems for the control of electronic equipment. Industry 4.0. In the 21st century, Industry 4.0 exploits the “Internet of Things” or IoT (Ashton, 2009) with wired or wireless communications connecting cyber-physical systems (CPSs), which share and analyze information and use it to guide actions. Industry 4.0 is based on six principles (Hermann et al., 2016). Interoperability: the ability of machines, devices, sensors, and people to connect and interact to achieve a common goal. Virtualization: CPSs monitor physical processes and continuously compare a model of the actual world (based on sensor data) with an editable model of the desired world. CPSs even monitor each other and provide alarms when they sense a failure. Decentralization: The increasing demand for customized products and services makes it increasingly difficult to control systems centrally. Embedded computers enable CPSs to make decisions on their own. Nevertheless, it is still necessary to keep track of the whole system at all times. In the context of Industry 4.0 “Smart Factories,” decentralization might mean radio-frequency identification (RFID) tags on components “tell” production machines which working steps are necessary, making central planning and control obsolete. Real-Time Adaptability: CPSs collect, share, and analyze data in real time. Thus, the plant can react to the failure of any system component and re-route information or parts to another machine. Service Orientation: The services provided by “smart” systems can be shared by other participants across company, discipline, and international boundaries. All CPSs can offer their functionalities as a stand-alone service, making it possible to assemble the proper combination of CPSs to make a specific product or service that meets any end-user needs. Modularity: Systems can adapt to changing requirements by replacing or expanding individual “Plug & Play” modules. With standardized software and hardware interfaces, new modules can be identified and requisitioned automatically and can be utilized immediately. Thus, Industry 4.0 is a new way to develop and adapt manufacturing technologies based on automation and instantaneous exchange of data across potentially physically separated CPS components. Currently, Industry 4.0 is the topic of many scientific conferences (e.g., Industry-4.eu, 2019), which are held all over the world, and address both general organizational issues and individual tasks. In fact, every scientific conference is in one way or another a stage in the advancement of Industry 4.0 technology. With the support of NATO/OTAN Science for Peace and Security (SfPS) Program, Project G5014 - “Holographic and Impulse Subsurface Radar for Landmine and IED Detection” (http://www.nato-sfpslandmines.eu/) and its successor Project G5731 “Multi-Sensor Cooperative Robots for Shallow-Buried Explosive Threat Detection,” we are developing Demining 4.0; designing cooperating robotic search-detection-discrimination platforms for humanitarian demining. The platforms are intended to exploit new electromagnetic and physical-acoustic methods and technologies for landmine detection in an open design environment. Humanitarian demining is a high-risk and high-cost task that can benefit from the Industry 4.0 approach. This paper illustrates the ways the systems interact. The first platform, “Ugo 1st,” incorporates ultrawideband (UWB) ground penetrating radar (GPR) for rapid target detection and XYZ coordinate determination, as well as holographic subsurface radar (HSR) for discrimination of mines from clutter. These are combined with GPS positioning (real time kinematic for sub-cm precision), and light detection and ranging (LiDAR) and optical sensors (PMD Pico Flexx and Teraranger) for remote navigation, obstacle avoidance, tripwire detection, and HSR image correction. These systems and their connectivity are depicted in Figure 1 on the next page. The prototype Ugo 1st operating in an outdoor test bed is shown in Figure 2 on the next page. In the newly begun successor project, the sensors will be spread across four robotic platforms that will sequentially scan a designated area. The UWB robot will first detect targets and send coordinates to the others, who will interrogate them with HSR and a metal detector. The team will be protected by a “shepherd” robot which provides detection of tripwires and obstacles that could impede the others. Additional Industry 4.0 principles are invoked in the ways data from these systems can be shared, archived, and processed in a decentralized manner accessible to the worldwide community, and in the use of artificial intelligence to standardize the detection and classification of targets according to their level of potential threat. In the successor project, additional platforms “Ugo 2nd” and “Ugo 3rd” will incorporate an imaging metal detector and the HSR, while “Shepherd” looks ahead for boobytrap tripwires, pits, and other obstacles to be avoided. In true Industry 4.0 fashion, these robots will cooperate autonomously. A preliminary test with Ugo 1st was carried out on three buried targets in natural soil at shallow depth for 20 days. The operator (see Figure 2) drove the robot along the lane with a maximum deviation of ±2 cm (as quantified from the real-time 3D video recorded during the traverse). The signals from the impulse GPR were acquired every 3 cm and processed automatically to determine target positioning on the ground relative to the antenna reference system. Figure 3 shows the results of repeated auto-detection of a single PMN-1 plastic-cased landmine (diameter 95 mm, depth 30 mm) by the moving impulse GPR. Ideally, the positions on this graph should be on a straight line and separated by 3 cm. The errors are within the experimental uncertainties and are due to the variable system speed and soil surface influence on the reflected GPR signals. Figure 4 depicts the plan-view HSR image of the buried landmine on a remote computer screen. The Industry 4.0 paradigm also allows replication of our robotic platform (as well as improvement and adaptation of its design), in different parts of the world with delocalized manufacturing of the physical components. Both experimental and operational field data from the system can be shared and accessed in real time at different locations owing to the web-based software architecture. The generation of large data archives by the system will soon be possible with the design and deployment of continuously connected radar systems.
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
This chapter examines the relevance of blockchain technology for medical electronics in general, as well as internet-connected portable things. Considering the faults of centralized and private organizations for accessing the data of the patients, the transformational role of blockchain technology for managing the Electronic Health Records (EHR) is to be analyzed. The effective blockchain basics, applications and how it can benefit to avoid data breaches in the healthcare services are elaborated. A reliable and secure process of storing, recording and distributing sensitive medical and clinical data using blockchain is to be addressed. Patients will also be able to choose when and to whom to authorize access to their medical records. The emerging division of Internet of Medical Things and the consumer medical electronics are also to be elaborated.
With the development of modern equipment system and science and technology, equipment is more and more toward the direction of digital and intelligent development. Complex equipment often undertakes important tasks, once the failure occurs, the result will be particularly serious. Based on this, the main work of this paper is as follows. (1) This paper builds the whole life cycle data chain of complex equipment from design, parts production, equipment transportation and installation based on blockchain technology so that the data is effectively used, and the data privacy is protected. (2) The blockchain technology is applied to the construction of digital twin, and the mapping from physical entity to virtual space is realized. It is more effective and accurate to diagnose the state and forecast the future trend of complex equipment. (3) A new intelligent maintenance framework is proposed, which provides new ideas and solutions for the intelligent maintenance of complex equipment.
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.
Abstract To assess and improve process plants safety and integrity in real time and intervene in timely manner. Assets are safe and we can prove it by employing Real Time Data Analytics to reduce operational process safety risk and improve plant safety and integrity assurance. Real time IPF/Bypass management and validation guarantee the overall effective safety system for safe Operational Excellence. Petronas Upstream employed Data Analytical tool to facilitate analysis of Instrumented Protective Functions performance, Bypass Management, documentation of proof testing, and management of required test schedules for Process Safety. Automated reporting of analytical tool provides testing requirements for current and future protective functions throughout the facility. All Bypasses of Instrumented Safety System (IPF) are monitored in Real Time and Descriptive Analytics has been developed to provide insights to safe operation of the facility. The software analytical tool implemented enable validation testing when the system is activated. In the event of a failure or fault of the safety system, plant personnel are notified and all information updated in centralized Web Based Dashboard for high level of data transparency across all stake holders. Descriptive analytical system helps to identify potential equipment malfunction/failures in advance. Automatic Alert & Notification of anomalies are sent to identified stakeholders. There is no room for error, yet there are many ways to bypass IPF from Safety Instrumented System(SIS) so Real Time Bypass Management is a key success factor for Process Safety Assurance & Asset Integrity. Real Time Web Based Dashboard designed to customize to user specific actionable analytics to Visualize Current Risk and Current availability of Instrumented Protective functions to identify risk. Performance Reporting strengthen the Analysis and reporting of IPF performance against design criteria. Real Time Analytics resulted from IPF Design Data for Design time, process safety time, testing interval, risk, consequence, severity, SIL level and more. Real Time Analytics are available on-demand through a single web based and mobile-enabled user interface (withing domain). Real Time Analytics for multiple user level provides indispensable capabilities to guarantee effective management of IPF Performance, Bypass Management, Demand on safety System rate tracking, Excusions Management, Safety and Operational Risk Visualization. The Data Analytics is aligned to management aspiration of Going Digital for sustainable future and to cultivate effective collaboration. Additional Information Standards, such as API RP 754, IEC 61508 and 61511, ISA 18.2; govern the monitoring and maintenance of Asset's Instrumented Protective Functions including tracking and documenting availability, demand rate and failures and proof testing. Employing Real Time Analytical tool will help PETRONAS to achieve proven in use high quality equipment Reliability data for value creation. Analytical information enables assurance of process safety governance to achieve objective of -
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.
Most things are driven by money. A constant and recurring theme in hospitals is how the monies for medical equipment maintenance should be budgeted and managed. There are two main schools of thought. Distributed model: All charges are paid by the department that uses the services. This means that surgery would budget and pay for all surgery expenses, parts, and contracts. Centralized model: All costs are centralized into a single medical equipment maintenance budget, controlled by biomedical engineering. This budget includes all in-house costs, external costs, contracts, and all overhead for biomedical engineering department operations. In the decentralized model, finance allocates all expenses as closely as possible to the departments that generate the revenue. However, in order for product managers to set rates and manage expenses, they need to have ALL of the expense data to analyze. Missing such a large part like equipment maintenance costs would skew the break-even point and lead to incorrect pricing. The benefits to the centralized system include: 1. The biomedical manager or director is a service management professional. His or her job, training, and professional life have been spent in the maintenance, repair, and support of patient care equipment. Who in the hospital can better oversee contracts, parts, and outside maintenance? A surgery manager whose main focus is patient care, not equipment costs and performance, is not the optimal person to manage equipment costs. 2. A central budget allows decisions to be made across departmental lines. The same type of equipment is owned and used by many different departments in the hospital. The decisions and choices made by a single department are often different than if the entire facility is considered. 3. Outside vendors will always be used to some extent. These vendors must be monitored and their work added to the comprehensive medical device record. Compliance and cooperation from the outside vendors cannot be attained except by the person responsible for paying for the service. 4. Unless the funds are in the biomedical department budget, they will not be closely watched and scrutinized. Clinical department managers do not focus on maintenance activities. But a biomedical manager who must meet budget and explain variances can more effectively manage a single cost center than try to manage the costs of 30 or 40 subaccounts scattered across the institution. 5. If hospitals are ever to consider less costly (and often more effective) service options than the original equipment manufacturer, the control and oversight must be given to the biomedical department. 6. As required by the Joint Commission, every biomedical department maintains a complete and upto-date medical equipment inventory. The software that keeps this inventory generally has the ability to track costs of every medical device and generate reports of labor and parts costs. These reports can be as granular as necessary, all the way to the device level, or summarized for each department. These reports can be the charge-back mechanism product line managers need to identify and manage their department costs. If a hospital is going to employ a professional manager of medical equipment service, that hospital must not tie his or her hands by removing the most powerful tool available—the money to negotiate and control costs and activities of outside companies and vendors. A centralized medical equipment maintenance budget in the hand of a professional biomedical manager is the best and cheapest way to care for your equipment. n
It is a fact of history that many of us who pioneered simulation in healthcare took as inspiration the experience of other intrinsically high-hazard industries such as commercial aviation. Many of us have argued forcefully that healthcare should adopt simulation comprehensively in part to follow the model set by these industries. Yet, with simulation in healthcare having some roots that go back decades, and with even aviation-inspired curricula in healthcare (like ACRM) approaching 20th anniversaries (ACRM was first offered in September 1990) one can wonder why the healthcare industry has not embraced and implemented simulation as fully as has been done by other industries including commercial aviation, the military, or nuclear power. I would like to explore this analogy a little more deeply, reviewing what I see as the meaningful similarities, as well as the profound differences, between healthcare as an industry and these other risky human endeavors, focusing especially on commercial aviation. This paper is a combination of my own personal recollections and an objective assessment of the industries. Some elements of this analysis are taken from my paper Structures and Organizational Issues in Patient Safety: A Comparison of Health Care to Other High Hazard Industries (California Management Review, Fall, 2000)1 while other aspects come from the talks I have been giving in the last 7 years. The Flight Deck as a Cognitive Parallel for Dynamic Healthcare Settings A little known point about my own laboratory's development of the mannequin-based simulator in the late 1980s was that we started with the goal of creating a tool for the understanding of cognition of anesthesiologists in the handling of adverse events. We were driven to this by our analysis of the “chain of accident evolution” in anesthesia – the ways in which inciting triggers end up as major catastrophes if not interrupted by the intervention of the anesthesiologist. We made a number of conjectures about cognition in these settings: “To recover from anesthesia incidents, the anesthesiologist must: 1) detect one or more of the manifestations of the incident in progress; 2) verify the manifestations and reject false alarms; 3) recognize that the manifestations represent an actual or potential threat; 4) assure continued maintenance of life-sustaining functions; 5) implement “generic” diagnostic or corrective strategies to provide failure compensation and allow continuation of surgery if possible; 6) achieve specific diagnosis and therapy for the underlying causes; and 7) provide follow-up of recovery to ensure adequate correction or compensation.”2 To provide empirical data to confirm these conjectures we needed a way to provide standardized adverse events to different clinicians to tease out the typical response behaviors. My own background as an avowed “aviation and space nut” stood me in good stead, giving me the knowledge of the existence of simulators in these arenas. That led us to create the CASE simulator series initially for this cognition research.3 Our experiments were described in several papers appearing in Anesthesia and Analgesia.4–6 As we looked for models in medicine of such dynamic decision making processes we didn't find them. Most of the literature on medical decision making were about quite static decisions, and concerned highly mathematical and probabilistic techniques that couldn't readily account for the behavior of anesthesiologists that we had observed. But we did find models for such cognition in other industries, particularly aviation. The flight deck of the aircraft does have a number of cognitive parallels with that of anesthesia and other medical domains, such as intensive care or emergency medicine. In all such settings most time will be spent in ordinary and routine activities. Like in the operating room, even in a busy ICU or emergency department the true crises will be rare. Hours of boredom, moments of terror is a mantra on the flight deck as well as in the operating room. A “flight” has many similarities to “an anesthetic.” Each has a phase of preparation – analysis of the situation (weather versus underlying diseases) – and equipment checks. Take off is like induction, cruise is like maintenance, and landing is like emergence. Certain flights, and certain surgical cases have key midstream milestones that must be anticipated and planned for. On both the flight deck and in the operating room there is a plethora of information sources, some of them mutually redundant, requiring dynamic allocation of attention but allowing cross-checking between sources. Our own research had shown that anesthesiologists did not solve acute problems by direct application of deep abstract reasoning but rather by applying “precompiled” knowledge, doing the usual things about the usual problems. Aviation does the same thing except they have codified many of the responses into an emergency procedures manual; pilots are expected to know from memory the first few items of these procedures, but to use the written protocols themselves for anything further. Both commercial flying and healthcare are conducted in crews and teams. We consider a crew to be one or more individuals each from a specific discipline, sometimes with their own hierarchy; multiple crews working together makeup a team. The flight deck crew are pilots (the days of nonpilot flight engineers in a 3 person flight deck are almost gone) with a “Captain” and “First-Officer”. The flight deck crew combine with the cabin crew to make up the aircraft team, and the team is larger still when air traffic control, airline dispatch and maintenance are included. Similarly, the OR has a surgeon crew, a nursing crew, an anesthesia crew, and sometimes specialist technicians (like cardiopulmonary bypass perfusionists). The crews work together as a team. Aviation found that intracrew and intercrew coordination was a major feature of good problem solving and that failures of coordination were at the root of many accidents.7 We also felt, by introspection, that a substantial part of expertise in anesthesiology lay in the ability to coordinate the anesthesia crew members (whenever there was more than one) and in the ability to coordinate with the other crews, especially the surgeons. Given these similarities, it is no wonder that we believed intuitively then, and still do now, that it was worth adapting many practices of aviation for use in healthcare. The use of simulation in aviation has been extensive, both for teaching practical “stick and rudder” skills but since the mid 1980s, also for the so called “nontechnical” skills known as Crew Resource Management (CRM).8,9 Having first heard about CRM possibly in an episode of the PBS show NOVA called “Why Planes Crash” we were fortunate that one key architect of early CRM worked at the nearby NASA Ames Research Center. This contact facilitated our exposure to the CRM approach allowing us to rapidly adapt many elements of CRM into a simulation-based curriculum for anesthesiology (ACRM). The wide spread of the simulation-based CRM approach within anesthesiology and across health care disciplines and domains has been gratifying to watch. Clearly, the resonance that we perceived between the cognitive and social psychologic aspects of work on the flight deck with that in the hospital has been shared by thousands of others. Nearly 20 years down the line in applying aviation concepts to health care, I still stand by the marked parallels at the level of the “sharp end” work itself. The dynamic thinking of people in dynamic fields of health care is much like that of pilots (and where it isn't yet, it probably should be closer). The 2 activities are not the same of course. Patients are not airplanes. Some aspects of health care are intrinsically different from aviation because of this fact. Other aspects are different not because of an intrinsic difference in the work but rather because of differences in the organizational structure of health care as an industry versus air transport as an industry. Let me explore some of each kind of these differences. What Does It Mean That Patients Are Not Airplanes? A major feature of the notion that “patients are not airplanes” is that people don't design and build human beings whereas they do design and build airplanes. I like that say that no one provides the instruction manual for humans. These facts mean that the level of uncertainty about human beings is enormously greater than that about airplanes. Each plane of a given type will behave in nearly the same fashion given the same small set of key characteristics (eg, thrust, weight, altitude, angle of attack) whereas the diversity between human beings is enormous. Designers instrument airplanes to give key data that can be relied on to fly the plane, whereas in health care clinicians typically obtain a smattering of data (eg, blood pressure, ECG, oxygen saturation) from noninvasive external sources. Airplanes are usually in good shape when we fly them, and there aren't mechanics in the back working on the aircraft during a flight, liable to sever a hydraulic line or the like. A daily variable in flying is weather and in this regard has some parallel to the routine diversity of “patient acuity” that we deal with in health care. Still, in commercial flying if the weather is bad enough, the planes don't fly regardless of how badly the passengers need to get where they're going. In health care, if the surgery is important enough it must go ahead regardless of whether the underlying disease state poses a danger. Another consideration is that health care is very personal. Most people don't care who the pilot of their airliner is as long as she or he is good at the job, and we don't care if the same pilot flies us on one leg of our trip as on the next leg. But we do care a lot about our physicians and having a personal relationship with physicians is perceived to be very important. Moreover, health care is full of issues of social norms and ethics that rarely enter into the sphere of aviation. Organizational Differences Between Healthcare and Other Industries Integration and Economies of Scale: Both aviation and health care are extremely decentralized, in contrast to some other high hazard undertakings that have been studied extensively (like aircraft carriers, of which the entire world has only 20, in the hands of but a few nations' navies). Annually in the U.S. there are more than 11 million departures by large airlines and somewhat over 30 million surgical procedures (the actual number is hard to come by), a roughly comparable figure. Both endeavors are conducted at hundreds or thousands of sites scattered all across the nation, some very large, and some relatively small. In this respect the industries are comparable. But whereas only about 10 airlines are responsible for the 11 million flights, the surgical procedures are conducted at (on the order of) 4,000–6,000 hospitals and a similar number of standalone surgicenters (not to mention office-based surgery). These are owned by on the order of 1,000 – 6,000 firms (no one really knows the number of firms; there are some large hospital chains, but most hospitals are one or 2 of a kind). The small number of firms gives airlines a huge economy of scale and it greatly simplifies both official and unofficial safety regulation of the industry. A good safety idea, even if not an official “de jure” regulation, can be adopted by the industry nationwide if 10 firms think it is worth doing. In healthcare one would have to convince each one of the many thousands of firms. There are a few examples of very large integrated health care organizations in the U.S (the Veterans Affairs health system is one and Kaiser Permanente is another; both have had much publicized safety efforts). Whether safety is actually greater in such integrated systems than in any nonintegrated collection of institutions of similar size, diversity, and scope remains to be seen, but it is conceivable that a system with some of the economies of scale and integration like that of the airline industry could come into being and demonstrate safety benefits of such organization. Accidents and the Means of Production The rates of fatal accidents in these industries is markedly disparate. Between 2002 and 2006 there were on the order of 10.5 to 11.5 million departures on major airlines (Part 121– see http://www.ntsb.gov/aviation/Table5.htm) with between 0 to 3 fatal accidents killing 0–50 people (median about 20). This yields rates of fatal crashes on the order of 0.020 per 100,000 departures. In healthcare we do not know the rates of fatal accidents so clearly. An airplane is never supposed to crash, and when it does it is highly public and may harm dozens or hundreds of people. When it comes to health and disease, all humans are destined to die, and in the industrial world nearly all will die in proximity to healthcare activities. Sorting out those that were due to accidents and those that were due only to the natural course of disease is difficult. Further, healthcare accidents are hidden and usually harm only one person. Still if taking the most wildly optimistic estimates of healthcare success – say the rate of fatal accidents due only to anesthesia care for healthy patients having routine surgery, which are on the order of 0.5 deaths per 100,000 cases,10,11 healthcare is still 25 times more dangerous than flying. For healthcare as a whole the gap is probably considerably larger. In aviation and other hazardous industries accidents harm workers and often the public, are highly publicized by the media, generate lawsuits, and (cynically) of even more concern is that a catastrophic accident destroys the means of production. In such a case the airplane (or even worse in the power industry, a power plant) is removed from service and has to be replaced at great cost and disruption. This gives even the hard-hearted “bean-counters” a healthy interest in avoiding accidents. In healthcare by contrast, accidents or other episodes of suboptimal care harm a patient but do not (generally) harm workers or the means of production. When clinicians hurt a patient they may feel bad about it but then they send for the next patient scheduled for that site. The recent announcement by the major U.S. government payer for healthcare (Centers for Medicare and Medicaid Services) that it will no longer pay for certain preventable conditions, mistakes or infections resulting from a hospital stay is a slight step in the direction of greater “business reasons” to avoid accidents, but even so the means of production are left intact. Imagine how much more seriously this would be taken if every time there was a serious problem in care in the OR or ICU that site had to be taken out of service for a year. Regulation In the U.S. a single federal agency regulates air transport, and comprehensively oversees nearly every level of equipment, personnel, and detailed operations all the way down to some flight crew processes. Beyond the official regulator there is a national independent agency (the National Transportation Safety Board) that investigates accidents and makes safety recommendations to the regulatory body. The firms themselves exert strong control over the pilots with standard operating procedures and company policies that are strongly adhered to. In health care, while a federal agency regulates drugs and devices, it does not regulate the practice of medicine. Each of 50 states and 3 federal jurisdictions (Department of of Veterans and the Health regulates the practice of medicine. The level of government regulatory control is and in it is very (or at the level of actual firms only over the practices of There is no independent official safety for health care. 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Healthcare could use the same of and to assure the and of The is a of and not the or consideration of how to achieve the by some the healthcare system could be from it would probably be and it much more like some of the other high-hazard industries. In I sometimes wonder – thinking about my own of anesthesiology – whether things would have been different had flight been in and anesthesia only in rather than the other way it isn't to the healthcare system from first must be made while the and organizational elements of the system intact. This is one of the key to the processes of simulation-based and assessment we in aviation. has been made in the last years from such industries, but it will the of this approach are fully in healthcare. of this will be in this but I have over more than 20 years that we had have a lot of and a long in
To contain the escalating cost of health care, a prospective payment system is being introduced into the military. The authors propose a proactive approach to this change in health care financing by evaluating the experiences of nurses to a similar change in the civilian sector. These approaches are presented as nine interrelated lessons: responding optimistically; shifting into a business mode; valuing clinical nursing experts; understanding the implications for documentation; moving to decentralized management; changing outpatient care delivery; considering the effects on job satisfaction; evaluating the relationship between costs and nursing resource consumption; and basing nursing practice in science.
Prologues: We live in an age of high technology, whether the subject is medicine, the exploration of space, or unraveling other mysteries of mankind. One small reflection of this reality is the presence on Capitol Hill of the Office of Technology Assessment, an arm of Congress created to assist legislators to better understand the relentless pace of technological change in our society. One of the pursuits of OTA involves a wide range of health related issues, which command the attention of a professional staff numbering about a dozen at any given time. David Banta, a board-certified specialist in preventive medicine, directs this work. One of his senior associates is Jane Willems, a Ph.D economist with a particular interest in technology. The thinking of Banta, as it relates to the appropriate role of government in monitoring technology, has changed in the seven years he has worked on Capitol Hill. Once a devout believer in strong regulation as the primary force to improve the use of technology, Banta has come around to thinking that a variety of instruments may be needed to undertake such tasks in the United States. Willems, on the other hand, has maintained a firm belief in the workings of the marketplace and in decentralized decisionmaking. The paper written by Banta and Willems reflects this tandem of thought. Three congressional committees in particular make use of the OTA 's health work: the Senate Labor and Human Resources Committee, the Senate Finance Committee, and the House Energy and Commerce Committee. Recent studies undertaken by the OTA include technology transfer at the National Institutes of Health, the role of technology in Medicare, alternatives to health and safety regulation in the work place, and technologies for handicapped people.