COMMENTARYodern increases in computers' capacity to store and process data have made pos sible the emergence and advancement of countless tech nologies that directly impact the daily lives of the world's population. Although discussion of some of the most powerful new technologies began in the last century, only recent ly has their development generated large paradigm shifts. Important examples include artificial intelli gence, data mining, and machine learning, among others. These tech nologies have then become re sponsible for the socalled Fourth Industrial Revolution (or Industry 4.0), where there is convergence be tween the digital, physical, and biological environment.The concept of Health 4.0 ad dresses the broad possibility of applying the technologies of Indus try 4.0 to improve healthcare (as shown in Table 1; cf. Democracy 4.0 [1]). In this commentary we address the challenges and perspectives of Health 4.0 and how the risk of lack of control can delay its emergence (see also [2]).In recent years, research on dis ruptive technologies in Industry 4.0 has increased exponentially in several fields, both in industrial M
(1) Background: Any disturbance in the pharmaceutical supply chain (PSC) can disrupt the supply of medicines and affect the efficiency of health systems. Due to shortages in the global pharma supply chain over the past few years and the complex nature of free trade and its limitations when confronted by a major global health and humanitarian crisis, many countries have taken steps to mitigate the risks of disruption, including, for example, recommending the adoption of a plus one diversification approach, increasing safety stock, and nationalizing the medical supply chains. (2) Objective: To scope findings in the academic literature related to decision criteria to guide national policy decisions for the “Partial Nationalization of Pharmaceutical Supply Chain” (PNPSC) from the viewpoints of the three main stakeholders: industry, payers (government and health insurance), and patients. (3) Methods: These consist of a scoping review of the peer-reviewed literature. (4) Results: A total of 115 studies were included. For local manufacturing decisions, five criteria and 15 sub-criteria were identified. Weighting, decision-making, risk assessment, and forecasting were the main data analysis tools applied; (5) Conclusions: The findings could serve as a baseline for constructing PNPSC frameworks after careful adaptation to the local context.
Background The high off-label use of albumin persuaded the pharmacy to introduce a request form that uses the internationals guidelines to assess whether the use of albumin is appropriate. This has resulted in a clear reduction in the costs. Purpose To monitor the wards using six sigma methodology (a statistical concept that measures a process in terms of defects); to ensure that all procedures have been followed correctly. Materials and Methods 696 forms for albumin prescriptions coming from 26 wards (August to December 2011) were analysed using Minitab software, which cheques the frequency of the best correct requests (type 1), partially correct requests (type 2) and incorrect requests (type 3). For each ward the β coefficient was used to connect the relationship between the ward and their requests. The wards were grouped into 4 ranges on confidence intervals for the odds ratio (OR) of a width equal to 0.3 called A,B,C,D and then a final logistic regression analysis was made. ResultsThe analysis showed that group A was the most efficient in terms of probability of obtaining better results, followed by groups D (OR 0.36), C (OR 0.19) and B (OR 0.09). The total number of requests received was: 43% type 1 (299/696); 26% type 2 (181/696); 31% type 3 (216/696). Group B showed the worst result with 51% type 3 requests (58% of the total requests for type 3). Using six sigma we have achieved a cost saving of about €15,000. Conclusions The results encourage us to apply this methodology to other fields. No conflict of interest.
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