Japan and Taiwan in the wake of bio-globalization : drugs, race and standards

Thesis (Ph. D. in History and Social Study of Science and Technology (HASTS))--Massachusetts Institute of Technology, Program in Science, Technology and Society, 2005.Also issued in a 2 v. set, printed in leaves.MIT Dewey Library copy: 2 v. set.Includes bibliographical references (p. 518-545).This is a study of Japan and Taiwan's different responses to the expansion of the global drug industry. The thesis focuses on the problematic of "voicing," of how a state can make its interests heard in the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). The ICH is a unique project that facilitates the formation of a single global market by creating universal standards for clinical trials and drug approvals. Tracing, through "slow motion" ethnography, step by step, why Japan claims a racial difference requires additional local clinical trials with "Asian bodies," this thesis rejects conventional interpretations of protectionism for Japan's resistance to globalization. It argues that more than protectionism is involved, and that a rich ethnographic understanding of Japan's medical infrastructure is required to understand the claim of biological, cultural, and national differences, as well as biostatistical arguments about the ambiguities of "extrapolation" of clinical data from one place to another.(cont.) The inherent ambiguities of efforts to create "bridging" studies as a temporary solution to these problematics created a deadlock in the ICH, and provided an opening for Taiwan, another Asian state, which does not enjoy formal recognition from the world, to speak for itself to this conference, and to create the fragile, but politically critical, possibility of becoming a clinical trial center for Asian populations. The language of genomics and biostatistics become in the more recent period the vehicles for both Japanese and Taiwanese efforts at "voicing" their concerns. Both genomics and biostatistics look different in these contexts than they do from the United States or European Union. In sum, (1) Japan's and Taiwan's response, as well as "global ethnographic objects" such as the ICH, provide important tools to rethink the comparative method as well as universalizing claims of harmonization. (2) Race, culture, and the nation-state are transformed as categories through the contemporary reworkings of genomics and biostatistics. (3) The thesis demonstrates that abstract accounts of the spread of clinical trials and resistance in various parts of the world are not to be trusted unless they include detailed probings of local understandings, identity issues, and problems of voicing.by Wen-Hua Kuo.Ph.D.in History and Social Study of Science and Technology (HAST

Managing and Recovering from the COVID-19 Pandemic – Dubai We Learn Research Report

In this time of crisis, benchmarking and learning from global best practices have never been more important. It was for this reason the Dubai Government Excellence Program (DGEP) launched an accelerated benchmarking initiative called “Dubai We learn – Conquering COVID-19” to provide best practices and ideas to Dubai Executive Council’s Supreme Committee of Crisis and Disaster Management with the aim “for Dubai to become a Global Best Practice in Managing and Recovering from the COVID-19 Pandemic”

Big Data Challenges to Privacy: Merits and Limits of the GDPR

Big Data technologies are required due to the enormous expansion in data. The enormous amount of data poses privacy concerns

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

Smoking cessation problem-based learning: Virtual experience

Background and Objectives: Problem-based learning (PBL) is a student-centered teaching and learning methodology where students collaboratively address specific issues. Tobacco use is a major health issue globally. Health professions and students need to have knowledge and skills to facilitate smoking cessation. The objective of this study is to assess feasibility of PBL during a virtual attachment involving institutions from Malaysia and the USA. Methods: A 4-week smoking cessation virtual attachment was conducted for three third-year University of Pittsburgh, USA pharmacy students. Malaysian smoking cessation experts designed and facilitated a PBL smoking cessation module. It was split into two 2-hour sessions with 3 triggers; Trigger 1: ‘Chief Presentation’, Trigger 2: ‘History & Motivational Interview’, and Trigger 3: ‘Brief 5A’s Intervention’. Students received Trigger 1 a day earlier and discussed amongst themselves. In session 1, Triggers 1-3 were given sequentially and discussed after completing all tasks from each trigger. In session 2 one-week later, facilitators gave formative assessment and students provided reflection regarding the PBL session. Upon completing the four-week virtual attachment, students provided feedback and facilitators graded the students. Result and Discussion: A comprehensive and interactive PBL session was successfully conducted virtually. Based on the clinical practice guidelines of both countries, there were differences in terms of availability and use of cessation medications, but the general principles of smoking cessation consultation and interventions were similar. Students were able to discuss the case openly, putting forth ideas and questions in both sessions. All students provided positive feedbacks regarding the PBL. Conclusions: With the extensive development of online platforms connecting the world over, student virtual attachment and mobility programmes can be easily conducted with minimal cost. A suitable module embedding PBL can be designed and conducted to best suit the online platform and the intended students

Smoking cessation problem-based learning: Virtual experience

Background and Objectives: Problem-based learning (PBL) is a student-centered teaching and learning methodology where students collaboratively address specific issues. Tobacco use is a major health issue globally. Health professions and students need to have knowledge and skills to facilitate smoking cessation. The objective of this study is to assess feasibility of PBL during a virtual attachment involving institutions from Malaysia and the USA. Methods: A 4-week smoking cessation virtual attachment was conducted for three third-year University of Pittsburgh, USA pharmacy students. Malaysian smoking cessation experts designed and facilitated a PBL smoking cessation module. It was split into two 2-hour sessions with 3 triggers; Trigger 1: ‘Chief Presentation’, Trigger 2: ‘History & Motivational Interview’, and Trigger 3: ‘Brief 5A’s Intervention’. Students received Trigger 1 a day earlier and discussed amongst themselves. In session 1, Triggers 1-3 were given sequentially and discussed after completing all tasks from each trigger. In session 2 one-week later, facilitators gave formative assessment and students provided reflection regarding the PBL session. Upon completing the four-week virtual attachment, students provided feedback and facilitators graded the students. Result and Discussion: A comprehensive and interactive PBL session was successfully conducted virtually. Based on the clinical practice guidelines of both countries, there were differences in terms of availability and use of cessation medications, but the general principles of smoking cessation consultation and interventions were similar. Students were able to discuss the case openly, putting forth ideas and questions in both sessions. All students provided positive feedbacks regarding the PBL. Conclusions: With the extensive development of online platforms connecting the world over, student virtual attachment and mobility programmes can be easily conducted with minimal cost. A suitable module embedding PBL can be designed and conducted to best suit the online platform and the intended students

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports the needs of healthcare providers. Another reason is the information overload, which makes healthcare providers often misunderstand, misinterpret, ignore, or overlook vital data. The emergence of a type of computational system known as visual analytics (VA), has the potential to reduce the complexity of EHR data by combining advanced analytics techniques with interactive visualizations to analyze, synthesize, and facilitate high-level activities while allowing users to get more involved in a discourse with the data. The purpose of this research is to demonstrate the use of sophisticated visual analytics systems to solve various EHR-related research problems. This dissertation includes a framework by which we identify gaps in existing EHR-based systems and conceptualize the data-driven activities and tasks of our proposed systems. Two novel VA systems (VISA_M3R3 and VALENCIA) and two studies are designed to bridge the gaps. VISA_M3R3 incorporates multiple regression, frequent itemset mining, and interactive visualization to assist users in the identification of nephrotoxic medications. Another proposed system, VALENCIA, brings a wide range of dimension reduction and cluster analysis techniques to analyze high-dimensional EHRs, integrate them seamlessly, and make them accessible through interactive visualizations. The studies are conducted to develop prediction models to classify patients who are at risk of developing acute kidney injury (AKI) and identify AKI-associated medication and medication combinations using EHRs. Through healthcare administrative datasets stored at the ICES-KDT (Kidney Dialysis and Transplantation program), London, Ontario, we have demonstrated how our proposed systems and prediction models can be used to solve real-world problems