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chart</title> <meta name="description" content="Search results for: prescription chart"> <meta name="keywords" content="prescription chart"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="prescription chart"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 403</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: prescription chart</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">403</span> Challenges to Safe and Effective Prescription Writing in the Environment Where Digital Prescribing is Absent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prashant%20Neupane">Prashant Neupane</a>, <a href="https://publications.waset.org/abstracts/search?q=Asmi%20Pandey"> Asmi Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumna%20Ehsan"> Mumna Ehsan</a>, <a href="https://publications.waset.org/abstracts/search?q=Katie%20Davies"> Katie Davies</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Lowsby"> Richard Lowsby</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction/Background & aims: Safe and effective prescribing in hospitals, directly and indirectly, impacts the health of the patients. Even though digital prescribing in the National Health Service (NHS), UK has been used in lots of tertiary centers along with district general hospitals, a significant number of NHS trusts are still using paper prescribing. We came across lots of irregularities in our daily clinical practice when we are doing paper prescribing. The main aim of the study was to assess how safely and effectively are we prescribing at our hospital where there is no access to digital prescribing. Method/Summary of work: We conducted a prospective audit in the critical care department at Mid Cheshire Hopsitals NHS Foundation Trust in which 20 prescription charts from different patients were randomly selected over a period of 1 month. We assessed 16 multiple categories from each prescription chart and compared them to the standard trust guidelines on prescription. Results/Discussion: We collected data from 20 different prescription charts. 16 categories were evaluated within each prescription chart. The results showed there was an urgent need for improvement in 8 different sections. In 85% of the prescription chart, all the prescribers who prescribed the medications were not identified. Name, GMC number and signature were absent in the required prescriber identification section of the prescription chart. In 70% of prescription charts, either indication or review date of the antimicrobials was absent. Units of medication were not documented correctly in 65% and the allergic status of the patient was absent in 30% of the charts. The start date of medications was missing and alternations of the medications were not done properly in 35%of charts. The patient's name was not recorded in all desired sections of the chart in 50% of cases and cancellations of the medication were not done properly in 45% of the prescription charts. Conclusion(s): From the audit and data analysis, we assessed the areas in which we needed improvement in prescription writing in the Critical care department. However, during the meetings and conversations with the experts from the pharmacy department, we realized this audit is just a representation of the specialized department of the hospital where access to prescribing is limited to a certain number of prescribers. But if we consider bigger departments of the hospital where patient turnover is much more, the results could be much worse. The findings were discussed in the Critical care MDT meeting where suggestions regarding digital/electronic prescribing were discussed. A poster and presentation regarding safe and effective prescribing were done, awareness poster was prepared and attached alongside every bedside in critical care where it is visible to prescribers. We consider this as a temporary measure to improve the quality of prescribing, however, we strongly believe digital prescribing will help to a greater extent to control weak areas which are seen in paper prescribing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=safe%20prescribing" title="safe prescribing">safe prescribing</a>, <a href="https://publications.waset.org/abstracts/search?q=NHS" title=" NHS"> NHS</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20prescribing" title=" digital prescribing"> digital prescribing</a>, <a href="https://publications.waset.org/abstracts/search?q=prescription%20chart" title=" prescription chart"> prescription chart</a> </p> <a href="https://publications.waset.org/abstracts/150545/challenges-to-safe-and-effective-prescription-writing-in-the-environment-where-digital-prescribing-is-absent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">402</span> Creation and Implementation of A New Palliative Care Drug Chart, via A Closed-Loop Audit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asfa%20Hussain">Asfa Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee%20Tang"> Chee Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Mien%20Nguyen"> Mien Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The safe usage of medications is dependent on clear, well-documented prescribing. Medical drug charts should be regularly checked to ensure that they are fit for purpose. Aims: The purpose of this study was to evaluate whether the Isabel Hospice drug charts were effective or prone to medical errors. The aim was to create a comprehensive palliative care drug chart in line with medico-legal guidelines and to minimise drug administration and prescription errors. Methodology: 50 medical drug charts were audited from March to April 2020, to assess whether they complied with medico-legal guidelines, in a hospice within East of England. Meetings were held with the larger multi-disciplinary team (MDT), including the pharmacists, nursing staff and doctors, to raise awareness of the issue. A preliminary drug chart was created, using the input from the wider MDT. The chart was revised and trialled over 15 times, and each time feedback from the MDT was incorporated into the subsequent template. In the midst of the COVID-19 pandemic in September 2020, the finalised drug chart was trialled. 50 new palliative drug charts were re-audited, to evaluate the changes made. Results: Prescribing and administration errors were high prior to the implementation of the new chart. This improved significantly after introducing the new drug charts, therefore improving patient safety and care. The percentage of inadequately documented allergies went down from 66% to 20% and incorrect oxygen prescription from 40% to 16%. The prescription drug-drug interactions decreased by 30%. Conclusion: It is vital to have clear standardised drug charts, in line with medico-legal standards, to allow ease of prescription and administration of medications and ensure optimum patient-centred care. This closed loop audit demonstrated significant improvement in documentation and prevention of possible fatal drug errors and interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=palliative%20care" title="palliative care">palliative care</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20chart" title=" drug chart"> drug chart</a>, <a href="https://publications.waset.org/abstracts/search?q=medication%20errors" title=" medication errors"> medication errors</a>, <a href="https://publications.waset.org/abstracts/search?q=drug-drug%20interactions" title=" drug-drug interactions"> drug-drug interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20safety" title=" patient safety"> patient safety</a> </p> <a href="https://publications.waset.org/abstracts/142480/creation-and-implementation-of-a-new-palliative-care-drug-chart-via-a-closed-loop-audit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142480.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">401</span> A Prospective Audit to Look into Antimicrobial Prescribing in the Clinical Setting: In a Teaching Hospital in the UK</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Richa%20Sinha">Richa Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Irfan%20Javed"> Mohammad Irfan Javed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Singh"> Sanjay Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Good antimicrobial prescribing reduces length of stay in hospital, risk of adverse events, antimicrobial resistance, and unnecessary hospital expenditure. The aim of this prospective audit was to identify any problems with antimicrobial prescribing including documentation of the relevant aspects as well as appropriateness of antibiotics use. The audit was conducted on the surgical wards in a teaching hospital in the UK. Methods: Standards included the indication, duration, choice, and prescription of antibiotic should be in line with current Regional Guidelines and should be clearly documented on the prescription chart. There should be an entry in each patients’ medical record of the diagnosis and indication for each acute antibiotic prescription issued. All prescriptions should clearly document the route, frequency and dose of antibiotic. Data collection was done for 2 weeks in the month of March 2014. A proforma including all the questions above was completed for all the patients. The results were analysed using Excel. Results: 35 patients in total were selected for the audit. 85.7% of patients had indication of antibiotic documented on the prescription chart and 68.5% of patients had indication documented in the notes. The antibiotic used was in line with hospital guidelines in 45.7% of patients, however, in a further 28.5% of patients the reason for the antibiotic prescription was microbiology approved. Therefore, in total 74.2% of patients had been prescribed appropriate antibiotics. The duration of antibiotic was documented in 68.6% of patients and the antibiotic was reviewed in 37.1% of patients. The dose, frequency and route was documented clearly in 100% of patients. Conclusion: Overall, prescribing can be improved on the surgical wards in this hospital. Only 37.1% of patients had clear documentation of a review of antibiotics. It may be that antibiotics have been reviewed but this should be clearly highlighted on the prescription chart or the notes. Failure to review antibiotics can lead to poor patient care and antimicrobial resistance and therefore it is important to address this. It is also important to address the appropriateness of antibiotics as inappropriate antibiotic prescription can lead to failure of treatment as well as antimicrobial resistance. The good points from the audit was that all patients had clear documentation of dose, route and frequency which is extremely important in the administration of antibiotics. Recommendations from this audit included to emphasize good antimicrobial prescribing at induction (twice yearly), an antimicrobial handbook for junior doctors, and re-audit in 6 months time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prescribing" title="prescribing">prescribing</a>, <a href="https://publications.waset.org/abstracts/search?q=antimicrobial" title=" antimicrobial"> antimicrobial</a>, <a href="https://publications.waset.org/abstracts/search?q=indication" title=" indication"> indication</a>, <a href="https://publications.waset.org/abstracts/search?q=duration" title=" duration"> duration</a> </p> <a href="https://publications.waset.org/abstracts/11047/a-prospective-audit-to-look-into-antimicrobial-prescribing-in-the-clinical-setting-in-a-teaching-hospital-in-the-uk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">400</span> The Variable Sampling Interval Xbar Chart versus the Double Sampling Xbar Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20B.%20C.%20Khoo">Michael B. C. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Khoo"> J. L. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20C.%20Yeong"> W. C. Yeong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20L.%20Teoh"> W. L. Teoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Shewhart Xbar control chart is a useful process monitoring tool in manufacturing industries to detect the presence of assignable causes. However, it is insensitive in detecting small process shifts. To circumvent this problem, adaptive control charts are suggested. An adaptive chart enables at least one of the chart’s parameters to be adjusted to increase the chart’s sensitivity. Two common adaptive charts that exist in the literature are the double sampling (DS) Xbar and variable sampling interval (VSI) Xbar charts. This paper compares the performances of the DS and VSI Xbar charts, based on the average time to signal (ATS) criterion. The ATS profiles of the DS Xbar and VSI Xbar charts are obtained using the Mathematica and Statistical Analysis System (SAS) programs, respectively. The results show that the VSI Xbar chart is generally superior to the DS Xbar chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20charts" title="adaptive charts">adaptive charts</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20time%20to%20signal" title=" average time to signal"> average time to signal</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20sampling" title=" double sampling"> double sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=charts" title=" charts"> charts</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20sampling%20interval" title=" variable sampling interval"> variable sampling interval</a> </p> <a href="https://publications.waset.org/abstracts/45295/the-variable-sampling-interval-xbar-chart-versus-the-double-sampling-xbar-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">399</span> An EWMA P-Chart Based on Improved Square Root Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowanit%20Sukparungsee">Saowanit Sukparungsee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=number%20of%20defects" title="number of defects">number of defects</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average" title=" exponentially weighted moving average"> exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title=" average run length"> average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=square%20root%20transformations" title=" square root transformations"> square root transformations</a> </p> <a href="https://publications.waset.org/abstracts/10613/an-ewma-p-chart-based-on-improved-square-root-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">398</span> Improving Compliance in Prescribing Regular Medications for Surgical Patients: A Quality Improvement Project in the Surgical Assessment Unit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Tahir">Abdullah Tahir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The omission of regular medications in surgical patients poses a significant challenge in healthcare settings and is associated with increased morbidity during hospital stays. Human factors such as high workload, poor communication, and emotional stress are known to contribute to these omissions, particularly evident in the surgical assessment unit (SAU) due to its high patient burden and long wait times. This study aimed to quantify and address the issue by implementing targeted interventions to enhance compliance in prescribing regular medications for surgical patients at Stoke Mandeville Hospital, United Kingdom. Data were collected on 14 spontaneous days between April and May 2023, and the frequency of prescription omissions was recorded using a tally chart. Subsequently, informative posters were introduced in the SAU, and presentations were given to the surgical team to emphasize the importance of compliance in this area. The interventions were assessed using a second data collection cycle, again over 14 spontaneous days in May 2023. Results demonstrated an improvement from 40% (60 out of 150) to 74% (93 out of 126) of patients having regular medications prescribed at the point of clerking. These findings highlight the efficacy of frequent prompts and awareness-raising interventions in increasing workforce compliance and addressing the issue of prescription omissions in the SAU. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prescription%20omissions" title="prescription omissions">prescription omissions</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20improvement" title=" quality improvement"> quality improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=regular%20medication" title=" regular medication"> regular medication</a>, <a href="https://publications.waset.org/abstracts/search?q=surgical%20assessment%20unit" title=" surgical assessment unit"> surgical assessment unit</a> </p> <a href="https://publications.waset.org/abstracts/170849/improving-compliance-in-prescribing-regular-medications-for-surgical-patients-a-quality-improvement-project-in-the-surgical-assessment-unit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">397</span> Statistical Design of Synthetic VP X-bar Control Chat Using Markov Chain Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akbar%20Heydari">Ali Akbar Heydari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20approach" title=" markov chain approach"> markov chain approach</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20design" title=" statistical design"> statistical design</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic" title=" synthetic"> synthetic</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20parameter" title=" variable parameter"> variable parameter</a> </p> <a href="https://publications.waset.org/abstracts/146094/statistical-design-of-synthetic-vp-x-bar-control-chat-using-markov-chain-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">396</span> A Brief History of Kampo Extract Formulations for Prescription in Japan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazunari%20Ozaki">Kazunari Ozaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mitsuru%20Kageyama"> Mitsuru Kageyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenki%20Miyazawa"> Kenki Miyazawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshio%20Nakamura"> Yoshio Nakamura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Kampo (Japanese Traditional medicine) is a medicine traditionally practiced in Japan, based on ancient Chinese medicine. Most Kampo doctors have used decoction of crude drug pieces for treatment. 93% of the Kampo drugs sold in Japan are Kampo products nowadays. Of all Kampo products, 81% of them are Kampo extract formulations for prescription, which is prepared in powdered or granulated form from medicinal crude drug extracts mixed with appropriate excipient. Physicians with medical license for Western medicine prescribe these Kampo extract formulations for prescription in Japan. Objectives: Our study aims at presenting a brief history of Kampo extract formulations for prescription in Japan. Methods: Systematic searches for relevant studies were conducted using not only printed journals but also electronic journals from the bibliographic databases, such as PubMed/Medline, Ichushi-Web, and university/institutional websites, as well as search engines, such as Google and Google Scholar. Results: The first commercialization of Kampo extract formulations for general use (or OTC (over-the-counter) Kampo extract formulation) was achieved after 1957. The number of drugs has been subsequentially increased, reaching 148 Kampo extract formulation for prescription currently. Conclusion: We provide a history of Kampo extract formulations for prescription in Japan. The originality of this research is that it analyzes the background history of Kampo in parallel with relevant transitions in the government and insurance systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health%20insurance%20system" title="health insurance system">health insurance system</a>, <a href="https://publications.waset.org/abstracts/search?q=history" title=" history"> history</a>, <a href="https://publications.waset.org/abstracts/search?q=Kampo" title=" Kampo"> Kampo</a>, <a href="https://publications.waset.org/abstracts/search?q=Kampo%20extract%20formulation%20for%20prescription" title=" Kampo extract formulation for prescription"> Kampo extract formulation for prescription</a>, <a href="https://publications.waset.org/abstracts/search?q=OTC%20Kampo%20extract%20formulation" title=" OTC Kampo extract formulation"> OTC Kampo extract formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20corresponding%20prescription%20%28Ho-sho-so-tai%29%20system" title=" pattern corresponding prescription (Ho-sho-so-tai) system"> pattern corresponding prescription (Ho-sho-so-tai) system</a> </p> <a href="https://publications.waset.org/abstracts/81559/a-brief-history-of-kampo-extract-formulations-for-prescription-in-japan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">395</span> Prescription of Maintenance Fluids in the Emergency Department</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Craig">Adrian Craig</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Easaw"> Jonathan Easaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Rose%20Jordan"> Rose Jordan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Hall"> Ben Hall</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prescription of intravenous fluids is a fundamental component of inpatient management, but it is one which usually lacks thought. Fluids are a drug, which like any other can cause harm when prescribed inappropriately or wrongly. However, it is well recognised that it is poorly done, especially in the acute portals. The National Institute for Health and Care Excellence (NICE) recommends 1mmol/kg of potassium, sodium, and chloride per day. With various options of fluids, clinicians tend to face difficulty in choosing the most appropriate maintenance fluid, and there is a reluctance to prescribe potassium as part of an intravenous maintenance fluid regime. The aim was to prospectively audit the prescription of the first bag of intravenous maintenance fluids, the use of urea and electrolytes results to guide the choice of fluid and the use of fluid prescription charts, in a busy emergency department of a major trauma centre in Stoke-on-Trent, United Kingdom. This was undertaken over a week in early November 2016. Of those prescribed maintenance fluid only 8.9% were prescribed a fluid which was most appropriate for their daily electrolyte requirements. This audit has helped to highlight further the issues that are faced in busy Emergency Departments within hospitals that are stretched and lack capacity for prompt transfer to a ward. It has supported the findings of NICE, that emergency admission portals such as Emergency Departments poorly prescribed intravenous fluid therapy. The findings have enabled simple steps to be taken to educate clinicians about their fluid of choice. This has included: posters to remind clinicians to consider the urea and electrolyte values before prescription, suggesting the inclusion of a suggested intravenous fluid of choice in the prescription chart of the trust and the inclusion of a session within the introduction programme revising intravenous fluid therapy and daily electrolyte requirements. Moving forward, once the interventions have been implemented then, the data will be reaudited in six months to note any improvement in maintenance fluid choice. Alongside this, an audit of the rate of intravenous maintenance fluid therapy would be proposed to further increase patient safety by avoiding unintentional fluid overload which may cause unnecessary harm to patients within the hospital. In conclusion, prescription of maintenance fluid therapy was poor within the Emergency Department, and there is a great deal of opportunity for improvement. Therefore, the measures listed above will be implemented and the data reaudited. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chloride" title="chloride">chloride</a>, <a href="https://publications.waset.org/abstracts/search?q=electrolyte" title=" electrolyte"> electrolyte</a>, <a href="https://publications.waset.org/abstracts/search?q=emergency%20department" title=" emergency department"> emergency department</a>, <a href="https://publications.waset.org/abstracts/search?q=emergency%20medicine" title=" emergency medicine"> emergency medicine</a>, <a href="https://publications.waset.org/abstracts/search?q=fluid" title=" fluid"> fluid</a>, <a href="https://publications.waset.org/abstracts/search?q=fluid%20therapy" title=" fluid therapy"> fluid therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=intravenous" title=" intravenous"> intravenous</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance" title=" maintenance"> maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=major%20trauma" title=" major trauma"> major trauma</a>, <a href="https://publications.waset.org/abstracts/search?q=potassium" title=" potassium"> potassium</a>, <a href="https://publications.waset.org/abstracts/search?q=sodium" title=" sodium"> sodium</a>, <a href="https://publications.waset.org/abstracts/search?q=trauma" title=" trauma"> trauma</a> </p> <a href="https://publications.waset.org/abstracts/64854/prescription-of-maintenance-fluids-in-the-emergency-department" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">394</span> A Cros Sectional Observational Study of Prescription Pattern of Gastro-Protective Drugs with Non-Steroidal Anti-Inflammatory Drugs in Nilgiris, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.S.%20Roopa">B.S. Roopa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: To investigate the prevalence of concomitant use of GPDs in patients treated with NSAIDs and GPDs in recommended dose and frequency as prophylaxis. And also to know the association between risk factors and prescription of GPDs in patients treated with NSAIDs. Methods: Study was a prospective, observational, cross-sectional survey. Data from patients with prescription of NSAIDs at the out-patient departments of secondary care Hospital, Nilgiris, India were collected in a specially designed proforma for a period of 45 days. Analysis using χ2 tests for discrete variables. Factors that might be associated with prescription of GPD with NSIADs were assessed in multiple logistic regression models. Results: Three hundred and three patients were included in this study, and the rate of GPD prescription was 89.1%. Most of the patients received H2-receptor antagonist, and, to a lesser degree, antacid and proton pump inhibitor. Patients with history of GI ulcer/bleeding were much more likely to be co-prescribed GPD than those who had no history of GI disorders .Compared with patients who were managed in general outpatient clinic, those managed in Secondary care hospital in Nilgrisis, India were more likely to receive GPD. Conclusions: The prescription rate of GPD with NSAIDs is high. Patients were prescribed with H2RA with dose of 150mg twice daily, which are not effective in reducing the risk of NSAIDs induced gastric ulcer. Only the frequency of NSAIDs prescription was considered significant determinant for the co-prescription with GPAs in patients who are < 65 years and ≥ 65 years old. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gastro%20protective%20agents" title="gastro protective agents">gastro protective agents</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20steridol%20anti%20inlfammatory%20agents" title=" non steridol anti inlfammatory agents"> non steridol anti inlfammatory agents</a> </p> <a href="https://publications.waset.org/abstracts/5957/a-cros-sectional-observational-study-of-prescription-pattern-of-gastro-protective-drugs-with-non-steroidal-anti-inflammatory-drugs-in-nilgiris-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5957.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">393</span> An Engineered Epidemic: Big Pharma's Role in the Opioid Crisis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Donna%20L.%20Roberts">Donna L. Roberts</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 2019 marked 23 years since Purdue Pharma launched its flagship drug, OxyContin, that unleashed an unprecedented epidemic touching both celebrities and common citizens, metropolitan, suburbia and rural areas and all levels of socioeconomic status. From rural Appalachia to East LA individuals, families and communities have been devastated by a trajectory of addiction that often began with the legitimate prescription of a pain killer for anything from a tooth extraction to a sports injury to recovery from surgery or chronic arthritis. Far from being a serendipitous progression of events, the proliferation of this new breed of 'miracle drug' was instead a carefully crafted marketing program aimed at both the medical community and common citizens. This research represents and in-depth investigation of the evolution of the marketing, distribution and promotion of prescription opioids by pharmaceutical companies and its relationship to the propagation of the opioid crisis. Specifically, key components of Purdue Pharma’s aggressive marketing campaign, including its bonus system and sales incentives, were analyzed in the context of the sociopolitical environment that essential created the proverbial 'perfect storm' for the changing manner in which pain is treated in the U.S. The analyses of these series of events clearly indicate their role in first, the increase in prescription of opioids for non-terminal pain relief and subsequently, the incidence of related addiction, overdose, and death. Through this examination of the conditions that facilitated and maintained this drug crisis, perhaps we can begin to chart a course toward its resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=addiction" title="addiction">addiction</a>, <a href="https://publications.waset.org/abstracts/search?q=opioid" title=" opioid"> opioid</a>, <a href="https://publications.waset.org/abstracts/search?q=opioid%20crisis" title=" opioid crisis"> opioid crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=Purdue%20Pharma" title=" Purdue Pharma"> Purdue Pharma</a> </p> <a href="https://publications.waset.org/abstracts/112631/an-engineered-epidemic-big-pharmas-role-in-the-opioid-crisis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">392</span> Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yupaporn%20Areepong">Yupaporn Areepong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20parameters" title=" optimal parameters"> optimal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average%20%28EWMA%29" title=" exponentially weighted moving average (EWMA)"> exponentially weighted moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/10653/optimal-design-for-sarmapql-process-of-ewma-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">391</span> Evaluation of Medication Errors in Outpatient Pharmacies: Electronic Prescription System vs. Paper System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mera%20Ababneh">Mera Ababneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayer%20Al-Azzam"> Sayer Al-Azzam</a>, <a href="https://publications.waset.org/abstracts/search?q=Karem%20%20Alzoubi"> Karem Alzoubi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abeer%20Rababa%27h"> Abeer Rababa'h</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Medication errors are among the most common medical errors. Their occurrences result in patient’s mortality, morbidity, and additional healthcare costs. Continuous monitoring and detection is required. Objectives: The aim of this study was to compare medication errors in outpatient’s prescriptions in two different hospitals (paper system vs. electronic system). Methods: This was a cross sectional observational study conducted in two major hospitals; King Abdullah University Hospital (KAUH) and Princess Bassma Teaching Hospital (PBTH) over three months period. Data collection was conducted by two trained pharmacists at each site. During the study period, medication prescriptions and dispensing procedures were screened for medication errors in both participating centers by two trained pharmacist. Results: In the electronic prescription hospital, 2500 prescriptions were screened in which 631 medication errors were detected. Prescription errors were 231 (36.6%), and dispensing errors were 400 (63.4%) of all errors. On the other side, analysis of 2500 prescriptions in paper-based hospital revealed 3714 medication errors, of which 288 (7.8%) were prescription errors, and 3426 (92.2%) were dispensing errors. A significant number of 2496 (67.2%) were inadequately and/or inappropriately labeled. Conclusion: This study provides insight for healthcare policy makers, professionals, and administrators to invest in advanced technology systems, education, and epidemiological surveillance programs to minimize medication errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medication%20errors" title="medication errors">medication errors</a>, <a href="https://publications.waset.org/abstracts/search?q=prescription%20errors" title=" prescription errors"> prescription errors</a>, <a href="https://publications.waset.org/abstracts/search?q=dispensing%20errors" title=" dispensing errors"> dispensing errors</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20prescription" title=" electronic prescription"> electronic prescription</a>, <a href="https://publications.waset.org/abstracts/search?q=handwritten%20prescription" title=" handwritten prescription"> handwritten prescription</a> </p> <a href="https://publications.waset.org/abstracts/64923/evaluation-of-medication-errors-in-outpatient-pharmacies-electronic-prescription-system-vs-paper-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64923.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">390</span> Application of Hyperbinomial Distribution in Developing a Modified p-Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed">Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria"> M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20distribution" title="binomial distribution">binomial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title=" cumulative distribution function"> cumulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper%20binomial%20distribution" title=" hyper binomial distribution"> hyper binomial distribution</a> </p> <a href="https://publications.waset.org/abstracts/90750/application-of-hyperbinomial-distribution-in-developing-a-modified-p-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">389</span> Optimal Bayesian Chart for Controlling Expected Number of Defects in Production Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Makis">V. Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Jafari"> L. Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20u-chart" title="Bayesian u-chart">Bayesian u-chart</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20design" title=" economic design"> economic design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20stopping" title=" optimal stopping"> optimal stopping</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a> </p> <a href="https://publications.waset.org/abstracts/62841/optimal-bayesian-chart-for-controlling-expected-number-of-defects-in-production-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">573</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">388</span> Design Data Sorter Circuit Using Insertion Sorting Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Abugharsa">Hoda Abugharsa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=insert%20sorting%20algorithm" title="insert sorting algorithm">insert sorting algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ASM%20chart" title=" ASM chart"> ASM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=sorter%20circuit" title=" sorter circuit"> sorter circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20machine" title=" state machine"> state machine</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20unit" title=" control unit"> control unit</a> </p> <a href="https://publications.waset.org/abstracts/5614/design-data-sorter-circuit-using-insertion-sorting-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">387</span> Regular or Irregular: An Investigation of Medicine Consumption Pattern with Poisson Mixture Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lichung%20Jen">Lichung Jen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Chun%20Liu"> Yi Chun Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuan-Wei%20Lee"> Kuan-Wei Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fruitful data has been accumulated in database nowadays and is commonly used as support for decision-making. In the healthcare industry, hospital, for instance, ordering pharmacy inventory is one of the key decision. With large drug inventory, the current cost increases and its expiration dates might lead to future issue, such as drug disposal and recycle. In contrast, underestimating demand of the pharmacy inventory, particularly standing drugs, affects the medical treatment and possibly hospital reputation. Prescription behaviour of hospital physicians is one of the critical factor influencing this decision, particularly irregular prescription behaviour. If a drug’s usage amount in the month is irregular and less than the regular usage, it may cause the trend of subsequent stockpiling. On the contrary, if a drug has been prescribed often than expected, it may result in insufficient inventory. We proposed a hierarchical Bayesian mixture model with two components to identify physicians’ regular/irregular prescription patterns with probabilities. Heterogeneity of hospital is considered in our proposed hierarchical Bayes model. The result suggested that modeling the prescription patterns of physician is beneficial for estimating the order quantity of medication and pharmacy inventory management of the hospital. Managerial implication and future research are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20model" title="hierarchical Bayesian model">hierarchical Bayesian model</a>, <a href="https://publications.waset.org/abstracts/search?q=poission%20mixture%20model" title=" poission mixture model"> poission mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=medicines%20prescription%20behavior" title=" medicines prescription behavior"> medicines prescription behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=irregular%20behavior" title=" irregular behavior"> irregular behavior</a> </p> <a href="https://publications.waset.org/abstracts/106808/regular-or-irregular-an-investigation-of-medicine-consumption-pattern-with-poisson-mixture-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">386</span> Process Monitoring Based on Parameterless Self-Organizing Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Young%20Jae%20Choung">Young Jae Choung</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling’s T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter-less%20self-organizing%20map" title=" parameter-less self-organizing map"> parameter-less self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20property" title=" time-varying property"> time-varying property</a> </p> <a href="https://publications.waset.org/abstracts/52108/process-monitoring-based-on-parameterless-self-organizing-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">385</span> A Comparison of Dietary Quality and Nutritional Adequacy of Meal Plans of a Diet Prescription Generator Web App against the Australian Guidelines to Healthy Eating</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ananda%20Perera">Ananda Perera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diet therapy has a positive impact on many diseases in General Practice. If a meal plan can be generated as easily as writing a drug prescription for dyspepsia, then the evidence and practice gap in nutrition therapy can be narrowed. Meal plans of 50 diet prescriptions were compared with the criteria for a healthy diet given by Australian authorities. The energy value of each meal plan was compared with the recommended daily energy requirements of the authorities for Diet Prescription Generator (DPG) accuracy. Meal plans generated were within the criteria laid down by the Australian authorities for a healthy diet. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dieting" title="dieting">dieting</a>, <a href="https://publications.waset.org/abstracts/search?q=obesity" title=" obesity"> obesity</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20loss" title=" weight loss"> weight loss</a>, <a href="https://publications.waset.org/abstracts/search?q=computerized%20decision%20support%20systems" title=" computerized decision support systems"> computerized decision support systems</a>, <a href="https://publications.waset.org/abstracts/search?q=dieting%20software" title=" dieting software"> dieting software</a>, <a href="https://publications.waset.org/abstracts/search?q=CDSS" title=" CDSS"> CDSS</a>, <a href="https://publications.waset.org/abstracts/search?q=meal%20plans" title=" meal plans"> meal plans</a> </p> <a href="https://publications.waset.org/abstracts/150628/a-comparison-of-dietary-quality-and-nutritional-adequacy-of-meal-plans-of-a-diet-prescription-generator-web-app-against-the-australian-guidelines-to-healthy-eating" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">384</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">383</span> The Role of Trust in Intention to Use Prescribed and Non-prescribed Connected Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean-michel%20Sahut">Jean-michel Sahut</a>, <a href="https://publications.waset.org/abstracts/search?q=Lubica%20Hikkerova"> Lubica Hikkerova</a>, <a href="https://publications.waset.org/abstracts/search?q=Wissal%20Ben%20Arfi"> Wissal Ben Arfi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet of Things (IoT) emerged over the last few decades in many fields. Healthcare can significantly benefit from IoT. This study aims to examine factors influencing the adoption of IoT in eHealth. To do so, an innovative framework has been developed which applies both the Technology Acceptance Model (TAM) and the United Theory of Acceptance and Use of Technology (UTAUT) model and builds on them by analyzing trust and perceived-risk dimensions to predict intention to use IoT in eHealth. In terms of methodology, a Partial Least Approach Structural Equation Modelling was carried out on a sample of 267 French users. The findings of this research support the significant positive effect of constructs set out in the TAM (perceived ease of use) on predicting behavioral intention by adding the effects identified for UTAUT variables. This research also demonstrates how perceived risk and trust are significant factors for models examining behavioral intentions to use IoT. Perceived risk enhanced by the trust has a significant effect on patients’ behavioral intentions. Moreover, the results highlight the key role of prescription as a moderator of IoT adoption in eHealth. Depending on whether an individual has a prescription to use connected devices or not, ease of use has a stronger impact on adoption, while trust has a negative impact on adoption for users without a prescription. In accordance with the empirical results, several practical implications can be proposed. All connected devices applied in a medical context should be divided into groups according to their functionality: whether they are essential for the patient’s health and whether they require a prescription or not. Devices used with a prescription are easily accepted because the intention to use them is moderated by the medical trust (discussed above). For users without a prescription, ease of use is a more significant factor than for users who have a prescription. This suggests that currently, connected e-Health devices and online healthcare systems have to take this factor into account to better meet the needs and expectations of end-users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title="internet of things">internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=Healthcare" title=" Healthcare"> Healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20acceptance" title=" consumer acceptance"> consumer acceptance</a> </p> <a href="https://publications.waset.org/abstracts/126430/the-role-of-trust-in-intention-to-use-prescribed-and-non-prescribed-connected-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">382</span> EWMA and MEWMA Control Charts for Monitoring Mean and Variance in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro">L. A. Toro</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas"> J. J. Vargas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title="control charts">control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20moving%20average%20%28MEWMA%29" title=" multivariate exponentially moving average (MEWMA)"> multivariate exponentially moving average (MEWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20moving%20average%20%28EWMA%29" title=" exponentially moving average (EWMA)"> exponentially moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20process" title=" industrial control process"> industrial control process</a> </p> <a href="https://publications.waset.org/abstracts/38377/ewma-and-mewma-control-charts-for-monitoring-mean-and-variance-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">381</span> Economic Design of a Quality Control Chart for the Proportion of Defective Items</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Encarnaci%C3%B3n%20%C3%81lvarez-Verdejo">Encarnación Álvarez-Verdejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Amor-Pulido"> Raúl Amor-Pulido</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20J.%20Moya-Fern%C3%A1ndez"> Pablo J. Moya-Fernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20F.%20Mu%C3%B1oz-Rosas"> Juan F. Muñoz-Rosas</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J.%20Blanco-Encomienda"> Francisco J. Blanco-Encomienda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proportion" title="proportion">proportion</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20I%20error" title=" type I error"> type I error</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20plan" title=" economic plan"> economic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20function" title=" distribution function"> distribution function</a> </p> <a href="https://publications.waset.org/abstracts/42442/economic-design-of-a-quality-control-chart-for-the-proportion-of-defective-items" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">380</span> A Study on the False Alarm Rates of MEWMA and MCUSUM Control Charts When the Parameters Are Estimated</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Farouk%20Abbas">Umar Farouk Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Danjuma%20Mustapha"> Danjuma Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamisu%20Idi"> Hamisu Idi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean µ and standard deviation σ parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=MCUSUM%20chart" title=" MCUSUM chart"> MCUSUM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=MEWMA%20chart" title=" MEWMA chart"> MEWMA chart</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title=" false alarm rate"> false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/106504/a-study-on-the-false-alarm-rates-of-mewma-and-mcusum-control-charts-when-the-parameters-are-estimated" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">379</span> On the Bootstrap P-Value Method in Identifying out of Control Signals in Multivariate Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Ikpotokin">O. Ikpotokin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20control%20limit" title="bootstrap control limit">bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/77853/on-the-bootstrap-p-value-method-in-identifying-out-of-control-signals-in-multivariate-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">378</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">377</span> Introduction of a Standardised Proforma to Optimise Post-Operative Analgesia after Caesarean Section</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prashant%20Neupane">Prashant Neupane</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumitra%20Kafle"> Sumitra Kafle</a>, <a href="https://publications.waset.org/abstracts/search?q=Asmi%20Pandey"> Asmi Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Mitchell"> Laura Mitchell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pain following caesarean section can influence recovery, patient satisfaction, breast feeding success and mother-child bonding. Since the introduction of enhanced recovery protocols, mothers are often discharged 24 hours later. We identified concerns within our hospital with mothers tolerating poorly controlled pain in order to achieve earlier discharge and subsequently suffering significant pain at home with inadequate analgesia. Methods: We conducted a prospective audit of analgesic prescribing and post-operative pain scores after caesarean section. Mothers were seen on post-operative day one, their pain score recorded on a verbal analogue score from 0-10, and their prescription chart reviewed. A follow-up phone call was then made on post-operative day 3-7 to enquire about pain scores and analgesia use at home. Following this, a standardized proforma for prescribing after the caesarean section was introduced, including the addition of dihydrocodeine that patients can take home following discharge. There were educational update sessions for anesthetists and midwifes, and then a re-audit was conducted months later. Results: Data was collected from 50 women before and after the introduction of the change. Initial audit showed that there was considerable variation in prescribing, with four women prescribed no regular analgesia at all and inconsistency in the dose of oral morphine prescribed. Women were not given any form of analgesia to take home after discharge and were advised to take regular paracetamol and ibuprofen. However, 31/50 (62%) reported that they needed additional analgesia and eight women (16%) even sought prescription for additional analgesia from elsewhere. After the introduction of the change, prescribing was more consistent with all patients prescribed regular analgesia. 46/50 patients were given dihydrocodeine on discharge. Mean pain scores on post-operative day one improved from 5.16 to 3.9, and at home improved from 6.18 to 2.58. Use of dihydrocodeine at home significantly improved patients reporting of severe pain at home from 24% to zero. Discussion: Lack of strong analgesia out of the hospital and the increased demands on activity levels means that women are frequently in more pain at home after discharge. Introduction of a standardized prescription proforma, including the use of to-take-out dihydrocodeine, was successful in improving patient pain scores and the requirement for additional analgesia, both in hospital and at home. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analgesia" title="analgesia">analgesia</a>, <a href="https://publications.waset.org/abstracts/search?q=caesarean%20section" title=" caesarean section"> caesarean section</a>, <a href="https://publications.waset.org/abstracts/search?q=post-operative%20pain" title=" post-operative pain"> post-operative pain</a>, <a href="https://publications.waset.org/abstracts/search?q=standardised" title=" standardised"> standardised</a> </p> <a href="https://publications.waset.org/abstracts/150541/introduction-of-a-standardised-proforma-to-optimise-post-operative-analgesia-after-caesarean-section" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150541.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">376</span> Adaptive Process Monitoring for Time-Varying Situations Using Statistical Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seulki%20Lee">Seulki Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20method" title=" nonparametric method"> nonparametric method</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20data%20description" title=" support vector data description"> support vector data description</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20process" title=" time-varying process"> time-varying process</a> </p> <a href="https://publications.waset.org/abstracts/52078/adaptive-process-monitoring-for-time-varying-situations-using-statistical-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52078.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">375</span> Analysis of Diabetes Patients Using Pearson, Cost Optimization, Control Chart Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Poovarasan"> R. Poovarasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=congenital%20diabetics" title=" congenital diabetics"> congenital diabetics</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20relationship" title=" linear relationship"> linear relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=monotonic%20function" title=" monotonic function"> monotonic function</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking%20samples" title=" ranking samples"> ranking samples</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric" title=" pediatric"> pediatric</a> </p> <a href="https://publications.waset.org/abstracts/72132/analysis-of-diabetes-patients-using-pearson-cost-optimization-control-chart-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">374</span> Malaria Management among Dispensers in Drug Retail Outlets in Buea Community: An Assessment of Knowledge of Malaria and Antimalarial Drug Prescription and Dispensing Practices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcelus%20U.%20Ajonina">Marcelus U. Ajonina</a>, <a href="https://publications.waset.org/abstracts/search?q=Deodata%20B.%20Ngonga"> Deodata B. Ngonga</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenric%20B.%20Ware"> Kenric B. Ware</a>, <a href="https://publications.waset.org/abstracts/search?q=Carine%20K.%20Nfor"> Carine K. Nfor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Lack of knowledge of rational use of antimalarial drugs among dispensers is a serious problem, especially in areas of intense transmission, thus increasing the risk of resistance and adverse drug reactions. This study was aimed at assessing the knowledge of malaria as well as perception and dispensing practices of antimalarials among vendors in Buea community. Methods: A community-based cross-sectional survey of a random sample of 140 drug vendors living within the Buea community was conducted between March and June 2017. A questionnaire was designed to obtain information from drug vendors on the general knowledge of malaria as well as dispensing practices. Data were analyzed using SPSS Statistics 20.0 and were considered significant at p ≤ 0.05. Results: Knowledge of malaria symptoms, transmission, and prevention was reasonable among 55.8% (77) of the respondents. Only 33.6% (47) of the respondents could attribute the cause of malaria to protozoan of genus Plasmodium species. Of the 140 vendors, 115 (82.7%) prescribe antimalarial drugs. The knowledge of the national protocol was malaria case management among dispensers was 35.0%. Vendors in hospital/community pharmacies were 2.4 times (OR = 3.14, 95% CI: 4.14 - 8.74, p < 0.001) more knowledgeable about malaria treatment protocol than those of in drugstores. The prevalence of self-prescription of antimalarials was 39.3%. Self-prescription was significantly higher in drugstores than hospital/community pharmacies (p=0.004). In all, 56 (40.6%) of vendors showed good practices regarding antimalarial drug dispensing with the majority (51.7%) from community pharmacies (OR=2.27,95% CI: 1.13-4.56). Conclusion: Findings reveal moderate knowledge of malaria but poor prescription and dispensing practices of antimalarial drugs among vendors, thus indicating a need for routine monitoring and evaluation to prevent the emergence of resistant strains to current efficacious antimalarials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antimalarials" title="antimalarials">antimalarials</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20retail%20outlets" title=" drug retail outlets"> drug retail outlets</a>, <a href="https://publications.waset.org/abstracts/search?q=dispensing" title=" dispensing"> dispensing</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20resistance" title=" drug resistance"> drug resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=prescription" title=" prescription"> prescription</a> </p> <a href="https://publications.waset.org/abstracts/108565/malaria-management-among-dispensers-in-drug-retail-outlets-in-buea-community-an-assessment-of-knowledge-of-malaria-and-antimalarial-drug-prescription-and-dispensing-practices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prescription%20chart&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prescription%20chart&page=3">3</a></li> <li class="page-item"><a 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