scholarly journals Quality of antimicrobial prescribing improved by the introduction of ePrescribing at Auckland City Hospital

2020 ◽  
Vol 26 (4) ◽  
pp. 2375-2382
Author(s):  
Tayla R Bowers ◽  
Eamon J Duffy

Improving antimicrobial prescribing is a difficult process often requiring labour-intensive, multi-modal interventions. Many hospitals have introduced ePrescribing systems but the effect on antimicrobial prescribing, without treatment choice decision support systems, has not been well described. We sought to determine whether the introduction of ePrescribing improved prescribing quality. Patient records for inpatients on four rehabilitation wards, two using ePrescribing and two using the National Medication Chart, during February 2017, were retrospectively reviewed to identify all antimicrobial prescriptions, which were then reviewed for quality. Documentation of indication was significantly better on ePrescribing wards (45/46, 98%) compared to National Medication Chart wards (47/59, 80%). Adherence to guidelines (32/46, 70% vs 33/59, 56%), appropriateness of therapy (42/46, 91% vs 50/59, 85%) and documentation of duration, stop or review dates (35/46, 76% vs 38/59, 64%) did not significantly differ. ePrescribing can improve the quality of antimicrobial prescribing when Antimicrobial Stewardship principles are used in system customisation but cannot address all factors impacting on prescribing quality.

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.


Author(s):  
InduShobha Chengalur-Smith ◽  
M. Pamela Neely ◽  
Thomas Tribunella

A database is only as good as the data in it. Transaction-processing systems and decision-support systems provide data for strategic planning and operations. Thus, it is important to not only recognize the quality of information in databases, but also to deal with it. Research in information quality has addressed the issues of defining, measuring and improving quality in databases; commercial tools have been created to automate the process of cleaning and correcting data; and new laws have been created that deal with the fallout of poor information quality. These issues will be discussed in the next sections.


2019 ◽  
Vol 3 (1) ◽  
pp. 73
Author(s):  
Yustria Handika Siregar ◽  
Adi Widarma

Abstract - White oyster mushroom is a micro business that is popular in most parts of Indonesia. At present many farmers cultivate white oyster mushrooms. In order for the quality of mushrooms to be optimal, the farmers in the cultivation of white oyster mushrooms always attend training. The quality of oyster mushrooms can be considered by factors that affect the growth of oyster mushrooms themselves. The good quality of mushrooms can increase the selling value of the mushrooms themselves. The decision support system can be used to determine the quality of oyster mushrooms. In this study also the implications of fuzzy logic. It is hoped that the farmers can use the quality of the best oyster mushrooms.Keywords - Decision Support Systems, Best Quality, White Oyster Mushrooms and Fuzzy Logic.  Abstract - Jamur  tiram  putih  merupakan  usaha  mikro  yang  populer  sebagian  besar  di  wilayah  Indonesia. Pada saat ini banyak para petani yang membudidayakan jamur tiram putih. Agar kualitas jamur dapat optimal maka para petani dalam budidaya jamur tiram putih selalu mengikuti pelatihan- pelatihan.  Kualitas  jamur  tiram  dapat  diperhatikan  dengan  faktor-faktor  yang  mempengaruhi pertumbuhan  jamur  tiram  itu  sendiri.  Kualitas  jamur  yang  baik  dapat  meningkatkan  nilai  jual jamur  itu  sendiri.  Sistem  pedukung  keputusan  dapat  digunakan  dalam  menentukan  kualitas jamur  tiram.  Dalam  penelitian  ini  juga  di  impletasikan  logika  fuzzy.  Diharapkan  dapat digunakan para petani dalam menentukan kualitas jamur tiram terbaik. Kata kunci - Sistem Pendukung Keputusan, Kualitas Terbaik, Jamur Tiram Putih dan Logika Fuzzy.


Author(s):  
Anuta Porutiu

In the current economic context, decision making requires complex and multiple actions on the part of the policy makers, who are more challenged than in previous situations, due to the crisis that we are facing. Decision problems cannot be solved by focusing on manager’s own experience or intuition, but require constant adaptation of the methods used effectively in the past to new challenges. Thus, a systemic analysis and modeling of arising issues is required, resulting in the stringent use of Decision Support Systems (DSS), as a necessity in a competitive environment. DSS optimize the situation by getting a timely decision because the decision making process must acquire, process and interpret an even larger amount of data in the shortest possible time. A solution for this purpose is the artificial intelligence systems, in this case Decision Support Systems (DSS), used in a wider area due to expansion of all the new information technologies in decisionmaking processes. These substantial cyber innovations have led to a radical shift in the relationship between enterprise success and quality of decisions made by managers.


Author(s):  
L. P. Vershinina ◽  

The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.


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