Enterprise intelligence: searching for a single version of the truth

Multiple versions of information and associated problems are well documented in both academic research and industry best practices. Many solutions have proposed a single version of the truth, with Business intelligence being adopted by many organizations. Business Intelligence (BI), however, is largely based on the collection of data, processing and presentation of information to meet different stakeholders’ requirement. This paper reviews the promise of Enterprise Intelligence, which promises to support decision-making based on a defined strategic understanding of the organizations goals and a unified version of the truth.

The impact of Group Intelligence software on enquiry-based learning

Despite the increasing use of groupware technologies in education, there is little evidence of their impact, especially within an enquiry-based learning (EBL) context. In this paper, we examine the use of a commercial standard Group Intelligence software called GroupSystems®ThinkTank. To date, ThinkTank has been adopted mainly in the USA and supports teams in generating ideas, categorising, prioritising, voting and multi-criteria decision-making and automatically generates a report at the end of each session. The software was used by students carrying out an EBL project, set by employers, for a full academic year. The criteria for assessing the impact of ThinkTank on student learning were those of creativity, participation, productivity, engagement and understanding. Data was collected throughout the year using a combination of interviews and questionnaires, and written feedback from employers. The overall findings show an increase in levels of productivity and creativity, evidence of a deeper understanding of their work but some variation in attitudes towards participation in the early stages of the project.

Artificial Intelligence: the basics

Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined, whether machines can 'think', sensory input in machine systems, the nature of consciousness, the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis

Background: Medication errors are common in primary care and are associated with considerable risk of patient harm. We tested whether a pharmacist-led, information technology-based intervention was more effective than simple feedback in reducing the number of patients at risk of measures related to hazardous prescribing and inadequate blood-test monitoring of medicines 6 months after the intervention. Methods: In this pragmatic, cluster randomised trial general practices in the UK were stratified by research site and list size, and randomly assigned by a web-based randomisation service in block sizes of two or four to one of two groups. The practices were allocated to either computer-generated simple feedback for at-risk patients (control) or a pharmacist-led information technology intervention (PINCER), composed of feedback, educational outreach, and dedicated support. The allocation was masked to general practices, patients, pharmacists, researchers, and statisticians. Primary outcomes were the proportions of patients at 6 months after the intervention who had had any of three clinically important errors: non-selective non-steroidal anti-inflammatory drugs (NSAIDs) prescribed to those with a history of peptic ulcer without co-prescription of a proton-pump inhibitor; β blockers prescribed to those with a history of asthma; long-term prescription of angiotensin converting enzyme (ACE) inhibitor or loop diuretics to those 75 years or older without assessment of urea and electrolytes in the preceding 15 months. The cost per error avoided was estimated by incremental cost-eff ectiveness analysis. This study is registered with Controlled-Trials.com, number ISRCTN21785299. Findings: 72 general practices with a combined list size of 480 942 patients were randomised. At 6 months’ follow-up, patients in the PINCER group were significantly less likely to have been prescribed a non-selective NSAID if they had a history of peptic ulcer without gastroprotection (OR 0∙58, 95% CI 0∙38–0∙89); a β blocker if they had asthma (0∙73, 0∙58–0∙91); or an ACE inhibitor or loop diuretic without appropriate monitoring (0∙51, 0∙34–0∙78). PINCER has a 95% probability of being cost eff ective if the decision-maker’s ceiling willingness to pay reaches £75 per error avoided at 6 months. Interpretation: The PINCER intervention is an effective method for reducing a range of medication errors in general practices with computerised clinical records. Funding: Patient Safety Research Portfolio, Department of Health, England.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Focusing the kaleidoscope: investigating the newly formed role of 'Academic Lead’ at a research-led University

Now, more than ever, higher education institutions are reflecting on the need for flexible leadership models to help adapt to the fast changing academic environment. Rapid shifts in the sector are contributing to a kaleidoscopic ‘supercomplexity’ of challenges, structures, processes and value frameworks for academics who lead and for those who are led. How are institutions’ leadership structures and roles developing in response to these changes? And how do these responses affect academic staff in relation to their identity, status and career trajectory? This paper reports on a Leadership Foundation funded research project exploring the ways in which one UK institution has implemented a new ‘distributed’ leadership model. Crucially, the project examines the impact of the model on both those who are leaders and those being led.

Description and process evaluation of pharmacists’ interventions in a pharmacist-led information technology-enabled multicentre cluster randomised controlled trial for reducing medication errors in general practice (PINCER trial)

Objective To undertake a process evaluation of pharmacists' recommendations arising in the context of a complex IT-enabled pharmacist-delivered randomised controlled trial (PINCER trial) to reduce the risk of hazardous medicines management in general practices. Methods PINCER pharmacists manually recorded patients’ demographics, details of interventions recommended, actions undertaken by practice staff and time taken to manage individual cases of hazardous medicines management. Data were coded and double entered into SPSS v15, and then summarised using percentages for categorical data (with 95% CI) and, as appropriate, means (SD) or medians (IQR) for continuous data. Key findings Pharmacists spent a median of 20 minutes (IQR 10, 30) reviewing medical records, recommending interventions and completing actions in each case of hazardous medicines management. Pharmacists judged 72% (95%CI 70, 74) (1463/2026) of cases of hazardous medicines management to be clinically relevant. Pharmacists recommended 2105 interventions in 74% (95%CI 73, 76) (1516/2038) of cases and 1685 actions were taken in 61% (95%CI 59, 63) (1246/2038) of cases; 66% (95%CI 64, 68) (1383/2105) of interventions recommended by pharmacists were completed and 5% (95%CI 4, 6) (104/2105) of recommendations were accepted by general practitioners (GPs), but not completed at the end of the pharmacists’ placement; the remaining recommendations were rejected or considered not relevant by GPs. Conclusions The outcome measures were used to target pharmacist activity in general practice towards patients at risk from hazardous medicines management. Recommendations from trained PINCER pharmacists were found to be broadly acceptable to GPs and led to ameliorative action in the majority of cases. It seems likely that the approach used by the PINCER pharmacists could be employed by other practice pharmacists following appropriate training.

Can nutrition be promoted through agriculture-led food price policies? A systematic review

Abstract Objective: To systematically review the available evidence on whether national or international agricultural policies that directly affect the price of food influence the prevalence rates of undernutrition or nutrition-related chronic disease in children and adults. Design: Systematic review. Setting: Global. Search strategy: We systematically searched five databases for published literature (MEDLINE, EconLit, Agricola, AgEcon Search, Scopus) and systematically browsed other databases and relevant organisational websites for unpublished literature. Reference lists of included publications were hand-searched for additional relevant studies. We included studies that evaluated or simulated the effects of national or international food-price-related agricultural policies on nutrition outcomes reporting data collected after 1990 and published in English. Primary and secondary outcomes: Prevalence rates of undernutrition (measured with anthropometry or clinical deficiencies) and overnutrition (obesity and nutrition-related chronic diseases including cancer, heart disease and diabetes). Results: We identified a total of four relevant reports; two ex post evaluations and two ex ante simulations. A study from India reported on the undernutrition rates in children, and the other three studies from Egypt, the Netherlands and the USA reported on the nutrition related chronic disease outcomes in adults. Two of the studies assessed the impact of policies that subsidised the price of agricultural outputs and two focused on public food distribution policies. The limited evidence base provided some support for the notion that agricultural policies that change the prices of foods at a national level can have an effect on population-level nutrition and health outcomes. Conclusions: A systematic review of the available literature suggests that there is a paucity of robust direct evidence on the impact of agricultural price policies on nutrition and health.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

The intelligence paradox; will ET get the metabolic syndrome? Lessons from and for Earth

Mankind is facing an unprecedented health challenge in the current pandemic of obesity and diabetes. We propose that this is the inevitable (and predictable) consequence of the evolution of intelligence, which itself could be an expression of life being an information system driven by entropy. Because of its ability to make life more adaptable and robust, intelligence evolved as an efficient adaptive response to the stresses arising from an ever-changing environment. These adaptive responses are encapsulated by the epiphenomena of “hormesis”, a phenomenon we believe to be central to the evolution of intelligence and essential for the maintenance of optimal physiological function and health. Thus, as intelligence evolved, it would eventually reach a cognitive level with the ability to control its environment through technology and have the ability remove all stressors. In effect, it would act to remove the very hormetic factors that had driven its evolution. Mankind may have reached this point, creating an environmental utopia that has reduced the very stimuli necessary for optimal health and the evolution of intelligence – “the intelligence paradox”. One of the hallmarks of this paradox is of course the rising incidence in obesity, diabetes and the metabolic syndrome. This leads to the conclusion that wherever life evolves, here on earth or in another part of the galaxy, the “intelligence paradox’” would be the inevitable side-effect of the evolution of intelligence. ET may not need to just “phone home” but may also need to “phone the local gym”. This suggests another possible reason to explain Fermi’s paradox; Enrico Fermi, the famous physicist, suggested in the 1950s that if extra-terrestrial intelligence was so prevalent, which was a common belief at the time, then where was it? Our suggestion is that if advanced life has got going elsewhere in our galaxy, it can’t afford to explore the galaxy because it has to pay its healthcare costs.