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Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables.

Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable.

This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput.

Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.

The paper aims to present an integrated foresight framework and method to support decision-makers who are confronted with today’s complex and rapidly changing world. The method aims at reducing the degree of uncertainty by addressing the inertia or duration of unfolding trends and by placing individual trends in a broader context.

The paper presents a three-layered framework and method for assessing megatrends based on their inertia or duration. It suggests that if long-term trends and key future uncertainties are studied in conjunction at a meta-level and placed in a broader multi-layered framework of trends, it can result in new insights.

The application of the proposed foresight method helps to systematically place a wide range of unrelated trends and key uncertainties in the context of a broader framework of trends, thereby improving the ability to understand the inertia, direction and mutual interaction of these trends.

The elaboration of identified trends and key uncertainties is partly case-specific and subject to interpretation. It is aimed at illustrating the potential use of the framework.

The paper presents a new approach that may, by itself or in combination with existing foresight methods, offer new means for anticipating future developments.

The use of the proposed framework has potential to provide better insight in the complexity of today’s rapid-changing world and the major transitions taking place. It aims to result in sharper foresight by reducing epistemic uncertainty for decision-makers.

The paper demonstrates how megatrends, Kondratieff waves and century-long trends can be placed in an integrated framework and analysed in conjunction.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Organizations are in a constant struggle to predict the future, and be able to identify and jump on opportunities before they arise for the competition. This review looks at a three-layer framework that looks to aid the prediction and analysis of trends.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Measuring patient satisfaction plays an increasingly important role in the growing push toward healthcare provider accountability. This study seeks to evaluate G.B. Pant Hospital (a North Indian tertiary care centre) patient satisfaction with clinical laboratory services.

A total of 100 out‐ and in‐patients were randomly selected and interviewed about microbiological services using a standard format, a method which can be easily used to compare patient satisfaction with laboratory services elsewhere.

Patients represented all age groups: females and males were balanced. Few were from poor socio‐economic backgrounds. Patients do not have problems getting tests done, but the laboratory's inconvenient location caused dissatisfaction. Patients do not have problems communicating with staff, but medical terms are not understood by patients. Hospital cleanliness needs improving, especially toilets, which causes the most patient dissatisfaction. Hospital staff were deemed highly competent and judged to give excellent technical help to patients. The questionnaire's financial subscale shows 100 per cent satisfaction because all tests in the microbiology department are free. The overall satisfaction with services stood at 83 per cent. Satisfaction scores for G.B. Pant Hospital appear to be satisfactory.

This study does not compare patient satisfaction in two or more hospitals and findings may not be generalisable.

Patient satisfaction surveys are the best way to identify deficiencies and improve hospital services. Repeating studies at six monthly intervals is a useful managerial intervention aimed at delivering and maintaining quality healthcare.

This laboratory satisfaction survey is the first of its kind for government hospitals in India. The survey revealed a positive feedback and helped to identify the areas of concern along with estimating the patient satisfaction scores. This is the best way to identify the areas of deficiencies and improving the services provided by the hospital. The authors feel that repeating such studies at a regular interval of six months would be a useful guide for the managerial interventions.

This chapter discussed the role of business intelligence (BI) in healthcare twofold strategic decision making of the organization and the stakeholders. The visualization techniques of data mining are applied for the early and correct diagnosis of the disease, patient’s satisfaction quotient and also helpful for the hospital to know their best commanders.

In this chapter, the usefulness of BI is shown at two levels: at doctor level and at hospital level. As a case study, a hospital is taken which deals with three different kinds of diseases: Breast Cancer, Diabetes, and Liver disorder. BI can be applied for taking better strategic decisions in the context of hospital and its department’s growth. At the doctor level, on the basis of various symptoms of the disease, the doctor can advise the suitable treatment to the patients. At the hospital level, the best department among all can be identified. Also, a patient’s type of admission, continued their treatments with the hospital, patient’s satisfaction quotient, etc., can be calculated. The authors have used different methods like Correlation matrix, decision tree, mosaic plots, etc., to conduct this analysis.