Search results

This paper aims to analyze how decision support systems manage Big data to obtain value.

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

Advanced Electronic Medical Records (EMR) provide many potential benefits to hospitals. However, because of their broad scope, many stakeholders deal with the EMR and a continuous effort has to be made to keep up with internal and external change. Therefore, hospitals need to deliberately shape their organizational competencies considering the pursuit of alignment, i.e. making sure that the EMR remains optimally aligned with strategies, goals and needs of the hospital and its stakeholders. This paper aims to investigate the evolutionary paths of these alignment competencies and their drivers, from a theoretical perspective of co-evolutionary information systems alignment (COISA).

This paper reports on a longitudinal multiple case study of three Dutch hospitals which each recently implemented an advanced EMR system. The authors conducted 35 in-depth interviews in 2 phases (before and after go-live of the EMR), and studied documentation related to the EMR implementations.

The findings show that each hospital's COISA capability shows a different evolutionary path. However, two of the three case hospitals ended up coordinating part of their COISA capability to an ecosystem level, i.e. they incorporated other hospitals using the same EMR system to coordinate their alignment efforts, either from an operational perspective, or in terms of orchestration and strategy. The found evolutionary paths' key drivers include “stakeholder initiative”, “accumulating experience”, “driving events” and “emerging issues”.

The findings help healthcare practitioners to deliberately shape their organization's COISA capability in pursuit of EMR alignment. Furthermore, the authors add to the knowledge base on co-evolutionary approaches to alignment through the longitudinal approach.

The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.

The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.

Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.

The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.

The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

The past decade has witnessed a tremendous and progressive growth in the number of Nigerians who engage in medical tourism from Nigeria to India. Various commentators have advanced diverse reasons for this trend. However, there is a dearth of research that has sought to provide empirical insights. This paper aims to investigate the decision-making process of Nigerian medical tourists and why they prefer medical tourism to India to medical care locally.

Eight Nigerian medical tourists are interviewed on a one-on-one basis with open-ended questions using purposive criterion sampling technique from an interpretivist mind-set.

The paper identifies two major motivators, namely, inadequate medical infrastructure and poor medical, and customer service from health workers in Nigeria, which spurred medical tourism from Nigeria to India. Further, it finds that first timers premise their decisions on advice from reference groups, while previous personal experiences guide decisions on subsequent medical travels. Findings are explained using the template provided by the theory of planned behaviour.

This exploratory nature of this research provides a useful basis to elucidate the course of decision-making of Nigerian patients so that appropriate marketing communication channels can be applied. It improves the process of recruiting and engaging Nigerian patients and nurturing wholesome relationships between Nigerian patients and hospitals.

The purpose of this paper is to understand how women with a diagnosis of schizophrenia or bipolar disorder approach medication decision making in pregnancy.

The study was co-produced by university academics and charity-based researchers. Semi-structured interviews were conducted by three peer researchers who have used anti-psychotic medication and were of child bearing age. Participants were women with children under five, who had taken anti-psychotic medication in the 12 months before pregnancy. In total, 12 women were recruited through social media and snowball techniques. Data were analyzed following a three-stage process.

The accounts highlighted decisional uncertainty, with medication decisions situated among multiple sources of influence from self and others. Women retained strong feelings of personal ownership for their decisions, whilst also seeking out clinical opinion and accepting they had constrained choices. Two styles of decision making emerged: shared and independent. Shared decision making involved open discussion, active permission seeking, negotiation and coercion. Independent women-led decision making was not always congruent with medical opinion, increasing pressure on women and impacting pregnancy experiences. A common sense self-regulation model explaining management of health threats resonated with women’s accounts.

Women should be helped to manage decisional conflict and the emotional impact of decision making including long term feelings of guilt. Women experienced interactions with clinicians as lacking opportunities for enhanced support except in specialist perinatal services. This is an area that should be considered in staff training, supervision, appraisal and organization review.

This paper uses data collected in a co-produced research study including peer researchers.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

This article analyzes the cognitive microfoundations, conflicting institutional logics and professional hybridization in a case characterized by conflict.

In contrast to the majority of earlier studies focusing on special health care, the study was conducted in a Finnish basic health care organization. The empirical data include 36 interviews, accounting reports, budgets, newspaper articles and meeting notes collected 2013–2018.

The use of accounting techniques in this case did not offer professionals sufficient support under conditions of conflict. The authors suggest that this perceived lack of support intensified the negative emotions toward accounting techniques. These negative emotions aggregated into incompatible professional-level institutional logics, which contributed to the lack of hybridization between such logics. The authors highlight the importance of the cognitive microfoundations, that is, the individual-level interpretations and emotional responses, in the analysis of conflicting institutional logics.

Managerial attention needs to be directed to accounting practices perceived as frustrating or threatening, a perception that can prevent the use of accounting techniques in the creation of professional hybrids. The Finnish basic health care context involves inconsistent political decision-making, multiple tasks, three institutional logics and individual interpretations and emotions in various decision-making situations.

This study develops microfoundational accounting research by illustrating how individual-level cognitive microfoundations such as dissatisfaction with budgeting, aggregate into professional-level institutional logics, and in our case, prevent professional hybridization in a basic health care setting characterized by conflict and three separate institutional logics.