Search results

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

The purpose of this paper is to examine whether healthcare leaders use evidence-based management (EBMgt) when facing major decisions and what types of evidence healthcare administrators consult during their decision-making. This study also intends to identify any relationship that might exist among adoption of EBMgt in healthcare management, attitudes towards EBMgt, demographic characteristics and organizational characteristics.

A cross-sectional study was conducted among US healthcare leaders. Spearman’s correlation and logistic regression were performed using the Statistical Package for the Social Sciences (SPSS) 23.0.

One hundred and fifty-four healthcare leaders completed the survey. The study results indicated that 90 per cent of the participants self-reported having used an EBMgt approach for decision-making. Professional experiences (87 per cent), organizational data (84 per cent) and stakeholders’ values (63 per cent) were the top three types of evidence consulted daily and weekly for decision-making. Case study (75 per cent) and scientific research findings (75 per cent) were the top two types of evidence consulted monthly or less than once a month. An exploratory, stepwise logistic regression model correctly classified 75.3 per cent of all observations for a dichotomous “use of EBMgt” response variable using three independent variables: attitude towards EBMgt, number of employees in the organization and the job position. Spearman’s correlation indicated statistically significant relationships between healthcare leaders’ use of EBMgt and healthcare organization bed size (r_s = 0.217, n = 152, p < 0.01), attitude towards EBMgt (r_s = 0.517, n = 152, p < 0.01), and the number of organization employees (r_s = 0.195, n = 152, p = 0.016).

This study generated new research findings on the practice of EBMgt in US healthcare administration decision-making.

The risk of adverse events in a hospital evaluation is an important process in healthcare management. It involves several technical, social, and economical aspects. The purpose of this paper is to propose an integrated approach to evaluate the risk of adverse events in the hospital sector.

This paper aims to provide a decision-making framework to evaluate hospital service. Three well-known methods are applied. More specifically are proposed the following methods: analytic hierarchy process (AHP), a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology developed by Thomas L. Saaty in the 1970s; decision-making trial and evaluation laboratory (DEMATEL) to construct interrelations between criteria/factors and VIKOR method, a commonly used multiple-criteria decision analysis technique for determining a compromise solution and improving the quality of decision making.

The example provided has demonstrated that the proposed approach is an effective and useful tool to assess the risk of adverse events in the hospital sector. The results could help the hospital identify its high performance level and take appropriate measures in advance to prevent adverse events. The authors can conclude that the promising results obtained in applying the AHP–DEMATEL–VIKOR method suggest that the hybrid method can be used to create decision aids that it simplifies the shared decision-making process.

This paper presents a novel approach based on the integration of AHP, DEMATEL and VIKOR methods. The final aim is to propose a robust methodology to overcome disadvantages associated with each method.

Consumer confusion is an emerging phenomenon of interest that significantly drives choice behaviour. Considering the dearth of scholarly focus on confusion faced by consumers in a healthcare setting, this paper aims to conceptualize and validate a patient confusion model consisting of its drivers and outcomes.

Drawing upon adaptive decision-making framework and consumer confusion literature, patient confusion model has been developed. Empirical data of 310 patients from three private sector hospitals in India was collected through pen and paper survey administration. The hypothesized patient confusion model was tested using partial least squares structural equation modelling (PLS-SEM) to derive confirmatory results.

The results confirm the role of decision-making variables such as information overload, information similarity, information ambiguity, information asymmetry, patient involvement and physician-patient communication in the occurrence of patient confusion. A significant impact of confusion on switching intention was also confirmed, providing insights for healthcare managers.

The effect of confusion on switching intention of consumers found through the present study holds significant implications from a healthcare management standpoint. Dissemination of credible information, improved communication between doctors and patients and creation of organized channels of health information provision also represent some of the notable implications for healthcare managers to mitigate patient confusion.

This study presents an empirically validated model of patient confusion creating a research agenda for theory development in this emerging area. Consumer confusion represents a core consumer behaviour problem that is of utmost significance in the healthcare sector. This paper is one of the first and early attempts to address this research problem.

Health care organizations’ decision-making for the future relies on anticipating changes. Reliable predictions are becoming increasingly difficult, creating anxiety and requires long-term adaptive planning to cope with unforeseen circumstances. The purpose of this study is to gain insights into the awareness of uncertainties that decision makers in healthcare have, particularly when making long-term investments.

This is a qualitative study with an explorative purpose. The data were collected through semi-structured and open interviews with board members of long-term care organizations.

The study revealed that respondents are most uncertain about the future financing of their real estate system. Another concern revealed is about the shortage of care professionals combined with an increasing demand for future care. Despite most decision makers do recognize uncertainties during the decision-making process, decision makers hardly address the level of these uncertainties. Although this study did find that some decision makers are aware of deep uncertainties, in terms of “unknown unknowns,” they have no actual approaches for dealing with such situations.

Decision makers at healthcare organizations are uncertain as to their ability to anticipate technological, economic, social and political developments, as well as predict future healthcare system transformations. Some decision makers are aware of deep uncertainties, in terms of “unknown unknowns” and “unidentified unknowns,” but they lack an actual approach to deal with such situations. This study examines how strategies adapt to unforeseen developments or how to deal with deep uncertainties in healthcare as complex adaptive system.

The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.

This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.

This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.

This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.

This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Research on strategic decision‐making has emphasized the importance of team decision‐making as it brings the benefits of synergy. Literature on healthcare is silent on the role of professional doctors in the strategic decision‐making process and their impact on decision outcomes. The purpose of the present paper is to empirically examine the outcomes of decisions when physician executives were involved in strategic decision‐making process in healthcare organizations.

Using a structured survey instrument, this paper gathered data from 361 senior executives from 109 hospitals in USA and analyzed the data using regression techniques on whether the presence of physicians in strategic decision‐making processes enhanced decision quality, commitment, and understanding.

Results showed the presence of professional doctors in the decision‐making process enhances commitment and decision quality in healthcare organizations.

Only the healthcare industry was considered. Self‐report measures may have some inherent social desirability bias.

This study contributes to both practicing managers as well as to strategic management literature. This study suggests that healthcare administrators need to engage physician executives in strategic decision‐making to have successful decision outcomes.

To the extent strategic decision‐making process is similar in other industries, the findings can be generalizable across other industries.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.

This paper looks at a number of approaches to health informatics that support decision‐making relevant to the integrated development and management of information systems with clinical and managerial practices in healthcare. Its main aim is to explore three such approaches for integrated development, the soft information systems and technologies methodology, participative simulation modelling and stakeholder analysis. A description of the health informatics research and development environment in the UK is given as necessary background to the paper. Organisational and social aspects are examined through these approaches including information and clinical process development, telemedicine, ethical issues of drug use and management, health policies and information management and strategies, tele‐education and modelling structures. In the conclusion the synergies between the three approaches are discussed and some principles are extracted for future research and development in integrated approaches to health informatics research.