Fostering performance management in healthcare: insights into the role of big data

Spanó, Rosanna; Ginesti, Gianluca

doi:10.1108/medar-12-2020-1123

Cited by 8 publications

(4 citation statements)

References 79 publications

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“…To accelerate digitalization, hospitals must invest in technology to automate processes and streamline operations, moving in two distinct directions: focusing on the organizational level (moving from episodic to coordinated care), where telemedicine is prioritized, and introducing digital solutions to enable new models of care (progressing toward personalized care and increasing the focus on prevention and wellness) [ 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ].…”

Section: Resultsmentioning

confidence: 99%

“…The optimal use of resources has a critically important role for healthcare operators in assessing the quality of the healthcare service provided and also requires appropriate technologies to ensure the rational use of resources [107][108][109]. Benzidia et al (2021) claim that extracting new insights from existing volumes of structured and unstructured data related to medical treatments and products improves decision-making and enables a better understanding of each patient's costs [93,110].…”

Section: Big Data and Digital Transformationmentioning

confidence: 99%

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Towards the Use of Big Data in Healthcare: A Literature Review

et al. 2022

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The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

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Section: Resultsmentioning

confidence: 99%

Section: Big Data and Digital Transformationmentioning

confidence: 99%

Towards the Use of Big Data in Healthcare: A Literature Review

et al. 2022

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“…Tus, the development of smart healthcare has become an important and widely followed research issue. In recent years, with the development of big data technology, many hospitals have been mining and analyzing medical big data through big data-related technologies, laying the foundation for further building smart hospitals [2][3][4]. It has also been confrmed that the medical application of big data technology plays an important role in safeguarding human lives [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

Zhang

Liu²,

Zhou

et al. 2023

Mobile Information Systems

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The purpose of this paper is to identify the medical risk of applying big data technology and to build a medical big data risk (MBDR) control process and manage medical big data risk (MBDR) from a systematic perspective. In this process, we firstly used systematic literature reviews (SLRs) method to systematically search 322 papers in web of science with the topics of “medical risk” and “big data risk” to build a dimensional system of medical big data risk (MBDR) from the theoretical level. Based on a case study of a hospital in Shanghai, we explored the formation mechanism and interaction effect of medical big data risk (MBDR) by using Bayesian belief networks (BBNs) method, and built a systematic risk control process. This paper finally finds that: the dimensional system of medical big data risk (MBDR) includes 24 subdimensions and 5 major categories of dimensions, which helps to explore the medical application of big data technology from a risk perspective. In addition, the medical big data risk (MBDR) control process constructed in this paper includes: risk prediction, reverse reasoning, risk control, and risk prevention in 4 aspects, which is important for hospitals to actually carry out medical big data risk (MBDR) control.

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“…Indeed, the implementation of digital technologies are becoming increasingly relevant for corporate and performance management (Oliver, 2018;Marrone and Hazelton, 2019;Wang et al, 2020aWang et al, , 2020bChatterjee et al, 2021;Jun et al, 2021), and especially ETs have demonstrated in the last years to be particularly supportive in fostering these issues in health care (Spanò and Ginesti, 2021), transportation (Chhabra et al, 2021), manufacturing (Rezaei et al, 2017) and so on.…”

Section: Introductionmentioning

confidence: 99%

Disaster management and emerging technologies: a performance-based perspective

Vermiglio

Noto

Bolívar

et al. 2021

MEDAR

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Purpose This paper aims to analyse how emerging technologies (ETs) impact on improving performance in disaster management (DM) processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation). Design/methodology/approach The methodology is based on a systematic review of the literature. Scopus, ProQuest, EBSCO and Web of Science were used as data sources, and an initial sample of 373 scientific articles was collected. After abstracts and full texts were read and refinements to the search were made, a final corpus of 69 publications was analysed using VOSviewer software for text mining and cluster visualisation. Findings The results highlight how ETs foster the preparedness and resilience of specific systems when dealing with different phases of the DM cycle. Simulation and disaster risk reduction are the fields of major relevance in the application of ETs to DM. Originality/value This paper contributes to the literature by adding the lenses of performance measurement, management and accountability in analysing the impact of ETs on DM. It thus represents a starting point for scholars to develop future research on a rapidly and continuously developing topic.

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Fostering performance management in healthcare: insights into the role of big data

Cited by 8 publications

References 79 publications

Towards the Use of Big Data in Healthcare: A Literature Review

Towards the Use of Big Data in Healthcare: A Literature Review

Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

Disaster management and emerging technologies: a performance-based perspective

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