A robust model for big healthcare data analytics

Chauhan, Ritu; Jangade, Rajesh

doi:10.1109/confluence.2016.7508117

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Such approaches should consider organizational and regulatory factors and newly emerged challenges like implication on IAF's independence or the possibility of counter-analytics. This type of research may use insight from similar research in a different field, such as the use of BDA for crime prevention (e.g., as exemplified by Chauhan and Aluvalu 2016;Craja, Kim, and Lessmann 2020).…”

Section: Relationships Among Factorsmentioning

confidence: 99%

Driving and Inhibiting Factors for Implementing Audit Analytics in an Internal Audit Function

Ramadhan

Janssen

Voort

2023

Journal of Emerging Technologies in Accounting

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Internal audit function (IAF) effectiveness can be improved by embracing Audit Analytics (AA). However, despite its promises, AA implementation remains limited. Although there is research on AA implementation in general, there needs to be an overview of insight into inhibiting and driving factors for internal auditing. This paper examines those driving and inhibiting factors by exploring the literature on AA implementation. The initial search revealed 98 uniquely identified papers. Further filtering and the additional search returned 42 articles, which were analyzed in detail. The analysis resulted in 12 driving and 23 inhibiting factors, grouped into internal, regulation, data, infrastructure, and audit practice categories. The literature shows that IAF encounters multiple and intertwined factors in AA implementation and needs to anticipate those factors. Moreover, AA implementation affects IAF’s parts and stakeholders differently, requiring internal and external collaboration. Building on these insights, we provide recommendations for further research. JEL Classifications: M42; M49; O32.

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Section: Relationships Among Factorsmentioning

confidence: 99%

Driving and Inhibiting Factors for Implementing Audit Analytics in an Internal Audit Function

Ramadhan

Janssen

Voort

2023

Journal of Emerging Technologies in Accounting

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“…With big data analytics, there is the opportunity to discover association, patterns and future trends to improve healthcare and save lives. All healthcare data is exponentially growing and several "data analysis techniques such as statistical modelling, predictive analytics, artificial intelligence, data mining and machine learning techniques are used in exploration to retrieve effective and efficient patterns from structured and unstructured big data" (Chauhan, Jangade, 2016). Furthermore, as the use of other technologies such as embedded sensors grows, the availability of data will increase as will the opportunity to put data findings into action.…”

Section: Health 40: Technologymentioning

confidence: 99%

Design for Health 4.0: Exploration of a New Area

Bause¹,

Esfahani²,

Forbes³

et al. 2019

Proc. Int. Conf. Eng. Des.

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Driven by networked Electronic Health Record systems, Artificial Intelligence, real-time data from wearable devices with an overlay of invisible user interfaces and improved analytics, Health 4.0 is changing the healthcare industry. The focus on collaboration, coherence, and convergence that will make healthcare more predictive and personalised. Furthermore, Health 4.0 realises the value of data more consistently and effectively. It can pinpoint areas of improvement and enable more informed decisions. What it also does is help move the entire healthcare industry from a system that is reactive and focused on fee-for-service to a system that is value-based, which measures outcomes and ensures proactive prevention.In this paper, the authors will first explore the realm of the emerging area of Health 4.0 and identify its opportunities and challenges. This includes understanding the relevant base technologies as well as the design principles for the realization of smart healthcare product, systems and product-service-systems of the future. Following on from there, the authors focus on the role of design in the specific context of healthcare.

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“…Later, the authors focused on ED waiting time, to understand the variables that can have the most influence on the patient waiting time, plus the used algorithms. Regarding predictive analytics in the healthcare industry, some authors studied its advantages and possible applications, like M. M. Malik et al [10] that reviewed and analyzed applications of predictive analytics and data mining in the healthcare industry or R. Chauhan and R. Jangade [9] that claim that predictive analytics in healthcare can be beneficial as it would allow for patient disease prediction, fraud detection and cost management initiatives.…”

Section: Related Workmentioning

confidence: 99%

“…According to [8], predictive analytics is a tactic that healthcare organizations should adopt, allowing the stratification of risk to predict outcomes, that in healthcare can be harmful to the patients. Other advantages would be the adoption of more sensor based technologies that would help patients to be more aware about their health, provide lifestyle suggestions by determining some diseases that he could suffer if he kept the same lifestyle [9], help the management of high risk and high cost patients during hospital care and after discharge follow-up care [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis in Healthcare: Emergency Wait Time Prediction

Gonçalves

Pereira

Ferreira

et al. 2018

Advances in Intelligent Systems and Computing

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Emergency departments are an important area of a hospital, being the major entry point to the healthcare system. One of the most important issues regarding patient experience are the emergency department waiting times. In order to help hospitals improving their patient experience, the authors will perform a study where the Random Forest algorithm will be applied to predict emergency department waiting times. Using data from a Portuguese hospital from 2013 to 2017, the authors discretized the emergency waiting time in 5 different categories: "Really Low", "Low", "Average", "High", "Really High". Plus, the authors considered as waiting time, the time from triage to observation. The authors expect to correctly evaluate the proposed classification algorithm efficiency and accuracy in order to be able to conclude if it is valuable when trying to predict ED waiting times.

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A robust model for big healthcare data analytics

Cited by 16 publications

References 12 publications

Driving and Inhibiting Factors for Implementing Audit Analytics in an Internal Audit Function

Driving and Inhibiting Factors for Implementing Audit Analytics in an Internal Audit Function

Design for Health 4.0: Exploration of a New Area

Predictive Analysis in Healthcare: Emergency Wait Time Prediction

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