A health data analytics maturity model for hospitals information systems

Carvalho, João Vidal; Rocha, Álvaro; Vasconcelos, José Braga de; Abreu, António

doi:10.1016/j.ijinfomgt.2018.07.001

Cited by 61 publications

(47 citation statements)

References 19 publications

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“…MM defines an improvement path for the development of these organizational capabilities (Carvalho et al 2018), displaying the best procedures to obtain a higher level of maturity . Although it is not an indispensable requisite to obtain the MM maximum level (Hamel et al, 2013) since each organization has its optimum level, which is defined as "the level that delivers the organization's strategic objectives most effectively and efficiently", which does not correspond to the scale's highest level (Introna et al 2014).…”

Section: Pcmmentioning

confidence: 99%

“…Maturity Levels are often five, and each one has its procedures to implement in order to achieve that level. (Antunes et al, 2014;Brooks et al, 2015;J. Carvalho et al, 2018;Cleven et al, 2014;Frick et al 2013;Introna et al, 2014;Lahrmann et al, 2010;Mettler & Rohner, 2009;Nord et al, 2016;Serenko et al 2016;Vezzetti et al, 2014) Table 10: Types of Improvement/Implementation Path…”

Section: Concepts Description Referencesmentioning

confidence: 99%

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Improvement of IT Infrastructure Management by Using Configuration Management and Maturity Models: A Systematic Literature Review and a Critical Analysis

Serrano

Pereira

2020

Organizacija

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Background and purpose: This research aims to investigate which benefits one may expect using Maturity Models in Configuration Management (CM) domain. CM is a support process that helps organizations have better management of their infrastructure. Its importance, in the Information Technology (IT) domain, has increased in recent years, despite this process not being technologically new, and the fact that many organizations implement this process in a haphazard way, which results in it not producing the benefits that it should produce. With the intention of assessing and improving the organizations’ IT processes practices and capabilities, MMs have been developed and implemented. However, the application of MM in the CM domain is yet to be explored.Design/Methodology/Approach: Two Systematic Literature Reviews (SLRs) and a Critical Analysis were performed. In sum, 80 scientific articles of the most rated conferences and scientific journals were analyzed and conclusions were drawn.Results: This research concludes that despite the CM process being badly implemented, using a MM this process could decrease operational costs and increase the quality management of the infrastructure.Conclusion: However, no MM has been developed so far for the CM process practices. This MM would be a viable support tool for the IT organizations providers since this would help organizations have a mature CM process and better control of their IT infrastructure. Therefore, the existence of a MM for the CM domain would be a welcome advancement that should be developed in the future.

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Section: Pcmmentioning

confidence: 99%

Section: Concepts Description Referencesmentioning

confidence: 99%

Improvement of IT Infrastructure Management by Using Configuration Management and Maturity Models: A Systematic Literature Review and a Critical Analysis

Serrano

Pereira

2020

Organizacija

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“…Biometric devices are used to monitor patients' health data in real time to make better and timely decisions (Carvalho, Rocha, Vasconcelos, & Abreu, 2018). A study has proposed an approach to stimulate the real-time falling action of humans, which can be collected by the accelerometer and triaxial gyroscope to detect the occurrence of a fall (Zhang, Lai, Lai, Wu, & Chao, 2017).…”

Section: Biometric Devicesmentioning

confidence: 99%

Real-time big data processing for anomaly detection: A Survey

Habeeb¹,

Nasaruddin²,

Gani³

et al. 2019

International Journal of Information Management

273

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The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.

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“…Particularly the Healthcare industry manages an extensive amount of data from a wide variety of sources, such as traditional interviews, clinical and laboratory processes, medical equipment, hospital bills, pharmacy deliveries, insurance companies, wearable devices for telemonitoring, etc. [4].…”

Section: Introductionmentioning

confidence: 99%

“…The LogLoss, also known as cross-entropy, is defined as the negative of the logarithm of the likelihood function [93] (see [3][4][5][6][7][8]. This metric captures the extent to which predicted probabilities differ from the class label.…”

mentioning

confidence: 99%

Machine Learning to Predict High-Cost Hospitalizations

PEREZ¹

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Healthcare providers are evolving their management models, developing proactive programs to improve the quality and efficiency of their health services, considering the available historical information. Proactive strategies seek not only to prevent and detect diseases but also to enhance hospitalization outcomes. In this sense, one of the most challenging tasks is to identify which patients should be included in proactive health programs. To this end, forecasting and modeling cost-related variables are among the most widely used approaches for identifying such patients, since these variables are potential indicators of the patients' hospitalization risk, their severity, and their medical resources consumption. Most of the existing research works in this area aim to model cost variables from an overall perspective and predict cost variations for specific periods. In contrast, this work focuses on predicting the costs of a particular event. Specifically, this thesis prescribes a solution for identifying high-cost hospitalizations, to support health service managers in their proactive actions. To this end, the Design Science Research (DSR) methodology was combined with the Data Science life cycle in a real scenario of a health consulting company. The data provided describes patients' hospitalizations through their demographic characteristics and their medical resource consumption. Different statistical and Machine Learning techniques were used to predict high-cost hospitalizations, such as Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Classification and Regression Trees (CART), Random Forest (RF), and Extreme Gradient Boosting (XGB). The experimental results showed that RF and XGB presented the best performance, reaching an Area Under the Curve Precision-Recall (AUCPR) of 0.732 and 0.644, respectively. In the case of RF, the model was able to detect, on average, 72% of the high-cost hospitalizations with a 33% of Precision, which represents 78.7% of the total cost generated by the high-cost hospitalizations. Moreover, the obtained results showed that the use of prior cost and aggregated variables of resource consumption increased the model's ability to predict high-cost hospitalizations.

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A health data analytics maturity model for hospitals information systems

Cited by 61 publications

References 19 publications

Improvement of IT Infrastructure Management by Using Configuration Management and Maturity Models: A Systematic Literature Review and a Critical Analysis

Improvement of IT Infrastructure Management by Using Configuration Management and Maturity Models: A Systematic Literature Review and a Critical Analysis

Real-time big data processing for anomaly detection: A Survey

Machine Learning to Predict High-Cost Hospitalizations

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