Developing a decision support system for big data analysis and cost allocation in national healthcare

Tabari, Mahdiyeh Yousefi; Memariani, Azizollah; Ebadati, E Omid Mahdi

doi:10.1016/b978-0-12-815368-0.00003-8

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Although DSS are an important application of big data analytics and may benefit patient care [56][57][58], only two reviews assessed such systems [16,24]. One focused on predictive analytics for identifying patients at risk of drug-induced QTc interval prolongation, discussing the efficacy of a DSS that has shown evidence of reduced prescriptions for QT interval-prolonging drugs.…”

Section: Discussionmentioning

confidence: 99%

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

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Section: Discussionmentioning

confidence: 99%

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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“…Risk management issues involve different decisions for the relevant parties to make in various decision-making issues. Decision support systems can support complex problemsolving because they can be customized to support intelligent decisions [14], [15], [16]. Utilization of the decision support system can determine the risks that affect the achievement of the furniture industry targets.…”

Section: A Decision Support System For Risk Managementmentioning

confidence: 99%

Intelligent Decision Support System for Supply Chain Risk Management Process (SCRMP) with COBIT 5 in Furniture Industry

Andry¹,

Hadiyanto²,

Gunawan³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Sustainable supply chain management can be a new strategy in the industry, especially in the furniture sector. In its implementation, different sources of risk factors emerge because the nature of the supply chain is too complex, causing supply and demand uncertainty for production. The furniture industry needs to implement supply chain risk management to overcome problems and cause a loss of trust from customers. This study aims to identify and manage supply chain risks in the furniture industry using the Supply Chain Risk Management Process (SCRMP) and COBIT 5 methods. After identifying the risk, the risk impact measurement uses the COBIT 5 method with the Deliver, Service, and Support (DSS) domain implemented into the decision support system. To find out changes after system implementation, the author conducts a risk assessment according to the COBIT 5 standard and evaluates the possibility of a risk occurring. The result of this study is a mapping of the handling of negative scenarios of risk after implementing the system that includes governance standards and input data to produce output documentation. Documents are applied to the decision support system to reduce risks in the supply chain. The SCRMP and COBIT 5 methods can manage supply chain risk more effectively. It guides the risk assessment area and provides information technology governance to the system. The implication of future research is to define standard operating procedure documentation for applying decision support systems in all divisions of the furniture industry.

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Big Data Architecture for Network Security

Bansal

Jenipher

Jain

et al. 2022

Cyber Security and Network Security

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Developing a decision support system for big data analysis and cost allocation in national healthcare

Cited by 7 publications

References 37 publications

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Intelligent Decision Support System for Supply Chain Risk Management Process (SCRMP) with COBIT 5 in Furniture Industry

Big Data Architecture for Network Security

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