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Economic evaluations of big data analytics for clinical decision-making: a scoping review.

ABSTRACT: OBJECTIVE:Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. MATERIALS AND METHODS:We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term. RESULTS:The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes. DISCUSSION:The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

SUBMITTER: Bakker L

PROVIDER: S-EPMC7526472 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Economic evaluations of big data analytics for clinical decision-making: a scoping review.

Bakker Lytske L Aarts Jos J Uyl-de Groot Carin C Redekop William W

Journal of the American Medical Informatics Association : JAMIA 20200701 9

<h4>Objective</h4>Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making.<h4>Materials and methods</h4>We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers t ...[more]

PMID: 32642750

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Project description:BACKGROUND:The treatment of multimorbid patients is one crucial task in general practice as multimorbidity is highly prevalent in this setting. However, there is little evidence how to treat these patients and consequently there are but a few guidelines that focus primarily on multimorbidity. Big data analytics are defined as a method that obtains results for high volume data with high variety generated at high velocity. Yet, the explanatory power of these results is not completely understood. Nevertheless, addressing multimorbidity as a complex condition might be a promising field for big data analytics. The aim of this scoping review was to evaluate whether applying big data analytics on patient data does already contribute to the treatment of multimorbid patients in general practice. METHODS:In January 2018, a review searching the databases PubMed, The Cochrane Library, and Web of Science, using defined search terms for "big data analytics" and "multimorbidity", supplemented by a search of grey literature with Google Scholar, was conducted. Studies were not filtered by type of study, publication year or language. Validity of studies was evaluated independently by two researchers. RESULTS:In total, 2392 records were identified for screening. After title and abstract screening, six articles were included in the full-text analysis. Of those articles, one reported on a model generated with big data techniques to help caring for one group of multimorbid patients. The other five articles dealt with the analysis of multimorbidity clusters. No article defined big data analytics explicitly. CONCLUSIONS:Although the usage of the phrase "Big Data" is growing rapidly, there is nearly no practical use case for big data analysis techniques in the treatment of multimorbidity in general practice yet. Furthermore, in publications addressing big data analytics, the term is rarely defined. However, possible models and algorithms to address multimorbidity in the future are already published.

Project description:BACKGROUND:Community water fluoridation (CWF) is considered one of the 10 greatest public health achievements of the twentieth century and has been a cornerstone strategies for the prevention and control of dental caries in many countries. However, for decision-makers the effectiveness and safety of any given intervention is not always sufficient to decide on the best option. Economic evaluations (EE) provide key information that managers weigh, alongside other evidence. This study reviews the relevant literature on EE in CWF. METHODS:A systematic database search up to August 2019 was carried out using MEDLINE, EMBASE, Cochrane Library, LILACS, Paediatric Economic Database Evaluation and National Health Service Economic Evaluation Database. The review included full economic evaluations on CWF programs, written in English, Spanish or Portuguese. The selection process and data extraction were carried out by two researchers independently. A qualitative synthesis of the results was performed. RESULTS:Of 498 identified articles, 24 studies met the inclusion criteria; 11 corresponded to cost-benefit analysis; nine were cost-effectiveness analyses; and four cost-utility studies. Two cost-utility studies used Disability-Adjusted Life Years,, one used Quality-Adjusted Tooth Years, and another Quality-Adjusted Life Years. EEs were conducted in eight countries. All studies concluded that water fluoridation was a cost-effective strategy when it was compared with non-fluoridated communities, independently of the perspective, time horizon or discount rate applied. Four studies adopted a lifetime time horizon. The outcome measures included caries averted (n = 14) and savings cost of dental treatment (n = 4). Most of the studies reported a caries reduction effects between 25 and 40%. CONCLUSION:Findings indicated that CWF represents an appropriate use of communities' resources, using a range of economic evaluation methods and in different locations. These findings provide evidence to decision-makers which they could use as an aid to deciding on resource allocation.

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Economic evaluations of big data analytics for clinical decision-making: a scoping review.

Publications

Economic evaluations of big data analytics for clinical decision-making: a scoping review.

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