For Big Data, Big Questions Remain

Fallik, Dawn

doi:10.1377/hlthaff.2014.0522

Cited by 21 publications

(5 citation statements)

References 3 publications

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“…With regard to expertise and investment barriers, specialist studies have shown interesting aspects of BDA in SCM: (1) a lack of qualified IT staff and high investment cost [72,73]; and (2) a lack of funds and facilities for research and development of BDA instruments [70]. Data barriers have highlighted interesting aspects of the implementation of BDA in SCM, such as: (1) complexity of data integration and data quality [72,73,76]; and (2) data security, confidentiality, performance, and scalability [72,73]. Organizational barriers have been identified as negative limits in implementing BDA in SCM: (1) the absence of a data-sharing policy between organizations and a lack of thinking in terms of large data [69]; and (2) a lack of training facilities and time constraints [72,77].…”

Section: Benefits and Constraints Of Bda For Scmmentioning

confidence: 99%

The Impact of Big Data Analytics on Company Performance in Supply Chain Management

et al. 2019

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Big data analytics can add value and provide a new perspective by improving predictive analysis and modeling practices. This research is centered on supply-chain management and how big data analytics can help Romanian supply-chain companies assess their experience, strategies, and professional capabilities in successfully implementing big data analytics, as well as assessing the tools needed to achieve these goals, including the results of implementation and performance achievement based on them. The research method used in the quantitative study was a sampling survey, using a questionnaire as a data collection tool. It included closed questions, measured with nominal and ordinal scales. A total of 205 managers provided complete and useful answers for this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. The major contributions of this research highlight the fact that companies are concerned with identifying new statistical methods, tools, and approaches, such as cloud computing and security technologies, that need to be rigorously explored.

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Section: Benefits and Constraints Of Bda For Scmmentioning

confidence: 99%

The Impact of Big Data Analytics on Company Performance in Supply Chain Management

et al. 2019

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“…Payers, from the US Government to third-party private insurers and employers, are looking to this kind of big data analysis to inform coverage, decisions, regulatory actions, and policymaking decisions. [1][2][3][4][5] Exploration of large datasets requires access to specialized expertise, programmers, and computing unavailable to clinicians in daily practice. Physician professional organizations, on behalf of their members, have the resources to engage in analysis of these data and advocate for value and quality.…”

mentioning

confidence: 99%

Neurologist ambulatory care, health care utilization, and costs in a large commercial dataset

et al. 2016

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Objective: To determine the value of neurologist ambulatory care in chronic neurologic diseases in a large administrative claims dataset detailing costs, adverse events, and health care utilization. Methods:The Optum proprietary claims dataset (2010)(2011)(2012) was examined to describe direct health care costs, as well as specific outcome metrics for a large population of persons with chronic neurologic illnesses. In phase I of the study, we detail neurologist involvement and differences in annualized allowed third-party payments within episode treatment groups (ETGs) for 10 neurologic illnesses. For phase II, we examined health care utilization for ETGs of epilepsy, Parkinson disease (PD), stroke, and multiple sclerosis (MS) with and without neurologist involvement. Reported outcomes were unadjusted differences and odds ratios between treatment groups.Results: For phase I, a total of 1,913,605 ETGs for 10 neurologic conditions were identified, 30.1% meeting criteria for neurologist involvement. All conditions had higher direct costs when neurologists were involved with care, ranging from a 25% increase for Alzheimer dementia to 100% more for MS care. In phase II, fractures, infections, emergent care, and inpatient admission were less with neurologist ambulatory care, while neurologist care was associated with greater utilization of disease-specific treatments (immunotherapies in MS anticoagulation in atrial fibrillation-associated stroke, deep brain stimulation and dopaminergic therapies in PD). Conclusion:Neurologist involvement with care is associated with greater unadjusted allowed payments, but fewer adverse events and less acute care utilization. The effect of neurologist care on health care costs and utilization outcomes in persons with chronic neurologic illness is not well-characterized. Aggregation of health care information in large administrative claims datasets presents an opportunity to assess the effect of specialist physicians on downstream usage of health resources related to the diseases they treat. Payers, from the US Government to third-party private insurers and employers, are looking to this kind of big data analysis to inform coverage, decisions, regulatory actions, and policymaking decisions. 1-5Exploration of large datasets requires access to specialized expertise, programmers, and computing unavailable to clinicians in daily practice. Physician professional organizations, on behalf of their members, have the resources to engage in analysis of these data and advocate for value and quality.6 Therefore, the American Academy of Neurology (AAN) engaged Optum, Inc. (Philadelphia, PA), a health care consulting firm, for a guided exploratory analysis of their proprietary administrative claims dataset to assess the value of neurologist care.

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“…1–3 However, a clear definition of Big Data remains elusive, and the ways by which Big Data’s advent might shape the future of epidemiologic research and population health intervention remain unclear. 4 While previous authors have considered the role of Big Data in clinical care, 2, 5–7 we are herein concerned with its implications for the future of research and practice of epidemiology and population health.…”

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confidence: 99%

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2015

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Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

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For Big Data, Big Questions Remain

Cited by 21 publications

References 3 publications

The Impact of Big Data Analytics on Company Performance in Supply Chain Management

The Impact of Big Data Analytics on Company Performance in Supply Chain Management

Neurologist ambulatory care, health care utilization, and costs in a large commercial dataset

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