A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health

Paige, Ellie; Doyle, Kerry; Jorm, Louisa; Banks, Emily; Hsu, Meng Ping; Nedkoff, Lee; Briffa, Tom; Cadilhac, Dominique A.; Mahoney, Ray; Verjans, Johan; Dwivedi, Girish; Inouye, Michael; Figtree, Gemma A.

doi:10.1016/j.hlc.2021.04.023

Cited by 10 publications

(8 citation statements)

References 30 publications

(29 reference statements)

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“…Big data are often complex. Therefore, advanced analytical methods, such as machine learning, are increasingly being used to complement standard statistical methods [3], in order to navigate the nuances and complexities of big data. Moreover, big data often lack some clinically important predictors of stroke outcome (e.g., level of frailty, stroke severity) that are difficult, expensive, or not feasible to collect.…”

Section: Big Data Analytics For Stroke Outcomesmentioning

confidence: 99%

“…Survivors often report poor quality of life and increased levels of disability, and are at an elevated risk of recurrent vascular events [1,2]. In recent decades, there has been substantial interest in using large, routinely collected data from various sources, often called "big data," to generate real-world evidence for understanding and improving outcomes after a stroke or vascular event [3,4]. Big data-enabled research can be distinguished from other types of research based on the attributes described in the 5Vs framework, including: volume (the size or number of records), variety (heterogeneity or diversity of type, structure and setting of data), velocity (rapid generation and reporting of data), veracity (data quality and reliability), and variability (variations between different data sources and datasets) [4,5].…”

Section: Introductionmentioning

confidence: 99%

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The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

Olaiya

Sodhi-Berry

Dalli

et al. 2022

Curr Neurol Neurosci Rep

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Purpose of Review To critically appraise literature on recent advances and methods using “big data” to evaluate stroke outcomes and associated factors. Recent Findings Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Summary Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.

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Section: Big Data Analytics For Stroke Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

Olaiya

Sodhi-Berry

Dalli

et al. 2022

Curr Neurol Neurosci Rep

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“…Apparently, it is possible to use big data technology to build an intelligent platform for health and medical big data, which could promote the vigorous development of multicenter clinical research and fully stimulate the intrinsic economic value of medical big data. Australia had developed a versatile big data health system for cardiovascular health [ 17 ], and South Korea had developed a cancer big data platform for cancer research [ 18 ]. However, they both noted that one of their current barrier is data fusion among different institutions with different standards.…”

Section: Introductionmentioning

confidence: 99%

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

Yu¹,

Chen²,

Chen³

et al. 2023

JMIR Form Res

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Background Medical informatization has initially demonstrated its advantages in improving the medical service industry. Over the past decade, the Chinese government have made a lot of effort to complete infrastructural information construction in the medical and health domain, and smart hospitals will be the next priority according to policies released by Chinese government in recent years. Objective To provide strategic support for further development of medical information construction in China, this study aimed to investigate the current situation of medical information construction in tertiary class-A public hospitals and analyze the existing problems and countermeasures. Methods This study surveyed 23 tertiary class-A public hospitals in China who voluntarily responded to a self-designed questionnaire distributed in April 2020 to investigate the current medical information construction status. Descriptive statistics were used to summarize the current configurations of hospital information department, hospital information systems, hospital internet service and its application, and the satisfaction of hospital information construction. Interviews were also conducted with the respondents in this study for requirement analysis. Results The results show that hospital information construction has become one of the priorities of the hospitals’ daily work, and the medical information infrastructural construction and internet service application of the hospitals are good; however, a remarkable gap among the different level of hospitals can be observed. Although most hospitals had built their own IT team to undertake information construction work, the actual utilization rate of big data collected and stored in the hospital information system was not satisfactory. Conclusions Support for the construction of information technology in primary care institutions should be increased to balance the level of development of medical informatization in medical institutions at all levels. The training of complex talents with both IT and medical backgrounds should be emphasized, and specialized disease information standards should be developed to lay a solid data foundation for data utilization and improve the utilization of medical big data.

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“…Health registers, standardised datasets relevant to a health condition (e.g., cerebral palsy, cancer, rheumatic fever), collect demographic and clinical information from registered participants. As such, they differ from administrative datasets and play an important role in answering specific health questions and monitoring distribution of disease and quality of care [ 3 , 4 ]. However, poor ascertainment and data quality limit potential benefit to Indigenous health and equity.…”

Section: Introductionmentioning

confidence: 99%

Indigenous health equity in health register ascertainment and data quality: a narrative review

Wright

Tapera

Stott

et al. 2022

Int J Equity Health

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Background Health registers play an important role in monitoring distribution of disease and quality of care; however, benefit is limited if ascertainment (i.e., the process of finding and recruiting people on to a register) and data quality (i.e., the accuracy, completeness, reliability, relevance, and timeliness of data) are poor. Indigenous peoples experience significant health inequities globally, yet health data for, and about, Indigenous peoples is often of poor quality. This narrative review aimed to (i) identify perceived barriers for the ascertainment of Indigenous peoples on health registers, and (ii) collate strategies identified and used by health registers to support comprehensive ascertainment and high-quality data for Indigenous peoples. Methods A Kaupapa Māori theoretical framework was utilized to guide this work. Four electronic databases were systematically searched for original articles and screened for eligibility. Studies involving health registers with Indigenous population(s) identified were included if either ascertainment or data quality strategies were described. Data extraction focused on the reporting of research involving Indigenous peoples using the CONSIDER checklist domains, ascertainment, and data quality. Results Seventeen articles were included spanning publication between 1992 and 2020. Aspects of four of eight CONSIDER domains were identified to be included in the reporting of studies. Barriers to ascertainment were themed as relating to ‘ethnicity data collection and quality’, ‘systems and structures’, ‘health services/health professionals’, and ‘perceptions of individual and community-level barriers’. Strategies to support ascertainment were categorized as ‘collaboration’, ‘finding people’, and ‘recruitment processes’. Categorized strategies to support data quality were ‘collaboration’, ‘ethnicity data collection and quality’, ‘systems-level strategies’, and ‘health service/health professional-level strategies’. Conclusions Poor-quality data for Indigenous peoples in health registers prevents the achievement of health equity and exemplifies inaction in the face of need. When viewed through a critical structural determinants lens, there are visible gaps in the breadth of strategies, particularly relating to the inclusion of Indigenous peoples in health register and research governance, and actions to identify and address institutional racism. Indigenous led research, meaningful collaboration, and a sharing of knowledge and experiences between health registers is recommended to enable research and health registers that support Indigenous self-determination and health equity.

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A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health

Cited by 10 publications

References 30 publications

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

Indigenous health equity in health register ascertainment and data quality: a narrative review

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