Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

Rumbold, John; O’Kane, Maurice; Philip, Nada; Pierscionek, Barbara K.

doi:10.1111/dme.14044

Cited by 29 publications

(16 citation statements)

References 34 publications

(34 reference statements)

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“…The patients involved in using such systems may consent for their data to be used for the primary purpose of the in-home health remote monitoring and not the future/secondary use, e.g. building algorithms using the collected data (Big data analytics) [44].…”

Section: B Security Privacy Ethics and Lawmentioning

confidence: 99%

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Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions

Philip

Rodrigues

Wang

et al. 2021

IEEE J. Select. Areas Commun.

179

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Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations to increase support for personalized, integrated care to prevent and manage chronic conditions. Many applications related to In-Home Health Monitoring have been introduced over the last few decades, thanks to the advances in mobile and Internet of Things technologies and services. Such advances include improvements in optimized network architecture, indoor networks coverage, increased device reliability and performance, ultra-low device cost, low device power consumption, and improved device and network security and privacy. Current studies of in-home health monitoring systems presented many benefits including improved safety, quality of life and reduction in hospitalization and cost. However, many challenges of such a paradigm shift still exist, that need to be addressed to support scale-up and wide uptake of such systems, including technology acceptance and adoption by patients, healthcare providers and policymakers. The aim of this paper is three folds: First, review of key factors that drove the adoption and growth of the IoT-based in-home remote monitoring; Second, present the latest advances of IoT based in-home remote monitoring system architecture and key building blocks; Third, discuss future outlook and our recommendations of the in-home remote monitoring applications going forward.

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Section: B Security Privacy Ethics and Lawmentioning

confidence: 99%

“…However, misuse of data being collected via (e.g. such IoT applications), without consultation, infringes these requirements [44]. It has been noticed that one of the issues in the low adoption of IoT applications among end-users is the lack of trust in IoT devices concerning data protection, privacy and safety.…”

Section: B Security Privacy Ethics and Lawmentioning

confidence: 99%

Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions

Philip

Rodrigues

Wang

et al. 2021

IEEE J. Select. Areas Commun.

179

View full text Add to dashboard Cite

show abstract

“…The absence of an agreed definition of RWD and RWE is also linked to the difficulty of uniquely capturing the essence of big data; this should not be surprising, as big data and RWD are closely linked. Essential characteristics of big data, however, have been considered the high volume, variety, velocity (ie, high speed of generation), veracity (accurate data), and value (usefulness); 5 Vs) …”

Section: Future Applications Of Rwe In Diabetes Researchmentioning

confidence: 99%

The present and future scope of real‐world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

Zaccardi

Davies

Khunti

2020

Diabetes Obesity Metabolism

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The last decade has witnessed an exponential growth in the opportunities to collect and link health‐related data from multiple resources, including primary care, administrative, and device data. The availability of these “real‐world,” “big data” has fuelled also an intense methodological research into methods to handle them and extract actionable information. In medicine, the evidence generated from “real‐world data” (RWD), which are not purposely collected to answer biomedical questions, is commonly termed “real‐world evidence” (RWE). In this review, we focus on RWD and RWE in the area of diabetes research, highlighting their contributions in the last decade; and give some suggestions for future RWE diabetes research, by applying well‐established and less‐known tools to direct RWE diabetes research towards better personalized approaches to diabetes care. We underline the essential aspects to consider when using RWD and the key features limiting the translational potential of RWD in generating high‐quality and applicable RWE. Only if viewed in the context of other study designs and statistical methods, with its pros and cons carefully considered, RWE will exploit its full potential as a complementary or even, in some cases, substitutive source of evidence compared to the expensive evidence obtained from randomized controlled trials.

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“…Although numerous randomized controlled trials of cardiovascular disease in relation to diabetes are conducted worldwide, such trials are extremely costly, take enormous effort, and require the cooperation of study participants. To resolve those issues, it is essential to use new and alternative research sources such as real‐world data as represented by claims data 1‐5 …”

Section: Introductionmentioning

confidence: 99%

Accuracy of Japanese claims data in identifying diabetes‐related complications

Fujihara

Yamada-Harada

Matsubayashi

et al. 2021

Pharmacoepidemiology and Drug

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Purpose To evaluate the accuracy of various claims‐based definitions of diabetes‐related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis). Methods We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes‐related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims‐based definition associated with diabetes‐related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD‐10) codes, procedure codes and medication codes were calculated. Results DPC‐based definitions had higher sensitivity, specificity, and PPV than ICD‐10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963–1.000 and 0.905–0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD‐10 codes + medication were better for heart failure than the ICD‐10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes‐related complications using ICD‐10 codes only. Conclusion The DPC‐based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD‐10 codes with medication codes for heart failure could accurately identify these diabetes‐related complications from claims databases.

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Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

Cited by 29 publications

References 34 publications

Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions

Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions

The present and future scope of real‐world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

Accuracy of Japanese claims data in identifying diabetes‐related complications

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