Big Data

The use of “big data” in neurosurgical research has become increasingly popular. However, using this type of data comes with limitations.

Big data has transformed into a trending phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, Raju et al. give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care 1)

A study aimed to shed light on this new approach to clinical research.

Oravec et al. compiled a list of commonly used databases that were not specifically created to study neurosurgical procedures, conditions, or diseases. Three North American journals were manually searched for articles published since 2000 utilizing these and other non-neurosurgery-specific databases. A number of data points per article were collected, tallied, and analyzed.A total of 324 articles were identified since 2000 with an exponential increase since 2011 (257/324, 79%). The Journal of Neurosurgery Publishing Group published the greatest total number (n = 200). The National Inpatient Sample was the most commonly used database (n = 136). The average study size was 114,841 subjects (range, 30-4146,777). The most prevalent topics were vascular (n = 77) and neurooncology (n = 66). When categorizing study objective (recognizing that many papers reported more than 1 type of study objective), “Outcomes” was the most common (n = 154). The top 10 institutions by primary or senior author accounted for 45%-50% of all publications. Harvard Medical School was the top institution, using this research technique with 59 representations (31 by primary author and 28 by senior).The increasing use of data from non-neurosurgery-specific databases presents a unique challenge to the interpretation and application of the study conclusions. The limitations of these studies must be more strongly considered in designing and interpreting these studies 2).

“Big data” refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in many ways, it is unlikely to replace physicians in the foreseeable future 3).

The management, analysis, and integration of Big Data have received increasing attention in healthcare research as well as in medical bioinformatics. The J-ASPECT study is the first nationwide survey in Japan on the real-world setting of stroke care using data obtained from the diagnosis procedure combination-based payment system. The J-ASPECT study demonstrated a significant association between comprehensive stroke care (CSC) capacity and the hospital volume of stroke interventions in Japan; further, it showed that CSC capabilities were associated with reduced in-Hospital mortality rates. Our study aims to create new evidence and insight from 'real world' neurosurgical practice and stroke care in Japan using Big Data. The final aim of this study is to develop effective methods to bridge the evidence-practice gap in acute stroke healthcare. In this study, the authors describe the status and future perspectives of the development of a new method of stroke registry as a powerful tool for acute stroke care research 4).

Ethical discussions around health care reform typically focus on problems of social justice and health care equity.

A review, in contrast, focuses on ethical issues of particular importance to neurosurgeons, especially with respect to potential changes in the physician-patient relationship that may occur in the context of health care reform.The Patient Protection and Affordable Care Act (ACA) of 2010 (H.R. 3590) was not the first attempt at health care reform in the United States but it is the one currently in force. Its ambitions include universal access to health care, a focus on population health, payment reform, and cost control. Each of these aims is complicated by a number of ethical challenges, of which 7 stand out because of their potential influence on patient care: the accountability of physicians and surgeons to individual patients; the effects of financial incentives on clinical judgment; the definition and management of conflicting interests; the duty to preserve patient autonomy in the face of protocolized care; problems in information exchange and communication; issues related to electronic health records and data security; and the appropriate use of “Big Data.”Systematic social and economic reforms inevitably raise ethical concerns. While the ACA may have driven these 7 to particular prominence, they are actually generic. Nevertheless, they are immediately relevant to the practice of neurosurgery and likely to reflect the realities the profession will be obliged to confront in the pursuit of more efficient and more effective health care 5).

In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system 6).

Raju B, Jumah F, Ashraf O, Narayan V, Gupta G, Sun H, Hilden P, Nanda A. Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons. J Neurosurg. 2020 Oct 2:1-11. doi: 10.3171/2020.5.JNS201288. Epub ahead of print. PMID: 33007750.
Oravec CS, Motiwala M, Reed K, Kondziolka D, Barker FG 2nd, Michael LM 2nd, Klimo P Jr. Big Data Research in Neurosurgery: A Critical Look at this Popular New Study Design. Neurosurgery. 2017 Jul 21. doi: 10.1093/neuros/nyx328. [Epub ahead of print] PubMed PMID: 28973512.
Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2017 Jun 23. pii: S0953-6205(17)30261-3. doi: 10.1016/j.ejim.2017.06.017. [Epub ahead of print] PubMed PMID: 28651747.
Nishimura A, Nishimura K, Kada A, Iihara K; J-ASPECT Study GROUP.. Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research. Neurol Med Chir (Tokyo). 2016 Nov 15;56(11):655-663. Epub 2016 Sep 27. PubMed PMID: 27680330; PubMed Central PMCID: PMC5221776.
Dagi TF. Seven Ethical Issues Affecting Neurosurgeons in the Context of Health Care Reform. Neurosurgery. 2017 Apr 1;80(4S):S83-S91. doi: 10.1093/neuros/nyx017. PubMed PMID: 28375501.
Pablo RJ, Roberto DP, Victor SU, Isabel GR, Paul C, Elizabeth OR. Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology. J Integr Bioinform. 2021 Aug 18. doi: 10.1515/jib-2020-0035. Epub ahead of print. PMID: 34412176.
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