Improving patient matching: Single patient view for Clinical Decision Support using Big Data analytics

2015 ◽  
Author(s):  
Reena Duggal ◽  
Sunil Kumar Khatri ◽  
Balvinder Shukla
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Single Patient

scholarly journals Big Data Analytics with OENN Based Clinical Decision Support System

2022 ◽  
Vol 31 (2) ◽  
pp. 1241-1256
Author(s):  
Thejovathi Murari ◽  
L. Prathiba ◽  
Kranthi Kumar Singamaneni ◽  
D. Venu ◽  
Vinay Kumar Nassa ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Clinical Decision

scholarly journals Addressing the Complexities of Big Data Analytics in Healthcare: The Diabetes Screening Case

2015 ◽  
Vol 19 ◽  
Author(s):  
Daswin De Silva ◽  
Frada Burstein ◽  
Herbert F. Jelinek ◽  
Andrew Stranieri
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Data Analytics ◽  
Healthcare Delivery ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Optimization Techniques ◽  
Diabetes Screening

The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context.

scholarly journals The Benefits of Big Data Analytics in the Healthcare Sector

2015 ◽  
pp. 406-439 ◽  
Author(s):  
Andrea Darrel ◽  
Margee Hume ◽  
Timothy Hardie ◽  
Jeffery Soar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Improved Outcomes ◽  
Health Initiatives ◽  
Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Decision Support Framework for Big Data Analytics

2018 ◽  
Author(s):  
Sakshi Agarwal ◽  
Krishnaprasad Narayanan ◽  
Manjira Sinha ◽  
Rohit Gupta ◽  
Sharanya Eswaran ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Support Framework
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