The Need for Developing Learning Healthcare Organisations

2020 ◽  
pp. 1-15
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Digital Health ◽  
Medical Science ◽  
Healthcare Delivery ◽  
Healthcare Systems ◽  
Organisational Learning ◽  
Sequencing Data ◽  
Healthcare Environments ◽  
Data Analytic

As the volumes of data generated in healthcare delivery grows, the need for embracing big data strategies and data analytic techniques to better navigate dynamic and complex healthcare environments becomes more and more pressing. This focus has been further fuelled by the advances in technologies and medical science and the incorporation of digital health solutions that enable us to isolate genome sequencing data. However, it is the thesis of this chapter that unless healthcare organisations become learning organisations and incorporate the techniques of knowledge management and organisational learning, these large and essentially raw data assets will become a burden and not a benefit. Thus, healthcare systems need to be redesigned into intelligent health systems that maximise technology and utilise valuable knowledge assets. To do this, it is imperative to understand the link between the principles of organisational learning and knowledge management (KM) to facilitate the building of learning healthcare organisations.

scholarly journals Big Data Analytics in Healthcare

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Ashwin Belle ◽  
Raghuram Thiagarajan ◽  
S. M. Reza Soroushmehr ◽  
Fatemeh Navidi ◽  
Daniel A. Beard ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Care Delivery ◽  
Healthcare Delivery ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Medical Data ◽  
Unstructured Data ◽  
Process Of Care ◽  
Multimodal Data

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

scholarly journals The Technology of Biotechnology and Big Data in Medicine Development

2021 ◽  
pp. 58-73
Author(s):  
Eric D. Perakslis ◽  
Martin Stanley
Keyword(s):  
Clinical Trials ◽  
Big Data ◽  
Digital Health ◽  
Wearable Devices ◽  
Sequencing Data ◽  
Detailed Understanding ◽  
Regulatory Decision ◽  
Device Discovery ◽  
Medicine Development ◽  
Dirty Data

The rise of big data and digital health in medicine have been concurrent over the last two decades. Often confused, while virtually all digital health solutions, such as sensors wearable devices, and diagnostic algorithms, involve big data, not all big data in health care originates from digital health tools. Genomic sequencing data being one example of this. In this chapter, the role and importance of big data in medicines and medical device discovery and development are detailed with the specific focus of providing a detailed understanding of the product discovery, product development, clinical trials, regulatory authorization, and marketing processes. Concepts such as “dirty data,” regulatory decision-making, remote and virtualized clinical trials, and other key elements of digital health are discussed.

The Role for Knowledge Management in Modern Healthcare Delivery

2010 ◽  
Vol 2 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Knowledge Management ◽  
Medical Science ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Healthcare Setting ◽  
Full Potential ◽  
Information And Communications Technologies ◽  
Healthcare Operations ◽  
Knowledge Assets ◽  
Tools And Techniques

As medical science advances and the applications of information and communications technologies (ICTs) to healthcare operations diffuse more data, information begins to permeate healthcare databases and repositories. However, given the voluminous nature of these disparate data assets, it is no longer possible for healthcare providers to process these data without the aid of sophisticated tools and technologies. The goal of knowledge management is to provide the decision maker with appropriate tools, technologies, strategies and processes to turn data and information into valuable knowledge assets. This paper discusses the benefits of incorporating these tools and techniques to the healthcare arena in order to make healthcare delivery more effective and efficient. To ensure a successful knowledge management initiative in a healthcare setting, the paper proffers the knowledge management infrastructure (KMI) framework and intelligence continuum (IC) model. The benefits of these techniques lie not only in the ability of making explicit the elements of these knowledge assets, and in so doing enable their full potential to be realized, but also to provide a systematic and robust approach to structuring the conceptualization of knowledge assets.

An Experimental Healthcare System

2021 ◽  
pp. 301-308
Author(s):  
Miroslav M. Bojović ◽  
Veljko Milutinović ◽  
Dragan Bojić ◽  
Nenad Korolija
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Clinical Guidelines ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Starting Point ◽  
Data Architecture

Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.

scholarly journals Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Naoual El aboudi ◽  
Laila Benhlima
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Healthcare Delivery ◽  
Quality Of Healthcare ◽  
Healthcare Systems ◽  
Health Condition ◽  
Systems Architecture ◽  
Patient Health

The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

scholarly journals Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review

2021 ◽  
Author(s):  
Sera Whitelaw ◽  
Danielle M Pellegrini ◽  
Mamas A Mamas ◽  
Martin Cowie ◽  
Harriette G C Van Spall
Keyword(s):  
Organizational Support ◽  
Quantitative Methods ◽  
Digital Health ◽  
Healthcare Delivery ◽  
Health Technology ◽  
Barriers And Facilitators ◽  
User Friendliness ◽  
Eligibility Criteria ◽  
Patient Level ◽  
Cardiovascular Care

Abstract Digital health technology (DHT) has the potential to revolutionize healthcare delivery but its uptake has been low in clinical and research settings. The factors that contribute to the limited adoption of DHT, particularly in cardiovascular settings, are unclear. The objective of this review was to determine the barriers and facilitators of DHT uptake from the perspective of patients, clinicians, and researchers. We searched MEDLINE, EMBASE, and CINAHL databases for studies published from inception to May 2020 that reported barriers and/or facilitators of DHT adoption in cardiovascular care. We extracted data on study design, setting, cardiovascular condition, and type of DHT. We conducted a thematic analysis to identify barriers and facilitators of DHT uptake. The search identified 3075 unique studies, of which 29 studies met eligibility criteria. Studies employed: qualitative methods (n = 13), which included interviews and focus groups; quantitative methods (n = 5), which included surveys; or a combination of qualitative and quantitative methods (n = 11). Twenty-five studies reported patient-level barriers, most common of which were difficult-to-use technology (n = 7) and a poor internet connection (n = 7). Six studies reported clinician-level barriers, which included increased workload (n = 4) and a lack of integration with electronic medical records (n = 3).Twenty-four studies reported patient-level facilitators, which included improved communication with clinicians (n = 10) and personalized technology (n = 6). Four studies reported clinician-level facilitators, which included approval and organizational support from cardiology departments and/or hospitals (n = 3) and technologies that improved efficiency (n = 3). No studies reported researcher-level barriers or facilitators. In summary, internet access, user-friendliness, organizational support, workflow efficiency, and data integration were reported as important factors in the uptake of DHT by patients and clinicians. These factors can be considered when selecting and implementing DHTs in cardiovascular clinical settings.

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