Big Data in Healthcare Management: A Review of Literature

Sa, Senthilkumar; Bharatendara, K; Meshram, Amruta A; Gunasekaran, Angappa; Chandrakumarmangalam, S.

doi:10.11648/j.ajtab.20180402.14

Cited by 100 publications

(19 citation statements)

References 53 publications

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“…4 Word cloud depicting the strengths of the workshop as reported by the participants (translated from French) Legend: The size of each word is proportional to the frequency of its occurrence in the text of the answers provided by the learners. This picture was created using the website nuagesdemots.fr on "red flags" will also become more relevant when clinical decision support algorithms based on artificial intelligence and big data are deployed in the field of RD [33].…”

Section: Discussionmentioning

confidence: 99%

Raising rare disease awareness using red flags, role play simulation and patient educators: results of a novel educational workshop on Raynaud phenomenon and systemic sclerosis

Sangès

Farhat

Assaraf³

et al. 2020

Orphanet J Rare Dis

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Background: As lack of awareness of rare diseases (RDs) among healthcare professionals results in delayed diagnoses, there is a need for a more efficient approach to RD training during academic education. We designed an experimental workshop that used role-play simulation with patient educators and focused on teaching "red flags" that should raise the suspicion of an RD when faced with a patient with frequently encountered symptoms. Our objective was to report our experience, and to assess the improvement in learners' knowledge and the satisfaction levels of the participants. Results: The workshop consisted of 2 simulated consultations that both started with the same frequent symptom (Raynaud phenomenon, RP) but led to different diagnoses: a frequent condition (idiopathic RP) and an RD (systemic sclerosis, SSc). In the second simulated consultation, the role of the patient was played by a patient educator with SSc. By juxtaposing 2 seemingly similar situations, the training particularly highlighted the elements that help differentiate SSc from idiopathic RP. When answering a clinical case exam about RP and SSc, students that had participated in the workshop had a higher mean mark than those who had not (14 ± 3.7 vs 9.6 ± 5.5 points out of 20, p = 0.001). Participants mostly felt "very satisfied" with this training (94%), and "more comfortable" about managing idiopathic RP and SSc (100%). They considered the workshop "not very stressful" and "very formative" (both 71%). When asked about the strengths of this training, they mentioned the benefits of being put in an immersive situation, allowing a better acquisition of practical skills and a more interactive exchange with teachers, as well as the confrontation with

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Section: Discussionmentioning

confidence: 99%

Raising rare disease awareness using red flags, role play simulation and patient educators: results of a novel educational workshop on Raynaud phenomenon and systemic sclerosis

Sangès

Farhat

Assaraf³

et al. 2020

Orphanet J Rare Dis

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“…Both are distributed applications that enable users to work with large datasets. Moreover, Hadoop and HBase are the best combination for large-scale data [12,28,29,30], such as sensing data and social network data. In this configuration, we set it up to work with PLCs sensing data, which is generated every second in the factory.…”

Section: Designation Of Data Storementioning

confidence: 99%

Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment

Leang

Ean

Ryu

et al. 2019

Sensors

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The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.

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“…7 There are various sources of big data in the healthcare setting these include notes from physicians, medical prescription records, imaging technologies, laboratory data, data from the pharmacy, insurance data, and other administrative and medical process records. 8 Additionally, big data in the healthcare environment may be generated from Electronic Health Records, medical sensors data, customers feedbacks, and posts on social media sites such as Facebook, Twitter, Instagram, websites activities and news feeds among others. 8 To makes sense of data obtained from these sources, it is crucial for stakeholders in the healthcare sectors to be able to visualize this information in a format whereby they can observe trends, links, and patterns.…”

Section: Literature Review On Big Data and Big Data Analytics In Healthcarementioning

confidence: 99%

“…8 Additionally, big data in the healthcare environment may be generated from Electronic Health Records, medical sensors data, customers feedbacks, and posts on social media sites such as Facebook, Twitter, Instagram, websites activities and news feeds among others. 8 To makes sense of data obtained from these sources, it is crucial for stakeholders in the healthcare sectors to be able to visualize this information in a format whereby they can observe trends, links, and patterns. These needs necessitate the concept of big data analytics which refers to the process of analyzing raw big data from different sources with the aim of portraying the data in a manner where an individual can observe the trends or patterns or draw insights to help in improving decision making and service delivery.…”

Section: Literature Review On Big Data and Big Data Analytics In Healthcarementioning

confidence: 99%

Usability of Big Data Analytics Within Clinical Decision Support Systems

Christopher¹,

Tsinale²

2019

IJEAST

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The adoption of electronic health record systems and other digital technologies such as Magnetic Resonance Imaging (MRI) techniques, automated laboratory tests, and body sensors have brought the era of big data technology into the healthcare industry. The use of big data technologies has the potential to provide medical organizations with powerful tools to gather and analyze large data volumes and to use this information to their advantage. However, special skills, systems, and capabilities are required to be able to analyze and extract useful information from big data. The objective of this paper was to explore the literature regarding the usability of big data analytics in supporting medical decision making. This information will guide healthcare organizations in understanding how they can adopt the utilization of big data to enhance decision making. A systematic review of evidence-based research articles from within the past five years was used to gather information in regards to this topic. The articles were derived from scientific databases. Based on the literature review, big data and big data analytics has the capability to improve decision making in the healthcare sector, predict disease outbreaks as well as the trends and patterns of the spread of such diseases, predict occurrence of medical phenomenon's such as hospital readmission, reoccurrence of diseases, and risk of infection among others. Moreover, big data analytics has the capability to help healthcare organizations to streamline processes within the healthcare setting. However, the process of integrating big data analytics in the healthcare setting follows distinct phases. Healthcare organizations also have to consider the challenges associated with adopting big data analytics. Nevertheless, based on the literature, big data analytics has the capability to improve delivery systems and outcomes within the healthcare sector.

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Big Data in Healthcare Management: A Review of Literature

Cited by 100 publications

References 53 publications

Raising rare disease awareness using red flags, role play simulation and patient educators: results of a novel educational workshop on Raynaud phenomenon and systemic sclerosis

Raising rare disease awareness using red flags, role play simulation and patient educators: results of a novel educational workshop on Raynaud phenomenon and systemic sclerosis

Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment

Usability of Big Data Analytics Within Clinical Decision Support Systems

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