Navigating Healthcare Challenges Text Analytics, Data Integration, and Decision-Making in the COVID-19 Era

Shujana

doi:10.58776/ijitcsa.v2i1.123

Authors

Shujana Madnira

DOI:

https://doi.org/10.58776/ijitcsa.v2i1.123

Keywords:

Pharmacy, Clinic, Text Analysis, Data Mining, OLAP, Sentiment Analysis, Data Integration, Data Warehouse, Machine Learning

Abstract

In the context of the COVID-19 pandemic, Integrated Healthcare Systems have emerged as crucial components in effectively managing healthcare challenges. This study delves into the multifaceted role of integrated systems, with a particular focus on the pivotal aspects of text analytics. An exploration of various applications of text analytics unfolds, shedding light on its diverse utility within the healthcare landscape. Extensive reviews of problems encountered by different organizations and insights gleaned from research contribute to a comprehensive understanding of the challenges faced by Health and Human Services (HHS). These challenges, intricately linked to issues such as hospital strains and consumers' personal experiences, are thoroughly examined to provide actionable solutions. A key emphasis is placed on the indispensability of data integration, and the abstract discusses how various analytic approaches can be strategically employed within a well-integrated database system. The nuances of implementing an integrated model are scrutinized, highlighting the primary challenges that organizations, particularly HHS, may encounter. Subsequently, potential solutions are presented, leveraging the power of OLAP to construct a dashboard tailored to address the identified problems. Beyond the technical intricacies, the abstract explores the ramifications of an integrated approach on decision-making processes within HHS. The discussion extends to the acceleration of decision-making possibilities, underlining the imperative need for timely and informed actions in the face of healthcare challenges. In essence, this study provides a nuanced exploration of the role of Integrated Healthcare Systems during the COVID-19 pandemic, incorporating insights from text analytics, data integration, and analytic methodologies. The findings aim to contribute valuable perspectives to healthcare organizations, particularly HHS, as they navigate and mitigate the complexities posed by the ongoing global health crisis.

References

. G. Krishna and S. Howard, “How Yemen’s Healthcare has been destroyed,” BMJ, 2021. doi:10.1136/bmj.n3110.

. M. Chapman, J. Philip, and P. Komesaroff, “Systems of care and experience for Dying well,” Toward an Integrated Science of Wellbeing, 2023. doi:10.1093/oso/9780197567579.003.0013.

. G. Rey, “The core galilean idea and some crucial data,” Representation of Language, pp. 13–44, 2020. doi:10.1093/oso/9780198855637.003.0001.

. “Infrastructure for research data storage and preservation,” Exploring Research Data Management, pp. 147–158, 2018. doi:10.29085/9781783302802.016.

. J. of Healthcare Engineering, “Retracted: College physical education and training in big data: A big data mining and analysis system,” Journal of Healthcare Engineering, vol. 2023, pp. 1–1, 2023. doi:10.1155/2023/9808964.

. M. Soni and D. K. Singh, “A key exchange system for secure data coordination in Healthcare Systems,” Healthcare Analytics, vol. 3, p. 100138, 2023. doi:10.1016/j.health.2023.100138.

. H. Yeo, “A machine learning based natural language question and answering system for healthcare data search using complex queries,” 2018 IEEE International Conference on Big Data (Big Data), 2018. doi:10.1109/bigdata.2018.8622448.

. M. Hyder and G. M. Nafees, “Harnessing deep learning and natural language processing (NLP),” ENGINEERING THE FUTURE: MACHINE LEARNING AND DATA SCIENCE IN PRACTICE, 2023. doi:10.52458/9789388996747.nsp2023.eb.ch-02.

. A. Zago Jorgetto, Patient Safety & Pharmacovigilance Program for enhancement of patient safety system in medical institutions, 2023. doi:10.26226/m.638624aefdc1e80014da8d82.

. C. Schwemmer and S. Ziewiecki, Social Media Sellout - the increasing role of product promotion on YouTube, 2018. doi:10.31235/osf.io/tskjb.

. J. M. Kudari, “Data Analytics to predict, detect, and monitor chronic autoimmune diseases using machine learning algorithms,” Advances in Medical Technologies and Clinical Practice, pp. 150–182, 2021. doi:10.4018/978-1-7998-7188-0.ch012.

. M. Robakowska et al., “Increased demand for services provided in hospital emergency departments,” Emergency Medical Service, vol. 9, no. 1, pp. 43–47, 2022. doi:10.36740/emems202201107.

. A.-E. H. Abd-Elrahman, “Telecommunications service quality, customer satisfaction and customer loyalty in pandemic times,” Management Research Review, vol. 46, no. 8, pp. 1112–1131, 2022. doi:10.1108/mrr-08-2021-0595.

. T. Iyamu, “The integration of social media with healthcare big data for services,” Advancing Big Data Analytics for Healthcare Service Delivery, pp. 126–140, 2022. doi:10.4324/9781003251064-9.

. T. Ramathulasi, U. Kumaran, and K. Lokesh, “A survey on text-based topic summarization techniques,” Advanced Practical Approaches to Web Mining Techniques and Application, pp. 1–13, 2022. doi:10.4018/978-1-7998-9426-1.ch001.

. S. Misra, S. K. Saha, and C. Mazumdar, “A survey on Data Stream Analytics,” Handbook of Big Data Analytics. Volume 1: Methodologies, pp. 175–208, 2021. doi:10.1049/pbpc037f_ch6.

. E. Gavagsaz, “Efficient parallel processing of K-nearest neighbor queries by using a centroid-based and hierarchical clustering algorithm,” Artificial Intelligence Advances, vol. 4, no. 1, pp. 26–41, 2022. doi:10.30564/aia.v4i1.4668.

. M. H. Tekieh and B. Raahemi, “Importance of data mining in Healthcare,” Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015. doi:10.1145/2808797.2809367.

. N. Menachemi and Collum, “Benefits and drawbacks of Electronic Health Record Systems,” Risk Management and Healthcare Policy, p. 47, 2011. doi:10.2147/rmhp.s12985.

. L. Pereira, R. Rijo, C. Silva, and R. Martinho, “Text mining applied to electronic medical records,” International Journal of E-Health and Medical Communications, vol. 6, no. 3, pp. 1–18, 2015. doi:10.4018/ijehmc.2015070101.

. N. Arfaoui and J. Akaichi, “A new approach to generate hospital data warehouse schema,” Hospital Management and Emergency Medicine, pp. 1–26, 2020. doi:10.4018/978-1-7998-2451-0.ch001.