Abstract
Meaningful and reliable data evaluation is mandatory in medical studies, also essential to doctors, healthcare staff, and other medical specialists during daily practice routine because of interaction with patient databases to generate reports and analyze trends. At the same time, it is of little use with large datasets without visualization as it brings contemporary advancements in health care especially to identify patterns and correlations with better data analysis. Visualization of similar data points or low infographics features of a small dataset are an ideal source of social medial sharing. Besides, interactive dashboards can help to health specialists to do quick data analysis on huge datasets. Ultimately, it can save the time consumption and even save lives. Therefore, the authors explain available data-driven tools along with its importance in the healthcare industry. This chapter also provides insights into security issues and offers some ideas of how it can be efficient in real-time clinical practices.
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References
Battineni G, Chintalapudi N, Amenta F (2020) Model discovery, and replay fitness validation using inductive mining techniques in medical training of CVC surgery. Appl Comput Inform. https://doi.org/10.1016/j.aci.2020.01.001
Jothi N, Rashid NA, Husain W (2015) Data mining in healthcare—a review. https://doi.org/10.1016/j.procs.2015.12.145
Global Big Data in Healthcare: 2015–2022—Key Questions Answered for the $34.27 Billion Industry. https://www.prnewswire.com/news-releases/global-big-data-in-healthcare-2015-2022—key-questions-answered-for-the-3427-billion-industry-300221274.html. Accessed 18 Aug 2020
Data visualization to fine-tune healthcare. https://renci.org/news/data-visualization-to-fine-tune-healthcare/. Accessed 18 Aug 2020
Dowding D et al (2015) Dashboards for improving patient care: Review of the literature. Int J Med Inform. https://doi.org/10.1016/j.ijmedinf.2014.10.001
Stukowski A (2010) Visualization and analysis of atomistic simulation data with OVITO-the open visualization tool. Model Simul Mater Sci Eng. https://doi.org/10.1088/0965-0393/18/1/015012
Shneiderman B, Plaisant C, Hesse BW (2013) Improving healthcare with interactive visualization. Computer (Long. Beach. Calif). https://doi.org/10.1109/mc.2013.38
Bradley PS (2013) Implications of big data analytics on population health management. Big Data. https://doi.org/10.1089/big.2013.0019
Davey S, Davey A (2015) Effect of practice management among physicians of developing countries with special reference to Indian scenario by mixed method technique. J Fam Med Prim Care. https://doi.org/10.4103/2249-4863.154637
Dent M (2006) Patient choice and medicine in health care. Public Manag Rev. https://doi.org/10.1080/14719030600853360
Battineni G, Chintalapudi N, Karami V, Nittari G, Amenta F, Tayabati SK (2019) Process mining case study approach: extraction of unconventional event logs to improve performance in hospital information systems (HIS) corresponding author. Int J Comput Sci Inf Secur
Tseng P, Kaplan RS, Richman BD, Shah MA, Schulman KA (2018) Administrative costs associated with physician billing and insurance-related activities at an academic health care system. JAMA J Am Med Assoc. https://doi.org/10.1001/jama.2017.19148
Ball MJ, Lillis J (2001) E-health: Transforming the physician/patient relationship. Int J Med Inform. https://doi.org/10.1016/S1386-5056(00)00130-1
Mirmoeini SM, Shooshtari SSM, Battineni G, Amenta F, Tayebati SK (2019) Policies and challenges on the distribution of specialists and subspecialists in rural areas of Iran. Med (Lithuania). https://doi.org/10.3390/medicina55120783
Nittari G et al (2020) Telemedicine practice: review of the current ethical and legal challenges. Telemed e-Health. https://doi.org/10.1089/tmj.2019.0158
Harvard Business School (2014) How big data impacts healthcare. Harvard Bus Rev Anal Serv
Stadler JG, Donlon K, Siewert JD, Franken T, Lewis NE (2016) Improving the efficiency and ease of healthcare analysis through use of data visualization dashboards. Big Data. https://doi.org/10.1089/big.2015.0059
Bumblauskas D, Nold H, Bumblauskas P, Igou A (2017) Big data analytics: transforming data to action. Bus Process Manag J. https://doi.org/10.1108/BPMJ-03-2016-0056
GBD Compare|IHME Viz Hub. https://vizhub.healthdata.org/gbd-compare/. Accessed 18 Aug 2020
NCHS Data Visualization Gallery—Homepage. https://www.cdc.gov/nchs/data-visualization/index.htm. Accessed 18 Aug 2020
Battineni G, Sagaro GG, Nalini C, Amenta F, Tayebati SK (2019) Comparative machine-learning approach: a follow-up study on type 2 diabetes predictions by cross-validation methods. Machines. https://doi.org/10.3390/machines7040074
Chintalapudi N, Battineni G, Sagaro GG, Amenta F (2020) COVID-19 outbreak reproduction number estimations and forecasting in Marche, Italy. Int J Infect Dis. https://doi.org/10.1016/j.ijid.2020.05.029
Battineni G, Chintalapudi N, Amenta F (2020) Performance analysis of different machine learning algorithms in breast cancer predictions. EAI Endorsed Trans Pervasive Heal Technol 6(23):166010. https://doi.org/10.4108/eai.28-5-2020.166010
Mittal A et al (2020) Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images. Sensors (Switzerland). https://doi.org/10.3390/s20041068
Battineni G, Chintalapudi N, Amenta F (2020) AI Chatbot design during an epidemic like the novel coronavirus. Healthcare. https://doi.org/10.3390/healthcare8020154
Chawla S, Mittal M, Chawla M, Goyal L (2020) Corona virus—SARS-CoV-2: an insight to another way of natural disaster. EAI Endorsed Trans Pervasive Heal Technol. https://doi.org/10.4108/eai.28-5-2020.164823
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Battineni, G., Mittal, M., Jain, S. (2021). Data Visualization in the Transformation of Healthcare Industries. In: Roy, S., Goyal, L.M., Mittal, M. (eds) Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-16-0538-3_1
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