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Overview of the Role of Data Analytics in Advancing Health Service

DOI: 10.4236/oalib.1110207, PP. 1-19

Subject Areas: Cloud Computing

Keywords: Data Analytics, Medical Data, Medical Research, Analysis

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Abstract

Data analytics is crucial in health services, supporting healthcare delivery, research, and decision-making. This article provides an overview of data analytics’s benefits, challenges, and limitations in health services. The first section highlights how data analytics improves health service outcomes by identifying risk factors, enabling early disease detection, and creating personalized treatment plans. It aids in predicting disease progression, identifying potential drug interactions, and tracking disease spread, empowering informed decision-making and service quality improvement. The research also focuses on data analytics in health service research and drug development. Analyzing large datasets provides insights into drug development, personalized medicine, and specialized clinical trials. It identifies patients who would benefit from specific treatments based on their biological characteristics, enhancing patient outcomes and advancing health services. The third section emphasizes data analytics’ importance in improving efficiency and profitability. It identifies fraud, abuse, and unnecessary medical activities, leading to cost savings and improved financial performance. Data analytics also helps identify high-cost patients and offers solutions to reduce healthcare expenses, boosting profitability. The fourth section explores how data analytics enhances public health surveillance and outbreak detection. Automating data collection, analyzing diverse sources, and detecting patterns or anomalies enables early outbreak detection, targeted interventions, and resource allocation. It proactively responds to public health threats, safeguarding population health and preventing infectious disease spread. Despite the benefits, challenges exist in implementing data analytics, such as data quality, governance, privacy, bias, integration, expertise, and ethics. Addressing these challenges is crucial to fully harness the potential of data analytics, transforming patient care, operational efficiency, and cost-effectiveness in health services.

Cite this paper

Baiyewu, A. S. (2023). Overview of the Role of Data Analytics in Advancing Health Service. Open Access Library Journal, 10, e207. doi: http://dx.doi.org/10.4236/oalib.1110207.

References

[1]  McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D. (2012) Big Data: The Management Revolution. Harvard Business Review, 90, 60-68.
[2]  Lynch, C. (2008) Big Data: How Do Your Data Grow? Nature, 455, 28-29. https://doi.org/10.1038/455028a
[3]  Jacobs, A. (2009) The Pathologies of Big Data. Communications of the ACM, 52, 36-44. https://doi.org/10.1145/1536616.1536632
[4]  Zikopoulos, P., Eaton, C., de Roos, D., Deutsch, T. and Lapis, G. (2011) Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, New York.
[5]  Manyika, J., Chui, M., Brown, B., et al. (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, New York.
[6]  Borckardt, J., Nash, M.R., Murphy, M.D., Moore, M., Shaw, D. and O’Neil, P. (2008) Clinical Practice as Natural Laboratory for Psychotherapy Research: A Guide to Case-Based Time-Series Analysis. The American Psychologist, 63, 77-95. https://doi.org/10.1037/0003-066X.63.2.77
[7]  Celi, L.A., Mark, R.G., Stone, D.J. and Montgomery, R.A. (2013) “Big Data” in the Intensive Care Unit: Closing the Data Loop. American Journal of Respiratory and Critical Care Medicine, 187, 1157-1160. https://doi.org/10.1164/rccm.201212-2311ED
[8]  Mackenzie, C.F., Hu, P., Sen, A., et al. (2008) Automatic Pre-Hospital Vital Signs Waveform and Trend Data Capture Fills Quality Management, Triage and Outcome Prediction Gaps. AMIA Annual Symposium Proceedings, 2008, 318-322.
[9]  Bodo, M., Settle, T., Royal, J., Lombardini, E., Sawyer, E. and Rothwell, S.W. (2013) Multimodal Noninvasive Monitoring of Soft Tissue Wound Healing. Journal of Clinical Monitoring and Computing, 27, 677-688. https://doi.org/10.1007/s10877-013-9492-z
[10]  Hu, P., Galvagno Jr., S.M., Sen, A., et al. (2014) Identification of Dynamic Prehospital Changes with Continuous Vital Signs Acquisition. Air Medical Journal, 33, 27-33. https://doi.org/10.1016/j.amj.2013.09.003
[11]  Apiletti, D., Baralis, E., Bruno, G. and Cerquitelli, T. (2009) Real-Time Analysis of Physiological Data to Support Medical Applications. IEEE Transactions on Information Technology in Biomedicine, 13, 313-321. https://doi.org/10.1109/TITB.2008.2010702
[12]  Chen, J., Dougherty, E., Demir, S.S., Friedman, C.P., Li, C.S. and Wong, S. (2005) Grand Challenges for Multimodal Bio-Medical Systems. IEEE Circuits and Systems Magazine, 5, 46-52. https://doi.org/10.1109/MCAS.2005.1438739
[13]  Menachemi, N., Chukmaitov, A., Saunders, C. and Brooks, R.G. (2008) Hospital Quality of Care: Does Information Technology Matter? The Relationship between Information Technology Adoption and Quality of Care. Health Care Management Review, 33, 51-59. https://doi.org/10.1097/01.HMR.0000304497.89684.36
[14]  DesRoches, C.M., Campbell, E.G., Rao, S.R., et al. (2008) Electronic Health Records in Ambulatory Care—A National Survey of Physicians. The New England Journal of Medicine, 359, 50-60. https://doi.org/10.1056/NEJMsa0802005
[15]  McCullough, J.S., Casey, M., Moscovice, I. and Prasad, S. (2010) The Effect of Health Information Technology on Quality in U.S. Hospitals. Health Affairs, 29, 647-654. https://doi.org/10.1377/hlthaff.2010.0155
[16]  Blum, J.M., Joo, H., Lee, H. and Saeed, M. (2015) Design and Implementation of a Hospital Wide Waveform Capture System. Journal of Clinical Monitoring and Computing, 29, 359-362. https://doi.org/10.1007/s10877-014-9612-4
[17]  Freeman, D. (2009) The Future of Patient Monitoring. Health Management Technology, 30, Article 26.
[18]  Muhsin B. and Sampath, A. (2012) Systems and Methods for Storing, Analyzing, Retrieving and Displaying Streaming Medical Data. US Patent No. 8310336.
[19]  Malan, D., Fulford-Jones, T., Welsh, M. and Moulton, S. (2004) Codeblue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Vol. 5, London, 2004.
[20]  Page, A., Kocabas, O., Ames, S., Venkitasubramaniam, M. and Soyata, T. (2014) Cloud-Based Secure Health Monitoring: Optimizing Fully-Homomorphic Encryption for Streaming Algorithms. 2014 IEEE Globecom Workshops (GC Wkshps), Austin, 8-12 December 2014, 48-52. https://doi.org/10.1109/GLOCOMW.2014.7063384
[21]  Bange, J., Gryzwa, M., Hoyme, K., Johnson, D.C., LaLonde, J. and Mass, W. (2011) Medical Data Transport over Wireless Life Critical Network. US Patent No. 7978062.
[22]  Kara N. and Dragoi, O.A. (2007) Reasoning with Contextual Data in Telehealth Applications. Proceedings of the 3rd IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMoB 2007), White Plains, 8-10 October 2007, 69. https://doi.org/10.1109/WIMOB.2007.4390863
[23]  Li, G., Liu, J., Li, X., Lin, L. and Wei, R. (2014) A Multiple Biomedical Signals Synchronous Acquisition Circuit Based on Over-Sampling and Shaped Signal for the Application of the Ubiquitous Health Care. Circuits, Systems, and Signal Processing, 33, 3003-3017. https://doi.org/10.1007/s00034-014-9794-5
[24]  Bar-Or, A., Healey, J., Kontothanassis, L. and van Thong, J.M. (2004) BioStream: A System Architecture for Real-Time Processing of Physiological Signals. Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2004), San Francisco, 1-5 September 2004, 3101-3104.
[25]  Raghupathi, W. and Raghupathi, V. (2014) Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2, Article No. 3. https://doi.org/10.1186/2047-2501-2-3
[26]  Ahmad, S., Ramsay, T., Huebsch L., et al. (2009) Continuous Multiparameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults. PLOS ONE, 4, e6642. https://doi.org/10.1371/journal.pone.0006642
[27]  Goldberger, A.L., Amaral, L.A., Glass, L., et al. (2000) Physiobank, Physiotoolkit, and Physionet Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, e215-e220. https://doi.org/10.1161/01.CIR.101.23.e215
[28]  Siachalou, E.J., Kitsas, I.K., Panoulas, K.J., et al. (2005) ICASP: An Intensive-Care Acquisition and Signal Processing Integrated Framework. Journal of Medical Systems, 29, 633-646. https://doi.org/10.1007/s10916-005-6132-2
[29]  Saeed, M., Lieu, C., Raber, G. and Mark, R.G. (2002) Mimic Ii: A Massive Temporal ICU Patient Database to Support Research in Intelligent Patient Monitoring. Proceedings of the Computers in Cardiology, Memphis, 22-25 September 2002, 641-644.
[30]  Burykin, A., Peck, T. and Buchman, T.G. (2011) Using “Off-the-Shelf” Tools for Terabyte-Scale Waveform Recording in Intensive Care: Computer System Design, Database Description and Lessons Learned. Computer Methods and Programs in Biomedicine, 103, 151-160. https://doi.org/10.1016/j.cmpb.2010.10.004
[31]  Prasad S. and Sha, M.S.N. (2013) NextGen Data Persistence Pattern in Healthcare: Polyglot Persistence. Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT 2013), Tiruchengode, 4-6 July 2013, 1-8. https://doi.org/10.1109/ICCCNT.2013.6726734
[32]  Adrian, G., Francisco, G.E., Marcela, M., Baum, A., Daniel, L. and de Quiros Fernan, G.B. (2013) Mongodb: An Open Source Alternative for HL7-CDA Clinical Documents Management. Proceedings of the Open Source International Conference (CISL 2013), Buenos Aires, 2013.
[33]  Yu, W.D., Kollipara, M., Penmetsa, R. and Elliadka, S. (2013) A Distributed Storage Solution for Cloud Based E-Healthcare Information System. Proceedings of the IEEE 15th International Conference on e-Health Networking, Applications & Services (Healthcom 2013), Lisbon, 9-12 October 2013, 476-480.
[34]  Santos, M. and Portela, F. (2011) Enabling Ubiquitous Data Mining in Intensive Care: Features Selection and Data Pre-Processing. Proceedings of the 13th International Conference on Enterprise Information Systems, Beijing, 8-11 June 2011, 261-266.
[35]  Berndt, D.J., Fisher, J.W., Hevner, A.R. and Studnicki, J. (2001) Healthcare Data Warehousing and Quality Assurance. Computer, 34, 56-65. https://doi.org/10.1109/2.970578
[36]  Uzuner, O., South, B.R., Shen, S. and DuVall, S.L. (2011) 2010 i2b2/VA Challenge on Concepts, Assertions, and Relations in Clinical Text. Journal of the American Medical Informatics Association, 18, 552-556. https://doi.org/10.1136/amiajnl-2011-000203
[37]  Athey, B.D., Braxenthaler, M., Haas, M. and Guo, Y. (2013) TranSMART: An Open Source and Community-Driven Informatics and Data Sharing Platform for Clinical and Translational Research. AMIA Summits on Translational Science Proceedings, 2013, 6-8.
[38]  Hu, H., Wen, Y., Chua, T.S. and Li, X. (2014) Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. IEEE Access, 2, 652-687. https://doi.org/10.1109/ACCESS.2014.2332453
[39]  Batko, K. (2016) Mozliwosci wykorzystania Big Data w ochronie zdrowia. Roczniki Kolegium Analiz Ekonomicznych, 42, 267-282.
[40]  Aungst, T.D., Clauson, K.A., Misra, S. and Lewis, T.L. (2021) Virtual Healthcare and Mobile Health Technology: A Guide for Pharmacy Practice. Journal of the American Pharmacists Association, 61, e46-e58.
[41]  Dorsey, E.R., Topol, E.J. and the Virtual healthcare 2.0 Study Group (2016) State of Telehealth. New England Journal of Medicine, 375, 154-161. https://doi.org/10.1056/NEJMra1601705
[42]  Krittanawong, C., Zhang, H., Wang, Z., Aydar, M. and Kitai, T. (2020) Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 75, 2996-3013.
[43]  Shah, R.C., Borrego, M.E. and Letourneau, L.M. (2018) Electronic Health Records: Improving Quality of Care and Patient Outcomes in Primary Care. Journal of the American Association of Nurse Practitioners, 30, 355-362.
[44]  Wang, Y., Lu, X., Zhu, X., Zhang, Z. and Yu, J. (2018) Using Machine Learning to Identify Risk Factors for Postoperative Complications of Orthopedic Patients. Journal of Healthcare Engineering, 2018, 1-8.
[45]  Bächle, D., Köpcke, C.D., Falque-Pierrotin, D. and Seidl, T. (2020) Patient Stratification and Prediction Models for Cardiovascular Disease Using Machine Learning Technologies. International Journal of Medical Informatics, 144, 1-10.
[46]  Miotto, R., Wang, F., Wang, S., Jiang, X and Dudley, J.T. (2016) Deep Learning for Healthcare: Review, Opportunities and Challenges. Briefings in Bioinformatics, 19, 1236-1246. https://doi.org/10.1093/bib/bbx044
[47]  Mavragani, A. and Ochoa, G. (2018) The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak. Big Data and Cognitive Computing, 2, Article 2. https://doi.org/10.3390/bdcc2010002
[48]  Ekins, S., et al. (2019) Enhancing Drug Discovery with Artificial Intelligence. Molecular Pharmaceutics, 16, 1315-1319.
[49]  Roque, F.S., et al. (2020) Using Data Analytics to Identify Patients with Specific Biological Features for Clinical Trials. BMC Medical Informatics and Decision Making, 20, 1-14.
[50]  Wang, J., et al. (2020) Personalized Blood Glucose Prediction and Insulin Infusion for Diabetes Management Using Recurrent Neural Networks and Model Predictive Control. IEEE Transactions on Biomedical Engineering, 67, 687-697.
[51]  Wu, X., Huang, J., Zeng, X. and Chen, S. (2018) Data Analytics and Fraud Detection in the Healthcare System. International Journal of Engineering and Technology, 10, 75-78.
[52]  Kim, S., Lee, J. and Yoo, S. (2020) Healthcare Revenue Management Using Machine Learning Techniques: A Review. Healthcare Informatics Research, 26, 79-87.
[53]  Li, X., Wen, D. and Guo, X. (2019) Research on the Application of Big Data Analysis in Reducing the Rate of Repeated Inspection in the Hospital. Journal of Medical Systems, 43, 1-7.
[54]  Kim, J., Lee, J. and Yoo, S. (2019) Predicting Healthcare Costs Based on Patients’ Demographics and Diagnoses Using Machine Learning. Healthcare Informatics Research, 25, 118-125.
[55]  Alotaibi, M., Wyatt, J.C. and Lopez, D. (2021) The Challenges of Using Data Analytics in Healthcare: Recommendations for Overcoming Obstacles. Journal of Innovation in Health Informatics, 28, 131-138.
[56]  Johnson, B., Zhang, K. and Unhelkar, Y. (2018) Data Quality and Its Impacts on Decision-Making: How Managers Can Benefit from Good Data. Journal of Enterprise Information Management, 31, 63-79.
[57]  Kang, Y., Yao, C. and Li, S. (2019) Healthcare Data Breach: A Comprehensive Overview and Defense-In-Depth Framework. IEEE Journal of Biomedical and Health Informatics, 23, 1113-1123.
[58]  Rudin, C., Parekh, R.E. and Smyth, N.J. (2019) Machine Learning for Health: Introduction to the Special Issue. IEEE Journal of Biomedical and Health Informatics, 23, 1255-1260.
[59]  Gupta, A., Gupta, A.S. and Joshi, S. (2019) Data Integration for Healthcare Analytics: A Comprehensive Review. IEEE Access, 7, 90044-90067.
[60]  Mackenzie, T., Kerr, M.A. and Karim, M.P. (2020) Barriers to Data Analytics Adoption in Healthcare: A Literature Review. Journal of Healthcare Information Management, 34, 8-13.
[61]  Kung, W., Byrd, M.B. and Greene, P.J. (2018) A Formative Evaluation of the Utility of Health Information Exchange in Three Communities. Journal of the American Medical Informatics Association, 25, 1600-1606.

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