Skip to main content
Top

2020 | OriginalPaper | Chapter

11. The Role of Big Data and Twitter Data Analytics in Healthcare Supply Chain Management

Authors : Shoayee Alotaibi, Rashid Mehmood, Iyad Katib

Published in: Smart Infrastructure and Applications

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

It is estimated that healthcare spending in the world’s major regions will increase from 2.4% of GDP to 7.5% during 2015 to 2020. Healthcare providers are required to deliver high-quality medical services to their customers. Since most of their budgets are spent on high cost medical equipment and medicines, there is a pressing need for them to optimize their supply chain activities such that high-quality services could be provided at lower costs. Relatedly, medical equipment and devices generate massive amounts of unused data. Big data analytics is proven to be helpful in forecasting and decision-making, and, hence, can be a powerful tool to improve healthcare supply chains. This paper extends our earlier work and presents a review on the use of big data in healthcare supply chains. We review the various concepts related to the topic of this paper including big data, big data analytics, and the role of big data in healthcare, supply chain management (SCM) and healthcare supply chain management. The role of Twitter data in SCM is also explored. The opportunities and challenges for big data enabled healthcare supply chains are discussed along with several directions for future developments. We conclude that the use of big data in healthcare supply chains is of immense potential and demands further investigation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Deloitte: 2017 Global Health Care Sector Outlook. 2015 (2017) Deloitte: 2017 Global Health Care Sector Outlook. 2015 (2017)
2.
go back to reference Sultanow, E., Chircu, A.M.: Improving healthcare with data-driven track-and-trace systems. 65–82 Sultanow, E., Chircu, A.M.: Improving healthcare with data-driven track-and-trace systems. 65–82
3.
go back to reference Kwon, I.W.G., Kim, S.H., Martin, D.G.: Healthcare supply chain management; strategic areas for quality and financial improvement. Technol. Forecast. Soc. Change. 113, 422–428 (2016)CrossRef Kwon, I.W.G., Kim, S.H., Martin, D.G.: Healthcare supply chain management; strategic areas for quality and financial improvement. Technol. Forecast. Soc. Change. 113, 422–428 (2016)CrossRef
4.
go back to reference Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Networks Appl. 19, 171–209 (2014)CrossRef Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Networks Appl. 19, 171–209 (2014)CrossRef
5.
go back to reference Malik, M.M., Abdallah, S., Ala’raj, M.: Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Ann. Oper. Res. 1–26 (2016) Malik, M.M., Abdallah, S., Ala’raj, M.: Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Ann. Oper. Res. 1–26 (2016)
6.
go back to reference Alotaibi, S., Mehmood, R.: Big data enabled healthcare supply chain management: opportunities and challenges. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds.) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 207–215. Springer, Cham (2018) Alotaibi, S., Mehmood, R.: Big data enabled healthcare supply chain management: opportunities and challenges. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds.) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 207–215. Springer, Cham (2018)
7.
go back to reference Naoui, F.: Customer service in supply chain management: a case study. J. Enterp. Inf. Manag. 27, 786–801 (2014)CrossRef Naoui, F.: Customer service in supply chain management: a case study. J. Enterp. Inf. Manag. 27, 786–801 (2014)CrossRef
8.
go back to reference Akyuz, G.A., Rehan, M.: Requirements for forming an “e-supply chain”. Int. J. Prod. Res. 47, 3265–3287 (2009)CrossRef Akyuz, G.A., Rehan, M.: Requirements for forming an “e-supply chain”. Int. J. Prod. Res. 47, 3265–3287 (2009)CrossRef
9.
go back to reference Butner, K.: The smarter supply chain of the future. Strateg. Leadersh. 38, 22–31 (2010)CrossRef Butner, K.: The smarter supply chain of the future. Strateg. Leadersh. 38, 22–31 (2010)CrossRef
10.
go back to reference Hessman, T.: The Dawn of the Smart Factory. IndustryWeek. 14–19 (2013) Hessman, T.: The Dawn of the Smart Factory. IndustryWeek. 14–19 (2013)
11.
go back to reference Ahmad, N., Mehmood, R.: Enterprise systems: are we ready for future sustainable cities. Supply Chain Manag. An Int. J. 20, 264–283 (2015)CrossRef Ahmad, N., Mehmood, R.: Enterprise systems: are we ready for future sustainable cities. Supply Chain Manag. An Int. J. 20, 264–283 (2015)CrossRef
13.
go back to reference Mehmood, R., Faisal, M.A., Altowaijri, S.: Future Networked Healthcare Systems: A Review and Case Study. In: I. Management Association (ed.) Big Data: Concepts, Methodologies, Tools, and Applications. pp. 2429–2457. IGI Global (2016) Mehmood, R., Faisal, M.A., Altowaijri, S.: Future Networked Healthcare Systems: A Review and Case Study. In: I. Management Association (ed.) Big Data: Concepts, Methodologies, Tools, and Applications. pp. 2429–2457. IGI Global (2016)
14.
go back to reference Mehmood, R., Meriton, R., Graham, G., Hennelly, P., Kumar, M.: Exploring the influence of big data on city transport operations: a Markovian approach. Int. J. Oper. Prod. Manag. 37, 75–104 (2017)CrossRef Mehmood, R., Meriton, R., Graham, G., Hennelly, P., Kumar, M.: Exploring the influence of big data on city transport operations: a Markovian approach. Int. J. Oper. Prod. Manag. 37, 75–104 (2017)CrossRef
15.
go back to reference Suma, S., Mehmood, R., Albugami, N., Katib, I., Albeshri, A.: Enabling Next Generation Logistics and Planning for Smarter Societies. Procedia - Procedia Comput. Sci. 1–6 (2017) Suma, S., Mehmood, R., Albugami, N., Katib, I., Albeshri, A.: Enabling Next Generation Logistics and Planning for Smarter Societies. Procedia - Procedia Comput. Sci. 1–6 (2017)
17.
go back to reference Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access. 5, 9533–9554 (2017)CrossRef Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access. 5, 9533–9554 (2017)CrossRef
18.
go back to reference Mehmood, R., Alam, F., Albogami, N.N., Katib, I., Albeshri, A., Altowaijri, S.M.: UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access. 5, 2615–2635 (2017)CrossRef Mehmood, R., Alam, F., Albogami, N.N., Katib, I., Albeshri, A., Altowaijri, S.M.: UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access. 5, 2615–2635 (2017)CrossRef
19.
go back to reference Feki, M., Wamba, S.F.: Big Data Analytics-enabled Supply Chain Transformation: A Literature Review. 49th Hawaii Int. Conf. Syst. Sci. 1123–1132 (2016) Feki, M., Wamba, S.F.: Big Data Analytics-enabled Supply Chain Transformation: A Literature Review. 49th Hawaii Int. Conf. Syst. Sci. 1123–1132 (2016)
20.
go back to reference Hogarth, R.M., Soyer, E.: Using simulated experience to make sense of big data. MIT Sloan Manag. Rev. 56, 49–54 (2015) Hogarth, R.M., Soyer, E.: Using simulated experience to make sense of big data. MIT Sloan Manag. Rev. 56, 49–54 (2015)
21.
go back to reference Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manag. 35, 137–144 (2015)CrossRef Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manag. 35, 137–144 (2015)CrossRef
22.
go back to reference Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Busienss Logist. 34, 77–84 (2013)CrossRef Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Busienss Logist. 34, 77–84 (2013)CrossRef
23.
go back to reference Zhong, R.Y., Newman, S.T., Huang, G.Q., Lan, S.: Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput. Ind. Eng. 101, 572–591 (2016)CrossRef Zhong, R.Y., Newman, S.T., Huang, G.Q., Lan, S.: Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput. Ind. Eng. 101, 572–591 (2016)CrossRef
24.
go back to reference Samuels, K.: Practitioners understanding of big data and its applications in supply chain management. Electron. Libr. 35, 616–617 (2017)CrossRef Samuels, K.: Practitioners understanding of big data and its applications in supply chain management. Electron. Libr. 35, 616–617 (2017)CrossRef
25.
go back to reference Lamba, K., Singh, S.P.: Big data in operations and supply chain management: current trends and future perspectives. Prod. Plan. Control. 28, 877–890 (2017)CrossRef Lamba, K., Singh, S.P.: Big data in operations and supply chain management: current trends and future perspectives. Prod. Plan. Control. 28, 877–890 (2017)CrossRef
26.
go back to reference Brinch, M., Stentoft, J.: Big data and its applications in supply chain management: findings from a Delphi Study. 1351–1360 (2017) Brinch, M., Stentoft, J.: Big data and its applications in supply chain management: findings from a Delphi Study. 1351–1360 (2017)
27.
go back to reference Schoenherr, T., Speier-Pero, C.: Data science, predictive analytics, and big data in supply chain management: current state and future potential. J. Bus. Logist. 36, 120–132 (2015)CrossRef Schoenherr, T., Speier-Pero, C.: Data science, predictive analytics, and big data in supply chain management: current state and future potential. J. Bus. Logist. 36, 120–132 (2015)CrossRef
28.
go back to reference Benabdellah, A.C., Benghabrit, A., Bouhaddou, I., Zemmouri, E.M.: Big Data for Supply Chain Management: Opportunities and Challenges. 7, 20–26 (2016) Benabdellah, A.C., Benghabrit, A., Bouhaddou, I., Zemmouri, E.M.: Big Data for Supply Chain Management: Opportunities and Challenges. 7, 20–26 (2016)
29.
go back to reference Varela, I.R., Tjahjono, B.: Big data analytics in supply chain management: trends and related research. 6th Int. Conf. Oper. Supply Chain Manag. 1, 2013–2014 (2014) Varela, I.R., Tjahjono, B.: Big data analytics in supply chain management: trends and related research. 6th Int. Conf. Oper. Supply Chain Manag. 1, 2013–2014 (2014)
30.
go back to reference Altowaijri, S., Mehmood, R., Williams, J.: A quantitative model of grid systems performance in healthcare organisations. In: ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation. pp. 431–436 (2010) Altowaijri, S., Mehmood, R., Williams, J.: A quantitative model of grid systems performance in healthcare organisations. In: ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation. pp. 431–436 (2010)
31.
go back to reference Srinivasan, U., Arunasalam, B.: Leveraging big data analytics to reduce healthcare costs. IT Prof. 15, 21–28 (2013)CrossRef Srinivasan, U., Arunasalam, B.: Leveraging big data analytics to reduce healthcare costs. IT Prof. 15, 21–28 (2013)CrossRef
32.
go back to reference Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Heal. Inf. Sci. Syst. 2, 3 (2014)CrossRef Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Heal. Inf. Sci. Syst. 2, 3 (2014)CrossRef
34.
go back to reference Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33, 1123–1131 (2014)CrossRef Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33, 1123–1131 (2014)CrossRef
35.
go back to reference Tawalbeh, L.A., Mehmood, R., Benkhlifa, E., Song, H.: Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access. 4, 6171–6180 (2016)CrossRef Tawalbeh, L.A., Mehmood, R., Benkhlifa, E., Song, H.: Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access. 4, 6171–6180 (2016)CrossRef
36.
go back to reference Tawalbeh, L.A., Bakhader, W., Mehmood, R., Song, H.: Cloudlet-Based Mobile Cloud Computing for Healthcare Applications. In: 2016 IEEE Global Communications Conference (GLOBECOM). pp. 1–6. IEEE (2016) Tawalbeh, L.A., Bakhader, W., Mehmood, R., Song, H.: Cloudlet-Based Mobile Cloud Computing for Healthcare Applications. In: 2016 IEEE Global Communications Conference (GLOBECOM). pp. 1–6. IEEE (2016)
37.
go back to reference Mehmood, R., Graham, G.: Big data logistics: a health-care transport capacity sharing model. Procedia Comput. Sci. 64, 1107–1114 (2015)CrossRef Mehmood, R., Graham, G.: Big data logistics: a health-care transport capacity sharing model. Procedia Comput. Sci. 64, 1107–1114 (2015)CrossRef
38.
go back to reference Al Shehri, W., Mehmood, R., Alayyaf, H.: A smart pain management system using big data computing. In: Mehmood R., Bhaduri B., Katib I., Chlamtac I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. pp. 232–246 (2018) Al Shehri, W., Mehmood, R., Alayyaf, H.: A smart pain management system using big data computing. In: Mehmood R., Bhaduri B., Katib I., Chlamtac I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. pp. 232–246 (2018)
39.
go back to reference Alamoudi, E., Mehmood, R., Albeshri, A., Gojobori, T.: DNA profiling methods and tools: A review. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. pp. 216–231. Springer, Cham (2018) Alamoudi, E., Mehmood, R., Albeshri, A., Gojobori, T.: DNA profiling methods and tools: A review. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. pp. 216–231. Springer, Cham (2018)
40.
go back to reference Chae, B.: Insights from hashtag #supplychain and twitter analytics: considering twitter and twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247–259 (2015)CrossRef Chae, B.: Insights from hashtag #supplychain and twitter analytics: considering twitter and twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247–259 (2015)CrossRef
41.
go back to reference Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: Tracking flu infections on Twitter. Proc. NAACL-HLT 2013. 789–795 (2013) Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: Tracking flu infections on Twitter. Proc. NAACL-HLT 2013. 789–795 (2013)
42.
go back to reference Aramaki, E.: Twitter catches the flu: detecting influenza epidemics using twitter the University of Tokyo the University of Tokyo National Institute of. Comput. Linguist. 2011, 1568–1576 (2011) Aramaki, E.: Twitter catches the flu: detecting influenza epidemics using twitter the University of Tokyo the University of Tokyo National Institute of. Comput. Linguist. 2011, 1568–1576 (2011)
43.
go back to reference Achrekar, H., Gandhe, A., Lazarus, R., Yu, S., Liu, B.: Twitter improves seasonal influenza prediction. Proceding Heal. Informatics. 61–70 (2012) Achrekar, H., Gandhe, A., Lazarus, R., Yu, S., Liu, B.: Twitter improves seasonal influenza prediction. Proceding Heal. Informatics. 61–70 (2012)
44.
go back to reference Broniatowski, D.A., Paul, M.J., Dredze, M.: National and local influenza surveillance through twitter: an analysis of the 2012-2013 influenza epidemic. PLoS One. 8, e83672 (2013)CrossRef Broniatowski, D.A., Paul, M.J., Dredze, M.: National and local influenza surveillance through twitter: an analysis of the 2012-2013 influenza epidemic. PLoS One. 8, e83672 (2013)CrossRef
45.
go back to reference Parker, J., Wei, Y., Yates, A., Frieder, O., Goharian, N.: A framework for detecting public health trends with Twitter. Proc. 2013 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min. - ASONAM ’13. 556–563 (2013) Parker, J., Wei, Y., Yates, A., Frieder, O., Goharian, N.: A framework for detecting public health trends with Twitter. Proc. 2013 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min. - ASONAM ’13. 556–563 (2013)
46.
go back to reference Dobrzykowski, D., Saboori Deilami, V., Hong, P., Kim, S.C.: A structured analysis of operations and supply chain management research in healthcare (1982-2011). Int. J. Prod. Econ. 147, 514–530 (2014)CrossRef Dobrzykowski, D., Saboori Deilami, V., Hong, P., Kim, S.C.: A structured analysis of operations and supply chain management research in healthcare (1982-2011). Int. J. Prod. Econ. 147, 514–530 (2014)CrossRef
47.
go back to reference Xu, S., Tan, K.H.: Data-driven inventory management in the healthcare supply chain. (2016) Xu, S., Tan, K.H.: Data-driven inventory management in the healthcare supply chain. (2016)
48.
go back to reference Bughin, J., Chui, M., Manyika, J.: Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Q. 75–86 (2010) Bughin, J., Chui, M., Manyika, J.: Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Q. 75–86 (2010)
49.
go back to reference Tan, K.H., Zhan, Y.Z., Ji, G., Ye, F., Chang, C.: Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 165, 223–233 (2015)CrossRef Tan, K.H., Zhan, Y.Z., Ji, G., Ye, F., Chang, C.: Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 165, 223–233 (2015)CrossRef
Metadata
Title
The Role of Big Data and Twitter Data Analytics in Healthcare Supply Chain Management
Authors
Shoayee Alotaibi
Rashid Mehmood
Iyad Katib
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-13705-2_11