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Big Data and the Precision Medicine Revolution

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  • Wallace J. Hopp
  • Jun Li
  • Guihua Wang

Abstract

The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high‐dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well‐suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.

Suggested Citation

  • Wallace J. Hopp & Jun Li & Guihua Wang, 2018. "Big Data and the Precision Medicine Revolution," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1647-1664, September.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:9:p:1647-1664
    DOI: 10.1111/poms.12891
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    Cited by:

    1. Rubbio, Iacopo & Bruccoleri, Manfredi, 2023. "Unfolding the relationship between digital health and patient safety: The roles of absorptive capacity and healthcare resilience," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.
    3. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    4. Ben-Jebara, Marouen & Mishra, Saurabh & Modi, Sachin B. & Mahar, Stephen, 2023. "Product personalization focus in the pharmaceutical industry and shareholder wealth: The roles of marketing capability and financial leverage," Journal of Business Research, Elsevier, vol. 159(C).
    5. Minmin Zhang & Guihua Wang & Jun Li & Wallace J. Hopp & David D. Lee, 2023. "Pausing transplants in the face of a global pandemic: Patient survival implications," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1380-1396, May.
    6. Sriram Somanchi & Idris Adjerid & Ralph Gross, 2022. "To Predict or Not to Predict: The Case of the Emergency Department," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 799-818, February.
    7. Sunil Mithas & Zhi‐Long Chen & Terence J.V. Saldanha & Alysson De Oliveira Silveira, 2022. "How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4475-4487, December.
    8. Yinchu Zhu & Ilya O. Ryzhov, 2022. "Optimal data-driven hiring with equity for underrepresented groups," Papers 2206.09300, arXiv.org.
    9. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    10. Tortorella, Guilherme Luz & Fogliatto, Flávio Sanson & Espôsto, Kleber Francisco & Vergara, Alejandro Mac Cawley & Vassolo, Roberto & Mendoza, Diego Tlapa & Narayanamurthy, Gopalakrishnan, 2020. "Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    11. Katherine Bobroske & Michael Freeman & Lawrence Huan & Anita Cattrell & Stefan Scholtes, 2022. "Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation," Management Science, INFORMS, vol. 68(3), pages 2003-2015, March.
    12. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    13. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.

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