Advertisement

Emerging technologies and analytics for a new era of value-centered marketing in healthcare

  • Ritu AgarwalEmail author
  • Michelle Dugas
  • Guodong (Gordon) Gao
  • P. K. Kannan
Conceptual/Theoretical Paper
  • 90 Downloads

Abstract

The healthcare system is undergoing a fundamental transformation fueled by regulatory shifts that reward value over volume, coupled with unprecedented advances in technological capabilities. To address the processes involved in defining, measuring, and delivering value in this shifting landscape, we develop the framework of value-centered marketing (VCM). Building on existing approaches in both healthcare and marketing, we propose three core dimensions of value in VCM: preferences, precision, and process. We also provide an overview of a trifecta of technological advances including the digital capture of health data, improvements in methodologies for data analysis, and exponential increases in processing power and storage capacity, which have created a perfect storm of opportunity for VCM. We describe how these emerging technologies can be combined with insights from marketing science to develop successful VCM strategy and highlight critical research questions. Finally, we discuss potential unintended consequences in the use of tech- and analytics-enabled healthcare.

Keywords

Health analytics Health technologies Healthcare marketing Value-centered marketing 

Notes

Supplementary material

11747_2019_692_MOESM1_ESM.docx (24 kb)
ESM 1 (DOCX 23 kb)

References

  1. Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA Dermatology, 154(11), 1247–1248.  https://doi.org/10.1001/jamadermatol.2018.2348.CrossRefGoogle Scholar
  2. Agarwal, R., Gao, G. G., DesRoches, C., & Jha, A. K. (2010). Research commentary — The digital transformation of healthcare: current status and the road ahead. Information Systems Research, 21(4), 796–809.  https://doi.org/10.1287/isre.1100.0327.CrossRefGoogle Scholar
  3. Akerlof, G. A. (1970). The market for “lemons”: quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.CrossRefGoogle Scholar
  4. Allen, M. (2018a). Health insurers are vacuuming up details about you — And it could raise your rates. Retrieved from ProPublica website: https://www.propublica.org/article/health-insurers-are-vacuuming-up-details-about-you-and-it-could-raise-your-rates. Accessed 13 June 2019.
  5. Allen, M. (2018b). You snooze, you lose: Insurers make the old adage literally true. Retrieved from ProPublica website: https://www.propublica.org/article/you-snooze-you-lose-insurers-make-the-old-adage-literally-true. Accessed 13 June 2019.
  6. American Medical Association. (2016). Digital health study: Physicians’ motivations and requirements for adopting digital clinical tools. Retrieved from https://www.ama-assn.org/practice-management/digital. Accessed 13 June 2019.
  7. Ancker, J. S., Barrón, Y., Rockoff, M. L., Hauser, D., Pichardo, M., Szerencsy, A., & Calman, N. (2011). Use of an electronic patient portal among disadvantaged populations. Journal of General Internal Medicine, 26(10), 1117–1123.  https://doi.org/10.1007/s11606-011-1749-y.CrossRefGoogle Scholar
  8. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17.CrossRefGoogle Scholar
  9. Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469–490.  https://doi.org/10.1287/isre.1100.0335.CrossRefGoogle Scholar
  10. Anderson-Lewis, C., Darville, G., Mercado, R. E., Howell, S., & Di Maggio, S. (2018). mHealth technology use and implications in historically underserved and minority populations in the United States: Systematic literature review. JMIR MHealth and UHealth, 6(6), e128.  https://doi.org/10.2196/mhealth.8383.CrossRefGoogle Scholar
  11. Antheunis, M. L., Tates, K., & Nieboer, T. E. (2013). Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Education and Counseling, 92(3), 426–431.  https://doi.org/10.1016/j.pec.2013.06.020.CrossRefGoogle Scholar
  12. Asgari, E., & Mofrad, M. R. K. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics. PLOS ONE, 10(11), e0141287.  https://doi.org/10.1371/journal.pone.0141287.CrossRefGoogle Scholar
  13. Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522.CrossRefGoogle Scholar
  14. Ashwood, J. S., Mehrotra, A., Cowling, D., & Uscher-Pines, L. (2017). Direct-to-consumer telehealth may increase access to care but does not decrease spending. Health Affairs, 36(3), 485–491.  https://doi.org/10.1377/hlthaff.2016.1130.CrossRefGoogle Scholar
  15. Atwal, G., & Williams, A. (2009). Luxury brand marketing -- the experience is everything! Journal of Brand Management, 16(5/6), 338–346.  https://doi.org/10.1057/bm.2008.48.CrossRefGoogle Scholar
  16. Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206.  https://doi.org/10.1177/0092070399272005.CrossRefGoogle Scholar
  17. Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131.  https://doi.org/10.1377/hlthaff.2014.0041.CrossRefGoogle Scholar
  18. Bhatt, J., & Bathija, P. (2018). Ensuring access to quality health care in vulnerable communities. Academic Medicine, 93(9), 1271–1275.  https://doi.org/10.1097/ACM.0000000000002254.CrossRefGoogle Scholar
  19. Bosworth, B. (2018). Increasing disparities in mortality by socioeconomic status. Annual Review of Public Health, 39(1), 237–251.  https://doi.org/10.1146/annurev-publhealth-040617-014615.CrossRefGoogle Scholar
  20. Broderick, A. B., & Haque, F. H. (2015). Mobile health and patient engagement in the safety net: A survey of community health centers and clinics. The Commonwealth Foundation.Google Scholar
  21. Brown, E. J., Kangovi, S., Sha, C., Johnson, S., Chanton, C., Carter, T., & Grande, D. T. (2015). Exploring the patient and staff experience with the process of primary care. The Annals of Family Medicine, 13(4), 347–353.  https://doi.org/10.1370/afm.1808.CrossRefGoogle Scholar
  22. Burgess, E., Hassmén, P., & Pumpa, K. L. (2017). Determinants of adherence to lifestyle intervention in adults with obesity: a systematic review. Clinical Obesity, 7(3), 123–135.  https://doi.org/10.1111/cob.12183.CrossRefGoogle Scholar
  23. Burwell, S. M. (2015). Setting value-based payment goals — HHS efforts to improve U.S. health care. The New England Journal of Medicine, 372(10), 897–899.  https://doi.org/10.1056/NEJMp1500445.CrossRefGoogle Scholar
  24. Carey, D. J., Fetterolf, S. N., Davis, F. D., Faucett, W. A., Kirchner, H. L., Mirshahi, U., … Ledbetter, D. H. (2016). The Geisinger MyCode community health initiative: An electronic health record–linked biobank for precision medicine research. Genetics in Medicine, 18(9), 906–913.  https://doi.org/10.1038/gim.2015.187.CrossRefGoogle Scholar
  25. Centers for Medicare and Medicaid Services. (2017a). NHE Fact Sheet - Centers for Medicare & Medicaid Services. Retrieved from https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html. Accessed 19 June 2019.
  26. Centers for Medicare and Medicaid Services (2017b). Hospital HCAHPS. Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-instruments/hospitalqualityinits/hospitalHCAHPS.html. 26 Jan 2019.
  27. Chai, P. R., Castillo-Mancilla, J., Buffkin, E., Darling, C., Rosen, R. K., Horvath, K. J., et al. (2015). Utilizing an ingestible biosensor to assess real-time medication adherence. Journal of Medical Toxicology, 11(4), 439–444.  https://doi.org/10.1007/s13181-015-0494-8.CrossRefGoogle Scholar
  28. Chiu, C.-C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., … Zhang, X. (2017). Speech recognition for medical conversations. ArXiv:1711.07274 [Cs, Eess, Stat]. Retrieved from http://arxiv.org/abs/1711.07274. Accessed 10 Sept 2019.
  29. Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., … Sun, J. (2016). Multi-layer representation learning for medical concepts. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ‘16, 1495–1504.  https://doi.org/10.1145/2939672.2939823.
  30. Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A. (2017). Using mobile sensing to test clinical models of depression, social anxiety, state affect, and social isolation among college students. Journal of Medical Internet Research, 19(3).  https://doi.org/10.2196/jmir.6820.CrossRefGoogle Scholar
  31. Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87.  https://doi.org/10.1007/s11747-015-0441-x.CrossRefGoogle Scholar
  32. Cohen, R. A., Zammitti, E. P., & Martinez, M. E. (2018). Health insurance coverage: Early release of estimates from the National Health Interview Survey, 2017. Rockville, MD: National Center for Health Statistics.Google Scholar
  33. Crowley, K., Piper, J., Cummins, J., Gao, G., Burn, L., Igumbor, K., … Veronese, F. (2018). Ethnographic study for HIV prevention reveals a typology consisting of five distinct types among South African adolescents and young women. Presented at AIDS 2018, Amsterdam, July 23–27, 2018.Google Scholar
  34. Cuckler, G. A., Sisko, A. M., Poisal, J. A., Keehan, S. P., Smith, S. D., Madison, A. J., … Hardesty, J. C. (2018). National health expenditure projections, 2017–26: despite uncertainty, fundamentals primarily drive spending growth. Health Affairs, 37(3), 482–492.  https://doi.org/10.1377/hlthaff.2017.1655.CrossRefGoogle Scholar
  35. De Lew, N., & Greenberg, G. (1992). A layman’s guide to the U.S. health care system. Health Care Financing Review, 14(1), 151–169.Google Scholar
  36. Donevant, S. B., Estrada, R. D., Culley, J. M., Habing, B., & Adams, S. A. (2018). Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature. Journal of the American Medical Informatics Association, 25(10), 1407–1418.  https://doi.org/10.1093/jamia/ocy104.CrossRefGoogle Scholar
  37. Downing, N. S., Shah, N. D., Neiman, J. H., Aminawung, J. A., Krumholz, H. M., & Ross, J. S. (2016). Participation of the elderly, women, and minorities in pivotal trials supporting 2011–2013 U.S. Food and Drug Administration approvals. Trials, 17(1), 199.  https://doi.org/10.1186/s13063-016-1322-4.CrossRefGoogle Scholar
  38. Dugas, M., Crowley, K., Gao, G. G., Xu, T., Agarwal, R., Kruglanski, A. W., & Steinle, N. (2018). Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans. PLOS ONE, 13(3), e0192807.  https://doi.org/10.1371/journal.pone.0192807.CrossRefGoogle Scholar
  39. Elliott, M. N., Cohea, C. W., Lehrman, W. G., Goldstein, E. H., Cleary, P. D., Giordano, L. A., … Zaslavsky, A. M. (2015). Accelerating improvement and narrowing gaps: Trends in patients’ experiences with hospital care reflected in HCAHPS public reporting. Health Services Research, 50(6), 1850–1867.  https://doi.org/10.1111/1475-6773.12305.CrossRefGoogle Scholar
  40. Elwyn, G., Frosch, D., Thomson, R., Joseph-Williams, N., Lloyd, A., Kinnersley, P., … Barry, M. (2012). Shared decision making: a model for clinical practice. Journal of General Internal Medicine, 27(10), 1361–1367.  https://doi.org/10.1007/s11606-012-2077-6.CrossRefGoogle Scholar
  41. Feero, W. G., Wicklund, C. A., & Veenstra, D. (2018). Precision medicine, genome sequencing, and improved population health. JAMA, 319(19), 1979–1980.  https://doi.org/10.1001/jama.2018.2925.CrossRefGoogle Scholar
  42. Frank, L., Basch, E., & Selby, J. V. (2014). The PCORI perspective on patient-centered outcomes research. JAMA, 312(15), 1513–1514.  https://doi.org/10.1001/jama.2014.11100.CrossRefGoogle Scholar
  43. Gao, G., McCullough, J. S., Agarwal, R., & Jha, A. K. (2012). A changing landscape of physician quality reporting: analysis of patients’ online ratings of their physicians over a 5-year period. Journal of Medical Internet Research, 14(1), e38.  https://doi.org/10.2196/jmir.2003.CrossRefGoogle Scholar
  44. Gaziano, J. M., Concato, J., Brophy, M., Fiore, L., Pyarajan, S., Breeling, J., … O’Leary, T. J. (2016). Million Veteran Program: A mega-biobank to study genetic influences on health and disease. Journal of Clinical Epidemiology, 70, 214–223.  https://doi.org/10.1016/j.jclinepi.2015.09.016.CrossRefGoogle Scholar
  45. Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544–1547.  https://doi.org/10.1001/jamainternmed.2018.3763.CrossRefGoogle Scholar
  46. Goh, J. M., Gao, G., & Agarwal, R. (2016). The creation of social value: can an online health community reduce rural–urban health disparities? MIS Quarterly, 40(1), 247–263.CrossRefGoogle Scholar
  47. Goldzweig, C. L., Orshansky, G., Paige, N. M., Towfigh, A. A., Haggstrom, D. A., Miake-Lye, I., … Shekelle, P. G. (2013). Electronic patient portals: Evidence on health outcomes, satisfaction, efficiency, and attitudes: A systematic review. Annals of Internal Medicine, 159(10), 677.  https://doi.org/10.7326/0003-4819-159-10-201311190-00006.CrossRefGoogle Scholar
  48. Gostin, L. O., Halabi, S. F., & Wilson, K. (2018). Health data and privacy in the digital era. JAMA, 320(3), 233–234.  https://doi.org/10.1001/jama.2018.8374.CrossRefGoogle Scholar
  49. Greenwood, B. N., & Agarwal, R. (2016). Matching platforms and HIV incidence: An empirical investigation of race, gender, and socioeconomic status. Management Science, 62(8), 2281–2303.  https://doi.org/10.1287/mnsc.2015.2232.CrossRefGoogle Scholar
  50. Grier, S., & Bryant, C. A. (2005). Social marketing in public health. Annual Review of Public Health, 26(1), 319–339.  https://doi.org/10.1146/annurev.publhealth.26.021304.144610.CrossRefGoogle Scholar
  51. Gustafson, D. H., McTavish, F. M., Chih, M.-Y., Atwood, A. K., Johnson, R. A., Boyle, M. G., … Shah, D. (2014). A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry, 71(5), 566–572.  https://doi.org/10.1001/jamapsychiatry.2013.4642.CrossRefGoogle Scholar
  52. Hadland, S. E., Cerdá, M., Li, Y., Krieger, M. S., & Marshall, B. D. L. (2018). Association of pharmaceutical industry marketing of opioid products to physicians with subsequent opioid prescribing. JAMA Internal Medicine, 178(6), 861–863.  https://doi.org/10.1001/jamainternmed.2018.1999.CrossRefGoogle Scholar
  53. Hadland, S. E., Rivera-Aguirre, A., Marshall, B. D. L., & Cerdá, M. (2019). Association of pharmaceutical industry marketing of opioid products with mortality from opioid-related overdoses. JAMA Network Open, 2(1), e186007–e186007.  https://doi.org/10.1001/jamanetworkopen.2018.6007.CrossRefGoogle Scholar
  54. Halldorsdottir, T., & Binder, E. B. (2017). Gene × environment interactions: From molecular mechanisms to behavior. Annual Review of Psychology, 68(1), 215–241.  https://doi.org/10.1146/annurev-psych-010416-044053.CrossRefGoogle Scholar
  55. Halpern, J. (2003). What is clinical empathy? Journal of General Internal Medicine, 18(8), 670–674.  https://doi.org/10.1046/j.1525-1497.2003.21017.x.CrossRefGoogle Scholar
  56. Hanauer, D. A., Zheng, K., Singer, D. C., Gebremariam, A., & Davis, M. M. (2014). Public awareness, perception, and use of online physician rating sites. JAMA, 311(7), 734–735.  https://doi.org/10.1001/jama.2013.283194.CrossRefGoogle Scholar
  57. HealthIT.gov (2018). Hospitals participating in the CMS EHR incentive programs. Retrieved from https://dashboard.healthit.gov/quickstats/pages/FIG-Hospitals-EHR-Incentive-Programs.php. Accessed 18 Sept 2018.
  58. Huang, M.-H., & Rust, R. T. (2017). Technology-driven service strategy. Journal of the Academy of Marketing Science, 45(6), 906–924.  https://doi.org/10.1007/s11747-017-0545-6.CrossRefGoogle Scholar
  59. Intel Security-McAfee. (2014). Net losses: Estimating the global cost of cybercrime. Center for Strategic and International Studies. Retrieved from https://www.csis.org/analysis/net-losses-estimating-global-cost-cybercrime. Accessed 20 June 2019.
  60. IQVIA Institute for Human Data Science. (2017). The growing value of digital health: Evidence and impact on human health and the healthcare system. Retrieved from https://www.iqvia.com/institute/reports/the-growing-value-of-digital-health. Accessed 18 Sept 2018.
  61. Janakiraman, R., Lim, J. H., & Rishika, R. (2018). The effect of a data breach announcement on customer behavior: evidence from a multichannel retailer. Journal of Marketing, 82(2), 85–105.  https://doi.org/10.1509/jm.16.0124.CrossRefGoogle Scholar
  62. Jiang, P., Sellers, W. R., & Liu, X. S. (2018). Big data approaches for modeling response and resistance to cancer drugs. Annual Review of Biomedical Data Science, 1(1), 1–27.  https://doi.org/10.1146/annurev-biodatasci-080917-013350.CrossRefGoogle Scholar
  63. Kannan, P. K., & Li, H. (2017). Digital marketing: a framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22–45.  https://doi.org/10.1016/j.ijresmar.2016.11.006.CrossRefGoogle Scholar
  64. Knox, G., & van Oest, R. (2014). Customer complaints and recovery effectiveness: a customer base approach. Journal of Marketing, 78(5), 42–57.  https://doi.org/10.1509/jm.12.0317.CrossRefGoogle Scholar
  65. Koh, H. K. (2016). Improving health and health care in the United States: toward a state of complete well-being. JAMA, 316(16), 1679–1681.  https://doi.org/10.1001/jama.2016.12414.CrossRefGoogle Scholar
  66. Kullgren, J. T., Duey, K. A., & Werner, R. M. (2013). A census of state health care price transparency websites. JAMA, 309(23), 2437–2438.  https://doi.org/10.1001/jama.2013.6557.CrossRefGoogle Scholar
  67. Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36–68.CrossRefGoogle Scholar
  68. Lagu, T., Metayer, K., Moran, M., Ortiz, L., Priya, A., Goff, S. L., & Lindenauer, P. K. (2017). Website characteristics and physician reviews on commercial physician-rating websites. JAMA, 317(7), 766–768.  https://doi.org/10.1001/jama.2016.18553.CrossRefGoogle Scholar
  69. Langmead, B., & Nellore, A. (2018). Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics, 19(4), 208–219.  https://doi.org/10.1038/nrg.2017.113.CrossRefGoogle Scholar
  70. Lee, V. S., Miller, T., Daniels, C., Paine, M., Gresh, B., & Betz, A. L. (2016). Creating the exceptional patient experience in one academic health system. Academic Medicine, 91(3), 338–344.  https://doi.org/10.1097/ACM.0000000000001007.CrossRefGoogle Scholar
  71. Légaré, F., & Witteman, H. O. (2013). Shared decision making: examining key elements and barriers to adoption into routine clinical practice. Health Affairs, 32(2), 276–284.  https://doi.org/10.1377/hlthaff.2012.1078.CrossRefGoogle Scholar
  72. Lewis, K. E., Lu, K. H., Klimczak, A. M., & Mok, S. C. (2018). Recommendations and choices for BRCA mutation carriers at risk for ovarian cancer: a complicated decision. Cancers, 10(2), 57.  https://doi.org/10.3390/cancers10020057.CrossRefGoogle Scholar
  73. Lin, Y.-K., Chen, H., Brown, R. A., Li, S.-H., & Yang, H.-J. (2017). Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach. MIS Quarterly, 41(2), 473–495.CrossRefGoogle Scholar
  74. Loewenstein, G. (2005). Hot-cold empathy gaps in medical decision making. Health Psychology, 24(4), S49–S56.CrossRefGoogle Scholar
  75. Lown, B. A., Rosen, J., & Marttila, J. (2011). An agenda for improving compassionate care: a survey shows about half of patients say such care is missing. Health Affairs, 30(9), 1772–1778.  https://doi.org/10.1377/hlthaff.2011.0539.CrossRefGoogle Scholar
  76. Lynn, J., McKethan, A., & Jha, A. K. (2015). Value-based payments require valuing what matters to patients. JAMA, 314(14), 1445–1446.  https://doi.org/10.1001/jama.2015.8909.CrossRefGoogle Scholar
  77. Malhotra, N. K. (2006). Consumer well-being and quality of life: an assessment and directions for future research. Journal of Macromarketing, 26(1), 77–80.CrossRefGoogle Scholar
  78. Manary, M. P., Boulding, W., Staelin, R., & Glickman, S. W. (2013). The patient experience and health outcomes. New England Journal of Medicine, 368(3), 201–203.  https://doi.org/10.1056/NEJMp1211775.CrossRefGoogle Scholar
  79. Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155.  https://doi.org/10.1007/s11747-016-0495-4.CrossRefGoogle Scholar
  80. Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36–58.  https://doi.org/10.1509/jm.15.0497.CrossRefGoogle Scholar
  81. McClellan, M. B., & Leavitt, M. O. (2016). Competencies and tools to shift payments from volume to value. JAMA, 316(16), 1655–1656.  https://doi.org/10.1001/jama.2016.14205.CrossRefGoogle Scholar
  82. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. ArXiv:1301.3781 [Cs]. Retrieved from http://arxiv.org/abs/1301.3781. Accessed 10 Sept 2019.
  83. Moorman, C., & Matulich, E. (1993). A model of consumers’ preventive health behaviors: the role of health motivation and health ability. Journal of Consumer Research, 20(2), 208–228.  https://doi.org/10.1086/209344.CrossRefGoogle Scholar
  84. Morgan, B. (2015). NOwnership, no problem: Why millennials value experiences over owning things. Retrieved from https://www.forbes.com/sites/blakemorgan/2015/06/01/nownershipnoproblem-nowners-millennials-value-experiences-over-ownership/. Accessed 30 Jan 2019.
  85. Motyka, S., Grewal, D., Aguirre, E., Mahr, D., de Ruyter, K., & Wetzels, M. (2018). The emotional review–reward effect: How do reviews increase impulsivity? Journal of the Academy of Marketing Science, 46(6), 1032–1051.  https://doi.org/10.1007/s11747-018-0585-6.CrossRefGoogle Scholar
  86. Nakata, C., Izberk-Bilgin, E., Sharp, L., Spanjol, J., Cui, A. S., Crawford, S. Y., & Xiao, Y. (2019). Chronic illness medication compliance: A liminal and contextual consumer journey. Journal of the Academy of Marketing Science, 47(2), 192–215.  https://doi.org/10.1007/s11747-018-0618-1.CrossRefGoogle Scholar
  87. National Cancer Institute. (2018). BRCA mutations: Cancer risk & genetic testing. Retrieved from https://www.cancer.gov/about-cancer/causes-prevention/genetics/brca-fact-sheet. Accessed 13 Jan 2019.
  88. National Human Genome Research Institute. (2016). The cost of sequencing a human genome. Retrieved fromhttps://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/. Accessed 30 Jan 2019.
  89. Nitzan, I., & Libai, B. (2011). Social effects on customer retention. Journal of Marketing, 75(6), 24–38.  https://doi.org/10.1509/jm.10.0209.CrossRefGoogle Scholar
  90. Nuckols, T. K. (2017). With the merit-based incentive payment system, pay for performance is now national policy. Annals of Internal Medicine, 166(5), 368.  https://doi.org/10.7326/M16-2947.CrossRefGoogle Scholar
  91. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown.Google Scholar
  92. Oldenburg, B., Taylor, C. B., O’Neil, A., Cocker, F., & Cameron, L. D. (2015). Using new technologies to improve the prevention and management of chronic conditions in populations. Annual Review of Public Health, 36(1), 483–505.  https://doi.org/10.1146/annurev-publhealth-031914-122848.CrossRefGoogle Scholar
  93. Olenski, S. (2018). 3 Reasons why CMOs should embrace experiential marketing. Retrieved from https://www.forbes.com/sites/steveolenski/2018/08/15/3-reasons-why-cmos-should-embrace-experiential-marketing/. Accessed 30 Jan 2019.
  94. Page, L. (2013). The rise and further rise of concierge medicine. BMJ, 347, f6465.  https://doi.org/10.1136/bmj.f6465.CrossRefGoogle Scholar
  95. Patrick, V. M., MacInnis, D. J., & Park, C. W. (2007). Not as happy as I thought I’d be? Affective misforecasting and product evaluations. Journal of Consumer Research, 33(4), 479–489.  https://doi.org/10.1086/510221.CrossRefGoogle Scholar
  96. Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176.  https://doi.org/10.1509/jmkg.2005.69.4.167.CrossRefGoogle Scholar
  97. Pew Research Center. (2014). Public perceptions of privacy and security in the post-Snowden era. Retrieved from http://www.pewinternet.org/2014/11/12/public-privacy-perceptions/. Accessed 22 Jan 2019.
  98. Pew Research Center. (2018a). Lower-income Americans still lag in tech adoption. Retrieved from https://www.pewresearch.org/fact-tank/2019/05/07/digital-divide-persists-even-as-lower-income-americans-make-gains-in-tech-adoption/. Accessed 5 June 2019.
  99. Pew Research Center. (2018b). For 24% of rural Americans, high-speed internet is a major problem. Retrieved from https://www.pewresearch.org/fact-tank/2018/09/10/about-a-quarter-of-rural-americans-say-access-to-high-speed-internet-is-a-major-problem/. Accessed 5 June 2019.
  100. Porter, M. E., & Kaplan, R. S. (2016). How to pay for health care. Harvard Business Review, 94(July–August), 88–98.Google Scholar
  101. Porter, M. E., & Rivkin, J. W. (2000). Industry transformation (Rev. 00/07/10.). Boston: Harvard Business School.Google Scholar
  102. Rauscher, G. H., Khan, J. A., Berbaum, M. L., & Conant, E. F. (2013). Potentially missed detection with screening mammography: does the quality of radiologist’s interpretation vary by patient socioeconomic advantage/disadvantage? Annals of Epidemiology, 23(4), 210–214.  https://doi.org/10.1016/j.annepidem.2013.01.006.CrossRefGoogle Scholar
  103. Rice, T. (2013). The behavioral economics of health and health care. Annual Review of Public Health, 34(1), 431–447.  https://doi.org/10.1146/annurev-publhealth-031912-114353.CrossRefGoogle Scholar
  104. Rich, E., & Miah, A. (2017). Mobile, wearable and ingestible health technologies: towards a critical research agenda. Health Sociology Review, 26(1), 84–97.  https://doi.org/10.1080/14461242.2016.1211486.CrossRefGoogle Scholar
  105. Ruckenstein, M., & Schüll, N. D. (2017). The datafication of health. Annual Review of Anthropology, 46(1), 261–278.  https://doi.org/10.1146/annurev-anthro-102116-041244.CrossRefGoogle Scholar
  106. Schwartz, L. M., & Woloshin, S. (2019). Medical marketing in the United States, 1997–2016. JAMA, 321(1), 80–96.  https://doi.org/10.1001/jama.2018.19320.CrossRefGoogle Scholar
  107. Shukla, A., Gao, G., & Agarwal, R. (2018). How Digital Word-of-Mouth Affects Consumer Decision Making: Evidence from Doctor Appointment Booking (SSRN Scholarly Paper No. ID 2778683).Google Scholar
  108. Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre Díez, I., López-Coronado, M., & Saleem, K. (2015). Mobile-health: a review of current state in 2015. Journal of Biomedical Informatics, 56, 265–272.  https://doi.org/10.1016/j.jbi.2015.06.003.CrossRefGoogle Scholar
  109. Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., … Bates, D. W. (2016). Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Affairs, 35(12), 2310–2318.  https://doi.org/10.1377/hlthaff.2016.0578.CrossRefGoogle Scholar
  110. Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2016). Twitter as a tool for health research: a systematic review. American Journal of Public Health, 107(1), e1–e8.  https://doi.org/10.2105/AJPH.2016.303512.CrossRefGoogle Scholar
  111. Sirgy, M. J., & Lee, D. J. (2006). Macro measures of consumer well-being (CWB): a critical analysis and a research agenda. Journal of Macromarketing, 26(1), 27–44.CrossRefGoogle Scholar
  112. Spanjol, J., Cui, A. S., Nakata, C., Sharp, L. K., Crawford, S. Y., Xiao, Y., & Watson-Manheim, M. B. (2015). Co-production of prolonged, complex, and negative services: an examination of medication adherence in chronically ill individuals. Journal of Service Research, 18(3), 284–302.  https://doi.org/10.1177/1094670515583824.CrossRefGoogle Scholar
  113. Spitzer, J. (2018). 6.1M healthcare data breach victims in 2018: 5 of the biggest breaches so far. Becker’s Healthcare. Retrieved from: https://www.beckershospitalreview.com/cybersecurity/6-1m-healthcare-data-breach-victims-in-2018-5-of-the-biggest-breaches-so-far.html. Accessed 22 Jan 2019.
  114. Stacey, D., Légaré, F., & Lewis, K. B. (2017). Patient decision aids to engage adults in treatment or screening decisions. JAMA, 318(7), 657–658.  https://doi.org/10.1001/jama.2017.10289.CrossRefGoogle Scholar
  115. Statista. (2017). Direct-to-consumer genetic testing market size worldwide 2014–2022. Retrieved from https://www.statista.com/statistics/792022/global-direct-to-consumer-genetic-testing-market-size/. Accessed 17 Dec 2018.
  116. Steenkamer, B. M., Drewes, H. W., Heijink, R., Baan, C. A., & Struijs, J. N. (2016). Defining population health management: a scoping review of the literature. Population Health Management, 20(1), 74–85.  https://doi.org/10.1089/pop.2015.0149.CrossRefGoogle Scholar
  117. Susukida, R., Crum, R. M., Stuart, E. A., Ebnesajjad, C., & Mojtabai, R. (2016). Assessing sample representativeness in randomized controlled trials: application to the National Institute of Drug Abuse Clinical Trials Network. Addiction, 111(7), 1226–1234.  https://doi.org/10.1111/add.13327.CrossRefGoogle Scholar
  118. Tangri, N. (2011). A predictive model for progression of chronic kidney disease to kidney failure. JAMA, 305(15), 1553.  https://doi.org/10.1001/jama.2011.451.CrossRefGoogle Scholar
  119. Thomas, S., Fayter, D., Misso, K., Ogilvie, D., Petticrew, M., Sowden, A., … Worthy, G. (2008). Population tobacco control interventions and their effects on social inequalities in smoking: Systematic review. Tobacco Control, 17(4), 230–237.  https://doi.org/10.1136/tc.2007.023911.CrossRefGoogle Scholar
  120. Tieu, L., Schillinger, D., Sarkar, U., Hoskote, M., Hahn, K. J., Ratanawongsa, N., … Lyles, C. R. (2016). Online patient websites for electronic health record access among vulnerable populations: portals to nowhere? Journal of the American Medical Informatics Association, 24(e1), e47–e54.  https://doi.org/10.1093/jamia/ocw098.
  121. Toubia, O. (2018). Conjoint analysis. In N. Mizik & D. M. Hanssens (Eds.), Handbook of marketing analytics: Methods and applications in marketing management, public policy, and litigation support (pp. 59–75). Northampton, MA: Edward Elgar Publishing.Google Scholar
  122. Turner, S. D. (2016). Digital denied: The impact of systemic racial discrimination on home-internet adoption. FreePress.Google Scholar
  123. Ubel, P. A. (2012). Critical decisions: How you and your doctor can make the right medical choices together. New York: Harper Collins.Google Scholar
  124. Ubel, P. A., Zhang, C. J., Hesson, A., Davis, J. K., Kirby, C., Barnett, J., & Hunter, W. G. (2016). Study of physician and patient communication identifies missed opportunities to help reduce patients’ out-of-pocket spending. Health Affairs, 35(4), 654–661.  https://doi.org/10.1377/hlthaff.2015.1280.CrossRefGoogle Scholar
  125. Volpp, K. G., & Mohta, N. S. (2019). Health Care Has Much to Learn from Consumer-Friendly Industries. NEJM Catalyst Insights Report. Retrieved from https://catalyst.nejm.org/consumerization-health-care-consumer-friendly-industries/. Accessed 10 Jan 2019.
  126. Wang, W., Chen, M., Gao, G., & McCullough, J. S. (2018). Surfing the ocean of digital health data: A deep learning approach to precise readmission prediction. Presented at Conference on Information Systems and Technology (CIST), Phoenix, AZ, November 3–14, 2018.Google Scholar
  127. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.  https://doi.org/10.1509/jm.15.0413.CrossRefGoogle Scholar
  128. World Economic Forum. (2017). Value in healthcare: Laying the foundation for health system transformation (Insight Report REF 220317).Google Scholar

Copyright information

© Academy of Marketing Science 2019

Authors and Affiliations

  • Ritu Agarwal
    • 1
    Email author
  • Michelle Dugas
    • 1
  • Guodong (Gordon) Gao
    • 1
  • P. K. Kannan
    • 2
  1. 1.Center for Health Information and Decision Systems, Department of Decision, Operations, and Information Technologies, Robert H. Smith School of BusinessUniversity of Maryland College ParkUSA
  2. 2.Department of Marketing, Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA

Personalised recommendations