COVID-19 pandemic, the associated economic lockdown and the norms of social distancing have disrupted the business world. Most managers have struggled to make sense of the chaos and complexity around. Health insurance industry mangers are at the forefront of this challenge as new products and services covering COVID-19 had to be launched fast. This was both a market as well as the societal requirement. In the COVID-19 world, in different countries like United States of America (USA), United Kingdom (UK), Germany and India, attempts are being made to develop mobile applications for tracking COVID-19 patients. Emerging technologies have been altering the business landscape in most industries. The health insurance industry has also been witnessing the effects of technologies such as wearables technology, big data analytics, cloud technologies, blockchain, machine learning and such others. The advent of these technologies is fundamentally changing the health insurance industry. Given the realities of the COVID-19 world, the health insurance industry is poised at a crossroad of evolution where the industry would become data-intensive and data-driven. Health insurance firms have to enter into interfirm collaboration with wearable technology firms in the conversation on tracking social distancing from COVID-19 positive and potential cases. Health insurance firms might develop a service mechanism which could while maintaining the anonymity of COVID-19 positive or potential cases, ensure that customers who are using the wearable technology products and following social distancing norms are provided favourable premium for COVID-19 related health insurance products in case they were infected. This would be a novel addition to COVID-19 related products of health insurance firms. Deliberating on these aspects in this article, the authors propose a fundamental shift in the strategic orientation of health insurance firms.
AbbasA., BilalK., ZhangL., & KhanS. U. (2015). A cloud based health insurance plan recommendation system: A user centered approach. Future Generation Computer Systems, 43, 99–109.
2.
AbouelmehdiK., Beni-HssaneA., KhaloufiH., & SaadiM. (2017). Big data security and privacy in healthcare: A review. Procedia Computer Science, 113, 73–80.
3.
AsijaR., & NallusamyR. (2019). Healthcare SaaS based on a data model with built-in security and privacy. In Cloud security: Concepts, methodologies, tools, and applications (pp. 744–759). IGI Global.
4.
BaickerK., CongdonW. J., & MullainathanS. (2012). Health insurance coverage and take-up: Lessons from behavioral economics. The Milbank Quarterly, 90(1), 107–134.
5.
BermanP. (1995). Health sector reform: Making health development sustainable. Health policy, 32(1–3), 13–28.
6.
BhattG. D. (2000). A resource-based perspective of developing organizational capabilities for business transformation. Knowledge and Process Management, 7(2), 119–129.
7.
BinkleyP. F. (2003). Predicting the potential of wearable technology. IEEE Engineering in Medicine and Biology Magazine, 22(3), 23–27.
8.
BologaA. R., BologaR., & FloreaA. (2013). Big data and specific analysis methods for insurance fraud detection. Database Systems Journal, 4(4), 30–39.
9.
ButtS., & PhillipsJ. G. (2008). Personality and self reported mobile phone use. Computers in Human Behavior, 24(2), 346–360.
10.
CarterR. L. (1972). Economics and insurance: An introduction to the economic aspects of insurance. PH Press Limited.
11.
ChuaT. H. H., & ChangL. (2016). Follow me and like my beautiful selfies: Singapore teenage girls’ engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, 55, 190–197.
12.
ErdmierC., HatcherJ., & LeeM. (2016). Wearable device implications in the healthcare industry. Journal of Medical Engineering & Technology, 40(4), 141–148.
13.
FeldsteinP. J., & WickizerT. M. (1995). Analysis of private health insurance premium growth rates: 1985–1992. Medical Care, 33(10), 1035–1050.
14.
FriedmanB. (1974). Risk aversion and the consumer choice of health insurance option. The Review of Economics and Statistics, 56(2), 209–214.
15.
GaoY., LiH., & LuoY. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704–1723.
16.
GuptaL. P. (2014). Health insurance for rixch and poor in India, Bhopal. The author.
17.
HamidS. A., RobertsJ., & MosleyP. (2011). Evaluating the health effects of micro health insurance placement: Evidence from Bangladesh. World Development, 39(3), 399–411.
18.
IyengarA., KunduA., & PallisG. (2018). Healthcare informatics and privacy. IEEE Internet Computing, 22(2), 29–31.
19.
JakicicJ. M., DavisK. K., RogersR. J., KingW. C., MarcusM. D., HelselD., RickmanA.D., WahedA. S., & BelleS. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316(11), 1161–1171.
20.
KadamudimathaB. (2016). Digital wallet: The next way of growth. International Journal of Commerce and Management Research, 2(12), 22–24.
21.
KifmannM. (2005). Health insurance in a democracy: Why is it public and why are premiums income related. Public Choice, 124(3–4), 283–308.
22.
KlepperS. (1997). Industry life cycles. Industrial and Corporate Change, 6(1), 145–182.
23.
KoseI., GokturkM., & KilicK. (2015). An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing, 36, 283–299.
24.
KraatzM. S., & ZajacE. J. (2001). How organizational resources affect strategic change and performance in turbulent environments: Theory and evidence. Organization Science, 12(5), 632–657.
25.
KruseC. S., GoswamyR., RavalY. J., & MarawiS. (2016). Challenges and opportunities of big data in health care: A systematic review. JMIR Medical Informatics, 4(4), e38.
26.
KumarK. M., TejasreeS., & SwarnalathaS. (2016, January). Effective implementation of data segregation & extraction using big data in E-health insurance as a service. In 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1–5). IEEE.
27.
LakdawallaD., & SoodN. (2013). Health insurance as a two-part pricing contract. Journal of Public Economics, 102, 1–12.
28.
LiY. C., YenJ. C., & HsuM. H. (2015). Embracing the era of wearable devices. Journal of the Formosan Medical Association, 114(11), 1029–1030.
29.
McFallL. (2015). Is digital disruption the end of health insurance? Some thoughts on the devising of risk. Economic Sociology: The European Electronic Newsletter, 17(1), 32–44.
30.
McGahanA. M. (2000). How industries evolve. Business Strategy Review, 11(3), 1–16.
31.
McGahanA. M. (2004). How industries change. Harvard Business Review, 82(10), 86–94.
32.
MeingastM., RoostaT., & SastryS. (2006, August). Security and privacy issues with health care information technology. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5453–5458). IEEE.
33.
MettlerM. (2016, September) Blockchain technology in healthcare: The revolution starts here. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1–3). IEEE.
34.
MuellerE., SandovalJ. S., MudigondaS., & ElliottM. (2019). A cluster-based machine learning ensemble approach for geospatial data: Estimation of health insurance status in Missouri. ISPRS International Journal of Geo-Information, 8(1), 13.
35.
NayakB., & BhattacharyyaS. S. (2019). Integrating digital wisdom and human capital. The Journal for Quality and Participation, 41(4), 20–23.
36.
NayakB., BhattacharyyaS. S., & KrishnamoorthyB. (2019a). Integrating wearable technology products and big data analytics in business strategy: A study of health insurance firms. Journal of Systems and Information Technology, 21(2), 255–275.
37.
NayakB., BhattacharyyaS. S., & KrishnamoorthyB. (2019b). Democratizing health insurance services; accelerating social inclusion through technology policy of health insurance firms. Business Strategy & Development, 2(3), 242–252.
38.
NayakB., BhattacharyyaS. S., & KrishnamoorthyB. (2019c). Application of digital technologies in health insurance for social good of bottom of pyramid customers in India. International Journal of Sociology and Social Policy, 39(9/10), 752–772.
39.
NymanJ. A. (1999). The value of health insurance: the access motive. Journal of Health Economics, 18(2), 141–152.
40.
OrloskyJ., EzenwoyeO., YatesH., & BesenyiG. (2019, April). A look at the security and privacy of Fitbit as a health activity tracker. In Proceedings of the 2019 ACM Southeast Conference (pp. 241–244). ACM.
41.
ParkS., & JayaramanS. (2003). Enhancing the quality of life through wearable technology. IEEE Engineering in Medicine and Biology Magazine, 22(3), 41–48.
42.
PatilH. K., & SeshadriR. (2014, June). Big data security and privacy issues in healthcare. In 2014 IEEE International Congress on Big Data (pp. 762–765). IEEE. doi:10.1109/BigData.Congress.2014.11.
43.
PatukaleK. (2016). Mediclaim and health insurance (pp. 222). Prabhat Prakashan.
44.
RandallD., GoelP., & AbujamraR. (2017). Blockchain applications and use cases in health information technology. Journal Health Medical Information, 8(276), 2.
45.
SheerV. C. (2011). Teenagers’ use of MSN features, discussion topics, and online friendship development: The impact of media richness and communication control. Communication Quarterly, 59(1), 82–103.
46.
SiggelkowN., & TerwieschC. (2019). Connected strategy: Building continuous customer relationships for competitive advantage (p. 288). Harvard Business Review Press.
47.
SouthgateD. (2017). The emergence of Generation Z and its impact in advertising: Long-term implications for media planning and creative development. Journal of Advertising Research, 57(2), 227–235.
48.
SudheerC. S. (2018). Health insurance handbook. Suvision Holdings Private Limited.
49.
WatersH. R., & HusseyP. (2004). Pricing health services for purchasers—A review of methods and experiences. Health Policy, 70(2), 175–184.
50.
WernerW. J. (2009). Micro-insurance in Bangladesh: Risk protection for the poor?. Journal of Health, Population, and Nutrition, 27(4), 563.
51.
WrightR., & KeithL. (2014). Wearable technology: If the tech fits, wear it. Journal of Electronic Resources in Medical Libraries, 11(4), 204–216.
52.
WulanV. R. (2017). Financial technology (fintech) a new transaction in future. Journal of Electrical Engineering and Computer Sciences, 2(1), 177–182.
53.
YiuD., BrutonG. D., & LuY. (2005). Understanding business group performance in an emerging economy: Acquiring resources and capabilities in order to prosper. Journal of Management Studies, 42(1), 183–206.
54.
ZhangM., LuoM., NieR., & ZhangY. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, 97–109.
