Managed Care 17, 12 (Dec. 2011), 117–124.

17. Jones, S.S., Heaton, P.S., Rudin, R.S., and Schneider, E.C. Unraveling the IT Productivity Paradox: Lessons for Health Care. The New England Journal of Medicine

366, 24 (June 14, 2012), 2243–2245.

18. Jones, S.S., Rudin, R.S., Perry, T., and Shekelle, P.G.

Health information technology: An updated systematic review with a focus on meaningful use. Annals of Internal Medicine 160, 1 (Jan. 2014), 48–54.

19. Lapointe, L. The I T productivity paradox in health: A stakeholder’s perspective. International Journal of Medical Informatics 80, 2 (Feb. 2011), 102–115.

20. Leidner, D. E., Preston, D., and Chen, D. An examination of the antecedents and consequences of organizational IT innovation in hospitals. Journal of Strategic Information Systems 19, 3 (Sept. 2010),

154–170.

21. Menon, N.M., Yaylacicegi, U., and Cezar, A. Differential
effects of the two types of information systems:
A hospital-based study. Journal of Management
Information Systems 26, 1 (July 2009), 297–316.

22. New York eHealth Collaborative. State HIE Cooperative Agreement Program Strategic Plan. New York, 2009; https://www.healthit.gov/topic/onc-hitech-programs/state-health-information-exchange

23. Panko, R.R. Is office productivity stagnant? MIS Quarterly 15, 2 (June 1991), 191–203.

24. Payne, P. R.O., Lussier, Y., Foraker, R.E., and Embi, P. J.

Rethinking the role and impact of health information technology: Informatics as an interventional discipline. BMC Medical Informatics and Decision Making 16, 40 (Mar. 29, 2016), 1–7.

25. Rosenbaum, L. Transitional chaos or enduring harm?

The EHR and the disruption of medicine. The New England Journal of Medicine 373, 17 (Oct. 22, 2015),

1585–1588.

26. Rudin, R.S., Motala, A., Goldzweig, C.L., and Shekelle, P.G. Usage and effect of health information exchange: A systematic review. Annals of Internal Medicine 161,

11 (Dec. 2014), 803–812.

27. Rupasingha, A., Goetz, S.J., and Freshwater, D.

The production of social capital in U.S. counties.

The Journal of Socio-Economics 35, 1

(Feb. 2006), 83–101.

28. Schryen, G. Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems 22, 2 (Mar. 2013), 139–169.

29. Sharma, L., Chandrasekaran, A., Boyer, K.K., and McDermott, C.M. The impact of health information technology bundles on hospital performance: An econometric study. Journal of Operations Management 41 (Jan. 2016), 25–41.

30. Vest, J.R. and Gamm, L. D. Health information exchange: Persistent challenges and new strategies.

Journal of the American Medical Informatics Association 17, 3 (May 2010), 288–294.

Quang “Neo” Bui ( [e-mail]) is an assistant professor of management information systems in the Saunders College of Business of the Rochester Institute of Technology, Rochester, NY, USA.

Sean Hansen ( [e-mail]) is an associate professor of management information systems in the Saunders College of Business of the Rochester Institute of Technology, Rochester, NY, USA.

Manlu Liu ( [e-mail]) is an associate professor of management information systems and accounting in the Saunders College of Business of the Rochester Institute of Technology, Rochester, NY, USA. Qiang (John) Tu ( [e-mail]) is a professor of management information systems and the Senior Associate Dean in the Saunders College of Business of the Rochester Institute of Technology, Rochester, NY, USA.

© 2018 ACM 0001-0782/18/10 $15.00

fective, enhanced, or idiosyncratic use of IT resources. 2, 8 Insight from the research could inform the aforementioned efforts among healthcare system participants to identify and disseminate best practices and foster more productive use patterns.

Efforts by policymakers. Policymakers play a significant role in each of the measures we have proposed, as in community building through RHIOs and advancing outcome-oriented measures of HIT use. While they should work with academic researchers and the industry to identify more relevant metrics for healthcare providers, it is equally important they maintain a holistic view of the healthcare value chain. Instead of focusing on policies that incentivize only EHR adoption or HIE participation, policymakers should also consider how to promote experimentation both within and across geographic boundaries. This might include more flexible use-style incentive programs that reward not only hospital-by-hospital efforts but also cross-hospital, cross-state, and cross-boundary initiatives. It is difficult today to promote technologies that provide value across geographical locations (such as telemedicine) or across institutional boundaries (such as healthcare supply-chain systems). In order to promote innovation and collaboration, policymakers might thus want to consider measures that target multiple parties in a healthcare value chain rather than a limited number of dominant players. This would include support for public-pri-vate partnerships that bring together healthcare providers, payer organizations, and HIT providers or initiatives that include large-scale participation groups (such as the Precision Medicine Initiative). Such efforts could leverage emergent technologies (such as big data analytics platforms, mobile health apps, and social media) to quickly assess the efficacy of a diverse set of HIT projects and channel resources toward the ones that show the greatest promise for bridging the gap between HIT use and health outcomes across populations.

Conclusion

IT use in the healthcare industry has
experienced tremendous growth and
attention since 2007. Yet concrete
and credible evidence that HIT im-
proves health outcomes remains in-
conclusive. Our investigation of New
York State healthcare providers fur-
ther indicates the healthcare industry
may be experiencing an ongoing HIT
productivity paradox, mirroring earli-
er patterns in manufacturing and oth-
er industrial sectors. While potential
HIT contribution to health outcomes
remains an open question, we suggest
a collective approach is needed to ad-
dress the many issues raised by the
HIT productivity paradox and hope
our research invites further inquiry
into this important issue.

References

1. Adler-Milstein, J., Bates, D. W., and Jha, A.K. A survey of health information exchange organizations in the United States: Implications for meaningful use. Annals of Internal Medicine 154, 10 (May 2011), 666–671.

2. Bagayogo, F.F., Lapointe, L., and Bassellier, G. Enhanced use of IT: A new perspective on post-adoption. Journal of the Association for Information Systems 15, 7 (July 2014). 361–387.

3. Blumenthal, D. and Tavenner, M. The ‘meaningful use’ regulation for electronic health records. The New England Journal of Medicine 363, 6 (Aug. 5, 2010), 501–504.

4. Boyatzis, R.E. Transforming Qualitative Information: Thematic Analysis and Code Development. Sage Publications, Thousand Oaks, CA, 1998.

5. Brynjolfsson, E. The productivity paradox of information technology. Commun. ACM 36, 12 (Dec. 1993), 66–77.

6. Brynjolfsson, E. and Hitt, L.M. Beyond the productivity paradox. Commun. ACM 41, 8 (Aug. 1998), 49–55.

7. Buntin, M. B., Burke, M. F., Hoaglin, M.C., and Blumenthal, D. The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs 30, 3 (2011), 464–471.

8. Burton-Jones, A. and Grange, C. From use to effective use: A representation theory perspective. Information Systems Research 24, 3 (Mar. 2012), 632–658.

9. Chari, M. D., Devaraj, S., and David, P. The impact of information technology investments and diversification strategies on firm performance. Management Science 54, 1 (Jan. 2008), 224–234.

10. Cho, K. W., Bae, S.-K., Ryu, J.-H., Kim, K.N., An, C.-H., and Chae, Y.M. Performance evaluation of public hospital information systems by the information system success model. Healthcare Informatics Research 21, 1 (Jan. 2015), 43–48.

11. David, P.A. The dynamo and the computer: An historical perspective on the modern productivity paradox. The American Economic Review 80, 2 (May 1990), 355–361.

12. Dedrick, J., Gurbaxani, V., and Kraemer, K. L. Information technology and economic performance: A critical review of the empirical evidence. ACM Computing Surveys 35, 1 (Mar. 2003), 1–28.

13. Devaraj, S. and Kohli, R. Information technology payoff in the healthcare industry: A longitudinal study. Journal of Management Information Systems 16, 4 (Apr. 2000), 41–67.

14. Government Accountability Office. Electronic Health Record Programs: Participation Has Increased, but Action Is Needed to Achieve Goals, Including Improved Quality of Care. Washington, D. C., 2014; https://www.gao.gov/assets/670/661399.pdf

15. Harrison, M.I., Koppel, R., and Bar-Lev, S. Unintended consequences of information technologies in health care: An interactive sociotechnical analysis. Journal of the American Medical Informatics Association 14, 5 (Sept. 2007), 542–549.

16. Jha, A.K., Burke, M.F., DesRoches, C., Joshi, M.S., Kralovec, P. D., Campbell, E.G., and Buntin, M.B. Progress toward meaningful use: Hospitals’ adoption of electronic health records. American Journal of