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Using Electronic Health Records Data to Evaluate the Impact of Information Technology on Improving Health Equity: Evidence from China

  • Qing Ye
  • Zhaohua DengEmail author
  • Yanyan Chen
  • Jiazhi Liao
  • Gang Li
Mobile & Wireless Health
  • 96 Downloads
Part of the following topical collections:
  1. Mobile & Wireless Health

Abstract

This study evaluates the impact of health information technology in accessing medical resources and identifies its role in improving health equity. We used 262, 771 records from the electronic medical records and outpatient appointment systems of three clinics for logistic regression to analyze the impact of information technology on patients’ access to medical care. We interviewed a few health professionals to gauge their reactions and to validate and understand our quantitative results. The proportion of inpatients affected by information technology is low, accounting for only 16.7% (N = 43, 870). The difference between rural and urban groups is statistically significant, and rural households are more susceptible to information technology. In addition, distance has a significant positive effect. We demonstrate an inverted U-shaped relationship between severity of disease and the impact of information technology. Moreover, our interview results are consistent with our quantitative results. Quantitative and interview results suggest that health information technology plays a positive role in accessing medical care for patients with rural household and those in remote areas. Meanwhile, this effect is complex for patients with different severities of illnesses. Governments and managers should vigorously promote health information technology for healthcare delivery in the future and focus their attention on patients with serious diseases.

Keywords

Health equity Health disparity Information technology Electronic medical records 

Abbreviations

EMR

Electronic medical records

Notes

Acknowledgements

The authors thank Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology for providing data used for this empirical analysis.

Funding

This study was supported by the National Natural Science Foundation of China (award no. 71671073) and Natural Science Foundation of Hubei Province (award no. 2018CFB739).

Compliance with Ethical Standards

Conflict of Interest

All authors declare that there is no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Friel, S., Marmot, M., McMichael, A. J., Kjellstrom, T., and Vågerö, D., Global health equity and climate stabilisation: A common agenda. Lancet. 372:1677–1683, 2008.CrossRefGoogle Scholar
  2. 2.
    Braveman, P., What are health disparities and health equity? We need to be clear. Publ. Health Rep. 129(1_suppl2):5–8, 2014.CrossRefGoogle Scholar
  3. 3.
    Carney, T. J., and Kong, A. Y., Leveraging health informatics to foster a smart systems response to health disparities and health equity challenges. J. Biomed. Inform. 68:184–189, 2017.CrossRefGoogle Scholar
  4. 4.
    Braveman, P., Health disparities and health equity: Concepts and measurement. Annu. Rev. Publ. Health. 27:167–194, 2006.CrossRefGoogle Scholar
  5. 5.
    Welch, V. A., Norheim, O. F., Jull, J., Cookson, R., Sommerfelt, H., Tugwell, P. et al., Consort-equity 2017 extension and elaboration for better reporting of health equity in randomised trials. BMJ. 359:j5085, 2017.CrossRefGoogle Scholar
  6. 6.
    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 Aff. (Millwood). 30:464–471, 2011.CrossRefGoogle Scholar
  7. 7.
    Bardhan, I. R., and Thouin, M. F., Health information technology and its impact on the quality and cost of healthcare delivery. Decis. Support Syst. 55:438–449, 2013.CrossRefGoogle Scholar
  8. 8.
    Humphries, K. H., and van Doorslaer, E., Income-related health inequality in Canada. Soc. Sci. Med. 50:663–671, 2000.CrossRefGoogle Scholar
  9. 9.
    Tan, X., Liu, X., and Shao, H., Healthy China 2030: A vision for health care. Value Health Reg. Issues. 12:112–114, 2017.CrossRefGoogle Scholar
  10. 10.
    Hu, F. B., Liu, Y., and Willett, W. C., Preventing chronic diseases by promoting healthy diet and lifestyle: Public policy implications for China. Obes. Rev. 12:552–559, 2011.CrossRefGoogle Scholar
  11. 11.
    Wu, H., and Deng, Z., Knowledge collaboration among physicians in online health communities: A transactive memory perspective. Int. J. Inf. Manag. 49:13–33, 2019.CrossRefGoogle Scholar
  12. 12.
    Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., and Pounders, K., Health literacy and health information technology adoption: The potential for a new digital divide. J. Med. Internet Res. 18, 2016. doi: https://doi.org/10.2196/jmir.6349.CrossRefGoogle Scholar
  13. 13.
    van Deursen, A. J., and van Dijk, J. A., The digital divide shifts to differences in usage. New Media Soc. 16:507–526, 2014.CrossRefGoogle Scholar
  14. 14.
    Hall, A. K., Bernhardt, J. M., Dodd, V., and Vollrath, M. W., The digital health divide: Evaluating online health information access and use among older adults. Health Educ. Behav. 42:202–209, 2015.CrossRefGoogle Scholar
  15. 15.
    Sun, J., Guo, Y., Wang, X., and Zeng, Q., mHealth for aging China: Opportunities and challenges. Aging Dis. 7:53–67, 2016.CrossRefGoogle Scholar
  16. 16.
    Zhang, X., Lai, K., and Guo, X., Promoting China’s mHealth market: A policy perspective. Health Policy Technol. 6:383–388, 2017.CrossRefGoogle Scholar
  17. 17.
    O’Neill, J., Tabish, H., Welch, V., Petticrew, M., Pottie, K., Clarke, M. et al., Applying an equity lens to interventions: Using progress ensures consideration of socially stratifying factors to illuminate inequities in health. J. Clin. Epidemiol. 67:56–64, 2014.CrossRefGoogle Scholar
  18. 18.
    Hosseinpoor, A. R., Bergen, N., Koller, T., Prasad, A., Schlotheuber, A., Valentine, N. et al., Equity-oriented monitoring in the context of universal health coverage. PLOS Med. 11:e1001727, 2014.CrossRefGoogle Scholar
  19. 19.
    Fischer, S. H., David, D., Crotty, B. H., Dierks, M., and Safran, C., Acceptance and use of health information technology by community-dwelling elders. Int. J. Med. Inf. 83:624–635, 2014.CrossRefGoogle Scholar
  20. 20.
    Chib, A., van Velthoven, M. H., and Car, J., mHealth adoption in low-resource environments: A review of the use of Mobile healthcare in developing countries. J. Health Commun. 20:4–34, 2015.CrossRefGoogle Scholar
  21. 21.
    Nguyen, A., Mosadeghi, S., and Almario, C. V., Persistent digital divide in access to and use of the internet as a resource for health information: Results from a California population-based study. Int. J. Med. Inf. 103:49–54, 2017.CrossRefGoogle Scholar
  22. 22.
    Hoque, M. R., An empirical study of mHealth adoption in a developing country: The moderating effect of gender concern. BMC Med. Inform. Decis. Mak. 16:51, 2016.CrossRefGoogle Scholar
  23. 23.
    Khatun, F., Heywood, A. E., Hanifi, S. M. A., Rahman, M. S., Ray, P. K., Liaw, S.-T. et al., Gender differentials in readiness and use of mHealth services in a rural area of Bangladesh. BMC Health Serv. Res. 17:573, 2017.CrossRefGoogle Scholar
  24. 24.
    Cooper, C. P., Gelb, C. A., Rim, S. H., Hawkins, N. A., Rodriguez, J. L., and Polonec, L., Physicians who use social media and other internet-based communication technologies. J. Am. Med. Inform. Assoc. 19:960–964, 2012.CrossRefGoogle Scholar
  25. 25.
    Hao, H., Padman, R., Sun, B., and Telang, R., Quantifying the impact of social influence on the information technology implementation process by physicians: A hierarchical Bayesian learning approach. Inf. Syst. Res. 29:25–41, 2018.CrossRefGoogle Scholar
  26. 26.
    Hecht, C., Weber, M., Grote, V., Daskalou, E., Dell’Era, L., Flynn, D. et al., Disease associated malnutrition correlates with length of hospital stay in children. Clin. Nutr. 34:53–59, 2015.CrossRefGoogle Scholar
  27. 27.
    Asner, S. A., Science, M. E., Tran, D., Smieja, M., Merglen, A., and Mertz, D., Clinical disease severity of respiratory viral co-infection versus single viral infection: A systematic review and meta-analysis. PLOS ONE. 9:e99392, 2014.CrossRefGoogle Scholar
  28. 28.
    Harrell, F.E., Binary logistic regression. In: Harrell Jr Frank E, editor. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Cham: Springer International Publishing; 2015. p. 219–274. doi: https://doi.org/10.1007/978-3-319-19425-7_10.CrossRefGoogle Scholar
  29. 29.
    Friedman, J., Hastie, T., and Tibshirani, R., Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33:1–22, 2010.CrossRefGoogle Scholar
  30. 30.
    Wu, H., and Lu, N., Service provision, pricing, and patient satisfaction in online health communities. Int. J. Med. Inf. 110:77–89, 2018.CrossRefGoogle Scholar
  31. 31.
    Carney, T. J., and Kong, A. Y., Leveraging health informatics to foster a smart systems respbnse to health disparities. And health equity challenges. J. Biomed. Inform. 68:184–189, 2017.CrossRefGoogle Scholar
  32. 32.
    Fareed, N., Walker, D., Sieck, C. J., Taylor, R., Scarborough, S., Huerta, T. R. et al., Inpatient portal clusters: Identifying user groups based on portal features. J. Am. Med. Inform. Assoc. 26:28–36, 2019.CrossRefGoogle Scholar
  33. 33.
    Greysen, S. R., Harrison, J. D., Rareshide, C., Magan, Y., Seghal, N., Rosenthal, J. et al., A randomized controlled trial to improve engagement of hospitalized patients with their patient portals. J. Am. Med. Inform. Assoc. 25:1626–1633, 2018.CrossRefGoogle Scholar
  34. 34.
    Cronin, R. M., Conway, D., Condon, D., Jerome, R. N., Byrne, D. W., and Harris, P. A., Patient and healthcare provider views on a patient-reported outcomes portal. J. Am. Med. Inform. Assoc. 25:1470–1480, 2018.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Medicine and Health Management, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina

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