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Determinants of Satisfaction in the Usage of Healthcare Information Systems by Hospital Workers in Hyderabad, India: Neural Network and SEM Approach

  • Received : 2023.03.25
  • Accepted : 2023.08.25
  • Published : 2023.12.31

Abstract

This study focuses on the adoption of Healthcare Information System (HIS) in India's healthcare services, which has led to an increased use of HIS software for managing patient information in hospitals. The study aims to evaluate the factors that influence hospital workers' satisfaction with HIS usage and its impact on their intention to continue in the use of HIS. Primary data was collected through a survey questionnaire from 265 hospital workers. A new framework was developed, and Structural Equation Modeling (SEM) was used for analysis. Sensitivity analysis was also conducted on demographic data using an Artificial Neural Network (ANN) approach. The results indicated that all hypotheses were significant (p < 0.05). Effort expectancy was the most significant factor influencing hospital workers' satisfaction (p < 0.01). Sensitivity analysis showed that education (Model-A) and experience in use of HIS (Model-B) were the most important factors. The study contributes by proposing a new theoretical framework and extending the previous research on HIS usage satisfaction. Overall, the study highlights the importance of easiness and usefulness in predicting HIS usage satisfaction.

Keywords

Acknowledgement

Mr. Surya Neeragatti is the recipient of the Indian Council for Social Sciences Research (ICSSR) doctoral fellowship with grant no RFD/2021-22/SC/MGT/12/dated 17th December, 2021. Dr. Ranjit Kumar Dehury acknowledges financial support to UoH-IoE by the University of Hyderabad through the Institute of Eminence Project, Ministry of Education (Government of India) grant no. (F11/9/2019-U3(A)). However, the grant agencies have no role in designing and conduct of the study by the researchers.

References

  1. Ahmed, M. H., Bogale, A. D., Tilahun, B., Kalayou, M. H., Klein, J., Mengiste, S. A., and Endehabtu, B. F. (2020). Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: Using unified theory of acceptance and use technology 2(UTAUT2) model. BMC Medical Informatics and Decision Making, 20(1), 207. https://doi.org/10.1186/s12911-020-01222-x 
  2. Alam, M. M. D., Alam, M. Z., Rahman, S. A., and Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. Journal of Biomedical Informatics, 116, 103722. https://doi.org/10.1016/j.jbi.2021.103722 
  3. Almajali, D. A., Masa'deh, R., and Tarhini, A. (2016). Antecedents of ERP systems implementation success: A study on Jordanian healthcare sector. Journal of Enterprise Information Management, 29(4), 549-565. https://doi.org/10.1108/JEIM-03-2015-0024 
  4. AMIA. (2023). Informatics: Research and Practice | AMIA - American Medical Informatics Association. https://amia.org/about-amia/why-informatics/informatics-research-and-practice 
  5. Bawack, R. E., and Kala Kamdjoug, J. R. (2018). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109, 15-22. https://doi.org/10.1016/j.ijmedinf.2017.10.016 
  6. Blut, M., Chong, A., Tsigna, Z., and Venkatesh, V. (2022). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a research agenda in the Red Ocean. Journal of the Association for Information Systems, 23(1), 13-95. https://doi.org/10.17705/1jais.00719 
  7. Chan, F. T. S., and Chong, A. Y. L. (2012). A SEM-neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621-630. https://doi.org/10.1016/j.dss.2012.08.009 
  8. Damberg, S. (2022). Predicting future use intention of fitness apps among fitness app users in the United Kingdom: The role of health consciousness. International Journal of Sports Marketing and Sponsorship, 23(2), 369-384. https://doi.org/10.1108/IJSMS-01-2021-0013 
  9. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., and Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y 
  10. Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312 
  11. Gagnon, M. P., Ghandour, E. K., Talla, P. K., Simonyan, D., Godin, G., Labrecque, M., Ouimet, M., and Rousseau, M. (2014). Electronic health record acceptance by physicians: Testing an integrated theoretical model. Journal of Biomedical Informatics, 48, 17-27. https://doi.org/10.1016/j.jbi.2013.10.010 
  12. Gaskin, J., and Lim, J. (2016). Model fit measures [Computer software]. 
  13. Gaskin, J., James, M., and Lim, J. (2019). Master Validity Tool. AMOS Plugin In: Gaskination's StatWiki, 2019 [Computer software]. Gaskination's StatWiki. 
  14. Hair, J. F., Ringle, C. M., and Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001 
  15. Hassan, M. K., El Desouky, A. I., Elghamrawy, S. M., and Sarhan, A. M. (2019). Big data challenges and opportunities in healthcare informatics and smart hospitals. In A. E. Hassanien, M. Elhoseny, S. H. Ahmed, and A. K. Singh (Eds.), Security in Smart Cities: Models, Applications, and Challenges (pp. 3-26). Springer International Publishing. https://doi.org/10.1007/978-3-030-01560-2_1 
  16. Haykin, S. (2007). Neural Networks (world). Guide Books. https://doi.org/10.5555/521706 
  17. Hennemann, S., Beutel, M. E., and Zwerenz, R. (2017). Ready for eHealth? Health professionals' acceptance and adoption of eHealth interventions in inpatient routine care. Journal of Health Communication, 22(3), 274-284. https://doi.org/10.1080/10810730.2017.1284286 
  18. Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8 
  19. HIMSS. (2021, January 20). Electronic Medical Record Adoption Model (EMRAM) | HIMSS. Retrieved from https://www.himss.org/what-we-do-solutions/digital-health-transformation/maturity-models/electronic-medical-record-adoption-model-emram
  20. Holden, R. J., Brown, R. L., Scanlon, M. C., and Karsh, B. T. (2012). Modeling nurses' acceptance of bar coded medication administration technology at a pediatric hospital. Journal of the American Medical Informatics Association, 19(6), 1050-1058. https://doi.org/10.1136/amiajnl-2011-000754 
  21. Hoque, R., and Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84. https://doi.org/10.1016/j.ijmedinf.2017.02.002 
  22. Hu, L., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 
  23. Huang, J. C. (2010). Remote health monitoring adoption model based on artificial neural networks. Expert Systems with Applications, 37(1), 307-314. https://doi.org/10.1016/j.eswa.2009.05.063 
  24. Ifinedo, P. (2012). Technology Acceptance by Health Professionals in Canada: An Analysis with a Modified UTAUT Model. 2012 45th Hawaii International Conference on System Sciences (pp. 2937-2946). https://doi.org/10.1109/HICSS.2012.556 
  25. Iqbal, U., Ho, C. H., Li, Y. C., Nguyen, P. A., Jian, W. S., and Wen, H. C. (2013). The relationship between usage intention and adoption of electronic health records at primary care clinics. Computer Methods and Programs in Biomedicine, 112(3), 731-737. https://doi.org/10.1016/j.cmpb.2013.09.001 
  26. Jaana, M., and Pare, G. (2020). Comparison of mobile health technology use for self-tracking between older adults and the general adult population in Canada: Cross-sectional survey. JMIR MHealth and UHealth, 8(11), e24718. https://doi.org/10.2196/24718 
  27. Khaldi, R., El Afia, A., Chiheb, R., and Faizi, R. (2017). Artificial neural network based approach for blood demand forecasting: Fez transfusion blood center case study. In Proceedings of the 2nd International Conference on Big Data, Cloud and Applications (pp. 1-6). https://doi.org/10.1145/3090354.3090415 
  28. Kissi, J., Dai, B., Dogbe, C. S., Banahene, J., and Ernest, O. (2020). Predictive factors of physicians' satisfaction with telemedicine services acceptance. Health Informatics Journal, 26(3), 1866-1880. https://doi.org/10.1177/1460458219892162 
  29. Lee, V. H., Hew, J. J., Leong, L. Y., Tan, G. W. H., and Ooi, K. B. (2020). Wearable payment: A deep learning-based dual-stage SEM-ANN analysis. Expert Systems with Applications, 157, 113477. https://doi.org/10.1016/j.eswa.2020.113477 
  30. Limna, P., Siripipatthanakul, S., Siripipattanakul, S., Woodeson, K., and Auttawechasakoon, P. (2022). Applying the UTAUT to Explain Factors Affecting English Learning Intention Via Netflix (English Subtitle) Among Thai people. Asia-Pacific Review of Research in Education, 1(1), 1-19 
  31. Maida, M., Maier, K., Obwegeser, N., and Stix, V. (2012). The Effect of Sensitivity Analysis on the Usage of Recommender Systems. Decisions@RecSys. Retrieved from https://www.semanticscholar.org/paper/The-Effect-of-Sensitivity-Analysis-on-the-Usage-of-Maida-Maier/d81d030172c5f727623c0cfffbf728c45b5ad66a 
  32. Maillet, E., Mathieu, L., and Sicotte, C. (2015). Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics, 84(1), 36-47. https://doi.org/10.1016/j.ijmedinf.2014.09.004 
  33. Marinkovic, V., Dordevic, A., and Kalinic, Z. (2020). The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: A UTAUT-based perspective. Technology Analysis and Strategic Management, 32(3), 306-318. https://doi.org/10.1080/09537325.2019.1655537 
  34. National Health Authority. (n.d.). Prime Minister of India launches countrywide Ayushman Bharat Digital Mission. Retrieved March 19, 2023, from https://pib.gov.in/pib.gov.in/Pressreleaseshare.aspx?PRID=1758511 
  35. Nwankpa, J., and Roumani, Y. (2014). Understanding the link between organizational learning capability and ERP system usage: An empirical examination. Computers in Human Behavior, 33, 224-234. https://doi.org/10.1016/j.chb.2014.01.030 
  36. Ostern, N., Perscheid, G., Reelitz, C., and Moormann, J. (2021). Keeping pace with the healthcare transformation: A literature review and research agenda for a new decade of health information systems research. Electronic Markets, 31(4), 901-921. https://doi.org/10.1007/s12525-021-00484-1 
  37. Queen, J. T. (2021). EHR/EMR/PHR technology impact on the medical community and public opinion. Honors Capstones, 465. Retrieved from https://huskiecommons.lib.niu.edu/studentengagement-honorscapstones/465 
  38. Rouidi, M., Elouadi, A. E., Hamdoune, A., Choujtani, K., and Chati, A. (2022). TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: A systematic review. Informatics in Medicine Unlocked, 32, 101008. https://doi.org/10.1016/j.imu.2022.101008 
  39. Sadoughi, F., Khodaveisi, T., and Ahmadi, H. (2019). The used theories for the adoption of electronic health record: A systematic literature review. Health and Technology, 9(4), 383-400. https://doi.org/10.1007/s12553-018-0277-8 
  40. Sewandono, R. E., Thoyib, A., Hadiwidjojo, D., and Rofiq, A. (2022). Performance expectancy of E-learning on higher institutions of education under uncertain conditions: Indonesia context. Education and Information Technologies, 28, 4041-4068. https://doi.org/10.1007/s10639-022-11074-9 
  41. Sharma, A., and Shafiq, M. O. (2022). A Comprehensive Artificial Intelligence Based User Intention Assessment Model from Online Reviews and Social Media. Applied Artificial Intelligence, 36(1), 2014193. https://doi.org/10.1080/08839514.2021.2014193 
  42. Srinivas, A. S., Ramasubbareddy, S., S.s, M., and K, G. (2018). Predicting User Behaviour on E-Commerce Site Using Ann. International Journal of Engineering and Technology, 7(4.6), Article 4.6. https://doi.org/10.14419/ijet.v7i4.6.20454 
  43. Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., and Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology, 107(4), 1300-1307. https://doi.org/10.1152/japplphysiol.00465.2009 
  44. Talukder, Md. S., Sorwar, G., Bao, Y., Ahmed, J. U., and Palash, Md. A. S. (2020). Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change, 150, 119793. https://doi.org/10.1016/j.techfore.2019.119793 
  45. Tan, G. W. H., Ooi, K. B., Leong, L. Y., and Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198-213. https://doi.org/10.1016/j.chb.2014.03.052 
  46. Teo, T. (2014). Preservice teachers' satisfaction with e-learning. Social Behavior and Personality: An International Journal, 42, 3-6. https://doi.org/10.2224/sbp.2014.42.1.3 
  47. Venkatesh, V., Thong, J. Y. L., and Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412 
  48. Venkatesh, V., Thong, J., and Xu, X., (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. https://doi.org/10.17705/1jais.00428 
  49. Vichitkraivin, P., and Naenna, T. (2021). Factors of healthcare robot adoption by medical staff in Thai government hospitals. Health and Technology, 11(1), 139-151. https://doi.org/10.1007/s12553-020-00489-4 
  50. Wan, L., Xie, S., and Shu, A. (2020). Toward an understanding of university students' continued intention to use MOOCs: When UTAUT Model meets TTF Model. SAGE Open, 10(3), 21582440. https://doi.org/10.1177/2158244020941858 
  51. Wang, G., Liu, X., Wang, Z., and Yang, X. (2021). Research on the influence of interpretability of artificial intelligence recommendation system on users' behavior intention. In Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering (pp. 762-766). https://doi.org/10.1145/3443467.3443850 
  52. WHO. (2008). Health Information Systems. Retrieved from https://www.who.int/activities/integrating-rehabilitation-into-health-systems/information 
  53. Wu, C. G., and Wu, P. Y. (2018). Investigating user continuance intention toward library self-service technology: The case of self-issue and return systems in the public context. Library Hi Tech, 37(3), 401-417. https://doi.org/10.1108/LHT-02-2018-0025 
  54. Wu, P., Zhang, R., Luan, J., and Zhu, M. (2022). Factors affecting physicians using mobile health applications: An empirical study. BMC Health Services Research, 22(1), 24. https://doi.org/10.1186/s12913-021-07339-7 
  55. Zakaryazad, A., and Duman, E. (2016). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing, 175, 121-131. https://doi.org/10.1016/j.neucom.2015.10.042 
  56. Zhou, L. L., Owusu-Marfo, J., Asante Antwi, H., Antwi, M. O., Kachie, A. D. T., and Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses' adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC Medical Informatics and Decision Making, 19(1), 230. https://doi.org/10.1186/s12911-019-0956-z