Antecedents of Online Patient Experience of the Hospital Telemedicine Application and Its Consequences

Authors

  • Cynthia Graduate School of Management, Universitas Pelita Harapan, South Jakarta 12930, Indonesia
  • Ferdi Antonio Department of Hospital Administration, Faculty of Economics and Business,Universitas Pelita Harapan, South Jakarta 12930, Indonesia
  • Dewi Wuisan Department of Hospital Administration, Faculty of Economics and Business,Universitas Pelita Harapan, South Jakarta 12930, Indonesia

DOI:

https://doi.org/10.18196/jmmr.v12i2.51

Keywords:

Hospital telemedicine, Online patient experience, Antecedents

Abstract

Hospitals had to adjust by offering telemedicine services in response to the COVID-19 outbreak. In contrast to telemedicine, which is developed independently, telemedicine used to have adequate resources. Unfortunately, few studies have been conducted to capture online patient experience from hospital telemedicine services. Therefore, this study attempts to analyze factors that can affect Online Patient Experience (OPX) and assess the impact on Continue Usage Intention (CUI) and Intention to Recommend (ITR). The independent variables that become the antecedents of OPX are Professional Knowledge (PKL), Physician Rank (PRK), Physician Image (PIM), Response Speed (RSP), Service Commitment (SCM), Service Attitude (SAT), and Communication Efforts (CEF). This study is a quantitative survey with cross-sectional data obtained by questionnaire instrument. Respondents were taken purposively using judgment sampling to get those who had received telemedicine services managed by a large group of hospitals in Jakarta. Based on the result, 172 respondents were eligible. The result indicated that all the antecedents studied had a positive and significant relation (p-value < 0,05) with OPX. The results also found that RSP (β = 0.317), SCM (β = 0.216), and PIM (β = 0,186) are the three constructs that play the dominant role. Thus, it is important to create a positive online patient experience so that patients can continue to use teleconsultation services and recommend them to others.

References

Alexandra, S., Handayani, P. W., & Azzahro, F. (2021). Indonesian hospital telemedicine acceptance model: the influence of user behavior and technological dimensions. Heliyon, 7(12), e08599. https://doi.org/10.1016/j.heliyon.2021.e08599

Almutairi, I. L., Alazemi, B. F., & Almutairi, F. L. (2021). Kuwaiti hospital patients' continuance intention to use telemedical systems in the wake of the covid19 pandemic. Healthcare Technology Letters, 8(6), 159–168. https://doi.org/10.1049/htl2.12019

Chen, S., Guo, X., Wu, T., & Ju, X. (2020). Exploring the online doctor-patient interaction on patient satisfaction based on text mining and empirical analysis. Information Processing & Management, 57(5), 102253. https://doi.org/10.1016/j.ipm.2020.102253

Chomeya, R. (2010). Quality of psychology test between Likert scale 5 and 6 points. Journal of Social Sciences, 6(3), 399–403.

Deng, Z., Hong, Z., Zhang, W., Evans, R., & Chen, Y. (2019). The Effect of Online Effort and Reputation of Physicians on Patients’ Choice: 3-Wave Data Analysis of China’s Good Doctor Website. Journal of Medical Internet Research, 21(3), e10170. https://doi.org/10.2196/10170

Ding, X., You, X., Zhang, X., & Yu, Y. (2022). Can Patients Co-Create Value in an Online Healthcare Platform? An Examination of Value Co-Creation. International Journal of Environmental Research and Public Health, 19(19), 12823. https://doi.org/10.3390/ijerph191912823

Fatima, T., Malik, S. A., & Shabbir, A. (2018). Hospital Healthcare Service Quality, patient satisfaction and Loyalty. International Journal of Quality & Reliability Management, 35(6), 1195–1214. https://doi.org/10.1108/ijqrm-02-2017-0031

Gong, Y., Wang, H., Xia, Q., Zheng, L., & Shi, Y. (2021). Factors that determine a patient's willingness to physician selection in online healthcare communities: A trust theory perspective. Technology in Society, 64, 101510. https://doi.org/10.1016/j.techsoc.2020.101510

Grenier Ouimet, A., Wagner, G., Raymond, L., & Pare, G. (2020). Investigating patients' intention to continue using teleconsultation to anticipate postcrisis momentum: Survey study. Journal of Medical Internet Research, 22(11), e22081 https://doi.org/10.2196/22081

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203

Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in Second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Hamonangan, T. P., & Aruan, D. T. H. (2021). The antecedents of customer intention to use Mobile Health: An application of Extended Technology Acceptance Model. 2021 6th International Conference on Management in Emerging Markets (ICMEM). https://doi.org/10.1109/icmem53145.2021.9869410

Haris, F., Irawati, K., & Rahman, F. F. (2021). Adaptation of telemedicine amidst COVID-19 towards Indonesian physicians: Benefits, limitations, and burdens. Bali Medical Journal, 10(3), 1289–1293. https://doi.org/10.15562/bmj.v10i3.2900

Hefner, J. L., McAlearney, A. S., Spatafora, N., & Moffatt-Bruce, S. D. (2019). Beyond patient satisfaction: Optimizing the patient experience. Advances in Health Care Management, 255–261. https://doi.org/10.1108/s1474-823120190000018010

Kichloo, A., Albosta, M., Dettloff, K., Wani, F., El-Amir, Z., Singh, J., Aljadah, M., Chakinala, R. C., Kanugula, A. K., Solanki, S., & Chugh, S. (2020). Telemedicine, the current COVID-19 pandemic and the future: A narrative review and perspectives moving forward in the USA. Family Medicine and Community Health, 8(3), e000530. https://doi.org/10.1136/fmch-2020-000530

Kumah, E. (2017). Patient experience and satisfaction with a healthcare system: Connecting the dots. International Journal of Healthcare Management, 12(3), 173–179. https://doi.org/10.1080/20479700.2017.1353776

Lacap, J. P., & Alfonso, K. J. (2022). The Mediating Role of Patient Loyalty on the Relationship Between Satisfaction on Physical Environment and Intention to Recommend. Asia-Pacific Social Science Review, 22(2).

Lee, S. M., & Lee, D. H. (2020). Healthcare wearable devices: An analysis of key factors for continuous use intention. Service Business, 14(4), 503–531. https://doi.org/10.1007/s11628-020-00428-3

Li, Y., Ma, X., Song, J., Yang, Y., & Ju, X. (2019). Exploring the effects of online rating and the activeness of physicians on the number of patients in an online health community. Telemedicine and e-Health, 25(11), 1090–1098. https://doi.org/10.1089/tmj.2018.0192

Liengaard, B. D., Sharma, P. N., Hult, G. T., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020). Prediction: Coveted, yet Forsaken? Introducing a cross‐validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445

Liu, X., Xu, Z., Yu, X., & Oda, T. (2022). Using telemedicine during the COVID-19 pandemic: How service quality affects patients' consultation. International Journal of Environmental Research and Public Health, 19(19), 12384. https://doi.org/10.3390/ijerph191912384

Mazzarol, T., Soutar, G., & Limnios, E. M. (2019). Member loyalty and WOM in cooperative and mutual enterprises. Journal of Services Marketing, 33(3), 303–3015. https://doi.org/10.1108/jsm-07-2018-0195

McElroy, J. A., Day, T. M., & Becevic, M. (2020). The influence of telehealth for better health across communities. Preventing Chronic Disease, 17. https://doi.org/10.5888/pcd17.200254

Molinillo, S., Navarro-García, A., Anaya-Sánchez, R., & Japutra, A. (2020). The impact of affective and cognitive app experiences on loyalty towards retailers. Journal of Retailing and Consumer Services, 54, 101948. https://doi.org/10.1016/j.jretconser.2019.101948

Monkowski, D., Rhodes, L. V., Templer, S., Kromer, S., Hartner, J., Pianucci, K., & Kincaid, H. (2019). A retrospective cohort study to assess the impact of an inpatient infectious disease telemedicine consultation service on hospital and Patient Outcomes. Clinical Infectious Diseases. https://doi.org/10.1093/cid/ciz293

Mun, Y. W., Aziz, Y. A., & Bojei, J. (2018). Preliminary study of international students in Malaysia on perceived university and destination image towards intention to recommend. Journal of Research in Business, Economics and Management, 10(5), 2078-2091.

Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/imds-07-2015-0302

Octavius, G. S., & Antonio, F. (2021). Antecedents of intention to adopt Mobile Health (mHealth) application and its impact on intention to recommend: An evidence from Indonesian customers. International Journal of Telemedicine and Applications, 2021, 1–24. https://doi.org/10.1155/2021/6698627

Orrange, S., Patel, A., Mack, W. J., & Cassetta, J. (2021). Patient satisfaction and trust in telemedicine during the COVID-19 pandemic: Retrospective Observational Study. JMIR Human Factors, 8(2). https://doi.org/10.2196/28589

Permenkes no. 20 tahun 2019 Tentang penyelenggaraan pelayanan telemedicine antar fasilitas pelayanan kesehatan [JDIH bpk ri]. (n.d.). Retrieved March 25, 2023, from https://peraturan.bpk.go.id/Home/Details/138613/permenkes-no-20-tahun-2019

Pogorzelska, K., & Chlabicz, S. (2022). Patient satisfaction with telemedicine during the COVID-19 pandemic—a systematic review. International Journal of Environmental Research and Public Health, 19(10), 6113. https://doi.org/10.3390/ijerph19106113

Ramaswamy, A., Yu, M., Drangsholt, S., Ng, E., Culligan, P. J., Schlegel, P. N., & Hu, J. C. (2020). Patient satisfaction with telemedicine during the COVID-19 pandemic: Retrospective cohort study. Journal of Medical Internet Research, 22(9), e20786. https://doi.org/10.2196/20786

Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/imds-10-2015-0449

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064. https://doi.org/10.1002/mar.21640

Sayani, S., Muzammil, M., Saleh, K., Muqeet, A., Zaidi, F., & Shaikh, T. (2019). Addressing cost and time barriers in chronic disease management through telemedicine: An exploratory research in select low- and middle-income countries. Therapeutic Advances in Chronic Disease, 10, 204062231989158. https://doi.org/10.1177/2040622319891587

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using plspredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/ejm-02-2019-0189

Tiara, K., & Antonio, F. (2022). The Influence Of Telemedicine Usability On Patient Loyalty Mediated By Patients' Trust And Satisfaction: A Study At Hospitals Of State-Owned Enterprises In Indonesia. Jurnal Pendidikan Tambusai, 6(1), 2326–2341.

Uzir, M. U., Al Halbusi, H., Lim, R., Jerin, I., Abdul Hamid, A. B., Ramayah, T., & Haque, A. (2021). Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19. Technology in Society, 67, 101780. https://doi.org/10.1016/j.techsoc.2021.101780

Verma, S., & Singh, V. (2022). Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms. Computers in Human Behavior, 131, 107215. https://doi.org/10.1016/j.chb.2022.107215

Wan, Y., Zhang, Y., & Yan, M. (2020). What influences patients' willingness to choose in online health consultation? an empirical study with PLS–sem. Industrial Management & Data Systems, 120(12), 2423–2446. https://doi.org/10.1108/imds-11-2019-0633

Wang, T., Giunti, G., Melles, M., & Goossens, R. (2022). Digital Patient Experience: Umbrella Systematic Review. Journal of Medical Internet Research, 24(8), e37952. https://doi.org/10.2196/37952

Wijaya, J. H., Octavius, G. S., & Hwei, L. R. (2022). A literature review of telemedicine in Indonesia: Past, present, and future prospective. Jurnal Administrasi Kesehatan Indonesia, 10(2), 261–272. https://doi.org/10.20473/jaki.v10i2.2022.261-272

Wolf, J. A., Niederhauser, V., Marshburn, D., & LaVela, S. L. (2021). Reexamining "defining patient experience": The human experience in Healthcare. Patient Experience Journal, 8(1), 16–29. https://doi.org/10.35680/2372-0247.1594

Ye, C., Cao, C., Yang, J., & Shao, X. (2022). Explore how online healthcare can influence willingness to seek offline care. International Journal of Environmental Research and Public Health, 19(13), 7925. https://doi.org/10.3390/ijerph19137925

Zagita, T. C., Handayani, P. W., & Budi, N. F. (2019). Analysis of factors affecting the loyalty of using online health services: Case study of alodokter. 2019 International Conference on Advanced Computer Science and Information Systems (ICACSIS). https://doi.org/10.1109/icacsis47736.2019.8979973

Zhang, X., Zhang, R., & Lu, X. (2020). Exploring the effects of patient activation in online health communities on patient compliance. Telemedicine and e-Health, 26(11), 1373–1382. https://doi.org/10.1089/tmj.2019.0258

Zobair, K. M., Sanzogni, L., Houghton, L., & Islam, Md. Z. (2022). Combining deep neural network and PLS-SEM to predict patients' continuity with telemedicine. International Journal of Information Technology & Decision Making, 21(05), 1555–1589. https://doi.org/10.1142/s0219622022500249

Zobair, K. M., Sanzogni, L., & Sandhu, K. (2019). Expectations of telemedicine health service adoption in rural Bangladesh. Social Science & Medicine, 238, 112485. https://doi.org/10.1016/j.socscimed.2019.112485

Zobair, K. M., Sanzogni, L., & Sandhu, K. (2020). Telemedicine Healthcare Service Adoption Barriers in Rural Bangladesh. Australasian Journal of Information Systems, 24. https://doi.org/10.3127/ajis.v24i0.2165

Downloads

Published

2023-08-07