Influencing factors of patients' adoption intention toward online medical services: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

CAO Xuefei , NING Zhipeng ,  HOU Yanhong

China Pharmaceutical University, Nanjing 211198, P. R. China;

Corresponding?author：

HOU Yanhong, Email: 1020092084@cpu.edu.cn

Keywords：

Online medical service; Adoption intention; Influencing factor; Meta-analysis; Systematic review; Cross-sectional study

DOI：

10.7507/1672-2531.202110144

Video：

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Objective To systematically review the influencing factors of patients’ adoption intention toward online medical services. Methods CNKI, VIP, WanFang Data, Web of Science, PubMed and EMbase databases were electronically searched to collect cross-sectional studies on the influencing factors of patients’ adoption intention toward online medical services from inception to August 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies; then, meta-analysis was performed by using Stata 16.0 software. Results A total of 28 cross-sectional studies involving 10 200 patients were included. The results of meta-analysis showed that perceived usefulness, perceived ease of use, and performance expectations were highly positively correlated with adoption intention. Effort expectation, social influence, convenience, self-efficacy, perceived value, individual innovation and perceived behavior control were moderately positively correlated factors. Perceived health threat was a low positive correlation factor of adoption intention. Resistance to change and perception of risk were moderately negatively correlated. Technology anxiety was a low negative correlation factor of adoption intention. There was a highly positive correlation between adoption intention and use behavior. Conclusion Current evidence shows that there is a highly positive correlation between adoption intention and use behavior, influencing factors of patients’ adoption intention include perceived usefulness, perceived ease of use, perceived value, etc.

Citation： CAO Xuefei, NING Zhipeng, HOU Yanhong. Influencing factors of patients' adoption intention toward online medical services: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2022, 22(3): 316-323. doi: 10.7507/1672-2531.202110144 Copy

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2.	國務院辦公廳. 關于促進“互聯網+醫療健康”發展的意見. 當代農村財經, 2018, (6): 42-45.
3.	國家衛健委, 國家中醫藥管理局. 關于印發互聯網診療管理辦法(試行)等3個文件的通知. 中華人民共和國國務院公報, 2019, (2): 49-58.
4.	梁馨月, 李瑩. 互聯網診療模式的研究現狀與展望. 經營與管理, 2020, 432(6): 24-27.
5.	Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. NJ: Prentice-Hall Englewood Cliffs, 1980.
6.	Ajzen I. From intentions to actions: a theory of planned behavior. Berlin: Springer, 1985.
7.	Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol, 2002, 32: 665-683.
8.	Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 1989, 13(3): 319-340.
9.	Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res, 2012, 14(5): e133.
10.	Venkatesh V, Morris MG, Davis GB, et al. User acceptance of information technology: toward a unified view. MIS Q, 2003, 27(3): 425-478.
11.	Venkatesh V, Thong JYL. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q, 2012, 36(1): 157-178.
12.	Tan BC. The role of perceived consumer effectiveness on value-attitude-behaviour model in green buying behavior context. Aust J Basic Appl Sci, 2011, 5(12): 157-166.
13.	Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag, 2015, 28(3): 443-448.
14.	Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. New Jersey: Prentice-Hall, 1980.
15.	Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol, 2005, 90(1): 175-181.
16.	Wilson DB. Practical meta-analysis effect size calculator. Available at: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php.
17.	Cohen J. Statistical power analysis for the behavioral sciences. Mahwah: Lawrence Erlbaum Associates, 1988: 79-81.
18.	Alaiad A, Alsharo M, Alnsour Y. The determinants of m-health adoption in developing countries: an empirical investigation. Appl Clin Inform, 2019, 10(5): 820-840.
19.	Apolinário-Hagen J, Menzel M, Hennemann S, et al. Acceptance of mobile health apps for disease management among people with multiple sclerosis: web-based survey study. JMIR Form Res, 2018, 2(2): e11977.
20.	Breil B, Kremer L, Hennemann S, et al. Acceptance of mHealth apps for self-management among people with hypertension. Stud Health Technol Inform, 2019, 267: 282-288.
21.	Deng Z, Hong Z, Ren C, et al. What predicts patients' adoption intention toward mHealth services in China: empirical study. JMIR Mhealth Uhealth, 2018, 6(8): e172.
22.	Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J Med Inform, 2014, 83(3): 210-224.
23.	Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform, 2017, 101: 75-84.
24.	Li D, Hu Y, Pfaff H, et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res, 2020, 22(10): e22716.
25.	Miao R, Wu Q, Wang Z, et al. Factors that influence users' adoption intention of mobile health: a structural equation modeling approach. Int J Prod Res, 2017, 55(19): 5801-5815.
26.	Salgado T, Tavares J, Oliveira T. Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development. JMIR Mhealth Uhealth, 2020, 8(7): e17588.
27.	Tang Y, Yang YT, Shao YF. Acceptance of online medical websites: an empirical study in China. Int J Environ Res Public Health, 2019, 16(6): 943.
28.	Wang H, Liang L, Du C, et al. Implementation of online hospitals and factors influencing the adoption of mobile medical services in china: cross-sectional survey study. JMIR Mhealth Uhealth, 2021, 9(2): e25960.
29.	Zhang X, Han X, Dang Y, et al. User acceptance of mobile health services from users' perspectives: the role of self-efficacy and response-efficacy in technology acceptance. Inform Health Soc Care, 2017, 42(2): 194-206.
30.	Zhang X, Liu S, Wang L, et al. Mobile health service adoption in China Integration of theory of planned behavior, protection motivation theory and personal health differences. Online Inf Rev, 2019, 44(1): 1-23.
31.	Zhang Y, Liu C, Luo S, et al. Factors influencing patients' intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: web-based survey. J Med Internet Res, 2019, 21(8): e15023.
32.	Zhu Z, Liu Y, Che X, et al. Moderating factors influencing adoption of a mobile chronic disease management system in China. Inform Health Soc Care, 2018, 43(1): 22-41.
33.	卞毛毛, 宋健, 王文娟, 等. 糖尿病應用程序使用意向量表的編制. 中華全科醫學, 2020, 18(9): 1577-1580.
34.	鄧子豪, 陳志鋒, 曾淼坤, 等. 武漢市患者移動醫療服務使用意愿及影響因素研究. 中國衛生統計, 2020, 37(2): 206-209.
35.	竇凱麗. 慢病患者移動技術接受度模型的設計與實踐. 杭州: 浙江大學, 2018.
36.	賴玉珊. 移動中醫健康服務用戶采納意愿問卷研制及初步應用. 廣州: 廣州中醫藥大學, 2016.
37.	李冰雪. 高血壓病患者移動醫療管理平臺使用意愿模型的構建及驗證. 南京: 南京中醫藥大學, 2020.
38.	劉連英. 成都市門診患者互聯網診療服務使用影響因素研究. 成都: 成都中醫藥大學, 2019.
39.	聶麗, 張凱麗. 慢性病患者移動醫療服務使用意愿影響因素分析. 中國衛生事業管理, 2021, 38(6): 468-472.
40.	彭前. 基于UTAUT理論的在線健康社區用戶接受與使用行為影響因素研究. 廣州: 暨南大學, 2020.
41.	王堯. 病患醫療服務模式選擇行為研究. 成都: 西南財經大學, 2016.
42.	燕菊, 劉婷, 苗秀欣, 等. 痛風患者移動醫療使用意愿調查及影響因素分析. 中華現代護理雜志, 2021, 27(8): 1006-1011.
43.	楊佳. 移動醫療APP患者持續使用行為分析—基于信任的分析視角. 中醫藥管理雜志, 2018, 26(18): 52-55.
44.	鐘姝雅. 基于UTAUT理論的移動醫療APP用戶接受行為影響因素研究. 杭州: 杭州師范大學, 2017.
45.	周騰. 患者群體對移動醫療平臺使用意愿的影響因素研究. 廣州: 南方醫科大學, 2019.
46.	Dou K, Yu P, Deng N, et al. Patients' acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR Mhealth Uhealth, 2017, 5(12): e177.
47.	Dehzad F, Hilhorst C, de Bie C, et al. Adopting health apps, what’s hindering doctors and patients. Health, 2014, 6(16): 2204-2217.
48.	Kim HW, Kankanhalli A. Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Q, 2009, 33(3): 567-582.

1. 周潔. 公立醫院加速互聯網化的機遇與瓶頸. 新民周刊, 2020, (30): 84-87.
2. 國務院辦公廳. 關于促進“互聯網+醫療健康”發展的意見. 當代農村財經, 2018, (6): 42-45.
3. 國家衛健委, 國家中醫藥管理局. 關于印發互聯網診療管理辦法(試行)等3個文件的通知. 中華人民共和國國務院公報, 2019, (2): 49-58.
4. 梁馨月, 李瑩. 互聯網診療模式的研究現狀與展望. 經營與管理, 2020, 432(6): 24-27.
5. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. NJ: Prentice-Hall Englewood Cliffs, 1980.
6. Ajzen I. From intentions to actions: a theory of planned behavior. Berlin: Springer, 1985.
7. Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol, 2002, 32: 665-683.
8. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 1989, 13(3): 319-340.
9. Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res, 2012, 14(5): e133.
10. Venkatesh V, Morris MG, Davis GB, et al. User acceptance of information technology: toward a unified view. MIS Q, 2003, 27(3): 425-478.
11. Venkatesh V, Thong JYL. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q, 2012, 36(1): 157-178.
12. Tan BC. The role of perceived consumer effectiveness on value-attitude-behaviour model in green buying behavior context. Aust J Basic Appl Sci, 2011, 5(12): 157-166.
13. Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag, 2015, 28(3): 443-448.
14. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. New Jersey: Prentice-Hall, 1980.
15. Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol, 2005, 90(1): 175-181.
16. Wilson DB. Practical meta-analysis effect size calculator. Available at: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php.
17. Cohen J. Statistical power analysis for the behavioral sciences. Mahwah: Lawrence Erlbaum Associates, 1988: 79-81.
18. Alaiad A, Alsharo M, Alnsour Y. The determinants of m-health adoption in developing countries: an empirical investigation. Appl Clin Inform, 2019, 10(5): 820-840.
19. Apolinário-Hagen J, Menzel M, Hennemann S, et al. Acceptance of mobile health apps for disease management among people with multiple sclerosis: web-based survey study. JMIR Form Res, 2018, 2(2): e11977.
20. Breil B, Kremer L, Hennemann S, et al. Acceptance of mHealth apps for self-management among people with hypertension. Stud Health Technol Inform, 2019, 267: 282-288.
21. Deng Z, Hong Z, Ren C, et al. What predicts patients' adoption intention toward mHealth services in China: empirical study. JMIR Mhealth Uhealth, 2018, 6(8): e172.
22. Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J Med Inform, 2014, 83(3): 210-224.
23. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform, 2017, 101: 75-84.
24. Li D, Hu Y, Pfaff H, et al. Determinants of patients' intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. J Med Internet Res, 2020, 22(10): e22716.
25. Miao R, Wu Q, Wang Z, et al. Factors that influence users' adoption intention of mobile health: a structural equation modeling approach. Int J Prod Res, 2017, 55(19): 5801-5815.
26. Salgado T, Tavares J, Oliveira T. Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development. JMIR Mhealth Uhealth, 2020, 8(7): e17588.
27. Tang Y, Yang YT, Shao YF. Acceptance of online medical websites: an empirical study in China. Int J Environ Res Public Health, 2019, 16(6): 943.
28. Wang H, Liang L, Du C, et al. Implementation of online hospitals and factors influencing the adoption of mobile medical services in china: cross-sectional survey study. JMIR Mhealth Uhealth, 2021, 9(2): e25960.
29. Zhang X, Han X, Dang Y, et al. User acceptance of mobile health services from users' perspectives: the role of self-efficacy and response-efficacy in technology acceptance. Inform Health Soc Care, 2017, 42(2): 194-206.
30. Zhang X, Liu S, Wang L, et al. Mobile health service adoption in China Integration of theory of planned behavior, protection motivation theory and personal health differences. Online Inf Rev, 2019, 44(1): 1-23.
31. Zhang Y, Liu C, Luo S, et al. Factors influencing patients' intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: web-based survey. J Med Internet Res, 2019, 21(8): e15023.
32. Zhu Z, Liu Y, Che X, et al. Moderating factors influencing adoption of a mobile chronic disease management system in China. Inform Health Soc Care, 2018, 43(1): 22-41.
33. 卞毛毛, 宋健, 王文娟, 等. 糖尿病應用程序使用意向量表的編制. 中華全科醫學, 2020, 18(9): 1577-1580.
34. 鄧子豪, 陳志鋒, 曾淼坤, 等. 武漢市患者移動醫療服務使用意愿及影響因素研究. 中國衛生統計, 2020, 37(2): 206-209.
35. 竇凱麗. 慢病患者移動技術接受度模型的設計與實踐. 杭州: 浙江大學, 2018.
36. 賴玉珊. 移動中醫健康服務用戶采納意愿問卷研制及初步應用. 廣州: 廣州中醫藥大學, 2016.
37. 李冰雪. 高血壓病患者移動醫療管理平臺使用意愿模型的構建及驗證. 南京: 南京中醫藥大學, 2020.
38. 劉連英. 成都市門診患者互聯網診療服務使用影響因素研究. 成都: 成都中醫藥大學, 2019.
39. 聶麗, 張凱麗. 慢性病患者移動醫療服務使用意愿影響因素分析. 中國衛生事業管理, 2021, 38(6): 468-472.
40. 彭前. 基于UTAUT理論的在線健康社區用戶接受與使用行為影響因素研究. 廣州: 暨南大學, 2020.
41. 王堯. 病患醫療服務模式選擇行為研究. 成都: 西南財經大學, 2016.
42. 燕菊, 劉婷, 苗秀欣, 等. 痛風患者移動醫療使用意愿調查及影響因素分析. 中華現代護理雜志, 2021, 27(8): 1006-1011.
43. 楊佳. 移動醫療APP患者持續使用行為分析—基于信任的分析視角. 中醫藥管理雜志, 2018, 26(18): 52-55.
44. 鐘姝雅. 基于UTAUT理論的移動醫療APP用戶接受行為影響因素研究. 杭州: 杭州師范大學, 2017.
45. 周騰. 患者群體對移動醫療平臺使用意愿的影響因素研究. 廣州: 南方醫科大學, 2019.
46. Dou K, Yu P, Deng N, et al. Patients' acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR Mhealth Uhealth, 2017, 5(12): e177.
47. Dehzad F, Hilhorst C, de Bie C, et al. Adopting health apps, what’s hindering doctors and patients. Health, 2014, 6(16): 2204-2217.
48. Kim HW, Kankanhalli A. Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Q, 2009, 33(3): 567-582.

Chinese Journal of Evidence-Based Medicine

Influencing factors of patients' adoption intention toward online medical services: a meta-analysis

Abstract Full text Figures/Tables Video References Cited by

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