Table 1.
Factors influencing the adoption of intelligent healthcare services by medical institutions with integrated medical care.
| Author | Country/region of data collection | Methods/models | Factors | Type of research (qualitative or quantitative) | Qualitative | Quantitative | ||
|---|---|---|---|---|---|---|---|---|
| Number of cases | Methods for data collection | Sample size | Methods for data collection | |||||
| Wang et al. 22 | USA | Multiple regression analysis. Cross-sectional analysis. Multiple regression. Cross-sectional analysis. TOE framework |
Inaccurate forecasting of market trends. High risk of data leakage. Communication channels. Managed care pressures. Competition and community needs. Financial factors. |
Quantitative | 1441 | Survey | ||
| Zheng et al. 23 | USA | Social network analysis | Gender. Medical risks. Skepticism about AI processing power. Computer experience. Computer knowledge Computer optimism. Perceived usefulness of intelligent systems and their ease of use. | Quantitative | 55 | Survey | ||
| Callaway 24 | USA | Logit regression. Survival analysis. |
Economic benefits. Financial costs. Skepticism about AI processing capabilities. Patient access perceptions. |
Quantitative | 5082 | Survey | ||
| Lian et al. 25 | China | Regression analysis. ANOVA. TOE framework. IOR framework. |
Perceived usefulness. High risk of data leakage. System service
complexity. AI infrastructure synergy. Costs. Relative advantages. Leadership management support. Inability to share information. Economic benefits. Government policies. Perceived competitive pressures. |
Quantitative | 60 | Questionnaire | ||
| Chang et al. 10 | China | Regression analysis. TOE framework. |
User participation. Inability to share information. Hospital size. Difficulty in meeting complex needs of elderly patients. Lack of excellent vendor support. Government policies. Security protection. Complexity of system services. | Quantitative | 53 | Questionnaire | ||
| Chong and Chan 18 | Malaysia | Structural equation model. TOE framework. |
Relative advantages. AI infrastructure synergy. System service complexity. Financial costs. High risk of data breaches. Leadership management support. Organization size. Economics. Lack of awareness of value and benefits of healthcare + AI technology. Competitive pressures. Inaccurate forecasting of market trends. | Quantitative | 182 | Questionnaire | ||
| Liu 26 | China | Regression analysis. TOE framework. |
AI infrastructure synergy. Relative strengths. Lack of excellent vendor support. Leadership management support. Lack of awareness of value and benefit of healthcare + AI technology. Internal needs. Government support. Competitive business pressures. | Quantitative | 70 | Questionnaire | ||
| Kazley and Ozcan 13 | USA | One-way ANOVA. Logistic regression. TOE framework. | Competitiveness. Geographical tolerance. Lack of awareness of value and advantages of AI medical technology. Hospital size. System integration. Health insurance payments. Financial support. Training support. |
Quantitative | 4606 | Survey | ||
| Lin et al. 15 | China | Factor analysis. Logistic regression. Pearson chi-square test. | Hospital size. High risk of data leakage. System integration. Lack of complex talent. Leadership management support. Competitive environment. Inaccurate forecasting of market trends. | Quantitative | 119 | Questionnaire | ||
| Hung et al. 27 | China | Factor analysis. Regression analysis. | Hospital size. Lack of complex talent. Leadership management support. Knowledge management capabilities. Relative strengths. System service complexity. | Quantitative | 97 | Questionnaire | ||
| Ahmadi et al. 28 | Malaysia | DEMATEL. ANP. AHP. TOE framework. | Relative advantages. AI infrastructure synergy. System service complexity. System integration. Government policy. Hospital size. High risk of data breaches. Leadership management support. Competitive environment. Lack of excellent vendor support. | Quantitative | 12 | Questionnaire | ||
| Greenberg et al. 29 | Israel | Expert interview. VIKOR method. | Financial costs. Efficiency improvements. Policy support. Reputation contributions. Profitability improvements. Leadership management support. Industry pressures. Employee training. | Qualitative | 26 hospitals, 132 hospital executives | Interviews | ||
| Asagbra et al. 30 | USA | OLS regression. Multivariate analysis. TOE framework. | Lack of patient trust. Health insurance support. Geographic tolerance. Complexity of system services. Hospital size. System integration. Lack of clarity of hospital ownership. Training support. | Quantitative | 4176 | Survey | ||
| Young et al. 31 | USA | Cox proportional hazards model. | Leadership management support. System service complexity. Hospital size. | Quantitative | 150 | Survey | ||
| Chen et al. 32 | China | Factor analysis. Regression analysis. | Hospital climate. Hospital size. Inability to share information. Internal needs. Leadership management support. Staff attitudes. Skepticism about AI processing capabilities. Healthcare policies. Lack of excellent vendor support. High risk of data leakage. Lack of patient trust. | Quantitative | 227 | Questionnaire | ||
| Alam et al. 33 | Bangladesh | Regression analysis. ANOVA analysis. TOE framework. | IT infrastructure. AI infrastructure synergy. Complexity. Relative strengths. Management leadership support. Unclear hospital ownership. Formalization. Perceived costs. Competitive pressures. Lack of excellent vendor support. Government policy and support. Skeptical of AI processing capabilities. Ability to lead innovation. | Quantitative | 383 | Questionnaire | ||
| Lee et al. 4 | South Korea | Multivariable analyses. Structural equation model. | Provider performance projections. Provider effort expectations. Provider attitudes. Social influence. Lack of excellent provider support. | Quantitative | 383 | Survey | ||
| Yang et al. 16 | USA | Expert interviews. TOE framework. | Lack of excellent vendor support. Relative advantages. AI infrastructure synergy. Complexity. Hospital type. Unclear hospital ownership. Hospital size. Internal needs. Inability to share information. Uncertain technological knowledge. Knowledge management capabilities. Lack of qualified teamwork capabilities. Leadership management support. Government policy support. Lack of excellent partner relationships. Competitive market pressures. National guarantees. | Qualitative | 24 | Interviews | ||
| Tsagaankhuu et al. 19 | Mongolia | Negative binomial regression. Multiple regression. | Hospital size. Number of beds. Training support. Geographic location. Unclear ownership affiliation. HMO penetration. | Quantitative | 78 | Questionnaire | ||
| Fan et al. 34 | China | Regression analysis. Case study. | Trust orientation. Social influence. Perceived substitution crisis. Job expectations. | Quantitative | 191 | Questionnaire | ||
| Hoque 35 | Bangladesh | Structural equation model. Regression analysis. | Perceived usefulness and ease of use. Subjective norms. | Quantitative | 234 | Questionnaire | ||
| Wu 36 | China | Structural equation model. Regression analysis. | Perceived service availability. Skeptical of AI processing capabilities. Perceived usefulness and ease of use. Hospital size. Lack of excellent supplier support. | Quantitative | 140 | Questionnaire | ||
| Kijsanayotin et al. 37 | Thailand | Structural equation model. Regression analysis. | Performance and effort expectations. Social impact. Employees’ computer processing capabilities. Facilitation. | Quantitative | 1323 | Questionnaire | ||
| Faber et al. 38 | Netherlands | Structural equation model. Regression analysis. | Hospital size. Number of beds, Leadership management support. IT infrastructure, human resources, government support, and security. Financial foundation. Centralization of decisionmaking. Lack of complex talent. | Quantitative | 58 | Questionnaire | ||
| Tortorella et al.39,40 | Brazil | Cluster analysis. ANOVA. Multivariate analysis. | Regulatory changes. IT infrastructure. Working against hospitals’ strategies. High risk of data breaches. Implementation costs. Lack of technological knowledge, qualified teamwork skills, and excellent partner relationships. | Quantitative | 159 | Questionnaire | ||
| Sun and Medaglia 41 | China | Expert interviews. Multi-attribute decision-making. | Perceived usefulness of intelligent systems. High costs and meager profits for hospitals. High risk of data leakage. Misconceptions and lack of awareness of value and advantages of AI medical technology. Lack of innovation. Lack of ability to read structured medical data. Skepticism of AI processing capabilities | Qualitative | 17 | Interviews | ||
| Mardani et al. 42 | Vietnam | Expert interviews. Multi- attribute decision-making. | High risk of data breaches. Skepticism about AI processing capabilities. Low-security programing. Lack of awareness of value and benefits of AI healthcare technology. Training support. Medical source risks. Unaffordable costs. | Qualitative | 24 | Interviews | ||
| Xing et al. 43 | China | Thematic analysis. Focus group. | Difficulty maintaining stability in device performance. Lack of complex talent. Skepticism about AI processing capabilities. Lack of management leadership support, qualified teamwork, and clinical value. Fear of changes in clinical workloads. Imbalances of costs and expenses. Lack of sustainable business models and government policies. High risk of data breaches. Lack of patient trust. Difficulty meeting complex needs of older patients | Qualitative | 38 | Interviews | ||