Modeling and Predicting Outcomes of eHealth Usage by European Physicians: Multidimensional Approach from a Survey of 9196 General Practitioners.

BACKGROUND: The literature has noted the need to use more advanced methods and models to evaluate physicians' outcomes in the shared health care model that electronic health (eHealth) proposes. OBJECTIVE: The goal of our study was to design and evaluate a predictive multidimensional model of the outcomes of eHealth usage by European physicians. METHODS: We used 2012-2013 survey data from a sample of 9196 European physicians (general practitioners). We proposed and tested two composite indicators of eHealth usage outcomes (internal practices and practices with patients) through 2-stage structural equation modeling. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage outcomes indicators were also calculated. RESULTS: European general practitioners who were female (internal practices OR 1.15, 95% CI 1.10-1.20; practices with patients OR 1.19, 95% CI 1.14-1.24) and younger-aged <35 years (internal practices OR 1.14, 95% CI 1.02-1.26; practices with patients OR 1.32, 95% CI 1.13-1.54) and aged 36-45 years (internal practices OR 1.16, 95% CI 1.06-1.28; practices with patients OR 1.21, 95% CI 1.10-1.33)-had a greater propensity toward favorable eHealth usage outcomes in internal practices and practices with patients. European general practitioners who positively valued information and communication technology (ICT) impact on their personal working processes (internal practices OR 5.30, 95% CI 4.73-5.93; practices with patients OR 4.83, 95% CI 4.32-5.40), teamwork processes (internal practices OR 4.19, 95% CI 3.78-4.65; practices with patients OR 3.38, 95% CI 3.05-3.74), and the doctor-patient relationship (internal practices OR 3.97, 95% CI 3.60-4.37; practices with patients OR 6.02, 95% CI 5.43-6.67) had a high propensity toward favorable effects of eHealth usage on internal practices and practices with patients. More favorable eHealth outcomes were also observed for self-employed European general practitioners (internal practices OR 1.33, 95% CI 1.22-1.45; practices with patients OR 1.10, 95% CI 1.03-1.28). Finally, general practitioners who reported that the number of patients treated in the last 2 years had remained constant (internal practices OR 1.08, 95% CI 1.01-1.17) or increased (practices with patients OR 1.12, 95% CI 1.03-1.22) had a higher propensity toward favorable eHealth usage outcomes. CONCLUSIONS: We provide new evidence of predictors (sociodemographic issues, attitudes toward ICT impacts, and working conditions) that explain favorable eHealth usage outcomes. The results highlight the need to develop more specific policies for eHealth usage to address different realities.

Modelling and Predicting eHealth Usage in Europe: A Multidimensional Approach From an Online Survey of 13,000 European Union Internet Users.

BACKGROUND: More advanced methods and models are needed to evaluate the participation of patients and citizens in the shared health care model that eHealth proposes. OBJECTIVE: The goal of our study was to design and evaluate a predictive multidimensional model of eHealth usage. METHODS: We used 2011 survey data from a sample of 13,000 European citizens aged 16-74 years who had used the Internet in the previous 3 months. We proposed and tested an eHealth usage composite indicator through 2-stage structural equation modelling with latent variables and measurement errors. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage was calculated using health status and sociodemographic independent variables. RESULTS: The dimensions with more explanatory power of eHealth usage were health Internet attitudes, information health Internet usage, empowerment of health Internet users, and the usefulness of health Internet usage. Some 52.39% (6811/13,000) of European Internet users' eHealth usage was more intensive (greater than the mean). Users with long-term health problems or illnesses (OR 1.20, 95% CI 1.12-1.29) or receiving long-term treatment (OR 1.11, 95% CI 1.03-1.20), having family members with long-term health problems or illnesses (OR 1.44, 95% CI 1.34-1.55), or undertaking care activities for other people (OR 1.58, 95% CI 1.40-1.77) had a high propensity toward intensive eHealth usage. Sociodemographic predictors showed that Internet users who were female (OR 1.23, 95% CI 1.14-1.31), aged 25-54 years (OR 1.12, 95% CI 1.05-1.21), living in larger households (3 members: OR 1.25, 95% CI 1.15-1.36; 5 members: OR 1.13, 95% CI 0.97-1.28; ≥6 members: OR 1.31, 95% CI 1.10-1.57), had more children <16 years of age (1 child: OR 1.29, 95% CI 1.18-1.14; 2 children: OR 1.05, 95% CI 0.94-1.17; 4 children: OR 1.35, 95% CI 0.88-2.08), and had more family members >65 years of age (1 member: OR 1.33, 95% CI 1.18-1.50; ≥4 members: OR 1.82, 95% CI 0.54-6.03) had a greater propensity toward intensive eHealth usage. Likewise, users residing in densely populated areas, such as cities and large towns (OR 1.17, 95% CI 1.09-1.25), also had a greater propensity toward intensive eHealth usage. Educational levels presented an inverted U shape in relation to intensive eHealth usage, with greater propensities among those with a secondary education (OR 1.08, 95% CI 1.01-1.16). Finally, occupational categories and net monthly income data suggest a higher propensity among the employed or self-employed (OR 1.07, 95% CI 0.99-1.15) and among the minimum wage stratum, earning ≤€1000 per month (OR 1.66, 95% CI 1.48-1.87). CONCLUSIONS: We provide new evidence of inequalities that explain intensive eHealth usage. The results highlight the need to develop more specific eHealth practices to address different realities.