The implementation of Telemedicine in behavioral health centers can be expensive if proactive steps were not taken to minimize user perceptions towards the new technology. Despite the significant capital investments on new Telemedicine, no consensus identified and explained what factors determined the acceptance, or rejection, of the technology. The study purpose was to explore physician Telemedicine adoption (TA) as a patient diagnosis tool in Georgia's behavioral healthcare centers using the Technology Acceptance Model (Davis, 1989). One hundred and ninety of 500 physicians from Georgia behavioral healthcare centers responded to an online survey for a 38% response rate. Pearson correlations coefficients were calculated among nine predictor variables, ATTUR, CAHS, PEU, PSNTT, PTTS, PTTSB, PTTSR, TTAT, and TTC, and determined associations with the criterion variable, TA. Data analysis resulted in moderate to high correlation among all variables (p<0.01) with a majority of the variance of TA (55.1%) explained by the combined TAM predictors. Three implications were determined as result of the statistical analysis; (a) the correlation between the nine predictor variables may play in TA and implementation in behavioral healthcare organizations, (b) positive user attitude and perceptions were necessary for TA with behavioral healthcare physicians, and (c) when combined, user perceptions explained a majority of TA; yet, other explanatory factors remain. Three recommendations were made for professional practice: behavioral healthcare administrators should use the study results to (a) design appropriate intervention programs, (b) measure behavioral intentions and attitudes towards TA, and (c) implement Telemedicine technology perceived as user friendly and beneficial to their clients. Five recommendations were made for future research: (a) a quantitative correlation study with an expanded sample to encompass all U.S. behavioral healthcare centers, (b) a quantitative inferential study focused on one demographic characteristic such as gender as a confounding variable, (c) a qualitative study with behavioral healthcare physicians, (d) a quantitative study of age as a predictor of TA, and (e) an experimental study to examine if training on the Telemedicine technology affects physicians perceptions of some of the TAM predictor variables and the dependent variable of TA. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]