%0 Journal Article %1 LoCiganic2016 %A Lo-Ciganic, Wei-Hsuan %A Gellad, Walid F. %A Huskamp, Haiden A. %A Choudhry, Niteesh K. %A Chang, Chung-Chou H. %A Zhang, Ruoxin %A Jones, Bobby L. %A Guclu, Hasan %A Richards-Shubik, Seth %A Donohue, Julie M. %D 2016 %J Medical Care %K imported %N 7 %P 725-732 %R 10.1097/MLR.0000000000000549 %T Who Were the Early Adopters of Dabigatran? %U http://www.ncbi.nlm.nih.gov/pubmed/27116109 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4907845 https://insights.ovid.com/crossref?an=00005650-201607000-00012 %V 54 %X BACKGROUND Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption. OBJECTIVE Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant. METHODS A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions. OUTCOMES Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran. RESULTS We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ≤3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (≤3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6-10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27-16.1 as were younger prescribers (age 36-45 y: OR=1.49, 95% CI, 1.13-1.95; age 46-55: OR=1.34, 95% CI, 1.07-1.69 vs. >55 y). CONCLUSIONS Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.

@article{LoCiganic2016, abstract = {BACKGROUND Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption. OBJECTIVE Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant. METHODS A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions. OUTCOMES Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran. RESULTS We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ≤3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (≤3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6-10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt [odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27-16.1] as were younger prescribers (age 36-45 y: OR=1.49, 95% CI, 1.13-1.95; age 46-55: OR=1.34, 95% CI, 1.07-1.69 vs. >55 y). CONCLUSIONS Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Lo-Ciganic, Wei-Hsuan and Gellad, Walid F. and Huskamp, Haiden A. and Choudhry, Niteesh K. and Chang, Chung-Chou H. and Zhang, Ruoxin and Jones, Bobby L. and Guclu, Hasan and Richards-Shubik, Seth and Donohue, Julie M.}, biburl = {https://www.bibsonomy.org/bibtex/2de1731e36c3a3a17f9effc415302ed67/jepcastel}, doi = {10.1097/MLR.0000000000000549}, interhash = {74aff86ae077ae2c78cca9fc52af0210}, intrahash = {de1731e36c3a3a17f9effc415302ed67}, issn = {0025-7079}, journal = {Medical Care}, keywords = {imported}, month = {7}, note = {Anàlisi de dades; Group-based trajectory models}, number = 7, pages = {725-732}, pmid = {27116109}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Who Were the Early Adopters of Dabigatran?}, url = {http://www.ncbi.nlm.nih.gov/pubmed/27116109 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4907845 https://insights.ovid.com/crossref?an=00005650-201607000-00012}, volume = 54, year = 2016 }