OBJECTIVE: This study examines hospital motivations to acquire new medical technology, an issue of considerable policy relevance: in this case, whether, when, and why hospitals acquire a new capital-intensive medical technology, magnetic resonance imaging equipment (MRI). STUDY DESIGN: We review three common explanations for medical technology adoption: profit maximization, technological preeminence, and clinical excellence, and incorporate them into a composite model, controlling for regulatory differences, market structures, and organizational characteristics. All four models are then tested using Cox regressions. DATA SOURCES: The study is based on an initial sample of 637 hospitals in the continental United States that owned or leased an MRI unit as of 31 December 1988, plus nonadopters. Due to missing data the final sample consisted of 507 hospitals. The data, drawn from two telephone surveys, are supplemented by the AHA Survey, census data, and industry and academic sources. PRINCIPAL FINDING: Statistically, the three individual models account for roughly comparable amounts of variance in past adoption behavior. On the basis of explanatory power and parsimony, however, the technology model is "best." Although the composite model is statistically better than any of the individual models, it does not add much more explanatory power adjusting for the number of variables added. CONCLUSIONS: The composite model identified the importance a hospital attached to being a technological leader, its clinical requirements, and the change in revenues it associated with the adoption of MRI as the major determinants of adoption behavior. We conclude that a hospital's adoption behavior is strongly linked to its strategic orientation.
RCT Entities:
OBJECTIVE: This study examines hospital motivations to acquire new medical technology, an issue of considerable policy relevance: in this case, whether, when, and why hospitals acquire a new capital-intensive medical technology, magnetic resonance imaging equipment (MRI). STUDY DESIGN: We review three common explanations for medical technology adoption: profit maximization, technological preeminence, and clinical excellence, and incorporate them into a composite model, controlling for regulatory differences, market structures, and organizational characteristics. All four models are then tested using Cox regressions. DATA SOURCES: The study is based on an initial sample of 637 hospitals in the continental United States that owned or leased an MRI unit as of 31 December 1988, plus nonadopters. Due to missing data the final sample consisted of 507 hospitals. The data, drawn from two telephone surveys, are supplemented by the AHA Survey, census data, and industry and academic sources. PRINCIPAL FINDING: Statistically, the three individual models account for roughly comparable amounts of variance in past adoption behavior. On the basis of explanatory power and parsimony, however, the technology model is "best." Although the composite model is statistically better than any of the individual models, it does not add much more explanatory power adjusting for the number of variables added. CONCLUSIONS: The composite model identified the importance a hospital attached to being a technological leader, its clinical requirements, and the change in revenues it associated with the adoption of MRI as the major determinants of adoption behavior. We conclude that a hospital's adoption behavior is strongly linked to its strategic orientation.
Authors: B D Hillman; C R Neu; J D Winkler; J Aroesty; R A Rettig; A P Williams Journal: Int J Technol Assess Health Care Date: 1987 Impact factor: 2.188
Authors: Bruce L Jacobs; Yun Zhang; Ted A Skolarus; John T Wei; James E Montie; Florian R Schroeck; Brent K Hollenbeck Journal: Urology Date: 2012-09-19 Impact factor: 2.649
Authors: Yongren Shi; Craig E Pollack; Pamela R Soulos; Jeph Herrin; Nicholas A Christakis; Xiao Xu; Cary P Gross Journal: Med Care Date: 2019-06 Impact factor: 2.983
Authors: Muhammad Ali Chaudhary; Jeffrey J Leow; Matthew Mossanen; Ritam Chowdhury; Wei Jiang; Peter A Learn; Joel S Weissman; Steven L Chang Journal: BMC Urol Date: 2017-07-11 Impact factor: 2.264
Authors: Bruce L Jacobs; Jonathan G Yabes; Samia H Lopa; Dwight E Heron; Chung-Chou H Chang; Justin E Bekelman; Joel B Nelson; Julie P W Bynum; Amber E Barnato; Jeremy M Kahn Journal: Urology Date: 2020-01-13 Impact factor: 2.649