TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Hüsers, Jens
A1  - Esdar, Moritz
A1  - Weiß, Jan-Patrick
A1  - Hübner, Ursula Hertha
T1  - Diffusion Dynamics of Radiology IT : Systems in German Hospitals : A Bayesian Bass Model
T2  - German Medical Data Sciences : Shaping Change – Creative Solutions for Innovative Medicine
N2  - Radiology has a reputation for having a high affinity to innovation – particularly with regard to information technologies. Designed for supporting the peculiarities of radiological diagnostic workflows, Radiology Information Systems (RIS) and Picture Archiving and Communication Systems (PACS) developed into widely used information systems in hospitals and form the basis for advancing the field towards automated image diagnostics. RIS and PACS can thus serve as meaningful indicators of how quickly IT innovations diffuse in secondary care settings – an issue that requires increased attention in research and health policy in the light of increasingly fast innovation cycles. We therefore conducted a retrospective longitudinal observational study to research the diffusion dynamics of RIS and PACS in German hospitals between 2005 and 2017. Based upon data points collected within the “IT Report Healthcare” and building on Rogers’ Diffusion of Innovation (DOI) theory, we applied a novel methodological technique by fitting Bayesian Bass Diffusion Models on past adoption rates. The Bass models showed acceptable goodness of fit to the data and the results indicated similar growth rates of RIS and PACS implementations and suggest that market saturation is almost reached. Adoption rates of PACS showed a slightly higher coefficient of imitation (q = 0.25) compared to RIS (q = 0.11). However, the diffusion process expands over approximately two decades for both systems which points at the need for further research into how innovation diffusion can be accelerated effectively. Furthermore, the Bayesian approach to Bass modelling showed to have several advantages over the classical frequentists approaches and should encourage adoption and diffusion research to adapt similar techniques.
Y1  - 2019
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:959-opus-20479
SN  - 978-1-64368-016-3
SB  - 978-1-64368-016-3
SN  - 978-1-64368-017-0
SB  - 978-1-64368-017-0
U6  - https://doi.org/10.3233/SHTI190799
DO  - https://doi.org/10.3233/SHTI190799
N1  - 64th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology (GMDS e.V.), 08.11. - 11.09.2019, Dortmund
SP  - 11
EP  - 19
PB  - IOS Press
CY  - Amsterdam
ER  -