Abstract
| Original language | English |
|---|---|
| Number of pages | 22 |
| Publication status | Published - 24 Oct 2019 |
| Event | 5th International Conference on Information and Communication Technologies in Organizations and Society, ICTO 2019: The Impact of Artificial Intelligence on Business and Society - Lille, France Duration: 24 Oct 2019 → 25 Oct 2019 Conference number: 5 https://icto2019.webs.com |
Conference
| Conference | 5th International Conference on Information and Communication Technologies in Organizations and Society, ICTO 2019 |
|---|---|
| Abbreviated title | ICTO 2019 |
| Country | France |
| City | Lille |
| Period | 24/10/19 → 25/10/19 |
| Internet address |
Fingerprint
Keywords
- analytics; clinical decision support system; artificial intelligence adoption
Cite this
}
Barriers for Adoption of Analytical CDSS in Healthcare : Insights from Case Stakeholders. / Wijnhoven, Fons; Klein Koerkamp, Rick.
2019. Paper presented at 5th International Conference on Information and Communication Technologies in Organizations and Society, ICTO 2019, Lille, France.Research output: Contribution to conference › Paper › Academic › peer-review
TY - CONF
T1 - Barriers for Adoption of Analytical CDSS in Healthcare
T2 - Insights from Case Stakeholders
AU - Wijnhoven, Fons
AU - Klein Koerkamp, Rick
PY - 2019/10/24
Y1 - 2019/10/24
N2 - Most analytical Clinical Decision Support Systems (CDSS) remain within a research and development (R&D) environment and lack implementations in clinical contexts. This study discusses these implementation challenges as IT adoption barriers and gives possible solutions for these barriers. For this, we have studied the stakeholder perceptions of the case of an analytical CDSS implementation called ‘Big data for small babies’ (BD4SB) which analyzes medical data to predict the probability on sepsis for prematurely born babies and to support the physicians’ decision-making on ministering antibiotics. The stakeholders explain that the system shows promising results; however, the transition from the R&D environment to the clinical environment is complex. From this study, we learn new insights regarding the adoption of analytical DSS systems in the clinical field and we developed a new generalizable method for analytical DSS projects in an organizational context.
AB - Most analytical Clinical Decision Support Systems (CDSS) remain within a research and development (R&D) environment and lack implementations in clinical contexts. This study discusses these implementation challenges as IT adoption barriers and gives possible solutions for these barriers. For this, we have studied the stakeholder perceptions of the case of an analytical CDSS implementation called ‘Big data for small babies’ (BD4SB) which analyzes medical data to predict the probability on sepsis for prematurely born babies and to support the physicians’ decision-making on ministering antibiotics. The stakeholders explain that the system shows promising results; however, the transition from the R&D environment to the clinical environment is complex. From this study, we learn new insights regarding the adoption of analytical DSS systems in the clinical field and we developed a new generalizable method for analytical DSS projects in an organizational context.
KW - analytics; clinical decision support system; artificial intelligence adoption
M3 - Paper
ER -