A Risk Assessment of Two Interorganizational Clinical Information Systems
Bibliographic record
Abstract
A risk analysis framework was used to examine the implementation barriers that may hamper the successful implementation of interorganizational clinical information systems (ICIS). In terms of study design, an extensive literature review was first performed in order to elaborate a comprehensive model of project risk factors. To test the applicability of the model, we next conducted a longitudinal multiple-case study of two large-scale ICIS demonstration projects carried out in Quebec, Canada. Variations in the levels of several risk dimensions measured throughout the duration of the projects were analyzed to determine their impact on successful implementation. The analysis shows that the proposed framework, composed of five risk dimensions, was very robust, and suitable for conducting a thorough risk analysis. The results also show that there are links between the quality of the risk management and the level of project outcomes. To be successful, it is important that the implementation efforts be distributed proportionally according to the importance of each of the risk factors. Furthermore, because the risks evolve dynamically, there is a need for high responsiveness to emerging implementation problems. Thus, implementation success lies in the ability of the project management team to be aware of and to manage several risk threats simultaneously and coherently since they evolve dynamically through time and interact with one another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".