Prioritizing the risk factors influencing the success of clinical information system projects. A Delphi study in Canada.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to gain a better understanding of the risk factors influencing the success of clinical information system projects. METHODS: This study addresses this issue by first reviewing the extant literature on information technology project risks, and second conducting a Delphi survey among 21 experts highly involved in clinical information system projects in Québec, Canada, a region where government have invested heavily in health information technologies in recent years. RESULTS: Twenty-three risk factors were identified. The absence of a project champion was the factor that experts felt most deserves their attention. Lack of commitment from upper management was ranked second. Our panel of experts also confirmed the importance of a variable that has been extensively studied in information systems, namely, perceived usefulness that ranked third. Respondents ranked project ambiguity fourth. The fifth-ranked risk was associated with poor alignment between the clinical information systems' characteristics and the organization of clinical work. The large majority of risk factors associated with the technology itself were considered less important. This finding supports the idea that technology-associated factors rarely figure among the main reasons for a project failure. CONCLUSIONS: In addition to providing a comprehensive list of risk factors and their relative importance, the study presents a major contribution by unifying the literature on information systems and medical informatics. Our checklist provides a basis for further research that may help practitioners identify the effective countermeasures for mitigating risks associated with the implementation of clinical information systems.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".