TOWARDS AN ASSESSMENT OF CLOUD E-HEALTH PROJECT RISK: AN EMPIRICAL STUDY
Bibliographic record
Abstract
The introduction of information technology and telecommunications (ITC) in the health care sector has brought so many benefits to the health operators, managers, and patients. However, the increasing use and the application of ITC to the management and delivery of health care well known as e-health has been associated with several e-health risks that need to be examined. In this paper we point out several shortcomings of current risk conceptualization and operationalization, particularly they do not address the integration of a variety of risk components, which are crucial for capturing the essence of e-health risks. To fill this gap and drawing on risk analysis perspective we present and discuss a formal framework for e-health cloud computing project risks that captures potential scenarios, their likelihood and, the associated negative consequences. E-health risks were identified in the literature and a cluster analysis was used to classify different risks into several risk domains according to the developed e-health risk framework. Results show several domains including privacy, security, safety, liability, operational, project and business e-health risks. Implications for researchers and managers are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".