Electronic Health Record in Hospitals: A Theoretical Framework for Collaborative Lifecycle Risk Management
Bibliographic record
Abstract
Prior research has generated substantial knowledge about information technology (IT) risk management in general and clinical information systems in particular.Nonetheless, in recent years, important accumulated signs have shown that this wisdom, due to some limitations, might not be adequate in forging useful insights for managing risk associated with electronic health record (EHR) in a hospital context.I aim to shift thinking away from two such held major limitations of the extant literature on IT risk management: (1) one-phase focused, as opposed to considering the whole system lifecycle, and (2) client-centric or health care provider (adopting organization) view, as opposed to considering all key players (health care provider, health care payer, software vendor, payers, etc.).In doing so, this essay attempts to draw researchers' attention to the following issue: How should hospitals manage the risk of EHR throughout the entire system lifecycle?Drawing from Poba-Nzaou [1] and Poba-Nzaou and Raymond [2], I articulate a conceptual framework for addressing this issue and framing important questions for future research as well as generating insights for improving hospitals' practices.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".