Implementation of a sustainable enterprise risk management framework: The Administrator on Duty model
Bibliographic record
Abstract
Background: Today’s health care landscape requires a new standard of service delivery aimed at quality outcomes, cost-effective provisions of coordinated treatment, and access to equitable care. This standard has brought emerging risks that pose threats to the operational and financial well-being of health care organizations, especially safety net hospitals. The establishment of enterprise risk management (ERM) programs guided by the efforts of efficacious health care managers will promote deeper risk analysis, engagement of the entire health care organization, and structured, coordinated and cohesive mitigation responses to risk exposures.Objective: To establish and implement an ERM program using the Administrator on Duty (AOD) model that will promote a patient-centric paradigm of care while optimizing organizational performance and mitigating risk and exposure.Results: The AOD model significantly contributes to all phases of ERM, particularly risk identification, risk assessment, risk response and monitoring. The model, as perceived by both AODs and hospital senior leadership, provides tremendous benefits to a health care organization. These include, among many others, a substantial leadership presence, dynamic risk mitigation efforts, continuous education to staff and facilitation of problem solving and conflict resolution.Conclusions: The AOD program is a vital constituent of an ERM endeavor. AODs are pivotal to managing the global risk terrain of a health care organization and play a substantial role in promoting patient, staff and visitor safety while working to ensure potential and actual risk issues are addressed timely and appropriately.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".