Challenges and opportunities in achieving secure hospital clinical mobility management: An illustrative use case
Bibliographic record
Abstract
Objective: To qualitatively describe a use case at Yale New Haven Health System (YNHHS) illustrating the need for and effective deployment of innovative technologies to manage an enterprise-owned shared device (EOSD) management program. EOSD management provides clinicians with secure, rapid access to enterprise mobile devices and applications, maintains devices in functional, use ready condition for clinicians, and enables enterprise tracking and reduced loss of devices.Methods: Executive leaders in clinical information technology and informatics management at YNHHS were interviewed through written and telephonic communication. Qualitative data was gathered through communications between clinical and information technology executives and the implementation support team of a leading identity and access management (IAM) solutions and EOSD management solution provider. Use case information was gathered, integrated and shared with health system executives and health IT/informatics leaders to verify the description of unmet needs, solution objectives and impact/value delivered after implementation of the EOSD management solution.Results: Benefits realized from implementation of an enterprise-shared mobility management solution included establishment of a cohesive and comprehensive enterprise-owned, shared device management strategy. This included effective monitoring and dynamic management of the system’s mobile device fleet, and better IT resource management with reduced mobile device loss. The IT administrative burden was reduced. While not surveyed systematically, improved clinician experience and satisfaction were reported to IT leaders anecdotally. EOSD management solution deployment was rapid, as was the time to improved clinician mobile experience and clear demonstration of value.Conclusions: A leading US health system was able to rapidly deploy a shared mobile device management solution that enabled effective monitoring and dynamic management of the enterprise mobile device fleet, with easier and faster clinician device access and workflows, and reduced IT administrative demand and costs. While the complexities associated with increased clinical mobility in healthcare will likely continue to grow, issuing future device and mobile management challenges that require effective hospital system response, technologies have emerged that enable more effective, efficient and satisfactory organizational mobility performance.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Health IT use case on managing shared clinical mobile devices in a hospital system.
It describes a hospital technology-management use case, not research practice.
Hospital shared mobile-device management use case is clinical IT operations, not metaresearch.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".