Improving Provisioning of an Inpatient Portal: Perspectives from Nursing Staff
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Bibliographic record
Abstract
BACKGROUND: Inpatient portals are recognized to provide benefits for both patients and providers, yet the process of provisioning tablets to patients by staff has been difficult for many hospitals. OBJECTIVE: Our study aimed to identify and describe practices important for provisioning an inpatient portal from the perspectives of nursing staff and provide insight to enable hospitals to address challenges related to provisioning workflow for the inpatient portal accessible on a tablet. METHODS: Qualitative interviews were conducted with 210 nursing staff members across 26 inpatient units in six hospitals within The Ohio State University Wexner Medical Center (OSUWMC) following the introduction of tablets providing access to an inpatient portal, MyChart Bedside (MCB). Interviews asked questions focused on nursing staffs' experiences relative to MCB tablet provisioning. Verbatim interview transcripts were coded using thematic analysis to identify factors associated with tablet provisioning. Unit provisioning performance was established using data stored in the OSUWMC electronic health record about provisioning status. Provisioning rates were divided into tertiles to create three levels of provisioning performance: (1) higher; (2) average; and (3) lower. RESULTS: Three themes emerged as critical strategies contributing to MCB tablet provisioning success on higher-performing units: (1) establishing a feasible process for MCB provisioning; (2) having persistent unit-level MCB tablet champions; and (3) having unit managers actively promote MCB tablets. These strategies were described differently by staff from the higher-performing units when compared with characterizations of the provisioning process by staff from lower-performing units. CONCLUSION: As inpatient portals are recognized as a powerful tool that can increase patients' access to information and enhance their care experience, implementing the strategies we identified may help hospitals' efforts to improve provisioning and increase their patients' engagement in their health care.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it