Improving Provisioning of an Inpatient Portal: Perspectives from Nursing Staff
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.
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 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".