Digital Maturity as a Strategy for Advancing Patient Experience in US Hospitals
Bibliographic record
Abstract
Patient experience is globally recognized as an important indicator of health system performance, linked to health system quality and improving patient outcomes. Post COVID-19, health systems have embraced digital health and advanced digital transformation efforts; however, the relationship between digital health and patient experience outcomes is not well-documented. Using HCAHPS hospital survey data to measure patient experience, and HIMSS EMRAM Maturity Model data to measure digital maturity, a cross-sectional design using multivariate analyses examined the impact of digital maturity on patient experience in US hospitals. Our analysis shows that advanced digital maturity in US hospitals is associated with stronger patient experience outcomes, particularly relative to communication with nurses, doctors, and communication about medicines and therapies. The findings suggest that there are significant differences in patient experience associated with teaching versus nonteaching hospitals, urban versus rural hospitals. As hospitals advance and progress digital transformation initiatives, evidence to inform how transformation efforts can engage and advance patient experience will contribute to health system performance well into the future.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".