Disrupted and Restored Patient Experience With Transition to New Electronic Health Record System
Bibliographic record
Abstract
Transitioning from one electronic health record (EHR) system to another is of the most disruptive events in health care and research about its impact on patient experience for inpatient is limited. This study aimed to assess the impact of transitioning EHR on patient experience measured by the Hospital Consumer Assessment of Healthcare Providers and Systems composites and global items. An interrupted time series study was conducted to evaluate quarter-specific changes in patient experience following implementation of a new EHR at a Midwest health care system during 2017 to 2018. First quarter post-implementation was associated with statistically significant decreases in Communication with Nurses (−1.82; 95% CI, −3.22 to −0.43; P = .0101), Responsiveness of Hospital Staff (−2.73; 95% CI, −4.90 to −0.57; P = .0131), Care Transition (−2.01; 95% CI, −3.96 to −0.07; P = .0426), and Recommend the Hospital (−2.42; 95% CI, −4.36 to −0.49; P = .0142). No statistically significant changes were observed in the transition, second, or third quarters post-implementation. Patient experience scores returned to baseline level after two quarters and the impact from EHR transition appeared to be temporary.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".