Engaging patients through Multi-Disciplinary Rounding – The case study at a Michigan hospital
Bibliographic record
Abstract
Multi-Disciplinary Rounding (MDR) is a leading practice and a promising process innovation that seeks to enhance both patient experiences and healthcare outcomes for hospitals. It requires multiple hospital staff involved in patientcare visiting patients as a team at their bedside, so that they can address various issues related to patientcare and care transition and answer any patient questions. This paper discusses the implementation of two different models of patient engagement through MDR to gain input from patients while they are still in the hospital, as opposed to relying on patient satisfaction data, so that hospitals can alter their strategies to educate patients on care plans and help empower them to self-manage their care post-discharge. The MDR is implemented as a process innovation at a comprehensive community teaching hospital in Michigan, with the expectation that it can lead to improved organizational outcomes in both the short run (e.g., reduced length of stay [LOS]) and the long run (e.g., reduced patient readmission and improved patient satisfaction). The hospital implemented MDR in various units as a process innovation to improve patient engagement and patient satisfaction. The initial phase of MDR implementation was nurse-led to gain feedback from patients at three time periods (30, 60 and 90 days) on patient services. The hospital revised the MDR process in the second phase into a doctor led patient education process. While the results to date are not conclusive, they do show how MDR can be used by hospitals to engage patients inside the hospital to gain feedback for continuous improvement, using technology when appropriate, and support patient education on care plans post-discharge.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".