Delivering an Electronic Health Record Based Educational Intervention Promoting Peri-Operative Non-Pharmacological Pain Care as Part of a Randomized Controlled Trial: Mixed Method Evaluation of Inpatient Nurses’ Perspectives
Bibliographic record
Abstract
Background: Best practice guidelines recommend educating surgical patients about non-pharmacological pain care (NPPC) techniques that can be used in addition to pain medication for perioperative pain management, given the risks for opioid misuse following surgery. As part of the parent non-pharmacologic options in postoperative hospital-based and rehabilitation pain management (NOHARM) clinical trial, we implemented the Healing After Surgery initiative, which leveraged the Epic electronic health record (EHR) to provide patients with education on NPPC techniques perioperatively. We disseminated educational materials directly to patients via the EHR patient portal and prompted patients to select the techniques they were most interested in using, which auto-populated the EHR so that their care team could view their preferences. We also built clinical decision support elements in the EHR to prompt and support inpatient nurses in providing patients with education and reinforcement for using their preferred NPPC techniques. Print materials, a website, a DVD, videos on hospital televisions, a toll-free number, and Zoom-based group calls provided additional education on NPPC techniques. Objective: This study evaluated nurses' perceptions of barriers and facilitators to implementing the EHR-based Healing After Surgery initiative. Methods: We invited inpatient nursing leaders and bedside nurses to participate in a semistructured interview. Inpatient nursing leaders were invited to complete a brief survey that asked them to rate their agreement with 7 items using a numeric rating scale (1=not at all, 10=a great deal). Results: Interview findings from 29 nurses revealed: (1) nurses gravitated towards providing NPPC techniques they were familiar with, (2) the initiative was patient-centric with opportunities to better engage patients, and (3) nurses experienced challenges implementing and prioritizing the intervention in the inpatient setting due to competing demands in a pandemic and postpandemic environment. Interviews revealed mixed effectiveness of implementation strategies. We received survey responses from 47 nursing leaders who indicated that their staff knew about the Healing After Surgery initiative (mean=7.53, SD=1.77) and what they were expected to do (mean=7, SD=1.88). They thought the Healing After Surgery initiative supported patients' pain management needs (mean=6.76, SD=2.24), endorsed it as a priority (mean=7.02, SD=2.56), and encouraged staff to support it (mean=5.98, SD=2.78). They indicated staff experienced some burden supporting the initiative (mean=3.93, SD=2.47), but supported some variation of the initiative continuing once the parent trial ended (mean=7.72, SD=2.62). Conclusions: Nurses understood the intervention's benefit but struggled to implement unfamiliar NPPC techniques and prioritize the initiative due to other clinical demands. Additional implementation strategies may be needed to better engage patients and facilitate intervention delivery.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".