How a nursing navigation role enhances patient recovery across Orthopedic Integrated Pathways
Bibliographic record
Abstract
Introduction: Our academic urban institution developed an integrated care pathway that has significantly improved patient transitions to outpatient rehabilitation after total joint replacement (TJR) and as patients requested, supports their earlier discharge from a hospital to home to recover. Early recovery at home has identified a need for care navigation, particularly for patients with a higher complexity of medical and social issues. Implementation of a nursing role for care navigation into our existing integrated pathways would provide support for patients receiving TJR surgery ensuring better coordination of services in their community. Aims, Objectives and Methodology: Our objective was to evaluate an IC nursing lead (ICL) role embedded in our existing orthopedic TJR integrated pathway to support “extended” care patients to safely discharge home. Process mapping sessions conducted in 2019-20, that included key stakeholders and patient partners, identified gaps in communication and care coordination before and after surgery affecting overall patient experience and surgical recovery. “Extended” care patients were identified by the orthopedic care team using a standardized referral form developed in conjunction with the ICL. Patients were contacted 1-2 weeks pre-surgery and supported at various pre-operative time points with care coordination. An in-person visit with the patient occurred on the day of surgery and prior to discharge from hospital. A standard 24-hr post-op phone call was completed once the patient returned home. The ICL could be engaged by the patient or care team at any point across the TJR journey (between consent and day of surgery, acute inpatient stay, and up to 90 days post-surgery. Key Findings: The ICL role, piloted in March 2022, started with gradual enrolment of patients across 2 arthroplasty surgeons. By August 2022, all 6 arthroplasty care teams (surgeons, physiotherapist practitioners, fellows, physician assistants and admins) were committed to this model of care. ICL patients were 41% male and 59% female ranging in age from 50-92 (mean age of 78). Inpatient pts represented 94% (ALOS 1-2 days). Discharge (DC) disposition patterns aligned with existing best practice guidelines. Discharge coordination made up the bulk of support (82%) provided by the ICL. Other key themes and interventions included emotional reassurance (32%), education/expectation setting (30%), information on community resources and services (18%), arrangement of homecare (6%), other miscellaneous questions (5%). The average time spent for care coordination was 90 mins per patient over the 90 day period. Qualitative report from ICL patients revealed improved patient experience with the degree of detail and support provided. "I'm so glad I can call you. I don't always know who to talk to when I have questions". "It's so good to have someone pick up the phone when I need an answer when everything is closed". Conclusions: Early findings highlight the benefits of an ICL in improving care quality of patients undergoing TJR and enabling coordinated transitions post discharge from hospital through to their recovery at home. As hospital stays for TJR becoming exceedingly shorter this type of role becomes even more critical, especially with increased patient needs in the aftermath of COVID-19.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".