ROLE OF A CLINICIAN NURSE IN MANAGEMENT OF HOSPITAL CONSULTATION REFERRALS IN A TERTIARY CARE PAIN CLINIC. PRELIMINARY RESULTS
Bibliographic record
Abstract
A-1021-0080-01435 Abstract title: Role of a clinician nurse in management of hospital consultation referrals in a tertiary care pain clinic. Preliminary results Poster presentation (Human/Clinical)Role of a clinician nurse in management of hospital consultation referrals in a tertiary care pain clinic. Preliminary results.L. Guay1,, G. Vargas-Schaffer1,2, M. Eghtesadi1,21Pain Center of Centre Hospitalier de l'Universitu00e9 de Montru00e9al (CHUM), Montru00e9al, Canada, 2Centre de Recherche du CHUM (CRCHUM), Montru00e9al, Canada.Background and aimsUtilization of a clinician nurse for management of patients with chronic pain has been encouraged in the past as part of an interest to reduce the cost of medical care, but also to give the patient a more personalized and less crisis-oriented service. Here we present results of such a collaboration for patients who are admitted at a tertiary care hospital and medically stabilized but who require chronic pain services. MethodsThe clinician nurse time is dedicated to these referrals and begins with triage of elements used for priority. The clinician nurse intervention then involves significant teaching towards nursing and medical staff about overall use of analgesics, patient advocacy and presence amongst multidisciplinary meetings. ResultsThe pain clinic staff physician will only be consulted for 3 out of 4 new referrals and 1 out of 5 reassessments. There is a constant pool of 10-15 admitted patients for which our pain clinic is actively involved but only 1 out of 10 will require outpatient follow up at our clinic after discharge. Assessment facilitators include positive physician attitude towards a clinician nurse expertise and support from hospital administration. The most common barriers include staffing shortages and negative prejudice towards patients viewed as having u2018u2019pain or opioid seeking behaviouru2019u2019.ConclusionAn established collaborative agreement between a clinician nurse and a group of physicians leads to reduced discharge times for patients. References1.Schadewaldt V, McInnes E, Hiller JE, Gardner A. Views and experiences of nurse practitioners and medical practitioners with collaborative practice in primary health care - an integrative review. BMC family practice. 2013;14:132.2.McCaffrey RG, Hayes R, Stuart W, Cassell A, Farrell C, Miller-Reyes C, et al. A program to improve communication and collaboration between nurses and medical residents. Journal of continuing education in nursing. 2010;41(4):172-3.Tschannen D, Kalisch BJ. The impact of nurse/physician collaboration on patient length of stay. Journal of nursing management. 2009;17(7):796-803.4.Ettner SL, Kotlerman J, Afifi A, Vazirani S, Hays RD, Shapiro M, et al. An alternative approach to reducing the costs of patient care? A controlled trial of the multi-disciplinary doctor-nurse practitioner (MDNP) model. Medical decision making : an international journal of the Society for Medical Decision Making. 2006;26(1):9-17.5.Cowan MJ, Shapiro M, Hays RD, Afifi A, Vazirani S, Ward CR, et al. The effect of a multidisciplinary hospitalist/physician and advanced practice nurse collaboration on hospital costs. The Journal of nursing administration. 2006;36(2):79-85.6.Zwarenstein M, Goldman J, Reeves S. Interprofessional collaboration: effects of practice-based interventions on professional practice and healthcare outcomes. The Cochrane database of systematic reviews. 2009(3):Cd000072.7. Connelly SV, Connelly PA. Physicians' patient referrals to a nurse practitioner in a primary care medical clinic. American journal of public health. 1979;69(1):73-5.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".