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Board 273 - Program Innovations Abstract The Use of Hybrid Simulation to Teach Family Communication in Critical Care Fellows - A Novel Approach (Submission #516)

2013· article· en· W2315208310 on OpenAlexaffabout
Tobias Witter, Janice Chisholm, J. J. Evans, Doug Ferkol, Stephen Beed, D. Bruce Holmes

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsDalhousie University
Fundersnot available
KeywordsConsistency (knowledge bases)FidelityCurriculumMedical educationResource (disambiguation)Patient safetyCommunication skillsMedicinePsychologyComputer sciencePedagogyHealth care

Abstract

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Introduction/Background High fidelity simulation has been shown to be an effective tool in teaching various aspects of critical care practicd.1 This includes not only technical skills like inserting lines, performing intubations or chest tubes etc. but also some non-technical skills, in particular crisis resource management.2-6 Over the years, high fidelity simulation has become standard in our institution and we believe this provides a safe learning environment for our trainees and ultimately, improved patient safety. In the critical care environment, family interactions are a very important part of the daily routine and yet the skill of communicating with families has not been addressed in our simulated teaching. Given the deficiency, a new communication curriculum was developed that combines high fidelity simulation with simulated patients who act as family members. Methods In collaboration with communications skills experts from the Dalhousie Faculty of Medicine, Division of Medical Education, a scenario was designed which combined high fidelity simulation with family communication using simulated patients. In the high fidelity component, a critically ill patient was admitted to the ICU with a severe head injury and progressed to neurological death. To keep the fellow engaged in the high fidelity simulation, the fellows managed treatable complications (e.g. tension pneumothorax) but these did not alter the overall outcome. For the communication component, a family consisting of three family members was created using simulated patient actors.. During the interaction with the trainees, the "family" followed a script to make sure there was consistency in each meeting. Initially, they met the "family" for the first time, updated them on the current status of their loved one and informed them of the potential of a very poor outcome. Later, the fellow communicated to the family that their loved one had passed away. The fellows were assessed for their management of the critically ill patient in the ICU with regards to medical skills and crisis resource management skills. The family meeting was assessed using a communications checklist developed by the communication skills experts in consultation with ICU staff. The checklist was based on previously published work on breaking bad news in Palliative care and Oncology.7-12 It focuses on verbal and non-verbal communication, collecting and providing of information, the structure and planning of the meeting and empathy displayed during the meeting. Accordingly, the fellows received direct feedback from the "family" actors and an independent observer including comments on these topics. Results: Conclusion With this novel approach of combining high fidelity simulation with actors trained in giving trainees communication feedback, a realistic situation was created in which the fellow switched from patient care to compassionate family care and back. The scripted approach of the family meeting and the specifically developed standardized evaluation tool allowed us to compare different approaches trainees use for particular situations arising in the meeting. Since our family actors have a non-medical background but are very experienced and trained in giving feedback, many areas of improvement were highlighted that would have escaped the traditional checklist evaluation and led to a great acceptance of this novel approach by our trainees. References 1. Barsuk JH, McGaghie WC, Cohen ER, Balachandran JS, Wayne DB. Use of simulation-based mastery learning to improve the quality of central venous catheter placement in a medical intensive care unit. J Hosp Med. 2009;4(7):397-403. doi: 10.1002/jhm.468; 10.1002/jhm.468. 2. Britt RC, Novosel TJ, Britt LD, Sullivan M. The impact of central line simulation before the ICU experience. Am J Surg. 2009;197(4):533-536. doi: 10.1016/j.amjsurg.2008.11.016;10.1016/j.amjsurg.2008.11.016. 3. Cheruparambath V, Sampath S, Deshikar LN, Ismail HM, Bhuvana K. A low-cost reusable phantom for ultrasound-guided subclavian vein cannulation. Indian J Crit Care Med. 2012;16(3):163-165. doi: 10.4103/0972-5229.102097; 10.4103/0972-5229.102097. 4. Clapper T. Development of a hybrid simulation course to reduce central line infections. J Contin Educ Nurs. 2012;43(5):218-224. doi: 10.3928/00220124-20111101-06; 10.3928/00220124-20111101-06. 5. Figueroa MI, Sepanski R, Goldberg SP, Shah S. Improving teamwork, confidence, and collaboration among members of a pediatric cardiovascular intensive care unit multidisciplinary team using simulation-based team training. Pediatr Cardiol. 2013;34(3):612-619. doi: 10.1007/s00246-012-0506-2; 10.1007/s00246-012-0506-2. 6. Pastis NJ, Doelken P, Vanderbilt AA, Walker J, Schaefer JJ,3rd. Validation of simulated difficult bag-mask ventilation as a training and evaluation method for first-year internal medicine house staff. Simul Healthc. 2013;8(1):20-24. doi: 10.1097/SIH.0b013e318263341f; 10.1097/SIH.0b013e318263341f. 7. Buckman R. Breaking bad news: The S-P-I-K-E-S strategy. In: Community oncology. 2nd ed. ; 2005:138. 8. Davidson JE, Powers K, Hedayat KM, et al. Clinical practice guidelines for support of the family in the patient-centered intensive care unit: American college of critical care medicine task force 2004-2005. Crit Care Med. 2007;35(2):605-622. doi: 10.1097/01.CCM.0000254067.14607.EB. 9. Kaplan M. SPIKES: A framework for breaking bad news to patients with cancer. Clin J Oncol Nurs. 2010;14(4):514-516. doi: 10.1188/10.CJON.514-516; 10.1188/10.CJON.514-516. 10. Kramer BJ, Boelk AZ, Auer C. Family conflict at the end of life: Lessons learned in a model program for vulnerable older adults. J Palliat Med. 2006;9(3):791-801. doi: 10.1089/jpm.2006.9.791. 11. Platt F, Gordon G. Field guide to the difficult patient interview. 2nd ed. New York: Lippincott Williams & Wilkiens; 2004. 12. Silverman J, Kurtz S, Draper J. Skills for communication with patients. 2nd ed. Oxon, UK: Radcliffe Publishing; 2005. Disclosures Fresenius Kabi.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.203
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2030.031

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.

Opus teacher head0.332
GPT teacher head0.509
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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