Interprofessional Staffing Pattern Clusters in U.S. ICUs

Author:

Gershengorn Hayley B.12ORCID,Costa Deena Kelly34,Garland Allan5,Lizano Danny67,Wunsch Hannah8910

Affiliation:

1. Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, FL.

2. Division of Critical Care Medicine, Albert Einstein College of Medicine, Bronx, NY.

3. Yale School of Nursing, West Haven, CT.

4. Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT.

5. Department of Medicine, University of Manitoba, Winnipeg, MB, Canada.

6. Physician Assistant Program, Fort Lauderdale Dr. Pallavi Patel College of Healthcare Sciences Health Professions Division, Nova Southeastern University, Fort Lauderdale, FL.

7. HCA Florida Kendall Hospital, Miami, FL.

8. Department of Anesthesiology, Weill Cornell Medical College, New York, NY.

9. Sunnybrook Research Institute, Toronto, ON, Canada.

10. Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada.

Abstract

OBJECTIVES: To identify interprofessional staffing pattern clusters used in U.S. ICUs. DESIGN: Latent class analysis. SETTING AND PARTICIPANTS: Adult U.S. ICUs. PATIENTS: None. INTERVENTIONS: None. ANALYSIS: We used data from a staffing survey that queried respondents (n = 596 ICUs) on provider (intensivist and nonintensivist), nursing, respiratory therapist, and clinical pharmacist availability and roles. We used latent class analysis to identify clusters describing interprofessional staffing patterns and then compared ICU and hospital characteristics across clusters. MEASUREMENTS AND MAIN RESULTS: We identified three clusters as optimal. Most ICUs (54.2%) were in cluster 1 (“higher overall staffing”) characterized by a higher likelihood of good provider coverage (both intensivist [onsite 24 hr/d] and nonintensivist [orders placed by ICU team exclusively, presence of advanced practice providers, and physicians-in-training]), nursing leadership (presence of charge nurse, nurse educators, and managers), and bedside nursing support (nurses with registered nursing degrees, fewer patients per nurse, and nursing aide availability). One-third (33.7%) were in cluster 2 (“lower intensivist coverage & nursing leadership, higher bedside nursing support”) and 12.1% were in cluster 3 (“higher provider coverage & nursing leadership, lower bedside nursing support”). Clinical pharmacists were more common in cluster 1 (99.4%), but present in greater than 85% of all ICUs; respiratory therapists were nearly universal. Cluster 1 ICUs were larger (median 20 beds vs. 15 and 17 in clusters 2 and 3, respectively; p < 0.001), and in larger (> 250 beds: 80.6% vs. 66.1% and 48.5%; p < 0.001), not-for-profit (75.9% vs. 69.4% and 60.3%; p < 0.001) hospitals. Telemedicine use 24 hr/d was more common in cluster 3 units (71.8% vs. 11.7% and 14.1%; p < 0.001). CONCLUSIONS: More than half of U.S. ICUs had higher staffing overall. Others tended to have either higher provider presence and nursing leadership or higher bedside nursing support, but not both.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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