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Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES: To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN SETTING AND PARTICIPANTS: This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS: For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61-1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57-1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001). CONCLUSIONS AND RELEVANCE: Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

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BACKGROUND: Various intensivist staffing models have been suggested, but the long-term sustainability and outcomes vary and may not be sustained. We examined the impact of implementing a high-intensity intensivist coverage model with a nighttime in-house nocturnist (non-intensivist) and its effect on intensive care unit (ICU) outcomes. METHODS: We obtained historical control baseline data from 2007 to 2011 and compared the same data from 2011 to 2015. The Acute Physiological and Chronic Health Evaluation outcomes system was utilized to collect clinical, physiological, and outcome data on all adult patients in the medical ICU and to provide severity-adjusted outcome predictions. The model consists of a mandatory in-house daytime intensivist service that leads multidisciplinary rounds, and an in-house nighttime coverage is provided by nocturnist (nonintensivists) with current procedural skills in airways management, vascular access, and commitment to supervise house staff as needed. The intensivist continues to be available remotely at nighttime for house staff and consultation with the nocturnist. A backup intensivist is available for surge management. RESULTS: First year yielded improved throughput (2428 patients/year to 2627 then 2724 at fifth year). Case mix stable at 53.7 versus 55.2. The ICU length of stay decreased from 4.7 days (predicted 4.25 days) to 3.8 days (4.15) in first year; second year: 3.63 days (4.29 days); third year: 3.24 days (4.37), fourth year: 3.34 days (4.45), and fifth year: 3.61 days (4.42). Intensive care unit <24 hours readmission remained at 1%; >24 hours increased from 4% to 6%. Low-risk monitoring admissions remained at an average 17% (benchmark 17.18%). Intensive care unit mortality improved with standardized mortality ration averaging at 0.84. Resident satisfaction surveys improved. CONCLUSIONS: Implementing an intensivist service with nighttime nocturnist staffing in a high-intensity large teaching hospital is feasible and improved ICU outcomes in a sustained manner that persisted after the initial implementation phase. The model resulted in reduced and sustained observed-to-predicted length of ICU stay.

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PURPOSE: Although evidence supports the impact of intensivist physician staffing in improving intensive care unit (ICU) outcomes, the optimal coverage for progressive care units (PCU) is unknown. We sought to determine how physician staffing models influence outcomes for intermediate care patients. MATERIALS AND METHODS: We conducted a retrospective observational comparison of patients admitted to the medical PCU of an academic hospital during 12-month periods of high-intensity and low-intensity staffing. RESULTS: A total of 318 PCU patients were eligible for inclusion (143 high-intensity and 175 low-intensity). We found that low-intensity patients were more often stepped up from the emergency department and floor, whereas high-intensity patients were ICU transfers (61% vs 42%, P = .001). However, Mortality Probability Model scoring was similar between the 2 groups. In adjusted analysis, there was no association between intensity of staffing and hospital mortality (odds ratio, 0.84; 95% confidence interval, 0.36-1.99; P = .69) or PCU mortality (odds ratio, 0.96; 95% confidence interval, 0.38-2.45; P = .69). There was also no difference in subsequent ICU admission rates or in PCU length of stay. CONCLUSIONS: We found no evidence that high-intensity intensivist physician staffing improves outcomes for intermediate care patients. In a strained critical care system, our study raises questions about the role of the intensivist in the graded care options between intensive and conventional ward care.

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Critical care medicine is at a crossroads in which limited numbers of staff care for increasing numbers of patients as the population ages and use of ICUs increases. Also at this time health care spending must be curbed. The high-intensity intensivist staffing model has been linked to improved mortality, complications, and costs. Tele-ICU uses technology to implement this high-intensity staffing model in areas that are relatively underserved. When implemented correctly and in the right populations this technology has improved outcomes. Future studies regarding implementation, organization, staffing, and innovation are needed to determine the optimal use of this critical care professional enhanced technology.

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