The Impact of Implementing an Intensivist Model With Nighttime In-Hospital Nocturnist and Effect on ICU Outcomes

Defining Intensivists: A Retrospective Analysis of the Published Studies in the United States, 2010-2020

OBJECTIVES

The Society of Critical Care Medicine last published an intensivist definition in 1992. Subsequently, there have been many publications relating to intensivists. Our purpose is to assess how contemporary studies define intensivist physicians.

DESIGN

Systematic search of PubMed, Embase, and Web of Science (2010-2020) for publication titles with the terms intensivist, and critical care or intensive care physician, specialist, or consultant. We included studies focusing on adult U.S. intensivists and excluded non-data-driven reports, non-U.S. publications, and pediatric or neonatal ICU reports. We aggregated the study title intensivist nomenclatures and parsed Introduction and Method sections to discern the text used to define intensivists. Fourteen parameters were found and grouped into five definitional categories: A) No definition, B) Background training and certification, C) Works in ICU, D) Staffing, and E) Database related. Each study was re-evaluated against these parameters and grouped into three definitional classes (single, multiple, or no definition). The prevalence of each parameter is compared between groups using Fisher exact test.

SETTING

U.S. adult ICUs and databases.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 657 studies, 105 (16%) met inclusion criteria. Within the study titles, 17 phrases were used to describe an intensivist; these were categorized as intensivist in 61 titles (58%), specialty intensivist in 30 titles (29%), and ICU/critical care physician in 14 titles (13%). Thirty-one studies (30%) used a single parameter (B-E) as their definition, 63 studies (60%) used more than one parameter (B-E) as their definition, and 11 studies (10%) had no definition (A). The most common parameter "Works in ICU" (C) in 52 studies (50%) was more likely to be used in conjunction with other parameters rather than as a standalone parameter (multiple parameters vs single-parameter studies; 73% vs 17%; p < 0.0001).

CONCLUSIONS

There was no consistency of intensivist nomenclature or definitions in contemporary adult intensivist studies in the United States.

Descriptive Comparison of Two Models of Tele-Critical Care Delivery in a Large Multi-Hospital Health Care System

Introduction: The Society of Critical Care Medicine Tele-Critical Care (TCC) Committee has identified the need for rigorous comparative research of different TCC delivery models to support the development of best practices for staffing, application, and approaches to workflow. Our objective was to describe and compare outcomes between two TCC delivery models, TCC with 24/7 Bedside Intensivist (BI) compared with TCC with Private Daytime Attending Intensivist (PI) in relation to intensive care unit (ICU) and hospital mortality, ICU and hospital length of stay (LOS), cost, and complications across the spectrum of routine ICU standards of care. Methods: Observational cohort study at large health care system in 12 ICUs and included patients, ≥18, with Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores and predictions (October 2016-June 2019). Results: Of the 19,519 ICU patients, 71.7% (n = 13,993) received TCC with 24/7 BI while 28.3% (n = 5,526) received TCC with PI. ICU and Hospital mortality (4.8% vs. 3.1%, p < 0.0001; 12.6% vs. 8.1%, p < 0.001); and ICU and Hospital LOS (3.2 vs. 2.4 days, p < 0.001; 9.8 vs. 7.2 days, p < 0.001) were significantly higher among 24/7 BI compared with PI. The APACHE observed/expected ratios (odds ratio [OR]; 95% confidence interval [CI]) for ICU mortality (0.62; 0.58-0.67) vs. (0.53; 0.46-0.61) and Hospital mortality (0.95; 0.57-1.48) vs. (0.77; 0.70-0.84) were significantly different for 24/7 BI compared with PI. Multivariate mixed models that adjusted for confounders demonstrated significantly greater odds of (OR; 95% CI) ICU mortality (1.58; 1.28-1.93), Hospital mortality (1.52; 1.33-1.73), complications (1.55; 1.18-2.04), ICU LOS [3.14 vs. 2.59 (1.25; 1.19-1.51)], and Hospital LOS [9.05 vs. 7.31 (1.23; 1.21-1.25)] among 24/7 BI when compared with PI. Sensitivity analyses adjusting for ICU admission within 24 h of hospital admission, receiving active ICU treatments, nighttime admission, sepsis, and highest third acute physiology score indicated significantly higher odds for 24/7 BI compared with PI. Conclusion: Our comparison demonstrated that TCC delivery model with PI provided high-quality care with significant positive effects on outcomes. This suggests that TCC delivery models have broad-ranging applicability and benefits in routine critical care, thus necessitating progressive research in this direction.

Patient Outcomes and Unit Composition With Transition to a High-Intensity ICU Staffing Model: A Before-and-After Study

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.

The organizational and environmental characteristics associated with hospitals' use of intensivists

BACKGROUND

As large numbers of coronavirus disease 2019 (COVID-19) patients were admitted to intensive care units (ICUs) in 2020 and 2021, the United States faced a shortage of critical care providers. Intensivists are physicians specializing in providing care in the ICU. Although studies have explored the clinical and financial benefits associated with the use of intensivists, little is known about the organizational and market factors associated with a hospital administrator's strategic decision to use intensivists.

PURPOSE

The aim of this study was to use the resource dependence theory to better understand the organizational and market factors associated with a hospital administrator's decision to use intensivists.

METHODOLOGY

The sample consisted of the national acute care hospitals (N = 4,986) for the period 2007-2017. The dependent variable was the number of full-time equivalent intensivists staffed in hospitals. The independent variables were organizational and market-level factors. A negative binomial regression model with state and year fixed effects, clustered at the hospital level, was used to examine the relationship between the use of intensivists and organizational and market factors.

RESULTS

The results from the analyses show that administrators of larger, not-for-profit hospitals that operate in competitive urban markets with relatively high levels of munificence are more likely to utilize intensivists.

PRACTICE IMPLICATIONS

When significant strains are placed on ICUs like what was experienced during the COVID-19 pandemic, it is imperative that hospital administrators understand how to best staff their ICUs. With a better understanding of the organizational and market factors associated with the use of intensivists, practitioners and policymakers alike can better understand how to strategically utilize intensivists in the ICU, especially in the face of a continuing pandemic.

Impact of an internal medicine nocturnist service on care of patients with cancer at a large Canadian teaching hospital: a quality-improvement study

BACKGROUND

Nocturnists (overnight hospitalists) are commonly implemented in US teaching hospitals to adhere to per-resident patient caps and improve care but are rare in Canada, where patient caps and duty hours are comparatively flexible. Our objective was to assess the impact of a newly implemented nocturnist program on perceived quality of care, code status documentation and patient outcomes.

METHODS

Nocturnists were phased in between June 2018 and December 2019 at Toronto General Hospital, a large academic teaching hospital in Toronto, Ontario. We performed a quality-improvement study comparing rates of code status entry into the electronic health record at admission, in-hospital mortality, the 30-day readmission rate and hospital length of stay for patients with cancer admitted by nocturnists and by residents. Surveys were administered in June 2019 to general internal medicine faculty and residents to assess their perceptions of the impact of the nocturnist program.

RESULTS

From July 2018 to June 2019, 30 nocturnists were on duty for 241/364 nights (66.5%), reducing the mean maximum overnight per-resident patient census from 40 (standard deviation [SD] 4) to 25 (SD 5) (p < 0.001). The rate of admission code status entry was 35.3% among patients admitted by residents (n = 133) and 54.9% among those admitted by nocturnists (n = 339) (p < 0.001). The mortality rate was 10.5% among patients admitted by residents and 5.6% among those admitted by nocturnists (p = 0.06), the 30-day readmission rate was 8.3% and 5.9%, respectively (p = 0.4), and the mean acute length of stay was 7.2 (SD 7.0) days and 6.4 (SD 7.8) days, respectively (p = 0.3). Surveys were completed by 15/24 faculty (response rate 62%), who perceived improvements in patient safety, efficiency and trainee education; however, only 30/102 residents (response rate 29.4%) completed the survey.

INTERPRETATION

Although implementation of a nocturnist program did not affect patient outcomes, it reduced residents' overnight patient census, and improved faculty perceptions of quality of care and education, as well as documentation of code status. Our results support nocturnist implementation in Canadian teaching hospitals.