SWEAT ICU—An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs

Abstract

OBJECTIVES: The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality. DESIGN: Cross-sectional observational study. SETTING: Fourteen academic centers in the United States from August 2020 to July 2021. SUBJECTS: We enrolled ICU physicians and collected data on adult ICU patients under the physician’s care on the single physician-selected study day for each physician. MEASUREMENTS and MAIN RESULTS: The primary exposure was workload (self-reported number of patients’ physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10–14), and the overall prevalence of burnout was 26.4% ( n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24–2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92–1.91). CONCLUSIONS: In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

Intensivist physician-to-patient ratios and mortality in the intensive care unit

“…Our study makes important methodological advances over past studies in this area by (a) directly measuring intensivist-to-patient ratios rather than inferring them from surveys; and (b) modelling the ratio in a way that allows it to change from day to day rather than modelling as an average value over the course of an ICU stay, which better reflects actual practice. Nonetheless, our findings are largely consistent with past studies, which in general do not show that higher daily caseloads are associated with worse patient outcomes [ 16 – 18 ]. Taken together, these results suggest that guidelines recommending specific ratios are premature and policies capping intensivist-to-patient ratios at specific numbers are unlikely to improve ICU mortality [ 21 ].…”

“…Yet the empirical data demonstrating a relationship between intensivist-to-patient ratios and clinical outcomes are scant. Five published studies show mixed results, with two of them suggesting that higher ratios are associated with higher mortality [ 14 , 15 ] and three showing no relationship [ 16 – 18 ]. Importantly, these studies all have methodological limitations.…”

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio

“…Additionally, this strategy appropriately incentivizes and rewards investigators through: 1) professional advancement opportunities in the form of authorship and multicenter site participation and 2) providing a Post graduate year two research training experience, including training sessions, that can meet appropriate criteria for the program (ideally reducing some burden on program directors in exchange for participation). OPTIM also represents a unique approach to data collection, in that it focuses on not only daily characteristics of pharmacist workload but also daily patient-level variables, which have not been included in previous workload studies or included but at a smaller scale ( 19 , 20 , 22 ). Front end emphasis on data management strategies through high-end REDcap infrastructure has the potential to allow for relative ease for answering a variety of study questions beyond those discussed here.…”

“…Secondary independent variables will include pharmacist rounding ratio, pharmacist team ratio, patients rounded on versus not rounded on as a percent of time, (hour/shift) to patient ratio, and Pharmacist Workload Index. In addition to the independent variables previously described, other covariates were selected based on their potential impact on mortality following a literature search and consensus of the investigators a priori (Supplemental Digital Content Table 1, http://links.lww.com/CCX/B231) (7)(8)(9)(19)(20)(21)(22)(23)(24)(25)(26). These covariates include patient-level data (patient SOFA score, age, sex, MRC-ICU score, and ICU type); practice model features (weekend clinical coverage, rounding information, pharmacist years in practice, pharmacist board certification, pharmacist highest level of training, institution type, number of ICU beds); pharmacist daily workload features (total ICU patients care for, total ICU patients new to you, total patients cared for ICU and non-ICU, total patients rounded on, total rounding teams responsible for, cross-coverage assignment); and daily personnel workload factors (pharmacy trainees on service, number of patients the medical team/attending is caring for, nurse ratio for that patient).…”