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Observational Study

Assess, Prevent, and Manage Pain; Both Spontaneous Awakening and Breathing Trials; Choice of Analgesia/Sedation; Delirium: Assess, Prevent, and Manage; Early Mobility; Family Engagement and Empowerment Bundle Implementation: Quantifying the Association of Access to Bundle-Enhancing Supplies and Equipment

Jeffery, Alvin D. PhD, RN, CCRN-K, NPD-BC, FNP-BC1,2,3; Werthman, Jennifer A. PhD, RN1; Danesh, Valerie PhD, RN4,5; Dietrich, Mary S. PhD2,3; Mion, Lorraine C. PhD, RN6,7; Boehm, Leanne M. PhD, RN, ACNS-BC, FCCM2,8

Author Information
doi: 10.1097/CCE.0000000000000525

Abstract

The Assess, prevent and manage pain; Both spontaneous awakening and breathing trials; Choice of analgesia/sedation; Delirium: assess, prevent, and manage; Early mobility; Family engagement and empowerment (ABCDEF) bundle is a guideline-recommended, evidence-based approach to organizing ICU people, processes, and technology for improved care of the critically ill patient (1–3). ABCDEF bundle implementation is associated with reduced delirium, ventilator, ICU, and hospital days; improved survival; and increased likelihood of early mobilization and restraint-free care (4–6). Despite guideline recommendations, the bundle is still underused in ICUs across the world (7). Organizational structure and process factors (e.g., physical environment, staffing, autonomy, workload) have commonly been identified as barriers to bundle implementation (8–11).

The human errors framework by Reason (12) recognizes the influence of latent conditions that only become evident with triggering factors. In the context of ABCDEF implementation, the latent conditions of the physical environment such as unit configuration, building-related characteristics (e.g., distance to storage rooms), and the built environment of modifiable characteristics (e.g., presence of ceiling lifts, visibility of mobility equipment) may influence bundle adherence.

The relationship between the physical environment and care delivery variability has been explored in diverse healthcare settings and patient populations. For example, studies of furniture placement proximity affect self-disclosure during patient-provider history interviews (13). In acute care settings, time-motion studies assessing the variability of care between hospitalized patients with and without contact isolation precautions suggest that donning additional isolation gowns with each patient room entry is associated with adverse effects such as decreases in patient-clinician contact and decreases in patient satisfaction (14–16). Variability associated with the change in the physical environment is associated with increases in noninfectious adverse events and higher rates of medication errors (15,17,18). To date, associations between the physical environment and ABCDEF bundle adherence have not been explored.

The aim of this study is to describe the physical environment factors (i.e., availability, accessibility) of ABCDEF bundle-enhancing items in units implementing the bundle and the association of physical environment with bundle adherence.

MATERIALS AND METHODS

This is an exploratory multicenter cross-sectional study conducted with sites participating in two ICU-based randomized controlled trials (RCTs) (NCT01211522 and NCT01739933). Data collection occurred between 2011 and 2016. At the initiation of both studies, the bundle was still an evolving framework for critical care open to evidence-based modification. At the time of these RCTs, we applied the original Awakening and Breathing trial Coordination, Delirium assessment/management, Early mobility bundle framework (19,20). The bundle has since been modified to include current critical care guidelines (i.e., ABCDEF bundle, see www.iculiberation.org and www.icudelirium.org), and our aim applies to implementation of the current ABCDEF bundle framework (1,2). Ethical approval was obtained from the coordinating center (Vanderbilt Institutional Review Board, numbers 101082 and 121380) and at each of the participating centers.

Setting and Sample

We obtained information from medical and surgical ICUs (n = 10) at six academic medical centers across the continental United States. Hospital size ranged from 175 to 1,541 licensed beds and 10 to 40 beds per ICU. Size of participating units ranged from 987 to 3,412 m2 (median =1,981 m2).

Prospective observational patient data were obtained from two ICU-based RCTs (NCT01211522 and NCT01739933) that employed the bundle as standard of care and measured daily bundle adherence (21,22). Adults with qualifying respiratory failure and/or septic shock (e.g., mechanical ventilation [MV], vasopressor use) were included in the RCTs. Patients with severe cognitive impairment, drug allergy (e.g., haloperidol, propofol), moribund state, cardiac arrhythmias (i.e., torsades de pointes, second- or third-degree heart block), unable to speak or understand English, or incarcerated were excluded. Patient screening, randomization, follow-up, and analysis are published in the parent RCT reports (21,22).

Variables and Measures

Initially, we generated a comprehensive list of supplies and equipment (25 items) required for the completion of bundle components through communication with clinicians providing critical care. Subsequently, we measured both presence and minimum and maximum distances (in meters) from a head of bed closest to and farthest from each of the 25 bundle-enhancing items. In the event that items were in use, distances were measured from the item’s storage location as identified by unit staff. If items were stored in multiple locations within the unit, minimum and maximum distances were measured for multiple locations and values recorded based on and the lowest (minimum) and highest (maximum) distance measured. These metrics were then included in our models as unit-level variables that were hypothesized to be associated with adherence to the bundle. Patient-level variables also included in those models were age (yr), body mass index, and daily MV. The outcome of bundle adherence is defined as completing all five components on ICU days requiring MV and completing delirium assessment/management and early mobility components on MV-free days (4).

Procedures

The principal investigator (L.M.B.) personally visited each site to meet individually with the leadership of participating units and collected measurements using a standardized checklist. The principal investigator (L.M.B.) operated a measuring wheel to capture exact distances for the minimum and maximum distances.

The ICU teams completed the bundle at their discretion, guided by a standardized protocol as part of the parent RCTs (21,22). The investigators were not responsible for bundle performance. A bundle checklist was placed at the patient’s bedside and completed by the nurse or other healthcare professional each calendar day for RCT participants (9). Study staff distributed, collected, and recorded checklists daily. We collected and managed all study data using Research Electronic Data Capture tools hosted at Vanderbilt University (23).

Statistical Methods

Given the binary nature of the dependent variable and the nested nature of the independent variables (i.e., multiple observations per patient and multiple patients per unit), we developed hierarchical logistic regression models. We selected a Bayesian framework for the modeling process in order to overcome the issue of multiple testing within such a small sample because Bayesian analyses do not need to correct significance thresholds when testing multiple hypotheses. The use of a Bayesian framework has the additional benefit of greater precision in specifying priors for a larger, future study. We also used a Frequentist framework due to familiarity among most audiences. Both Frequentist and Bayesian frameworks contained the same predictors: geospatial measurements (unit size and distances to unique pieces of equipment), the patient’s age and body mass index, and whether a patient was on MV on a given day. We modeled two primary outcomes: 1) adherence with the full bundle and 2) adherence with the early mobility component, given it had the largest variance of all components and 23 of the 25 items are directly associated with early mobility performance.

For units that did not have a piece of equipment, we imputed the missing values with two methods: 1) replacing equipment variables with a dummy variable in which the lack of equipment was coded with a 0 and any observed value was coded with a 1—the “binary” models—and 2) replacing missing values with twice the maximum observed value to conceptually represent the equipment being even farther away—the “continuous” models. We imputed missing data for height (5.7% missing) and weight (4.8% missing) using the IterativeImputer function from Python’s scikit-learn library. As a final preprocessing step, we scaled all nonbinary, numeric variables to have a mean of 0 and variance of 1. Given the large number of predictors (p = 29) for the relatively small number of patient observations (n = 751), we conducted a redundancy analysis using subject-matter expertise, examining bivariate correlations coefficients, and building regression models to determine which variables could be predicted from remaining variables. This dimensionality reduction process resulted in the inclusion of six predictors in the binary models and seven predictors in the continuous models (see eFigs. 14 and eTable 1, https://links.lww.com/CCX/A766, for further details).

For the Frequentist models, we used the lme4 package in R Studio (1.1.463, R Core Team, 2015) (https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf). For the Bayesian models, we used PYMC3 (Version 3.7) in Python (Version 3.6.7; https://www.python.org). We used the No U-Turn Sampler suggested by Hoffman and Gelman (24), which is a variation of the Hamiltonian Monte Carlo simulation method that allows for automatic tuning of step size and number. We developed models with uninformative priors (normal distributions with mean of 0 and sds pulling from a half-Cauchy distribution with beta value of 5) and weakly informative priors (normal distributions with mean of –1 [for patient-level predictors and for equipment in the continuous models] or +1 [for equipment in the binary models] and sds pulling from a half-Cauchy distribution with beta value of 2). We adjusted the number of tuning steps, iterations, and acceptance rates to optimize sampling chain convergence, Gelman-Rubin R-hat values, and Geweke z scores. We compared models’ goodness-of-fit between imputation methods (both Frequentist and Bayesian approaches) and selection of priors (Bayesian approach only). We used the Akaike information criterion (AIC) for Frequentist models and the widely applicable information criterion (WAIC) for Bayesian models. We only interpreted results of the models with the lowest (best) AIC/WAIC values.

RESULTS

The sample represents 751 patient observations across 105 patients within 10 different ICUs. The availability of bundle-enhancing supplies and equipment ranged from eight to 15 items across units. All units had electronic charts, bag valve masks, oxygen tubing, positive end-expiratory pressure (PEEP) valves, and automated medication dispensing systems. The most commonly available bundle-enhancing items were standard walkers (n = 9 units), ceiling lifts (n = 7 units), and recliner chairs (n = 7 units). The least commonly available (n = 1–2 units) bundle-enhancing items were sit-to-stand aid, bariatric chair, portable ventilator, portable monitor, turning straps, stretch bands, and nonpharmacologic delirium aids. Table 1 provides descriptive summaries for patient- and unit-level variables. Full bundle adherence was achieved in 79.1% of patient days. Early mobility adherence was achieved in 89.2% of patient days. Dot plot graphical comparisons of beta coefficients across the four best-fit models for each predictor type (i.e., binary and continuous) are found in Figures 1 and 2, and corresponding numerical details are found in Tables 2 and 3. Beta coefficient estimate density plots for the best-fit Bayesian models are found in the Supplement (eFigs. 1–4, https://links.lww.com/CCX/A766).

TABLE 1. - Descriptive Statistics for Unit- and Patient-Level Variables
Patient Variables n Mean (sd) Median (IQR) Included in Final Analysis
Age 105 54 (14) 57 (47–63) Yes
Height (cm) 99 171 (12) 173 (164–178) Yes
Weight (kg) 100 100 (38) 95 (75–115) Yes
Body mass index 99 35 (17) 32 (25–38) Yes
Unit Variable n Mean (sd) Median (IQR) Included in Final Analysis
Unit size (m2) 10 2,088 (1,981) 1,981 (1,103–3,109) Yes
Distance From Head of Bed to Bundle-Enhancing Items n Units With Item (% Missing) Mean (sd) (m)c Median (IQR) (m)c Included in Final Analysis
Bag valve mask 10 (0) 1.5 (0.7) 1.4 (1.1–1.6)
Bariatric chair 2 (80) 67.2 (4.4) 67.2 (65.6–68.7) Yes
Canvas sling 8 (20) 52.8 (9.5) 51.2 (46.5–59) Yes
Ceiling lift 7 (30) 1.3 (0.3) 1.3 (1–1.7) Yes
Chart 10 (0) 15.16 (9.8) 11.7 (10–14.9)
Gait belt 3 (70) 50.3 (16.7) 40.9 (40.6–55.3)
High back chair 5 (50) 50.4 (21.5) 64.4 (30.7–66.8) Yes
Hover mat 4 (60) 68.2 (54.4) 68.2 (49–87.4)
Lift sheet 4 (60) 17.7 (1.1) 17.7 (17.3–18.1)
Medication dispensing system 10 (0) 26.4 (5.8) 26.4 (21.8–29.6)
Nonpharmacologic aidsa 2 (80) 40.6 (0.4) 40.6 (40.5–40.8) Yes
O2 tank 10 (0) 42 (13.2) 42.4 (30.3–49.8) Yes
O2 tubing 10 (0) 50.2 (14.6) 50.5 (43.5–57.5) Yes
Positive end-expiratory pressure valve 10 (0) 50.2 (14.6) 50.5 (43.5–57.5) Yes
Portable monitor 2 (80) 50.8 (18.7) 50.8 (44.2–57.5) Yes
Portable ventilator 2 (80) 46.1 (2) 46.1 (45.4–46.8) Yes
Radio 1 (90) 0.9 (NA) 0.9 (0.9–0.9)
Recliner chair 7 (30) 11.2 (11.9) 3.9 (2.4–18.4) Yes
Sit-to-stand 2 (80) 66.8 (0) 66.8 (66.8–66.8)
Sling lift 8 (20) 66 (1.4) 66.8 (65.6–66.8) Yes
Specialty walkerb 6 (40) 80.4 (67.1) 65.9 (46.4–69.5) Yes
Standard walker 9 (10) 55.1 (17.2) 66.8 (47.5–67.4) Yes
Standing lift 3 (70) 66 (1.4) 66.8 (65.6–66.8) Yes
Stretch band 2 (80) 40.6 (0.4) 40.6 (40.5–40.8)
Turning strap 2 (80) 50.3 (9.8) 50.3 (46.8–53.7) Yes
IQR = interquartile range.
aNonpharmacologic aids include ear plugs, eye covers, reading glasses, and/or amplifiers.
bWalker that allows ambulation with fewer staff members may include integrated features for O2 tanks, portable ventilator/monitor, telescoping IV pole, seat flaps, and leaning bar, among other features.
cFarthest distance.

TABLE 2. - Beta Coefficients for Variables of Best-Fit Models Examining Adherence With Full Bundle
Approach Frequentist, Mean (sd) Bayesian, Mean (2.5% HPD, 97.5% HPD)
Predictors Continuous Binary Continuous Binary
Daily variable
 Ventilator status –3.0 (0.5)a –3.0 (0.5)a –2.1 (–2.9, –1.3)b –2.6 (–3.5, –1.8)b
Patient variables
 Age –0.4 (0.2)b –0.4 (0.2)b –0.4 (–0.8, 0.07) –0.4 (–0.7, –0.01)b
 Body mass index 0.1 (0.2) 0.1 (0.2) 0.05 (–0.4, 0.6) 0.1 (–0.3, 0.5)
Unit variables
 Unit size 1.0 (1.6) 2.9 (3.5) –0.2 (–2.5, 1.9) –1.0 (–2.1, 0.03)
 Canvas sling 1.9 (1.3) NA –0.07 (–2.1, 2.2) NA
 Ceiling lift NA 1.9 (1.2) NA 1.1 (–0.6, 2.8)
 High back chair 1.2 (1.0) NA –0.4 (–2.4, 1.7) NA
 O2 tank –0.7 (0.8) NA –0.9 (–3.2, 1.5) NA
 O2 tubing/positive end-expiratory  pressure valve –0.4 (0.8) NA 0.1 (–1.4, 1.7) NA
 Portable lift NA –12.0 (11.6) NA 0.7 (–1.2, 2.9)
 Portable monitor/turning  straps/bariatric chair NA –8.7 (8.4) NA 0.4 (–1.6, 2.2)
 Portable ventilator/ nonpharmacologic aids NA –5.7 (3.3) NA –1.0 (–3.0, 0.8)
 Recliner chair –0.7 (0.5) 2.7 (2.7) 0.4 (–1.4,2.0) 1.2 (–0.6, 3.0)
 Specialty walker 0.4 (0.4) NA –0.6 (–2.3,1.1) NA
 Standard walker 0.4 (0.5) –5.3 (4.7) 1.0 (–0.4,2.6) 1.7 (0.3, 3.4)b
HPD = high probability density, NA = not applicable.
ap < 0.001.
bp < 0.05 (Frequentist) or 95% HPD excludes 0 (Bayesian).
Both Bayesian approaches assume uninformative priors.

TABLE 3. - Beta Coefficients for Variables of Best-Fit Models Examining Adherence With Early Mobility Component of Bundle
Approach Frequentist, Mean (sd) Bayesian, Mean (2.5% HPD, 97.5% HPD)
Predictors Continuous Binary Continuous Binary
Daily variable
 Daily ventilator status –2.4 (0.6)a –2.4 (0.6)a –2.2 (–3.3, –1.1)b –2.0 (–3.0, –1.0)b
Patient variables
 Age –0.3 (0.2) –0.3 (0.2) –0.6 (–1.6, 0.3) –0.3 (–0.8, 0.2)
 Body mass index –0.1 (0.2) –0.1 (0.2) –0.6 (–1.6, 0.5) –0.1 (–0.6, 0.3)
Unit variables
 Unit size –9.5 (599.1) 2.6 (4.1) –1.5 (–3.7, 0.5) –1.9 (–3.4, –0.6)b
 Canvas sling –0.8 (133.7) NA –0.5 (–2.4, 1.6) NA
 Ceiling lift NA 2.1 (1.4) NA 0.9 (–1.2, 2.9)
 High back chair –4.9 (379.9) NA –1.1 (–3.2, 0.7) NA
 O2 tank 0.8 (71.9) NA –1.3 (–3.5, 0.6) NA
 O2 tubing/positive end-expiratory pressure valve 4.1 (273.6) NA –0.7 (–2.4, 1.4) NA
 Portable lift NA –28.7 (65.4) NA 0.9 (–1.5, 3.4)
 Portable monitor/turning straps/bariatric chair NA –25.4 (65.3) NA 0.3 (–1.9, 2.5)
 Portable ventilator NA –21.9 (65.6) NA –1.3 (–3.7, 0.9)
 Recliner chair –2.4 (98.0) 3.0 (3.2) –0.5 (–2.4, 1.5) 0.9 (–1.0, 2.9)
 Specialty walker 1.8 (88.4) NA –1.1 (–3.0, 0.9) NA
 Standard walker –2.4 (148.4) 22.6 (65.4) –0.5 (–2.4, 1.5) 2.6 (0.8, 4.7)b
HPD = high probability density, NA = not applicable.
ap < 0.001.
bp < 0.05 (Frequentist) or 95% HPD excludes 0 (Bayesian).
Bayesian approaches assume uninformative priors for binary predictors and weakly informative priors for continuous predictors.

F1
Figure 1.:
Beta coefficient means and 95% upper/lower limits for “binary” models using predictors for equipment. Items are ranked in increasing order for mean of the Frequentist approach for full adherence. BMI = body mass index.
F2
Figure 2.:
Beta coefficient means and 95% upper/lower limits for “continuous” models using predictors for equipment. Items are ranked in increasing order for mean of the Frequentist approach for full adherence. BMI = body mass index, PEEP = positive end-expiratory pressure.

Full Bundle Adherence

In the Frequentist approach, the AIC values for full bundle adherence (655.9) and early mobility (448.9) in both binary models demonstrated better goodness-of-fit than the continuous models (657.8 and 450.4, respectively). eTable 2 (https://links.lww.com/CCX/A766) contains a comparison of all models’ AIC/WAIC values. Daily ventilator status and age were the only statistically significant predictors when modeling the outcome of full bundle adherence using various predictors in both the binary and continuous models (Table 2). Both variables had negative beta coefficients, indicating active MV and older age negatively influenced completion of the full bundle.

In the Bayesian approach, the WAIC values for models with uninformative priors (626.04 for binary and 634.18 for continuous) outperformed models with weakly informative priors (644.85 for binary and 643.27 for continuous); therefore, primary findings are reported from models with uninformative priors. When using binary predictors, availability of a standard walker, age, and daily ventilator status had 95% high probability densities (HPDs) excluding 0 (Table 2). When using continuous predictors, daily ventilator status had 95% HPDs excluding 0. The beta coefficient for walker availability was positive, indicating increased full bundle adherence on units with walkers. The beta coefficients for daily ventilator status and age were negative, indicating decreased full bundle adherence on days the patient is receiving MV and among older patients.

Early Mobility Adherence

For both binary and continuous models (Table 3), daily ventilator status was the only statistically significant predictor. Ventilator status had a negative regression weight, indicating active MV negatively influenced completion of early mobility.

In the Bayesian approach, the WAIC values for binary models with uninformative priors (428.21) outperformed binary models with weakly informative priors (431.79); conversely, continuous models with weakly informative priors (431.43) outperformed continuous models with uninformative priors (432.72). When using binary predictors, a unit’s size, availability of a walker, and daily ventilator status had 95% HPDs excluding 0 (Table 3). When using continuous predictors, daily ventilator status had 95% HPDs excluding 0. Similar to the full bundle adherence models, the beta coefficients were negative for daily ventilator status and positive for standard walker availability. The beta coefficient for unit size was negative, indicating decreased early mobility adherence on larger units.

DISCUSSION

We used multiple models to examine the availability and accessibility of ABCDEF bundle-enhancing items in order to generate hypotheses about geospatial factors influencing bundle implementation. After taking patient-level factors into account, for every 1 m2 increase in the unit size, the odds of receiving early mobility are decreased by 85%. Having a standard walker on the unit was associated with a patient being five times more likely to receive the full bundle and 13 times more likely to receive the early mobility component of the bundle. Overall, the findings suggest a measurable association of not only patient-level factors but also the presence and physical proximity to essential items with ABCDEF bundle and early mobility adherence.

At the unit level, we investigated multiple bundle-enhancing items and distances and only found two (unit size and standard walker) to be potentially significant associations. Although this could be a chance in the Frequentist results, the Bayesian results do not inherent the same false discovery rate as their Frequentist counterpart, thus, suggesting a potential relationship between bundle adherence and unit size and standard walker presence. However, it remains unclear if accessibility of equipment, administrative investment, or a combination of these factors drive successful ABCDEF bundle implementation. Our analysis builds on existing time-motion studies of the nurse work environment. In medical-surgical nursing units, when patient room assignments are clustered in close proximity and walking distances are minimized, the number of nurse-patient interactions increases (25). Similarly, our findings demonstrate that the geospatial location of mobility-related equipment could also be associated with use for full bundle adherence. Characteristics of the physical environment, both fixed (e.g., unit size) and modifiable (e.g., accessibility of a standard walker), may contribute to structure and process factors influencing bundle implementation. Research evaluating both the modifiable process factors for current practice and fixed structural factors to inform hospital architectural design are needed to develop evidence-based design in healthcare (26).

Although not particularly surprising, patient-level factors played a large role in bundle delivery. For every 1-year increase in a patient’s age, the odds of receiving the full bundle were decreased by 33%. MV patients were eight to 20 times less likely to receive full bundle care. Early mobility, the most complex bundle component to coordinate, was seven to 11 times less likely to occur with a MV patient. Previous studies describe aversion to ABCDEF bundle and early mobility adherence due to perceived safety concerns or risk of adverse events (e.g., tube dislodgment, desaturation, fall) (27,28). However, adverse events occurring with implementation of the ABCDEF bundle and related components are reportedly low (5,29,30). Similarly, increases in patient age were associated with decreases in full bundle adherence. In addition to safety concerns, this could be due to biased perceptions of older people as frail with diminished intrinsic capacity (i.e., the physical and mental capacity of an individual) (31). Diverse trajectories of aging means intrinsic capacity exists along a continuum (e.g. 90-yr-old marathon runner [high intrinsic capacity] vs 60-yr-old bedbound individual [frail]) (31). To address potential patient-level barriers to implementation, strategies may include interventions that overcome risk aversion to performing bundled care and implementing admission intrinsic capacity assessments to inform patient-centered care plans (e.g., mobility level goal) that maintain baseline function and avoid age-related bias.

Strengths of our study comprise the heterogenous representation of medical and surgical ICUs from geographically diverse medical centers as well as the use of multiple analytic methods for triangulation. Slowly gaining popularity in the biomedical literature, Bayesian analyses have the advantage of incorporating prior knowledge and do not introduce error inflation during multiple testing. Further, our findings can be used as prior distributions in future studies leveraging Bayesian statistics, which effectively reduces the sample size needed in the future.

Our study has limitations that warrant consideration. The sample size of 10 ICUs from six academic medical centers does not represent the majority of ICU contexts but does represent a mix of geographic regions in the Northeast, Southeast, Midwest, Southwest, and Pacific Northwest United States. The small sample size also limits our ability to make causal inference and resulted in sparse and collinear data for some pieces of equipment, necessitating their removal during final analyses. Evaluating bundle adherence by augmenting structural physical environment variables with process-related variables (e.g., team composition, staffing, and workflow) could also offer evidence-based insights for intervention development (32). Finally, our retrospective, observational study design precludes the ability to establish direct causation. It is possible that upstream factors (e.g., organizational commitment, architectural design) play a larger role in bundle adherence, and equipment availability is simply an artifact of these factors.

CONCLUSIONS

We identified unit- and patient-level factors that were associated with full bundle and early mobility implementation. There may be benefit to physical proximity of bundle-enhancing items, but we are limited in our ability to make causal inferences about the physical environment and adherence. There is benefit in testing implementation strategies that address hesitancy of performing bundled care with critically ill patients and applying early assessment of a patient’s intrinsic capacity. Future studies with larger sample sizes should explore how availability and accessibility (e.g., equipment location) could have implications to promote the implementation of evidence-based design.

ACKNOWLEDGMENTS

We would like to thank Dr. David Schlueter for contributing his knowledge and expertise during the technical design of the Bayesian modeling.

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Keywords:

ABCDEF bundle; critical care; environment; evidence-based design; intensive care unit; interdisciplinary

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