The Structure of Critical Care Nursing Teams and Patient Outcomes: A Network Analysis

Patterns of interactions among ICU interprofessional teams: A prospective patient-shift-level survey approach

BACKGROUND

The Awakening, Breathing Coordination, Delirium monitoring and Early mobility bundle (ABCDE) is associated with lower mortality for intensive care unit (ICU) patients. However, efforts to improve ABCDE are variably successful, possibly due to lack of clarity about who are the team members interacting when caring for each patient, each shift. Lack of patient shift-level information regarding who is interacting with whom limits the ability to tailor interventions to the specific ICU team to improve ABCDE.

OBJECTIVE

Determine the number and types of individuals (i.e., clinicians and family members) interacting in the care of mechanically ventilated (MV) patients, as reported by the patients' assigned physician, nurse, and respiratory therapist (RT) each shift, using a network science lens.

METHODS

We conducted a prospective, patient-shift-level survey in 2 medical ICUs. For each patient, we surveyed the assigned physician, nurse, and RT each day and night shift about who they interacted with when providing ABCDE for each patient-shift. We determined the number and types of interactions, reported by physicians, nurses, and RTs and day versus night shift.

RESULTS

From 1558 surveys from 404 clinicians who cared for 169 patients over 166 shifts (65% response rate), clinicians reported interacting with 2.6 individuals each shift (physicians: 2.65, nurses: 3.33, RTs: 1.86); this was fewer on night shift compared to day shift (1.99 versus 3.02). Most frequent interactions were with the bedside nurse, attending, resident, intern, and RT; family member interactions were reported in less than 1 in 5 surveys (12.2% of physician surveys, 19.7% of nurse surveys, 4.9% of RT surveys).

INTERPRETATION

Clinicians reported interacting with 3-4 clinicians each shift, and fewer on nights. Nurses interacted with the most clincians and family members. Interventions targeting shift-level teams, focusing on nurses and family members, may be a way to improve ABCDE delivery and ICU teamwork.

Characterizing intensive care unit rounding teams using meta-data from the electronic health record

PURPOSE

Teamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams.

METHODS

We analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization.

RESULTS

We identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0-706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0-99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07-0.23), degree centralization (0.50, 0.35-0.65) and closeness centralization (0.45, 0.11-0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics.

CONCLUSIONS

EHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.

The Use of Electronic Health Record Metadata to Identify Nurse-Patient Assignments in the Intensive Care Unit: Algorithm Development and Validation

Background

Nursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse’s unique skills are matched to each patient’s needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale.

Objective

This study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU.

Methods

We used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts.

Results

The analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions.

Conclusions

Metadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Variation in Provider Connectedness Associates With Outcomes of Inflammatory Bowel Diseases in an Analysis of Data From a National Health System

BACKGROUND & AIMS

Inflammatory bowel diseases (IBD) often require multidisciplinary care with tight coordination among providers. Provider connectedness, a measure of the relationship among providers, is an important aspect of care coordination that has been linked to higher quality care. We aimed to assess variation in provider connectedness among medical centers, and to understand the association between this established measure of care coordination and outcomes of patients with IBD.

METHODS

We conducted a national cohort study of 32,949 IBD patients with IBD from 2005 to 2014. We used network analysis to examine provider connectedness, defined using network properties that measure the strength of the collaborative relationship, team cohesiveness, and between-facility collaborations. We used multilevel modeling to examine variations in provider connectedness and association with patient outcomes.

RESULTS

There was wide variation in provider connectedness among facilities in complexity, rural designation, and volume of patients with IBD. In a multivariable model, patients followed in a facility with team cohesiveness (odds ratio, 0.38; 95% CI, 0.16-0.88) and where providers often collaborated with providers outside their facility (odds ratio, 0.48; 95% CI, 0.31-0.75) were less likely to have clinically active disease, defined by a composite of outpatient flare, inpatient flare, and IBD-related surgery.

CONCLUSIONS

A national study found evidence for heterogeneity in patient-sharing among IBD care teams. Patients with IBD seen at health centers with higher provider connectedness appear to have better outcomes. Understanding provider connectedness is a step toward designing network-based interventions to improve coordination and quality of care.

Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study

Background

Health care organizations (HCOs) adopt strategies (eg. physical distancing) to protect clinicians and patients in intensive care units (ICUs) during the COVID-19 pandemic. Many care activities physically performed before the COVID-19 pandemic have transitioned to virtual systems during the pandemic. These transitions can interfere with collaboration structures in the ICU, which may impact clinical outcomes. Understanding the differences can help HCOs identify challenges when transitioning physical collaboration to the virtual setting in the post–COVID-19 era.

Objective

This study aims to leverage network analysis to determine the changes in neonatal ICU (NICU) collaboration structures from the pre– to the intra–COVID-19 era.

Methods

In this retrospective study, we applied network analysis to the utilization of electronic health records (EHRs) of 712 critically ill neonates (pre–COVID-19, n=386; intra–COVID-19, n=326, excluding those with COVID-19) admitted to the NICU of Vanderbilt University Medical Center between September 1, 2019, and June 30, 2020, to assess collaboration between clinicians. We characterized pre–COVID-19 as the period of September-December 2019 and intra–COVID-19 as the period of March-June 2020. These 2 groups were compared using patients’ clinical characteristics, including age, sex, race, length of stay (LOS), and discharge dispositions. We leveraged the clinicians’ actions committed to the patients’ EHRs to measure clinician-clinician connections. We characterized a collaboration relationship (tie) between 2 clinicians as actioning EHRs of the same patient within the same day. On defining collaboration relationship, we built pre– and intra–COVID-19 networks. We used 3 sociometric measurements, including eigenvector centrality, eccentricity, and betweenness, to quantify a clinician’s leadership, collaboration difficulty, and broad skill sets in a network, respectively. We assessed the extent to which the eigenvector centrality, eccentricity, and betweenness of clinicians in the 2 networks are statistically different, using Mann-Whitney U tests (95% CI).

Results

Collaboration difficulty increased from the pre– to intra–COVID-19 periods (median eccentricity: 3 vs 4; P<.001). Nurses had reduced leadership (median eigenvector centrality: 0.183 vs 0.087; P<.001), and neonatologists with broader skill sets cared for more patients in the NICU structure during the pandemic (median betweenness centrality: 0.0001 vs 0.005; P<.001). The pre– and intra–COVID-19 patient groups shared similar distributions in sex (~0 difference), race (4% difference in White, and 3% difference in African American), LOS (interquartile range difference in 1.5 days), and discharge dispositions (~0 difference in home, 2% difference in expired, and 2% difference in others). There were no significant differences in the patient demographics and outcomes between the 2 groups.

Conclusions

Management of NICU-admitted patients typically requires multidisciplinary care teams. Understanding collaboration structures can provide fine-grained evidence to potentially refine or optimize existing teamwork in the NICU.

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study

Background

Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals.

Objective

We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected.

Methods

We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors.

Results

We detail the spatiotemporal distributions of staff members’ inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery.

Conclusions

Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network.