Provider Networks in the Neonatal Intensive Care Unit Associate with Length of Stay

Kim, Cindy; Lehmann, Christoph U.; Hatch, D.; Schildcrout, Jonathan S.; Chen, You

doi:10.1109/cic48465.2019.00024

Cited by 11 publications

(21 citation statements)

References 28 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research has shown that a 1-day window is enough to capture the meaningful, collaborative relationships among HCWs [20][21][22][23]. Therefore, we assumed that there was a connection between two HCWs who interacted with the same patient's EHR on the same day.…”

Section: Modeling Hcw Networkmentioning

confidence: 99%

See 1 more Smart Citation

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

Yan¹,

Zhang²,

Gao³

et al. 2021

JMIR Hum Factors

Self Cite

View full text Add to dashboard Cite

Background Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. Objective We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. Methods In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics—eigencentrality and betweenness—to quantify HCWs’ statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs’ broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients’ EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non–COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. Results HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non–COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non–COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non–COVID-19 care (P<.001). Compared to HCWs in non–COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). Conclusions Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non–COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.

show abstract

Section: Modeling Hcw Networkmentioning

confidence: 99%

“…As EHR adoption has spread, the proportion of HCW activities (eg, the review of notes, requests for x-rays, and the management of medication) that involve EHRs has increased [18,19]. Thus, interactions with EHRs provide an opportunity for studying the collaborations among HCWs [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

Yan¹,

Zhang²,

Gao³

et al. 2021

JMIR Hum Factors

Self Cite

View full text Add to dashboard Cite

show abstract

“…We extracted EHR data of 15 NICU gastrostomy patients from the day prior to the patient's surgery day until postoperative day 30. The study aims to validate the associations between health-care worker networks and post-surgical length of stay (PLOS) [36]. For each patient ICU stay, we built a directed network to show how information was received and disseminated among health-care workers in the NICU.…”

Section: Length Of Stay and Nicu Team Structuresmentioning

confidence: 99%

Learning Health-Care Worker Networks from Electronic Health Record Utilization

Chen¹

2021

Teamwork in Healthcare

Self Cite

View full text Add to dashboard Cite

The health-care system is a highly collaborative environment where health-care workers collaborate to care for patients. Health-care organizations (HCOs) design and develop various types of staffing plans to promote collaboration among healthcare workers. The existing staffing plans describe the cooperation at a coarsegrained level, such as team scheduling. They seldom consider connections among health-care workers and investigate how health-care workers receive and disseminate information, which is essential evidence to inform actionable staffing interventions to improve care quality and patient safety. In this chapter, we introduce how to apply network analysis methods to electronic health record (EHR) utilization data to learn connections among health-care workers and build networks to describe teamwork in a fine-grained level. The chapter includes: (i) a brief description of the EHR utilization data, (ii) approaches to learn connections among health-care workers, (iii) building health-care worker networks, (iv) developing survey instruments to validate health-care worker networks, (v) introducing sociometric measurements to quantify network structures and positions of health-care workers in the networks, (vi) using statistical models to test associations between teamwork structures and patient outcomes, and (vii) listing examples to learn health-care worker networks in an HCO and a specific setting, including neonatal intensive care unit and trauma.

show abstract

“…Highly granular and widely available EHR audit logs document HCW activities occurring within EHRs [20][21][22][23] and can be used to model latent collaboration among HCWs and the respective interactions between HCWs and EHR systems [13,16,[24][25][26][27][28][29]. Typically, each event documented in an audit log includes a timestamp, the type of action involved, the involved HCW and patient IDs, and further metadata, such as HCW specialties, patient demographics, and health conditions [13,16,[20][21][22][23][24][25][26][27][28][29]. EHR audit logs have been widely used to measure health care organizational structures [20,25], clinical workflows [20,30,31], trauma care team structures [13,20,26], and intensive care unit care structures [16,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Typically, each event documented in an audit log includes a timestamp, the type of action involved, the involved HCW and patient IDs, and further metadata, such as HCW specialties, patient demographics, and health conditions [13,16,[20][21][22][23][24][25][26][27][28][29]. EHR audit logs have been widely used to measure health care organizational structures [20,25], clinical workflows [20,30,31], trauma care team structures [13,20,26], and intensive care unit care structures [16,[27][28][29]. Existing studies have investigated audit log data at a coarse-grained level to build connections between HCWs [13,16,[20][21][22][23][24][25][26][27][28][29][30][31], and thus, much of the contextual information (eg, HCW-EHR system interactions) is lost.…”

Section: Introductionmentioning

confidence: 99%

Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study

Chen

Rhodes

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

Background Collaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods. Objective We aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage. Methods By extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center’s neonatal intensive care unit (NICU) to validate the effectiveness of our framework. Results There was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters. Conclusions Leveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.

show abstract

Provider Networks in the Neonatal Intensive Care Unit Associate with Length of Stay

Cited by 11 publications

References 28 publications

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

Learning Health-Care Worker Networks from Electronic Health Record Utilization

Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study

Contact Info

Product

Resources

About