A pilot study to determine the incidence, type, and severity of non-routine events in neonates undergoing gastrostomy tube placement.

BACKGROUND: Non-routine events (NRE) are defined as any suboptimal occurrences in a process being measured in the opinion of the reporter and comes from the field of human factors engineering. These typically occur well up-stream of an adverse event and NRE measurement has not been applied to the complex context of neonatal surgery. We sought to apply this novel safety event measurement methodology to neonates in the NICU undergoing gastrostomy tube placement. METHODS: A prospective pilot study was conducted between November 2016 and August 2020 in the Level IV NICU and the pediatric operating rooms of an urban academic children's hospital to determine the incidence, severity, impact, and contributory factors of clinician-reported non-routine events (NREs, i.e., deviations from optimal care) and 30-day NSQIP occurrences in neonates receiving a G-tube. RESULTS: Clinicians reported at least one NRE in 32 of 36 (89%) G-tube cases, averaging 3.0 (Standard deviation: 2.5) NRE reports per case. NSQIP-P review identified 7 cases (19%) with NSQIP-P occurrences and each of these cases had multiple reported NREs. One case in which NREs were not reported was without NSQIP-P occurrences. The odds ratio of having a NSQIP-P occurrence with the presence of an NRE was 0.695 (95% CI 0.06-17.04). CONCLUSION: Despite being considered a "simple" operation, >80% of neonatal G-tube placement operations had at least one reported NRE by an operative team member. In this pilot study, NRE occurrence was not significantly associated with the subsequent reporting of an NSQIP-P occurrence. Understanding contributory factors of NREs that occur in neonatal surgery may promote surgical safety efforts and should be evaluated in larger and more diverse populations. LEVEL OF EVIDENCE: IV.

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs.

BACKGROUND: In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). METHODS: The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. RESULTS: The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03). CONCLUSION: Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.