Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

BACKGROUND: Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. OBJECTIVES: The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. METHODS: Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RESULTS: RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. CONCLUSION: The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

Variability in Nursing Documentation Patterns across Patients' Hospital Stays.

This study explores the variability in nursing documentation patterns in acute care and ICU settings, focusing on vital signs and note documentation, and examines how these patterns vary across patients' hospital stays, documentation types, and comorbidities. In both acute care and critical care settings, there was significant variability in nursing documentation patterns across hospital stays, by documentation type, and by patients' comorbidities. The results suggest that nurses adapt their documentation practices in response to their patients' fluctuating needs and conditions, highlighting the need to facilitate more individualized care and tailored documentation practices. The implications of these findings can inform decisions on nursing workload management, clinical decision support tools, and EHR optimizations.

Identifying Reuse and Redundancies in Respiratory Flowsheet Documentation: Implications for Clinician Documentation Burden.

Documentation burden is experienced by clinical end-users of the electronic health record. Flowsheet measure reuse and clinical concept redundancy are two contributors to documentation burden. In this paper, we described nursing flowsheet documentation hierarchy and frequency of use for one month from two hospitals in our health system. We examined respiratory care management documentation in greater detail. We found 59 instances of reuse of respiratory care flowsheet measure fields over two or more templates and groups, and 5 instances of clinical concept redundancy. Flowsheet measure fields for physical assessment observations and measurements were the most frequently documented and most reused, whereas respiratory intervention documentation was less frequently reused. Further research should investigate the relationship between flowsheet measure reuse and redundancy and EHR information overload and documentation burden.