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.

Information Needs and the Use of Documentation to Support Collaborative Decision-Making: Implications for the Reduction of Central Line-Associated Blood Stream Infections.

It is clear that interdisciplinary communication and collaboration have the potential to mitigate healthcare-associated harm, yet there is limited research on how communication through documentation in the patient record can support collaborative decision making. Understanding what information is needed to support collaborative decision making is necessary to design electronic health information systems that facilitate effective communication and, ultimately, safe care. To explore this issue, we focused on information needs related to central venous catheter management and the prevention of central line-associated blood stream infections. Semistructured interviews were conducted with nurses working in an intensive care unit. Interview transcripts were analyzed using inductive thematic analysis. Three themes were identified: (1) challenges managing documentation in multiple places in the absence of formal documentation processes for central venous catheter management; (2) lack of standardized decision-making processes for managing central venous catheters; and (3) oral communication holds it together. Our findings provide a foundation for the development of EHR functional requirements that enhance communication regarding the management of central venous catheters and facilitate the prompt removal of unnecessary lines.

Identifying best practices in electronic health record documentation to support interprofessional communication for the prevention of central line-associated bloodstream infections.

BACKGROUND: There is a paucity of research on best practices for communication through the electronic health record (EHR) to support shared decision-making and to prevent adverse events. To explore this issue, this study focused on interprofessional communication around a specific clinical issue, the prevention of central line-associated bloodstream infections. METHODS: The Delphi technique was used to describe what information is needed to support decisions regarding central venous catheter (CVC) management, and best practices for communicating this information among the interprofessional team. RESULTS: The expert panel, 4 physicians and 6 nurses, identified 12 information types necessary for decisions regarding CVC management and the removal of unnecessary lines. For each of the 12 information types, the panel reached agreement regarding the best channel to communicate this information, including asynchronous EHR formats and synchronous oral channels. CONCLUSIONS: Findings address 2 important aspects of postinsertion CVC practices and a gap in the literature by moving beyond a description of current practices to a description of how the EHR ought to be used to support collaboration and interprofessional communication. Future research should examine how the communication practices identified in this study contribute to a reduction in central line-associated bloodstream infections.

Identifying nurses' concern concepts about patient deterioration using a standard nursing terminology.

OBJECTIVES: Nurse concerns documented in nursing notes are important predictors of patient risk of deterioration. Using a standard nursing terminology and inputs from subject-matter experts (SMEs), we aimed to identify and define nurse concern concepts and terms about patient deterioration, which can be used to support subsequent automated tasks, such as natural language processing and risk predication. METHODS: Group consensus meetings with nurse SMEs were held to identify nursing concerns by grading Clinical Care Classification (CCC) system concepts based on clinical knowledge. Next, a fundamental lexicon was built placing selected CCC concepts into a framework of entities and seed terms to extend CCC granularity. RESULTS: A total of 29 CCC concepts were selected as reflecting nurse concerns. From these, 111 entities and 586 seed terms were generated into a fundamental lexicon. Nursing concern concepts differed across settings (intensive care units versus non-intensive care units) and unit types (medicine versus surgery units). CONCLUSIONS: The CCC concepts were useful for representing nursing concern as they encompass a nursing-centric conceptual framework and are practical in lexicon construction. It enabled the codification of nursing concerns for deteriorating patients at a standardized conceptual level. The boundary of selected CCC concepts and lexicons were determined by the SMEs. The fundamental lexicon offers more granular terms that can be identified and processed in an automated fashion.