Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp.

BACKGROUND: Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States. Following the implementation of CONCERN, our team sought to develop the CONCERN Implementation Toolkit to enable other hospital systems to adopt CONCERN. OBJECTIVE: The aim of this study was to identify the optimal resources needed to implement CONCERN and package these resources into the CONCERN Implementation Toolkit to enable the spread of CONCERN to other hospital sites. METHODS: To accomplish this aim, we conducted qualitative interviews with nurses, prescribing providers, and information technology experts in two health systems. We recruited participants from July 2022 to January 2023. We conducted thematic analysis guided by the Donabedian model. Based on the results of the thematic analysis, we updated the α version of the CONCERN Implementation Toolkit. RESULTS: There was a total of 32 participants included in our study. In total, 12 themes were identified, with four themes mapping to each domain in Donabedian's model (i.e., structure, process, and outcome). Eight new resources were added to the CONCERN Implementation Toolkit. CONCLUSIONS: This study validated the α version of the CONCERN Implementation Toolkit. Future studies will focus on returning the results of the Toolkit to the hospital sites to validate the ß version of the CONCERN Implementation Toolkit. As the development of early warning systems continues to increase and clinician workflows evolve, the results of this study will provide considerations for research teams interested in implementing early warning systems in the acute care setting.

Understanding the Technical Implementation of a Clinical Decision Support SmartApp: A Qualitative Analysis.

CONCERN is a SmartApp that identifies patients at risk for deterioration. This study aimed to understand the technical components and processes that should be included in our Implementation Toolkit. In focus groups with technical experts five themes emerged: 1) implementation challenges, 2) implementation facilitators, 3) project management, 4) stakeholder engagement, and 5) security assessments. Our results may aid other teams in implementing healthcare SmartApps.

Reducing ICU Utilization, Length of Stay, and Cost by Optimizing the Clinical Use of Continuous Monitoring System Technology in the Hospital.

BACKGROUND: Continuous monitoring system technology (CMST) aids in earlier detection of deterioration of hospitalized patients, but whether improved outcomes are sustainable is unknown. METHODS: This interrupted time series evaluation explored whether optimized clinical use of CMST was associated with sustained improvement in intensive care unit (ICU) utilization, hospital length of stay, cardiac arrest rates, code blue events, mortality, and cost across multiple adult acute care units. RESULTS: A total of 20,320 patients in the postoptimized use cohort compared with 16,781 patients in the preoptimized use cohort had a significantly reduced ICU transfer rate (1.73% vs 2.25%, P = .026) corresponding to 367.11 ICU days saved over a 2-year period, generating an estimated cost savings of more than $2.3 million. Among patients who transferred to the ICU, hospital length of stay was decreased (8.37 vs 9.64 days, P = .004). Cardiac arrest, code blue, and mortality rates did not differ significantly. CONCLUSION: Opportunities exist to promote optimized adoption and use of CMST at acute care facilities to sustainably improve clinical outcomes and reduce cost.

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial.

BACKGROUND: Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients' risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses' increased surveillance to predict when patients are at the risk of clinical deterioration. OBJECTIVE: The objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients' negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates). METHODS: A multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives. RESULTS: Data collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay. CONCLUSIONS: The CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm. TRIAL REGISTRATION: ClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30238.

Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings.

OBJECTIVES: Nursing documentation behavior within electronic health records may reflect a nurse's concern about a patient and can be used to predict patient deterioration. Our study objectives were to quantify variations in nursing documentation patterns, confirm those patterns and variations with clinicians, and identify which patterns indicate patient deterioration and recovery from clinical deterioration events in the critical and acute care settings. METHODS: We collected patient data from electronic health records and conducted a regression analysis to identify different nursing documentation patterns associated with patient outcomes resulting from clinical deterioration events in the intensive care unit (ICU) and acute care unit (ACU). The primary outcome measures were whether patients were discharged alive from the hospital or expired during their hospital encounter. Secondary outcome measures were clinical deterioration events. RESULTS: In the ICU, the increased documentation of heart rate, body temperature, and withheld medication administrations were significantly associated with inpatient mortality. In the ACU, the documentation of blood pressure, respiratory rate with comments, singular vital signs, and withheld medications were significantly related to inpatient mortality. In contrast, the documentation of heart rate and "as needed" medication administrations were significantly associated with patient survival to discharge in the ACU. CONCLUSION: We successfully identified and confirmed the clinical relevancy of the nursing documentation patterns indicative of patient deterioration and recovery from clinical deterioration events in both the ICU and ACU.

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.

OBJECTIVE: There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals). MATERIALS AND METHODS: We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories. RESULTS: Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework. DISCUSSION: The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle. CONCLUSIONS: We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

An Implementation Science Approach to Promote Optimal Implementation, Adoption, Use, and Spread of Continuous Clinical Monitoring System Technology.

OBJECTIVES: This study aimed to apply implementation science tenets to guide the deployment and use of in-hospital Clinical Monitoring System Technology (CMST) and to develop a toolkit to promote optimal implementation, adoption, use, and spread of CMST. METHODS: Six steps were carried out to (1) establish leadership support; (2) identify, educate, and sustain champions; (3) enlist clinical staff users to learn barriers and facilitators; (4) examine initial qualitative data from 11 clinician group interviews; (5) validate barriers/facilitators to CMST use and toolkit content; and (6) propose a toolkit to promote utilization. Clinical Monitoring System Technology output before and after implementation were compared. RESULTS: The top 3 barriers to effective CMST use were as follows: (1) inadequate education/training/support, (2) clinical workflow challenges, and (3) lack of communication. Facilitators to CMST implementation and adoption included the following: (1) providing comprehensive and consistent CMST education, (2) presenting evidence early and often, (3) tailoring device and usage expectations to individual environments, and (4) providing regular feedback about progress. Empirical data drove the development of a CMST implementation toolkit covering 6 areas: (1) why, (2) readiness, (3) readiness and implementation, (4) patient/family introduction, (5) champions, (6) care team saves, and (7) troubleshooting. Clinical Monitoring System Technology positively impacted failure to rescue events. Monthly median cardiac alert responses decreased from 30 to 3.64 minutes (87.9%), and respiratory alert responses decreased from 26 to 4.85 minutes (81.4%). CONCLUSIONS: Using implementation science tenets to concurrently guide deployment and study performance of 2 CMST devices and impact on workload was effective for both learning CMST efficacy at 2 hospital systems and developing a toolkit to promote optimal implementation, adoption, use, and spread.