Publications by authors named "Sarah Rossetti"

Objectives: Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of HHC patients experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models.

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Objectives: Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows.

Materials And Methods: A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity.

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Background: Health professions trainees (trainees) are unique as they learn a chosen field while working within electronic health records (EHR). Efforts to mitigate EHR burden have been described for the experienced health professional (HP), but less is understood for trainees. EHR or documentation burden (EHR burden) affects trainees, although not all trainees use EHRs, and use may differ for experienced HPs.

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Objectives:  Efforts to reduce documentation burden (DocBurden) for all health professionals (HP) are aligned with national initiatives to improve clinician wellness and patient safety. Yet DocBurden has not been precisely defined, limiting national conversations and rigorous, reproducible, and meaningful measures. Increasing attention to DocBurden motivated this work to establish a standard definition of DocBurden, with the emergence of excessive DocBurden as a term.

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Article Synopsis
  • Late predictions of patient deterioration in hospitals can lead to delays in treatment; the CONCERN Early Warning System addresses this by identifying risks up to 42 hours earlier using nursing documentation patterns.
  • The study tested the hypothesis that patients with care teams informed by CONCERN would experience lower mortality rates and shorter hospital stays compared to those not using the system.
  • The trial involved over 60,000 patient encounters across two large U.S. health systems, and results showed a 35.6% decreased risk of in-hospital mortality for patients monitored by the CONCERN EWS.
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Background:  Studies have shown that documentation burden experienced by clinicians may lead to less direct patient care, increased errors, and job dissatisfaction. Implementing effective strategies within health care systems to mitigate documentation burden can result in improved clinician satisfaction and more time spent with patients. However, there is a gap in the literature regarding evidence-based interventions to reduce documentation burden.

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Article Synopsis
  • The study investigates the gap between order-based and clinically perceived nursing workloads to better understand the factors contributing to nurse burnout and shortages.
  • Using a retrospecitve cohort study design, the research analyzes data on patient flow, nurse assignments, and workload intensity to quantify these factors.
  • Findings reveal that over half of the samples show that clinically perceived workload exceeds order-based workload, influenced by factors like shift timing, patient admission stages, and patient demographics, indicating a need for targeted workload management interventions.
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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.

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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.

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Article Synopsis
  • CONCERN is a SmartApp designed to detect patients at risk of health deterioration.
  • The study focused on identifying key technical components and processes for an Implementation Toolkit through discussions with technical experts.
  • Five main themes were identified: challenges and facilitators of implementation, project management, stakeholder engagement, and security assessments, all of which could assist other teams in deploying healthcare SmartApps.
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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.

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Workflow fragmentation, defined as task switching, may be one proxy to quantify electronic health record (EHR) documentation burden in the emergency department (ED). Few measures have been operationalized to evaluate task switching at scale. Theoretically grounded in the time-based resource-sharing model (TBRSM) which conceives task switching as proportional to the cognitive load experienced, we describe the functional relationship between cognitive load and the time and effort constructs previously applied for measuring documentation burden.

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Computerized provider order entry (CPOE) systems have been cited as a significant contributor to clinician burden. Vendor-derived measures and data sets have been developed to help with optimization of CPOE systems. We describe how we analyzed vendor-derived Order Friction (OF) EHR log data at our health system and propose a practical approach for optimizing CPOE systems by reducing OF.

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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.

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Background The critical care literature has seen an increase in the development and validation of tools using artificial intelligence for early detection of patient events or disease onset in the intensive care unit (ICU). The hemodynamic stability index (HSI) was found to have an AUC of 0.82 in predicting the need for hemodynamic intervention in the ICU.

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Objective: This study aims to understand whether higher use of a patient portal can have an impact on mental health functioning and recovery.

Method: A mixed methods approach was used for this study. In 2019-2021, patients with mental health diagnoses at outpatient clinics in an academic centre were invited to complete World Health Organization Disability Assessment Scale 12 (WHODAS-12) and Mental Health Recovery Measure surveys at baseline, 3 months, and 6 months after signing up for the portal.

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Objectives: Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

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Background: Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available.

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Few computational approaches exist for abstracting electronic health record (EHR) log files into clinically meaningful phenomena like clinician shifts. Because shifts are a fundamental unit of work recognized in clinical settings, shifts may serve as a primary unit of analysis in the study of documentation burden. We conducted a proof- of-concept study to investigate the feasibility of a novel approach using time series clustering to segment and infer clinician shifts from EHR log files.

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Background: A growing body of literature has linked usability limitations within electronic health records (EHRs) to adverse outcomes which may in turn affect EHR system transitions. NewYork-Presbyterian Hospital, Columbia University College of Physicians and Surgeons (CU), and Weill Cornell Medical College (WC) are a tripartite organization with large academic medical centers that initiated a phased transition of their EHRs to one system, EpicCare.

Objectives: This article characterizes usability perceptions stratified by provider roles by surveying WC ambulatory clinical staff already utilizing EpicCare and CU ambulatory clinical staff utilizing iterations of Allscripts before the implementation of EpicCare campus-wide.

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Objective: Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED).

Methods: From February to June 2022, we conducted semistructured interviews among a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems' EHR. We recruited participants through professional listservs, social media, and email invitations sent to healthcare professionals.

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Study Objective: We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand.

Methods: We conducted a retrospective study in an ED site in a large academic hospital in New York City.

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Aims: To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits.

Design: A retrospective cohort study.

Methods: This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States.

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Article Synopsis
  • There is a need for better research on adverse events (AEs) in oncology patients, as current methods like voluntary safety reporting (VSR) and the Global Trigger Tool (GTT) show significant differences in detection rates.
  • A study conducted between 2013 and 2015 compared these two methods, finding that the modified GTT detected more AEs (0.90 per patient) primarily related to medications, whereas VSR detected only 0.24 AEs per patient, with many not causing harm.
  • The findings suggest that neither detection method is fully effective on its own in identifying AEs in oncology patients, emphasizing the need for improved strategies, such as using electronic health records and patient-reported data.
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