Citations

Objective: To characterize the relationship between surgeon-staff familiarity and the time required to perform an operation. Summary Background Data: The clinical and operational efficiency of operating rooms is a driver of hospital finances and patient outcomes. Surgeon-staff familiarity is an important focus for quality improvement and process optimization. Methods: A retrospective cohort study was performed analyzing operations performed at 4 academic hospitals between July 1, 2019 and December 31, 2023. Exclusion criteria (urgent operations, 2-specialty cases, low surgeon/specialty volume in data set, scheduled length > 8 hours) were applied to limit heterogeneity. The exposure of interest was familiarity between the surgeon and the allied health staff (scrub technicians and circulating nurses) present during an operation (represented as the median staff experience with the index surgeon in the previous 180 d). The primary outcome was whether the actual case length was (vs. was not) 10% shorter than the scheduled case length (“shorter case length”). A secondary outcome was the quantification of the difference between the observed and expected case length and whether the surgical time (cut-close) was shorter. Results: A total of 257,645 operations performed by 25 different specialties comprised the analyzed cohort. Higher levels of surgeon-staff familiarity were associated with a higher likelihood of a shorter case length (observed:expected ratio = 1.16 [1.14–1.18] for high/high levels of familiarity, compared with 0.90 [0.88–0.91] for low/low levels of familiarity). Greater familiarity was also associated with case lengths that were shorter relative to scheduled case length (high/high levels of familiarity 8.7% shorter than low/low levels, P < 0.001). Within the 4 most common types of cases, those operations performed by teams with higher levels of familiarity had shorter surgical times (cut-to-close). Conclusions: This study found that operations were more efficiently performed when the scrub technician and circulating nurse staff had a greater recent experience with the primary surgeon. Surgical team familiarity should be considered a focus of operational and clinical excellence.

Abstract Background/Objective: Decentralized research has many advantages; however, little is known about the representativeness of a source population in decentralized studies. We recruited participants aged 18-64 years from four states from June to December 2022 for a prospective cohort study to assess viral epidemiology. Our aim was to determine the association between age, gender, race/ethnicity, rurality, and socioeconomic status (SES) on study participation in a decentralized prospective cohort study. Methods: We consented 9,286 participants from 231,099 (4.0%) adults with the mean age of 45.6 years (±12.0). We used an electronic decentralized approach for recruitment. Consented participants were more likely to be non-Hispanic White, female, older, urban residents, have more health conditions, and possessed higher socioeconomic status (SES) compared to those non-consented. Results: We observed an interaction between SES and race-ethnicity on the odds of consent (P = 0.006). Specifically, SES did not affect non-Hispanic white participation rates(OR 1.24 95% CI 1.16 – 1.32] for the highest SES quartile compared to those with the lowest SES quartile) as much as it did participants combined across the other races (OR 1.73; 95% CI 1.45 – 2.98]) Conclusion: The relationship between SES and consent rates might be disproportionately greater in historically disadvantaged groups, compared to non-Hispanic White. It suggests that instead of focusing on enrollment of specific minority groups in research, there is value in future research exploring and addressing the diversity of barriers to trials within minority groups. Our study highlights that decentralized studies need to address social determinants of health, especially in under-resourced populations.

Dermatoneuro syndrome (DNS) is a rare diagnosis of exclusion characterised by fever, altered mental status and seizures in patients with scleromyxoedema. We present a complex case of a patient presenting with a fever, coma, seizures, rigidity and deep T2 fluid-attenuated inversion recovery hyperintensities in the context of both scleromyxoedema and influenza A with extensive, negative infectious workup. This case highlights the shared clinical features of DNS and influenza-associated encephalopathy, the latter being the eventual diagnosis for our patient. We review the DNS literature focusing on presentation, adequacy of investigation, treatment and prognosis. Finally, we provide an initial workup for patients with suspected DNS, including nasopharyngeal influenza PCR.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Objectives/Goals: Undergraduate Medical Education (UME) may apply Just-in-Time training (JITT) to provide medical students with learning experiences closely aligned with real-time clinical needs. The purpose of this scoping review is to offer an overview of the implementation of JITT training in UME. Methods/Study Population: Following the five-stage framework by Arksey and O’Malley to methodically collect and analyze studies on JITT in UME, five electronic databases were searched, and a supplemental search for grey literature was conducted. Studies exploring the integration of JITT principles into UME clinical training and their time to follow-up after training were included. Bloom’s Taxonomy was used to assess educational goals of JITT interventions. Results/Anticipated Results: The review yielded 21 studies across 4 countries. The majority were cohort studies (13) and randomized control trials (5). Assessment definitions and use of JITT varied widely. Most studies focused on short-term outcomes, defined by being measured immediately after JITT session (15) or at the end of JITT-based rotation or clerkship (3). Three studies evaluated outcomes at a period longer than 2 weeks after completion of session or clerkship. Attitudes (9), followed by skills (8) were the most common educational goals of intervention. The efficacy and utility of JITT in improving educational goal acquisition was demonstrated in 90% (17/19) of the studies with reported outcomes. Discussion/Significance of Impact: The introduction of JITT in UME has been shown to meet the immediate needs of healthcare environments; however, evidence is limited in the evaluation of longer-term outcomes. Further research to determine the impact of JITT on long-term learning retention and education goal acquisition in UME is merited.

Objectives/Goals: Community-based practices have limited research opportunities for providers. As a rural community-based health system, Mayo Clinic Health System (MCHS) sought to create intramural funding mechanisms to help early-stage researchers conduct pilot research so they are more competitive to compete for more robust extramural funding opportunities. Methods/Study Population: We created a Research Seed Grant Program infrastructure across our four MCHS regions (Southwest and Southeast Minnesota, Northwest and Southwest Wisconsin). This model program featured an initial research funding announcement call based on 6 prioritized programs (AI Validation & Stewardship, Cancer, Health Equity, Population Health, Rural Health, Learning Healthcare System [LHS]), with submissions uploaded to a central electronic repository. Proposal review and ranking was organized on a regional basis, with ranking of all proposals by at least 2 clinical scientist reviewers according to the NIH domain-specific framework (i.e., 1.0 best > 9.0 worst). Awards were competitively selected by conformity to prioritized research areas and through ranking of most competitive overall application scores. Results/Anticipated Results: For our inaugural RFA, we received 55 grant application submissions across the MCHS regions. Fifteen of the most highly ranked applications were selected for awards on a per region basis, providing direct funding as well as protected investigator research time of 10% for up to six months. The selected projects address several research priorities including improving access, reducing health disparities, improving behavioral health in our communities, increasing cancer screening and prevention, and community-based pragmatic trials and interventions. Outcomes from these now completed pilot projects remain pending at this time. Funding for this seed grant program was supported by philanthropy, Mayo Clinic Research Administration, Mayo Clinic Comprehensive Cancer Center, and the Mayo Clinic CCaTS Rural Core. Discussion/Significance of Impact: We present this framework for a LHS-focused Seed Grant program model for consideration of adoption by other national/international LHS. Future plans include tracking of outcome metrics (e.g., published peer review articles, extramural grant applications) of this initial cycle and future expansion of this program to support the goals of our LHS.

AbstractRegistered nurses increasingly work in remote care and digital interaction roles, offering flexibility and expansion of their scope of practice. These roles may expose nurses to digital compassion fatigue, a phenomenon proposed to be characterized by the negative psychological and emotional impact of caring for patients remotely through the use of technology.The first objective of this work is to propose the phenomenon of “digital compassion fatigue” as a potentially further evolved and differentiated form of “compassion fatigue.” The second objective is to produce a comparative analysis of attributes, antecedents, and consequences through literature reviews.An evolutionary concept analysis approach was selected as a guide for exploring, discussing, and defining the new concept of digital compassion fatigue. Concept analysis was selected given the need for a framework that accounts for the dynamic nature of technology and practice. The process of conducting a concept analysis includes consideration of diverse and multidisciplinary perspectives. As a result, those in caring, educational, and/or support service roles (e.g., social work, counseling, and teaching) for which distance suffering and technostress could feasibly be present were also included in the literature searches and reviews. Health care-specific studies often included nurses in the sample but may not have differentiated their specific insights or data points in the results.The concept analysis explored the attributes, antecedents, and consequences of digital compassion fatigue, differentiating it from its evolutionary parent, compassion fatigue. Key antecedents included technostress, distant suffering, and the unique challenges of delivering care remotely. A major confounding variable was the COVID-19 pandemic, which may have heightened or introduced new technology-related stressors or highlighted the existence of digital compassion fatigue.Further defining and understanding digital compassion fatigue is crucial for developing effective strategies to support nurses who may experience it or who are at risk.

Introduction: Online self-scheduling of medical appointments is increasingly common. An automated waitlist can be used for patients who desire an earlier appointment time if one becomes available after they are scheduled. Our study examines outcomes of an automated waitlist and self-rescheduling process. Methods: We studied outcomes of an automated waitlist self-rescheduling process in which patients with existing appointments elected to be placed on an automated waitlist for an earlier appointment offer. When software found earlier dates for the same visit type, patients were then notified through an automated process and could self-reschedule. We reviewed appointments for which patients were sent new offers when earlier appointment slots were found. We compared the accepted appointment offers with the original scheduled appointments and determined the number of days that the appointment had been moved up. Results: Spanning the calendar year 2023 there were 1 019 698 appointment offers generated by an automated waitlist process for 229 998 appointments and sent to 164 248 patients. The waitlist process automatically found open appointments as they became available and sent the first new appointment offer within 2 days after being placed on the waitlist for 74 736 (32.5%) of the 229 998 waitlisted appointments. Patients sent back at least 1 response for 104 554 (45.4%) of the waitlisted appointments. Of the responses, 56 636 accepted one of the sent offers for an accept rate of 24.6% (56 636/229 998). For accepted, moved-up visits, appointments were self-rescheduled earlier by a mean of 22.6 days (95%CI; 22.2, 22.9, P < .0001). Conclusion: New appointments can be successfully self-rescheduled using an automated waitlist process that allows patients to accept or decline new appointment offers. This process can increase the efficiency of scheduling and decrease appointment wait time for patients desiring more timely access to healthcare. In addition, this process can be successfully applied across several different appointment type categories.