Faculty Bibliography

INTRODUCTION/BACKGROUND:Menu labels were federally mandated in May 2018, but the authors are not aware of any work that has evaluated the impact of the national rollout of this legislation in restaurants using a comparison group to account for potential bias. METHODS:Using synthetic control methods, Taco Bell restaurants that implemented menu labels after nationwide labeling (n=5,060 restaurants) were matched to restaurants that added calorie labels to menus after local labeling legislation (and prior to nationwide labeling). The effect of menu labeling on calories purchased per transaction after nationwide labeling between groups (i.e., "later-treated" and "early-treated" restaurants) was estimated using a two-way fixed effects regression model, with time modeled as relative month from implementation and fixed effects for calendar month and restaurant. RESULTS:In the baseline period, average calories per transaction was 1,242 (SD=178) in the national menu labeling group and 1,245 (SD=183.9) in the comparison group, with parallel trends between groups. Difference-in-differences model results indicated that transactions from restaurants in the national menu labeling group included 7.4 (95% CI: 7.3, 7.5) more calories than was predicted based on the trend in the comparison group. Average number of total transactions per month decreased ∼2% more in the national menu labeling group relative to the comparison group. CONCLUSIONS:Negligible changes were observed in calories purchased and number of transactions in restaurants that added calorie labels due to national legislation, above and beyond secular changes. Other strategies may be necessary to promote meaningful decreases in daily calories purchased in restaurants going forward.

IMPORTANCE/UNASSIGNED:Hospital course (HC) summarization represents an increasingly onerous discharge summary component for physicians. Literature supports large language models (LLMs) for HC summarization, but whether physicians can effectively partner with electronic health record-embedded LLMs to draft HCs is unknown. OBJECTIVES/UNASSIGNED:To compare the editing effort required by time-constrained resident physicians to improve LLM- vs physician-generated HCs toward a novel 4Cs (complete, concise, cohesive, and confabulation-free) HC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Quality improvement study using a convenience sample of 10 internal medicine resident editors, 8 hospitalist evaluators, and randomly selected general medicine admissions in December 2023 lasting 4 to 8 days at New York University Langone Health. EXPOSURES/UNASSIGNED:Residents and hospitalists reviewed randomly assigned patient medical records for 10 minutes. Residents blinded to author type who edited each HC pair (physician and LLM) for quality in 3 minutes, followed by comparative ratings by attending hospitalists. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Editing effort was quantified by analyzing the edits that occurred on the HC pairs after controlling for length (percentage edited) and the degree to which the original HCs' meaning was altered (semantic change). Hospitalists compared edited HC pairs with A/B testing on the 4Cs (5-point Likert scales converted to 10-point bidirectional scales). RESULTS/UNASSIGNED:Among 100 admissions, compared with physician HCs, residents edited a smaller percentage of LLM HCs (LLM mean [SD], 31.5% [16.6%] vs physicians, 44.8% [20.0%]; P < .001). Additionally, LLM HCs required less semantic change (LLM mean [SD], 2.4% [1.6%] vs physicians, 4.9% [3.5%]; P < .001). Attending physicians deemed LLM HCs to be more complete (mean [SD] difference LLM vs physicians on 10-point bidirectional scale, 3.00 [5.28]; P < .001), similarly concise (mean [SD], -1.02 [6.08]; P = .20), and cohesive (mean [SD], 0.70 [6.14]; P = .60), but with more confabulations (mean [SD], -0.98 [3.53]; P = .002). The composite scores were similar (mean [SD] difference LLM vs physician on 40-point bidirectional scale, 1.70 [14.24]; P = .46). CONCLUSIONS AND RELEVANCE/UNASSIGNED:Electronic health record-embedded LLM HCs required less editing than physician-generated HCs to approach a quality standard, resulting in HCs that were comparably or more complete, concise, and cohesive, but contained more confabulations. Despite the potential influence of artificial time constraints, this study supports the feasibility of a physician-LLM partnership for writing HCs and provides a basis for monitoring LLM HCs in clinical practice.

This paper is an examination of the relationship between continuity of care with home health physical therapists following hospitalization and the likelihood of readmission. We conducted a retrospective cohort study. Using rehospitalization as the dependent variable, a continuity of care indicator variable was analyzed with a multivariable logistic regression. The indicator variable was created using the Bice-Boxerman Index to measure physical therapist continuity of care. The mean of the index (0.81) was used to separate between high continuity (0.81 or greater) of care and low continuity of care (lower than 0.81). The sample included 90,220 patients, with data coming from the linking of the Outcome Assessment and Information Set (OASIS) and an administrative dataset. All subjects lived in the NYC metro area. Inclusion criteria was a patient's admission to their first home health care site following discharge occurring between 2010 and 2015, and individuals who identified as Male or Female. In comparison to low continuity of physical therapy, high continuity of physical therapy significantly decreased hospital readmissions (OR = 0.74, 95% CI 0.71-0.76, p ≤ .001, AME = -4.28%). Interpersonal continuity of physical therapy care has been identified as a key factor in decreasing readmissions from the home care setting. The research suggests an increased emphasis in preserving physical therapist continuity following hospitalization should be explored, with the potential to reduce hospital readmissions.

Inflammation is the body's response to infection, trauma or injury and is activated in a coordinated fashion to ensure the restoration of tissue homeostasis and healthy physiology. This process requires communication between stromal cells resident to the tissue compartment and infiltrating immune cells which is dysregulated in disease. Clinical innovations in patient diagnosis and stratification include measures of inflammatory activation that support the assessment of patient prognosis and response to therapy. We propose that (i) the recent advances in fast, dynamic monitoring of inflammatory markers (e.g., cytokines) and (ii) data-dependent theoretical and computational modeling of inflammatory marker dynamics will enable the quantification of the inflammatory response, identification of optimal, disease-specific biomarkers and the design of personalized interventions to improve patient outcomes - multidisciplinary efforts in which biomedical engineers may potentially contribute. To illustrate these ideas, we describe the actions of cytokines, acute phase proteins and hormones in the inflammatory response and discuss their role in local wounds, COVID-19, cancer, autoimmune diseases, neurodegenerative diseases and aging, with a central focus on cardiac surgery. We also discuss the challenges and opportunities involved in tracking and modulating inflammation in clinical settings.

Objectives Improve concordance between patient end-of-life preferences and code status orders by incorporating data from a state registry with Clinical Decision Support (CDS) within the electronic health record (EHR) to preserve patient autonomy and ensure that patients receive care that aligns with their wishes. Methods Leveraging a Health Information exchange (HIE) interface between the New York State Medical Orders for Life-Sustaining Treatment (eMOLST) registry and the EHR of our academic health system, we developed a bundled CDS intervention that displays eMOLST information at the time of code status ordering and provides an in-line alert when providers enter a resuscitation order discordant with wishes documented in the eMOLST registry. To evaluate this intervention, we performed a segmented regression analysis of an interrupted times series to compare percentage of discordant orders before and after implementation among all hospitalizations for which an eMOLST was available. Results We identified a total of 3648 visits that had an eMOLST filed prior to inpatient admission and a code status order placed during admission. There was a statistically significant decrease of discordant resuscitation orders of -5.95% after the intervention went live, with a relative risk reduction of 25%, [95% CI: -9.95%, -1.94%, p=0.009] in the pre- and post-intervention period. Logistic regression model after adjusting for co-variates showed an average marginal effect of -5.12% after the intervention [CI =-9.75%, -0.50%, p=0.03]. Conclusions Our intervention resulted in a decrease in discordant resuscitation orders. This study demonstrates that accessibility to eMOLST data within the provider workflow supported by CDS can reduce discrepancies between patient end-of-life wishes and hospital code status orders.

AIM/OBJECTIVE:To describe the self-reported mental health of nurses from 35 countries who worked during the COVID-19 pandemic. BACKGROUND:There is little occupationally specific data about nurses' mental health worldwide. Studies have documented the impact on nurses' mental health of the COVID-19 pandemic, but few have baseline referents. METHODS:A descriptive, cross-sectional design structured the study. Data reflect a convenience sample of 9,387 participants who completed the opt-in survey between July 31, 2022, and October 31, 2023. Descriptive statistics were run to analyze the following variables associated with mental health: Self-reports of mental health symptoms, burnout, personal losses during the pandemic, access to mental health services, and self-care practices used to cope with pandemic-related stressors. Reporting of this study was steered by the STROBE guideline for quantitative studies. RESULTS:Anxiety or depression occurred at rates ranging from 23%-61%, with country-specific trends in reporting observed. Approximately 18% of the sample reported experiencing some symptoms of burnout. The majority of nurses' employers did not provide mental health support in the workplace. Most reported more frequently engaging with self-care practices compared with before the pandemic. Notably, 20% of nurses suffered the loss of a family member, 35% lost a friend, and 34% a coworker due to COVID-19. Nearly half (48%) reported experiencing public aggression due to their identity as a nurse. CONCLUSIONS:The data obtained establish a basis for understanding the specific mental health needs of the nursing workforce globally, highlighting key areas for service development. IMPLICATIONS FOR NURSING POLICY/CONCLUSIONS:Healthcare organizations and governmental bodies need to develop targeted mental health support programs that are readily accessible to nurses to foster a resilient nursing workforce.

PURPOSE:To compare the effects of enhanced and limited patient-clinician relationships during patient history taking on objective functional measures and pain appraisals for individuals with chronic low back pain (CLBP). METHODS:Fifty-two (52) participants with CLBP, unaware of the two groups, were randomized using concealed allocation to an enhanced (n=26) or limited (n=26) patient-clinician relationship condition. Participants shared their history of CLBP with a clinician who enacted either enhanced or limited communication strategies. Fingertip-to-floor, one-minute lift, and Biering-Sorensen tests, and visual analogue scale for pain at rest were assessed before and after the patient-clinician relationship conditions. FINDINGS:The enhanced condition resulted in significantly greater improvements in the one-minute lift test (F(1,49)=7.47, p&lt;.01, ηp2=0.13) and pain at rest (F(1,46)=4.63, p=.04, ηp2=0.09), but not the fingertip-to-floor or Biering-Sorensen tests, compared with the limited group. CONCLUSIONS:Even without physical treatment, differences in patient-clinician relationships acutely affected lifting performance and pain among individuals with CLBP.

BACKGROUND AND OBJECTIVES/UNASSIGNED:Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to build a machine learning (ML) model to predict incision times for robotic-assisted hysterectomies, enhancing scheduling accuracy and hospital finances. METHODS/UNASSIGNED:A retrospective study was conducted using data from robotic-assisted hysterectomy cases performed between January 2017 and April 2021 across 3 hospitals within a large academic health system. Cases were filtered for surgeries performed by high-volume surgeons and those with an incision time of under 3 hours (n = 2,702). Features influencing incision time were extracted from electronic medical records and used to train 5 ML models (linear ridge regression, random forest, XGBoost, CatBoost, and explainable boosting machine [EBM]). Model performance was evaluated using a dynamic monthly update process and novel metrics such as wait-time blocks and excess-time blocks. RESULTS/UNASSIGNED: < .001, 95% CI [-329 to -89]), translating to approximately 52-hours over the 51-month study period. The model predicted more surgeries within a 15% range of the true incision time compared to traditional methods. Influential features included surgeon experience, number of additional procedures, body mass index (BMI), and uterine size. CONCLUSION/UNASSIGNED:The ML model enhanced the prediction of incision times for robotic-assisted hysterectomies, providing a potential solution to reduce OR underutilization and increase surgical throughput and hospital revenue.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

BACKGROUND/UNASSIGNED:Hospital mergers remain common, but their influence on healthcare quality varies. Data on effects of European hospital mergers are ill defined, and academic hospitals in particular. This case study assesses early quality of care changes in two formerly competing Dutch academic hospitals that merged on June 6, 2018. METHODS/UNASSIGNED:Statistical process control and interrupted time series analysis were performed. All adult, non-psychiatric patients, admitted between 01-03-2016 and 01-10-2022 were eligible for analysis. Primary outcome measure was all cause in-hospital mortality (or hospice), secondary outcomes were unplanned 30-day readmissions to same hospital, length of stay, and patients' hospital rating. Data were obtained from electronic health records, and patient experience surveys. FINDINGS/UNASSIGNED:The mean (SD) age of the 573 813 included patients was 54·3 (18·9) years. The minority was female (277 817, 48·4 %), and most admissions were acute (308 597, 53·8 %). No merger related change in mortality was found in the first 20 months post-merger (limited to the pre-Covid-19 era). For this same period, the 30-day readmission incidence changed to a downward slope post-merger, and the length of stay shortened (immediate level-change -3·796 % (95 % CI, -5·776 % to -1·816 %) and trend-change -0·150 % per month (95 % CI, -0·307 % to 0·007 %)). Patients' hospital ratings seemed to improve post-merger. INTERPRETATION/UNASSIGNED:In this quality improvement study, a full- and gradual post-merger integration strategy for a Dutch academic hospital merger was not associated with changes in in-hospital mortality, and yielded slight improved results for secondary quality of care outcomes.