Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction.

Electronic health records offer great promise for early disease detection, treatment evaluation, information discovery, and other important facets of precision health. Clinical notes, in particular, may contain nuanced information about a patient's condition, treatment plans, and history that structured data may not capture. As a result, and with advancements in natural language processing, clinical notes have been increasingly used in supervised prediction models. To predict long-term outcomes such as chronic disease and mortality, it is often advantageous to leverage data occurring at multiple time points in a patient's history. However, these data are often collected at irregular time intervals and varying frequencies, thus posing an analytical challenge. Here, we propose the use of large language models (LLMs) for robust temporal harmonization of clinical notes across multiple visits. We compare multiple state-of-the-art LLMs in their ability to generate useful information during time gaps, and evaluate performance in supervised deep learning models for clinical prediction.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer.

BACKGROUND: Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However, <30% of eligible patients undergo DXA screening. Biomechanical computed tomography (BCT) is a radiomic technique that measures bone mineral density (BMD) and bone strength from computed tomography (CT) scans. OBJECTIVE: To evaluate the (1) correlations between BCT- and DXA-assessed BMD, and (2) associations between BCT-assessed metrics and subsequent fracture. DESIGN, SETTING, AND PARTICIPANTS: A multicenter retrospective cohort study was conducted among patients with mHSPC between 2013 and 2020 who received CT abdomen/pelvis or positron emission tomography/CT within 48 wk before ADT initiation and during follow-up (48-96 wk after ADT initiation). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used univariate logistic regression to assess the associations between BCT measurements and the primary outcomes of subsequent pathologic and nonpathologic fractures. RESULTS AND LIMITATIONS: Among 91 eligible patients, the median ([interquartile range) age was 67 yr (62-75), 44 (48.4%) were White, and 41 (45.1%) were Black. During the median follow-up of 82 wk, 17 men (18.6%) developed a pathologic and 15 (16.5%) a nonpathologic fracture. BCT- and DXA-assessed femoral-neck BMD T scores were strongly correlated (R2 = 0.93). On baseline CT, lower BCT-assessed BMD (odds ratio [OR] 1.80, 95% confidence interval or CI [1.10, 3.25], p = 0.03) was associated with an increased risk of a pathologic fracture. Lower femoral strength (OR 1.63, 95% CI [0.99, 2.71], p = 0.06) was marginally associated with an increased risk of a pathologic fracture. Neither BMD (OR 1.52, 95% CI [0.95, 2.63], p = 0.11) nor strength (OR 1.14, 95% CI [0.75, 1.80], p = 0.57) was associated with a nonpathologic fracture. BCT identified nine (9.9%) men eligible for antiresorptive therapy, of whom four (44%) were not treated. Limitations include low fracture numbers resulting in lower power to detect fracture associations. CONCLUSIONS: Among men diagnosed with mHSPC, BCT assessments were strongly correlated with DXA, predicted subsequent pathologic fracture, and identified additional men indicated for antiresorptive therapy. PATIENT SUMMARY: We assess whether biomechanical computer tomography (BCT) from routine computer tomography (CT) scans can identify fracture risk among patients recently diagnosed with metastatic prostate cancer. We find that BCT and dual-energy x-ray absorptiometry-derived bone mineral density are strongly correlated and that BCT accurately identifies the risk for future fracture. BCT may enable broader fracture risk assessment and facilitate timely interventions to reduce fracture risk in metastatic prostate cancer patients.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Mining for equitable health: Assessing the impact of missing data in electronic health records.

Electronic health records (EHR) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have a consistent, standardized format across institutions, particularly in the United States, and can present significant analytical challenges- they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHR can reflect inequity- patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for under-served groups may be less informative partly due to more fragmented care, which can be viewed as a type of missing data problem. For EHR data in this complex form, there is currently no framework for introducing realistic missing values. There has also been little to no work in assessing the impact of missing data in EHR. In this work, we first introduce a terminology to define three levels of EHR data and then propose a novel framework for simulating realistic missing data scenarios in EHR to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

Mental health among head and neck surgeons in Brazil during the COVID-19 pandemic: A national study.

PURPOSE: Head and neck surgeons are among the highest risk for COVID-19 exposure, which also brings great risk to their mental wellbeing. In this study, we aim to evaluate mental health symptoms among head and neck surgeons in Brazil surrounding the time it was declared the epicenter of the virus. MATERIALS AND METHODS: A cross-sectional, survey-based study evaluating burnout, anxiety, distress, and depression among head and neck surgeons in Brazil, assessed through the single-item Mini-Z burnout assessment, 7-item Generalized Anxiety Disorder scale, 22-item Impact of Event Scale-Revised, and 2-item Patient Health Questionnaire, respectively. RESULTS: 163 physicians completed the survey (74.2% males). Anxiety, distress, burnout, and depression symptoms were reported in 74 (45.5%), 43 (26.3%), 24 (14.7%), and 26 (16.0%) physicians, respectively. On multivariable analysis, female physicians were more likely to report a positive screening for burnout compared to males (OR 2.88, CI [1.07-7.74]). Physicians 45 years or older were less likely to experience anxiety symptoms than those younger than 45 years (OR 0.40, CI [0.20-0.81]). Physicians with no self-reported prior psychiatric conditions were less likely to have symptoms of distress compared to those with such history (OR 0.11, CI [0.33-0.38]). CONCLUSION: Head and neck surgeons in Brazil reported symptoms of burnout, anxiety, distress and depression during our study period within the COVID-19 pandemic. Institutions should monitor these symptoms throughout the pandemic. Further study is required to assess the long-term implications for physician wellness.