34 research outputs found
Large Language Models to Identify Social Determinants of Health in Electronic Health Records
Social determinants of health (SDoH) have an important impact on patient
outcomes but are incompletely collected from the electronic health records
(EHR). This study researched the ability of large language models to extract
SDoH from free text in EHRs, where they are most commonly documented, and
explored the role of synthetic clinical text for improving the extraction of
these scarcely documented, yet extremely valuable, clinical data. 800 patient
notes were annotated for SDoH categories, and several transformer-based models
were evaluated. The study also experimented with synthetic data generation and
assessed for algorithmic bias. Our best-performing models were fine-tuned
Flan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The
benefit of augmenting fine-tuning with synthetic data varied across model
architecture and size, with smaller Flan-T5 models (base and large) showing the
greatest improvements in performance (delta F1 +0.12 to +0.23). Model
performance was similar on the in-hospital system dataset but worse on the
MIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and
few-shot performance of ChatGPT-family models for both tasks. These fine-tuned
models were less likely than ChatGPT to change their prediction when
race/ethnicity and gender descriptors were added to the text, suggesting less
algorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of
patients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can
effectively extracted SDoH information from clinic notes, performing better
compare to GPT zero- and few-shot settings. These models could enhance
real-world evidence on SDoH and aid in identifying patients needing social
support.Comment: 38 pages, 5 figures, 5 tables in main, submitted for revie
Prostate cancer incidence across stage, NCCN risk groups, and age before and after USPSTF Grade D recommendations against prostateâ specific antigen screening in 2012
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153721/1/cncr32604.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153721/2/cncr32604_am.pd
Conservative management of lowâ risk prostate cancer among young versus older men in the United States: Trends and outcomes from a novel national database
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151903/1/cncr32332.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151903/2/cncr32332_am.pd
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Health literacy and English language comprehension among elderly inpatients at an urban safety-net hospital.
ObjectiveTo evaluate the relationship between health literacy and age in chronically-ill inpatients at a safety-net hospital.Setting and participantsWe recruited 399 English- and Spanish-speaking inpatients being evaluated or treated for Congestive Heart Failure or Coronary Artery Disease at a large, urban safety-net teaching hospital in Southern California.DesignParticipants were interviewed to ascertain education, English comprehension, and in-home language use. Health literacy was assessed using The Test of Functional Health Literacy in Adults (TOFHLA). We compared by age (aged 65 or more, 51 to 64 years of age, and less than age 50) levels of health literacy, educational attainment, English comprehension, and language use.ResultsPrevalence of inadequate health literacy significantly increased with increasing age (87.2% in > or = 65, 48.9% for 51-64, and 26.3% in < or = 50, p<0.001). The correlation between older age and lower health literacy persisted when controlling for educational achievement, race, ethnicity, gender, and immigration status. Additionally, older patients were more likely to have never learned to read (34.9% in > or = 65, 6.5% for 51-64, and 1.5% in < or = 50, p<0.001), no formal education (27.9% in > or = 65, 9.0% for 51-64, and 0.8% in < or = 50, p<0.001), have limited English comprehension (74.2% in > or = 65, 43.5% for 51-64, and 35.8% in < or = 50, p<0.001), and speak a non-English language at home (82.3% in > or = 65, 70.2% for 51-64, and 62.2% in < or = 50, p=0.015).ConclusionsTo prepare to meet the chronic disease needs of a growing older patient population, and ameliorate the negative health effects of associated low literacy, safety-net hospital leaders and providers need to prioritize the development and implementation of low-literacy educational materials, programs, and services
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Health literacy and English language comprehension among elderly inpatients at an urban safety-net hospital.
ObjectiveTo evaluate the relationship between health literacy and age in chronically-ill inpatients at a safety-net hospital.Setting and participantsWe recruited 399 English- and Spanish-speaking inpatients being evaluated or treated for Congestive Heart Failure or Coronary Artery Disease at a large, urban safety-net teaching hospital in Southern California.DesignParticipants were interviewed to ascertain education, English comprehension, and in-home language use. Health literacy was assessed using The Test of Functional Health Literacy in Adults (TOFHLA). We compared by age (aged 65 or more, 51 to 64 years of age, and less than age 50) levels of health literacy, educational attainment, English comprehension, and language use.ResultsPrevalence of inadequate health literacy significantly increased with increasing age (87.2% in > or = 65, 48.9% for 51-64, and 26.3% in < or = 50, p<0.001). The correlation between older age and lower health literacy persisted when controlling for educational achievement, race, ethnicity, gender, and immigration status. Additionally, older patients were more likely to have never learned to read (34.9% in > or = 65, 6.5% for 51-64, and 1.5% in < or = 50, p<0.001), no formal education (27.9% in > or = 65, 9.0% for 51-64, and 0.8% in < or = 50, p<0.001), have limited English comprehension (74.2% in > or = 65, 43.5% for 51-64, and 35.8% in < or = 50, p<0.001), and speak a non-English language at home (82.3% in > or = 65, 70.2% for 51-64, and 62.2% in < or = 50, p=0.015).ConclusionsTo prepare to meet the chronic disease needs of a growing older patient population, and ameliorate the negative health effects of associated low literacy, safety-net hospital leaders and providers need to prioritize the development and implementation of low-literacy educational materials, programs, and services
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ASO Author Reflections: The Need for Disaggregated Study among Hispanic Populations
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