Contemporary large language models (LLMs) may have utility for processing
unstructured, narrative free-text clinical data contained in electronic health
records (EHRs) -- a particularly important use-case for mental health where a
majority of routinely-collected patient data lacks structured, machine-readable
content.
A significant problem for the the United Kingdom's National Health Service
(NHS) are the long waiting lists for specialist mental healthcare. According to
NHS data, in each month of 2023, there were between 370,000 and 470,000
individual new referrals into secondary mental healthcare services. Referrals
must be triaged by clinicians, using clinical information contained in the
patient's EHR to arrive at a decision about the most appropriate mental
healthcare team to assess and potentially treat these patients.
The ability to efficiently recommend a relevant team by ingesting potentially
voluminous clinical notes could help services both reduce referral waiting
times and with the right technology, improve the evidence available to justify
triage decisions.
We present and evaluate three different approaches for LLM-based, end-to-end
ingestion of variable-length clinical EHR data to assist clinicians when
triaging referrals. Our model is able to deliver triage recommendations
consistent with existing clinical practices and it's architecture was
implemented on a single GPU, making it practical for implementation in
resource-limited NHS environments where private implementations of LLM
technology will be necessary to ensure confidential clinical data is
appropriately controlled and governed.

通过解决长期等待次名单和提供相关团队推荐，基于大型语言模型的端到端入食方法有助于缩短患者延迟时间和改善三级分类决策，适用于精神医疗保健。

面向精神健康护理的定制大型语言模型用于数字分诊辅助

Bespoke Large Language Models for Digital Triage Assistance in Mental  Health Care

Initial hours of hospital admission impact clinical trajectory, but early
clinical decisions often suffer due to data paucity. With clustering analysis
for vital signs within six hours of admission, patient phenotypes with distinct
pathophysiological signatures and outcomes may support early clinical
decisions. We created a single-center, longitudinal EHR dataset for 75,762
adults admitted to a tertiary care center for 6+ hours. We proposed a deep
temporal interpolation and clustering network to extract latent representations
from sparse, irregularly sampled vital sign data and derived distinct patient
phenotypes in a training cohort (n=41,502). Model and hyper-parameters were
chosen based on a validation cohort (n=17,415). Test cohort (n=16,845) was used
to analyze reproducibility and correlation with biomarkers. The training,
validation, and testing cohorts had similar distributions of age (54-55 yrs),
sex (55% female), race, comorbidities, and illness severity. Four clusters were
identified. Phenotype A (18%) had most comorbid disease with higher rate of
prolonged respiratory insufficiency, acute kidney injury, sepsis, and
three-year mortality. Phenotypes B (33%) and C (31%) had diffuse patterns of
mild organ dysfunction. Phenotype B had favorable short-term outcomes but
second-highest three-year mortality. Phenotype C had favorable clinical
outcomes. Phenotype D (17%) had early/persistent hypotension, high rate of
early surgery, and substantial biomarker rate of inflammation but second-lowest
three-year mortality. After comparing phenotypes' SOFA scores, clustering
results did not simply repeat other acuity assessments. In a heterogeneous
cohort, four phenotypes with distinct categories of disease and outcomes were
identified by a deep temporal interpolation and clustering network. This tool
may impact triage decisions and clinical decision-support under time
constraints.

通过研究对医院入院的最初几个小时进行聚类分析，提取生命体征数据，确定患者表型与病理生理特征及结果，以支持早期临床决策，对分析重现性和与生物标志物的相关性使用了一个规模较大的数据集。