Community socioeconomic disadvantage drives type of 30-day medical-surgical revisits among patients with serious mental illness.
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ABSTRACT: Background
Patients with serious mental illness (SMI) are vulnerable to medical-surgical readmissions and emergency department visits.Methods
We studied 1,914,619 patients with SMI discharged after medical-surgical admissions in Florida and New York between 2012 and 2015 and their revisits to the hospital within 30 days of discharge.Results
Patients with SMI from the most disadvantaged communities had greater adjusted 30-day revisit rates than patients from less disadvantaged communities. Among those that experienced a revisit, patients from the most disadvantaged communities had 7.3 % greater 30-day observation stay revisits.Conclusions
These results suggest that additional investments are needed to ensure that patients with SMI from the most disadvantaged communities are receiving appropriate post-discharge care.
SUBMITTER: Germack HD
PROVIDER: S-EPMC8256502 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.
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