The Relationship between Cognitive Impairment in Psychiatric Patients and Readmission Rate to an Inpatient Facility
Bibliographic record
Abstract
The primary intention of this study was to further understand the impact of assessing cognitive impairment in psychiatric patients, as a mediating factor on readmission rates. Mild cognitive dysfunction impacts a patient’s functional outcomes [1,2]. Little information exists to guide best practices in the treatment of adults with cognitive impairment who are hospitalized for acute conditions [2]. A cognitive impairment may impact patient prognosis and ability to function outside of a setting focused on stabilization. Neuropsychological testing is a valuable tool in predicting a patient’s cognitive potential. However, creating a battery of tests that must be short enough to fit the needs of patients within an inpatient setting, allowing them to demonstrate their ability, while still providing enough information for accurate diagnosis and prognosis is a challenge. Overall, this study aimed to investigate the utilization of brief cognitive screeners in identifying patients that may be more vulnerable to relapse and readmission. Cognitive impairment was assessed utilizing the Montreal Cognitive Assessment (MoCA). Readmission rate is defined by total admissions per individual patient. The use of community resources, such as medication management, case management, housing programs, and their relationship with readmission rates was examined. Archival data was taken from an inpatient facility’s HIPAA-compliant electronic medical record (EMR) database. All personal identifying information was de-identified to ensure minimal risk of breaching confidentiality. Although all objectives demonstrated significant correlations among the variables, there were no significant predictive models. This study found a positive linear relationship between admission rate and a higher MoCA score. This may partially be explained by Age (M = 69.09, SD = 8.25) having a negative relationship with inpatient readmission rate and a positive relationship with community resources. Within the inpatient setting, patients with higher cognitive functioning may be more aware of their mental health symptoms and are more likely to seek help. This may lead to more frequent admissions as they are able to seek help to address their mental health concerns. The relationship between patient demographics, community resource usage, and readmission rate was also examined. Limitations of this study included the use of archival data, a small sample size, and a lack of diversity within the sample. Future directions for research include norming the MoCA for psychiatric populations and examining deficit patterns within the cognitive domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".