Can We Improve Physical Health Monitoring for Patients Taking Antipsychotics on a Mental Health Inpatient Unit?
Bibliographic record
Abstract
BACKGROUND: Patients with severe mental illness are at risk of medical complications, including cardiovascular disease, metabolic syndrome, and diabetes. Given this vulnerability, combined with metabolic risks of antipsychotics, physical health monitoring is critical. Inpatient admission is an opportunity to screen for medical comorbidities. Our objective was to improve the rates of physical health monitoring on an inpatient psychiatry unit through implementation of an electronic standardized order set. METHODS: Using a clinical audit tool, we completed a baseline retrospective audit (96 eligible charts) of patients aged 18 to 100 years, discharged between January and March 2012, prescribed an antipsychotic for 3 or more days. We then developed and implemented a standard electronic admission order set and provided training to inpatient clinical staff. We completed a second chart audit of patients discharged between January and March 2016 (190 eligible charts) to measure improvement in physical health monitoring and intervention rates for abnormal results. RESULTS: In the 2012 audit, thyroid-stimulating hormone (TSH), blood pressure, blood glucose, fasting lipids, electrocardiogram (ECG), and height/weight were measured in 71%, 92%, 31%, 36%, 51%, and 75% of patients, respectively. In the 2016 audit, TSH, blood pressure, blood glucose, fasting lipids, ECG, and height/weight were measured in 86%, 96%, 96%, 64%, 87%, and 71% of patients, respectively. There were statistically significant improvements (P < 0.05) in monitoring rates for blood glucose, lipids, ECG, and TSH. Intervention rates for abnormal blood glucose and/or lipids (feedback to family doctor and/or patient, consultation to hospitalist, endocrinology, and/or dietician) did not change between 2012 and 2016. CONCLUSIONS: Electronic standardized order set can be used as a tool to improve screening for physical health comorbidity in patients with severe mental illness receiving antipsychotic medications.
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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 | 0.010 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".