Documentation of Functional Medication Management in Older Adults: A Retrospective Chart Review in Acute Care Hospitalization
Bibliographic record
Abstract
BACKGROUND: Functional skills can affect the ability of older adults to appropriately manage their medication regimens. Research evaluating a patient's functional ability or the assessment of medication management is limited. OBJECTIVES: Our objective was to describe the documented components of functional medication management (FMM) in adults aged ≥65 years during an acute hospital stay. The secondary objective was to describe the characteristics of the healthcare providers (HCP) who document FMM. METHODS: This study was a retrospective chart review of a sample of patients aged ≥65 years admitted to medical units in a tertiary hospital from January 2013 to October 2014. FMM was defined as the steps required to take medications-including ordering, picking up, organizing, preparing, administering, and monitoring medications-and the functional abilities necessary to perform these tasks. RESULTS: The mean (standard deviation [SD]) age of patients was 78.9 (8.4) years; 72 (52 %) were female. Of the 190 charts screened, 140 were eligible for inclusion. The mean (SD) number of documented scheduled oral medications was eight (3.1) per patient, and 108 (77.1 %) charts contained documented FMM-related information. Commonly documented FMM components included whether the patient could administer medications independently (73 [52 %]) or schedule medication (46 [33 %]). These activities were most frequently documented by physicians (124 [39 %]) and occupational therapists (108 [34 %]). CONCLUSION: FMM assessments for older adult inpatients with multiple comorbidities and complex medication regimens were not documented comprehensively or frequently. Given the complexity of medication regimens and the functional skills required to manage medications at home, failing to document these assessments when evaluating patients in hospital reflects a lost opportunity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".