P3‐540: CLINICIANS’ PERSPECTIVES ON BARRIERS AND ENABLERS TO OPTIMIZING PRESCRIBING IN PEOPLE WITH DEMENTIA AND COEXISTING CONDITIONS
Bibliographic record
Abstract
People with dementia often have coexisting conditions and experience high rates of polypharmacy and potentially inappropriate medication use. There is a dearth of guidance for clinicians on how to manage coexisting conditions and determine medication appropriateness in people with dementia. To begin developing a framework to optimize prescribing for this vulnerable patient group, our objective was to investigate clinician-perceived barriers to and facilitators of reducing polypharmacy and potentially inappropriate medication use in people with dementia. Semi-structured interviews of 12 primary care and 9 specialist clinicians in urban, suburban and rural settings. Qualitative content analysis was used to identify major themes. Interviews were conducted from March 1, 2017 to January 12, 2018. Participants comprised 19 physicians and 2 nurse practitioners with a mean (SD) age of 47 (9) and a mean 14 (10) years in practice. Clinicians described decisions about prescribing medicines to manage coexisting conditions in patients with dementia as complex and said they often relied on subjective impressions, rather than objective measures or tools. They cited the following drug classes as challenging: oral anticoagulants, anti-diabetic agents, statins and bladder antimuscarinics. Enablers of reducing polypharmacy and potentially inappropriate medication use included collaboration with pharmacists and other health care providers involved in the patient's care; frank communication with families about the progression of dementia; and clinical practice guidelines for chronic conditions such as diabetes addressing dementia. Barriers included: inertia; absence of an involved family caregiver; lack of non-pharmacologic resources (to assist with conditions such as incontinence); family caregivers’ perception that the clinician is “giving up;” and fear of harming the patient by stopping medications. The results of this qualitative study highlight a need for tools, such as decision aids and guidelines that explicitly address how to manage coexisting conditions in people with dementia. Such tools should be developed and tested to help clinicians make decisions about medication appropriateness in people with dementia, with the goal of reducing polypharmacy and use of potentially inappropriate medications in this population.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".