Facilitating the Process of Medication Re-evaluation and Withdrawal in the Long-Term Institutionalized Population: The Example of Cisapride
Bibliographic record
Abstract
INTRODUCTION It is well documented that many patients in long-term or complex continuing care* facilities receive medications that are considered inappropriate, are not indicated, or are not utilized optimally, and that these problems occur at a higher rate than among their counterparts living at home.1-3 The prevalence of inappropriate prescribing is reportedly as high as 40% in nursing homes.4-6 For many medications prescribed to patients living in nursing homes, the indications are often not documented, which makes it difficult to evaluate the effectiveness of drug therapy.7 The occurrence of avoidable adverse drug reactions is the most serious consequence of suboptimal medication use. Drugs identified as particularly hazardous in this population include antipsychotics, antidepressants, sedatives, digoxin, diuretics, and nonsteroidal anti-inflammatory drugs.8-11 It is challenging to recognize treatment-emergent symptoms as adverse effects because they may not present in the standard way in frail elderly or chronically ill patients and may be misinterpreted as part of the normal aging process. The economic burden of the cost of medications, the cost of medication administration time, and the cost (although difficult to measure) of the negative pharmacotherapeutic outcomes associated with suboptimal medication use are also of concern.12 Such concerns regarding cost often create the impetus for facilities to take on the challenge of reducing inappropriate or suboptimal medication use. Attempts to decrease inappropriate medication use and corresponding costs have been successful to varying degrees. Mechanisms include consultant pharmacists providing medication reviews, interdisciplinary medication review, face-to-face educational interventions for physicians and nurses, academic detailing, pharmacy and therapeutics committee policies on formulary management, application of criteria to identify inappropriate prescribing, and drug utilization review of specific drugs or categories of drugs.13-26 Studies examining the outcome of medication withdrawal in elderly patients have shown that a high percentage of medications can be discontinued without adverse consequences.27 The nursing home or long-term care setting is believed to be a good environment in which to attempt drug withdrawal because patients can be monitored closely for adverse drug withdrawal events.28 The SCO Health Service provides long-term care, complex continuing care, palliative care, and rehabilitation services through 3 main sites in Ottawa: Elisabeth Bruyere Health Centre, St. Vincent Hospital, and Residence St. Louis (the abbreviation SCO refers to the founding Soeurs de la charite d’Ottawa or Sisters of Charity of Ottawa). In addition to an ongoing patient-specific interdisciplinary medication review conducted every 4 months at this institution, it was decided, in 1999, to explore the possibility of a targeted medication withdrawal program. In this program a drug or group of drugs would be identified for withdrawal, and the withdrawal process would be applied to all 437 patients in the institution’s complex continuing care program at the same time, rather than waiting for individual medication reviews (during which numerous issues may have to be addressed). The first drug identified for withdrawal was cisapride. A number of patients were receiving it, it accounted for a substantial portion of the institution’s drug budget, and its potential side effects are significant. A random chart audit revealed that cisapride had usually been initiated upon insertion of a feeding tube to prevent symptoms of gastroesophageal reflux disease (GERD) or aspiration pneumonia. This article describes how a carefully planned and monitored medication withdrawal program for one drug was successful in decreasing medication use and costs in a complex continuing care facility and in identifying patients who required alternative or continued medication for their condition. This example illustrates the need for continuous re-evaluation of medication treatment in long-term institutionalized patients. The process described here is one method of ensuring that medication therapy does get re-evaluated in patients for whom pharmacists and physicians may have difficulty making medication therapy changes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".