Bedside execution, not schedule mismatch: characterizing inpatient carbidopa-levodopa administration timing in Parkinson disease
Plagenz, J.; Lin, A.; Harlow, T.
Show abstract
Background: Timely carbidopa-levodopa administration is a recognized inpatient safety priority in Parkinson disease, and mistiming is common, but where in the medication-use process it arises is uncharacterized. Objectives: To localize where inpatient mistiming arises and where to target intervention. Methods: In a single-center retrospective analysis of hospitalized adults with Parkinson disease on home carbidopa-levodopa, each dose's administration time was compared with the individualized home schedule. Mistiming was defined a priori as more than 15 minutes from the home time (Parkinson's Foundation Hospital Care Standard 2). We characterized the deviation distribution, tested whether administrations tracked the schedule or the standard grid, and examined length-of-stay and readmission. Results: Across 947 doses in 101 patients, ordering was accurate, yet 62.9% (596 of 947) missed the home time by more than 15 minutes and 99% of patients had at least one mistimed dose. Administrations tracked the individualized schedule almost exactly (Pearson r 0.98), not the standard grid: only 10% fell within 15 minutes of the default times, and the median dose sat 24 minutes from its home time but 76 from the nearest default. Deviation was symmetric drift (median absolute deviation 24 minutes; 16.5% beyond 60 minutes). Conclusions: Mistiming in this study reflected imprecise bedside execution, not ordering or a mismatch between fixed rounds and individualized regimens. These findings may point medication-safety efforts toward protecting bedside administration as complementary redesigning orders.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical features and outcomes of hospitalised patients with COVID-19 and Parkinsonian disorders: a multicentre UK-based study 92%
- Device-aided therapies (DATs) in Parkinson’s disease (PD). The DATs-PD GETM Spanish Registry Protocol Study 90%
- Pause characteristics of sentence production in Parkinson's disease: insights from sentence complexity and length 90%
Similar papers in this journal
- Disease progression strikingly differs in research and real-world Parkinson's populations 93%
- Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank 92%
- AccessPD - a ‘next generation’ registry to accelerate Parkinson’s disease research 92%
Similar papers in this journal
- α4β2 * Nicotinic Cholinergic Receptor Target Engagement in Parkinson Disease Gait-Balance Disorders 93%
- Persistent racial disparities in deep brain stimulation for Parkinson’s disease 91%
- Progression of daily-life tremor measures in early Parkinson disease: a longitudinal continuous monitoring study 91%
Similar papers in this journal
- Retrospective Case-Control Study of Pre-Diagnosis Observational and Prescription Data in Parkinson's Disease 93%
- Developing and Validating a New Web-Based Tapping Test for Measuring Distal Bradykinesia in Parkinson's Disease 93%
- Remote monitoring of progression in early Parkinson’s disease: reliability and validity of the Roche PD Mobile Application v2 92%
Similar papers in this journal
- How, Why And Under What Circumstances Does A Quality Improvement Collaborative Build Knowledge And Skills In Clinicians Working With People With Dementia? A Realist Informed Process Evaluation 88%
- Improving the impact of pharmacy interventions in hospitals 86%
- Evaluating the impact of an enhanced support implementation of the PReCePT (PRevention of Cerebral palsy in Pre-Term labour) quality improvement toolkit to increase the uptake of magnesium sulphate in pre-term deliveries for the prevention of neurodisabilities: study protocol for a cluster randomized controlled trial 85%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.