Reducing Medication Ordering Errors Through Indications-Based Prescribing
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 2,000
- 主要终点
- The combined rate of Wrong Drug, Wrong Duration, Wrong Dose and Wrong Frequency Retract-And-Reorder (RAR) events will be combined to create an overall rate of near-miss ordering errors in the control and intervention arm.
研究概览
简要总结
Indications-based prescribing is a medication ordering system in which a clinician selects an indication, and then the electronic health record (EHR) suggests an appropriate medication regimen. This approach was shown to significantly decrease medication ordering errors in a prototype environment. However, the effect of indications-based prescribing on preventing ordering errors has not been rigorously evaluated in a real-world healthcare setting. Antibiotics are the medication class most likely to contain ordering errors, which can lead to significant patient harm. At NewYork-Presbyterian (NYP) a robust antimicrobial indication-based order set was developed to help clinicians identify the appropriate antibiotic, dose, frequency, and duration, based on type of infection and patient-specific characteristics, but it is not widely used. The investigators propose a randomized controlled trial to assess the effectiveness of this indications-based order set for reducing antimicrobial ordering errors.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All providers placing inpatient orders on adult patients.
排除标准
- •Providers placing orders on patients who were ordered for antibiotics >24 hours in the past 72 hours and/or patients with positive cultures during that admission, and/or placing an order from the order set.
研究组 & 干预措施
Control Arm
Intervention Arm
干预措施: Clinical Decision Support (Other)
结局指标
主要结局
The combined rate of Wrong Drug, Wrong Duration, Wrong Dose and Wrong Frequency Retract-And-Reorder (RAR) events will be combined to create an overall rate of near-miss ordering errors in the control and intervention arm.
时间窗: Up to 18 months
Novel Health IT measures which utilize provider ordering patterns to capture near-miss ordering errors.
次要结局
未报告次要终点
研究者
Anne Grauer
Assistant Professor of Clinical Medicine at CUIMC
New York Presbyterian Hospital
