An educational implementation of a cancer pain algorithm for ambulatory care.
An educational implementation of a cancer pain algorithm for ambulatory care.
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DOI：
10.1053/jpmn.2000.19333
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发表时间：
2000-12-01
期刊：
影响因子：
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通讯作者：
Syrjala, K
中科院分区：
文献类型：
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作者：
Du Pen, A R;Du Pen, S;Syrjala, K
算法被提议作为操作指导方针或标准的手段，癌症疼痛管理。专业教育被用作将知识转化为实践的手段。结果测量是验证改进的金标准。本研究采用教育干预方法，将先前测试过的癌症疼痛管理算法的实施知识转移到社区门诊肿瘤诊所，并随后测量患者的结果。来自普吉特海湾9家诊所的医生和护士被随机分为“培训”组和“未培训”组。在为期一天的研讨会上，模范医生/护士团队是核心教员。向训练有素的医生/护士团队提供了书面参考材料和文件工具。在4个月的时间里，共收集和评估了105名受过培训和未受过培训的提供者的患者。与未经培训的医护人员相比，经过培训的医护人员的患者在4个月的数据收集期间通常疼痛明显减少（t = 2.0; p = 0.05）。阿片类镇痛药处方的改善是适度的，而神经性疼痛的联合镇痛药处方的改善是显著的。随着时间的推移，训练的效果明显恶化。最显著的效果发生在干预后的头140天内，随后逐渐恢复到基线练习。总之，算法干预可以成功地转移到社区实践中，但必须开展进一步的工作，以开发确保知识保留和保持改进结果的方法。
Algorithms are proposed as a means of operationalizing guidelines or standards for cancer pain management. Professional education is used as the means to translate knowledge into practice. Outcomes measurement is the gold standard for validating improvement. This study used an educational intervention to transfer knowledge on implementing a previously tested algorithm for cancer pain management into community outpatient oncology clinics and, subsequently, measuring patient outcomes. Physicians and nurses from 9 Puget Sound clinics were randomized by institution blocks to either "training" or "no training." Role model physician/nurse teams were the core faculty for a day-long seminar. Written reference materials and documentation tools were provided to the trained physician/nurse teams. A total of 105 patients of trained and untrained providers were accrued and assessed over 4 months. Patients of trained providers had a significant reduction in usual pain over the 4 months of data collection compared with patients of untrained providers (t = 2.0; p = .05). Improvements were modest in the prescription of opioid analgesics and dramatic in the prescription of co-analgesics for neuropathic pain. There was a clear deterioration in the impact of the training over time. The most significant effect occurred within the first 140 days after the intervention and was followed by a gradual return to baseline practice. In conclusion, algorithmic interventions can be successfully transferred into community practice, but further work must be performed to develop methods for securing retention of knowledge and maintaining improved outcomes.