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快速学习型医疗保健模式下的呼吸困难管理。

Management of dyspnea within a rapid learning healthcare model.

机构信息

Division of Medical Oncology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.

出版信息

Curr Opin Support Palliat Care. 2011 Jun;5(2):101-10. doi: 10.1097/SPC.0b013e32834582b3.

Abstract

PURPOSE OF REVIEW

This review addresses a distressing symptom experienced by many palliative care patients, for which available interventions have been only partially effective. A new model of healthcare delivery and research - rapid learning healthcare - provides a potential framework for improving clinical care for and outcomes of dyspnea. This review places dyspnea management in palliative care within the new systems approach offered by rapid learning healthcare.

RECENT FINDINGS

Results of important studies in dyspnea are briefly presented, though the focus of this review is on evidence supporting implementation of a rapid learning model for palliative symptom management. Recent findings suggest that a rapid learning system is feasible and acceptable to patients with advanced illness, helps monitor symptoms over time, facilitates study of the impact of novel interventions, and can identify unrecognized needs and concerns.

SUMMARY

A rapid learning model could improve comprehensive assessment, timeliness of intervention, accrual of data to support best practice, and tailoring of care to patients' needs as their disease and experiences change over time. Data collected in the process of routine care in a rapid learning model can simultaneously function as clinical information and a resource for research on patient-centered experiences and outcomes.

摘要

目的综述

本篇综述探讨了许多姑息治疗患者经历的一种令人痛苦的症状,针对该症状,目前已有部分干预措施,但效果有限。快速学习型医疗保健这一新的医疗保健提供和研究模式为改善呼吸困难的临床护理和结果提供了一个潜在的框架。本文将姑息治疗中的呼吸困难管理置于快速学习型医疗保健提供的新系统方法中。

最新发现

简要介绍了呼吸困难的重要研究结果,但本篇综述的重点是支持姑息症状管理快速学习模型实施的证据。最近的研究结果表明,快速学习系统对晚期疾病患者是可行且可接受的,有助于随时间监测症状,促进对新干预措施影响的研究,并能发现未被认识到的需求和关注点。

总结

快速学习模型可以改善全面评估、干预的及时性、支持最佳实践的数据积累,以及根据患者疾病和体验随时间变化而调整护理以满足患者的需求。在快速学习模型的常规护理过程中收集的数据可以同时作为临床信息和患者为中心的体验和结果研究的资源。

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