Adherence to Measuring What Matters Measures Using Point-of-Care Data Collection Across Diverse Clinical Settings

Journal of Pain and Symptom Management(2016)

引用 17|浏览14
暂无评分
摘要
Measuring What Matters (MWM) for palliative care has prioritized data collection efforts for evaluating quality in clinical practice. How these measures can be implemented across diverse clinical settings using point-of-care data collection on quality is unknown.To evaluate the implementation of MWM measures by exploring documentation of quality measure adherence across six diverse clinical settings inherent to palliative care practice.We deployed a point-of-care quality data collection system, the Quality Data Collection Tool, across five organizations within the Palliative Care Research Cooperative Group. Quality measures were recorded by clinicians or assistants near care delivery.During the study period, 1989 first visits were included for analysis. Our population was mostly white, female, and with moderate performance status. About half of consultations were seen on hospital general floors. We observed a wide range of adherence. The lowest adherence involved comprehensive assessments during the first visit in hospitalized patients in the intensive care unit (2.71%); the highest adherence across all settings, with an implementation of u003e95%, involved documentation of management of moderate/severe pain. We observed differences in adherence across clinical settings especially with MWM Measure #2 (Screening for Physical Symptoms, range 45.7%-81.8%); MWM Measure #5 (Discussion of Emotional Needs, range 46.1%-96.1%); and MWM Measure #6 (Documentation of Spiritual/Religious Concerns, range 0-69.6%).Variations in clinician documentation of adherence to MWM quality measures are seen across clinical settings. Additional studies are needed to better understand benchmarks and acceptable ranges for adherence tailored to various clinical settings.
更多
查看译文
关键词
Quality,quality measures,Measuring What Matters,implementation,alliance,collaboratives
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要