Quality of End-of-Life Care for Patients with Advanced Cancer in an Academic Medical Center

Published in: Journal of Palliative Medicine, v. 14, no. 4, Apr. 6, 2011, p. 451-457

Posted on RAND.org on April 01, 2011

by Sydney Dy, Steven M. Asch, Karl Lorenz, Kristina Weeks, Rashmi K. Sharma, Antonio C. Wolff, Jennifer Malin

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PURPOSE: We assessed key aspects of the quality of end-of-life care using validated explicit process quality measures in an academic medical center (hospital and cancer center) before expanding to a broader palliative care initiative. METHODS: We evaluated 21 indicators most relevant to end-of-life care from the Cancer Quality-ASSIST supportive oncology indicator set for 238 patients with advanced/metastatic solid tumors who died between 2–15 months after diagnosis. These included outpatient and hospital indicators for cancer symptoms and information and care planning that met criteria for feasibility, reliability, and validity. We abstracted detailed information from medical records to specify the necessary data elements. RESULTS: Overall adherence was 53% (95% confidence interval [CI], 50%–56%); this varied widely among indicators. Adherence was highest for pain indicators; in particular, 97% of eligible subjects' hospitalizations had documented screening for pain, and, after an outpatient pain medication was changed, 97% of patients had a pain assessment at the subsequent visit. For other symptoms, adherence ranged from 0% for documentation of life expectancy for patients starting parenteral or enteral nutrition to 87% for assessment of nausea or vomiting on hospital admission. For information and care planning, results ranged from 6% for documentation of ventilation preferences prior to intubation to 68% for documented communication of risks and benefits or prognosis prior to starting chemotherapy. CONCLUSION: In conclusion, Cancer Quality-ASSIST indicators are useful for practical quality assessment of cancer end-of-life care in an academic medical center. These results will serve as useful data for targeting areas for quality improvement and measuring progress.

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