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Invited Commentary
August 23, 2021

Reenvisioning End-of-Life Care Quality Measurement for Adolescents and Young Adults With Cancer—Novel Patient-Centered Indicators and Approaches

Author Affiliations
  • 1Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut
  • 2Yale Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, New Haven, Connecticut
JAMA Netw Open. 2021;4(8):e2122323. doi:10.1001/jamanetworkopen.2021.22323

Adolescents and young adults (AYAs) with cancer in the United States experience strikingly inferior outcomes compared with younger children and older adults.1 Furthermore, more than two-thirds of AYAs with cancer receive resource-intensive hospital care near the end of life, with infrequent referral to hospice.2 Despite intolerably high cancer mortality and widely observed patterns of intense end-of-life care, what defines high-quality care for AYAs with advanced, incurable cancer has previously remained elusive. In contrast, numerous end-of-life care quality indicators for older adults with cancer are routinely implemented to evaluate care and ensure accountability.2,3 For pragmatic reasons, endorsed quality indicators focus predominantly on appraising and reducing health care intensity, extracting data from administrative claims.3 Inferring from extant indicators, we might conclude that AYAs with cancer receive overall poor-quality end-of-life care. However, prior cancer quality indicators were not designed to accommodate the unique preferences and experiences of AYAs. Systematically measuring and benchmarking high-quality end-of-life care for AYAs, using quality indicators designed by and for AYAs with cancer, is crucial for establishing person-centered standards for care.

Elsewhere in JAMA Network Open, Mack and colleagues4 present a rigorous, multicenter qualitative study seeking to develop patient-centered indicators for end-of-life care quality in AYAs. A wide range of stakeholders were engaged in this study, recruited bicoastally through large health care institutions and a community-based organization. Notably, patients and parents who were facing poor-prognosis cancer were asked in a sensitive, thoughtful manner about the end-of-life care they might wish to receive. These are difficult topics to broach with living patients and caregivers, yet they amplify the voices of key stakeholders and offer critical insights into what constitutes high-quality end-of-life care for this population.

Mack et al4 identified 7 core domains and 20 subdomains of high-quality end-of-life care for AYAs. Select subdomains pertaining to symptom screening, anticipatory guidance around what to expect near the end of life, quality of life, spiritual support, and global psychosocial support build on indicators designed for adults with serious illness—primarily in the palliative care space.5 However, most subdomains identified in this qualitative work require novel quality indicator development, as they reflect a marked departure from how we traditionally conceptualize high-quality end-of-life care for adults.3 For instance, study participants uniformly favored sensitivity to patient preferences regarding use of cancer-directed or life-sustaining therapies near end of life, not restricting or discouraging their use a priori.

There is substantial synergy between the data described by Mack et al4 and prior studies focused on developing end-of-life care quality indicators for children with cancer.6,7 Across the vast age spectrum, communication, including direct communication with children and/or AYAs when desired; robust therapeutic alliance with interdisciplinary health care teams; symptom evaluation and management; honoring patient preferences; and supporting bereaved family are mutual priorities. Mack and colleagues4 highlight an added dimension for AYAs with cancer, who must simultaneously navigate a terminal diagnosis and an increasing desire for agency and ownership of decision-making apropos their developmental stage. As life goals for AYAs are likely substantially disrupted on account of a terminal cancer diagnosis, study participants articulated the importance of exploring and, if possible, enabling the achievement of key milestones. These findings capture the essence of the AYA experience and underscore why this challenging work of exploring end-of-life care priorities with AYAs and caregivers is formative to the field.

Among the limitations of this study are the lack of uptake of Spanish language interviews, although they were offered, and the relative racial/ethnic homogeneity of the AYA and caregiver participants. Limited diversity has been a challenge in prior studies as well.7 There is often selection bias inherent to qualitative research. This study may also have been affected by elements of social desirability bias. Findings should therefore be confirmed in geographically, racially, and ethnically diverse populations. As we strive to understand and ameliorate disparities in end-of-life care for AYAs with cancer, it is imperative that we elicit the perspectives of stakeholders who belong to racial/ethnic minority groups to explore unique tensions and priorities.

For decades, the National Academy of Medicine has emphasized patient-centeredness of care as a core component of health care quality.4,7 Nonetheless, how we operationalize patient-centered indicators is largely undefined. Most of the quality domains identified by Mack et al4 cannot be assessed using retrospective claims data and, as the authors aptly state, require patient and/or caregiver report. Therefore, a prospective framework for quality measurement should be considered, evaluating aspects of care quality prior to death by directly querying patients and families. In learning health systems, numerous data sources are integrated in real-time to identify deficiencies in care quality. This approach expedites quality improvement and innovation. Applying a rapid learning model to end-of-life oncology care for AYAs may seem daunting. As a field, we would need to arrive at a consensus regarding which patients to query, how to query them, and when in the illness trajectory to query about care quality. Leaders within and across health systems must invest in such an approach. However, the rapid learning model in this context may offer more immediate, concrete opportunities to intervene in the care of individuals with serious illness.5

Several novel data sources are gaining valence in quality measurement and may be leveraged to assess end-of-life care quality. Artificial intelligence–based methods, such as natural language processing, can capture rich content from the electronic health record and are more efficient than manual medical record abstraction. Natural language processing has been used to evaluate palliative care process indicators for the purposes of research and could be similarly harnessed in quality improvement efforts.5 Validated instruments to assess patient-reported quality of life and symptoms are increasingly being used in routine oncology clinical care. Adapting existing patient experience surveys may also allow us to characterize quality of interdisciplinary care delivery from the patient and/or caregiver perspective.5,7 Each of these sources of data, in isolation, has numerous limitations. We must determine reliability and validity of indicators that draw on these alternate data sources. Nevertheless, in combination, these varied approaches may offer a more holistic, nuanced, patient-centered picture of care quality for AYAs with cancer.

Mack et al4 signal a paradigm shift in our approach to end-of-life care quality measurement in oncology. It is evident that a one size fits all method of defining high-quality care is not attendant to the goals and preferences of AYAs. Therefore, we ought to broaden our notions of quality measurement, integrating indicators and data sources that allow for real-time assessment of patient-reported outcomes and experience. As we move toward personalized medicine in cancer treatment, there is an imminent need for personalization of end-of-life care quality measurement as well.

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Article Information

Published: August 23, 2021. doi:10.1001/jamanetworkopen.2021.22323

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Ananth P. JAMA Network Open.

Corresponding Author: Prasanna Ananth, MD, MPH, Department of Pediatrics, Yale School of Medicine, 330 Cedar St, LMP 2082C, New Haven, CT 06510 (prasanna.ananth@yale.edu).

Conflict of Interest Disclosures: Dr Ananth reported receiving funding from the St Baldrick’s Foundation and the National Cancer Institute (grant 1K08CA259222-01).

Disclaimer: Any opinions, findings, and conclusions expressed in this article are those of the author and do not reflect those of St Baldrick’s Foundation or the National Cancer Institute.

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