Measuring Quality in Cancer Care

Despite remarkable advances in cancer treatment, gaps in the quality of care can lead to poor outcomes, especially among vulnerable populations. We all want to believe that when someone gets cancer they receive the highest quality of medical care; however, persistent problems in the health care system limit our ability to realize this goal. These deficiencies are caused, in part, by the inherent complexity of multidisciplinary cancer care, exposing patients to potential harm from fragmented care, access issues, and medical error. The Institute of Medicine report on cancer care in the United States describes a system in crisis, suffering from increasing demands in the face of limited resources and a lack of coordination. Improving cancer care has been identified as a national priority, and establishing quality metrics has been prescribed as a requirement to chart a new course. There also is a growing interest in value-based reimbursement models tied to quality measures. Although not a panacea for health care, well-defined and well-implemented metrics provide an essential infrastructure on which to strengthen care delivery systems. Challenges to measuring quality in cancer arise from the heterogeneous nature of cancer, complex multimodal treatment algorithms, prolonged continuum of care, and the large number of health care professionals involved in treating an individual cancer patient. Furthermore, “traditional” cancer outcomes such as survival and recurrence, although important, are limited in their ability to address salient quality metrics including safety, timeliness, efficiency, cost, or patient experience. Health care professionals involved in cancer patient care must be actively and meaningfully engaged in the improvement process in order for quality efforts to have any significant impact. The effectiveness of quality metrics to enhance clinical outcomes is largely tied to the relevance of the metrics selected, the degree to which they are “actionable,” and the inherent quality of the data. Unfortunately, administrative/billing data sources commonly adapted for quality purposes often do not provide the level of detail or clinical relevance needed to inform care improvement. As illustrated in a survey of surgical oncologists, although most stated they were being monitored for quality, the majority felt that the measures were not adequate for patient care. National efforts have been initiated to help address the quality gap in cancer care through the development of metrics for monitoring, benchmarking, and public reporting. These efforts hope to promote practice-based quality improvement, as it is only at this local level that meaningful transformations in cancer care can be achieved. Quality metrics used to assess delivery of care can be broadly categorized into one of the 3 components originally defined by Donabedian: structure, process, and outcomes. Structural measures reflect the physical properties or system characteristics in which care is delivered (eg, number of specialists, infusion center capacity). Structure measures are often necessary to provide good care, but usually are insufficient to ensure excellent quality. The best structure measures have a demonstrated influence on processes of care and patient outcomes. For example, reports have linked higher case volume to increased survival for a variety of cancer types and surgical oncology procedures. This finding likely reflects a combination of structural and process factors present within high-volume facilities. This raises questions about whether to regionalize cancer care to high-volume centers versus developing strategies to replicate best practices at lower volume facilities. Structure measures tend to be very accessible, are commonly used for accreditation, but are not always actionable (eg, a cancer center may not be able to increase patient volume easily). Process measures reflect appropriateness of interventions (eg, appropriate administration of adjuvant chemotherapy). In order for a process measure to be credible, 639639 AJMXXX10.1177/1062860616639639American Journal of Medical QualityJames et al research-article2016