QUESTION ASKED: Can an academic oncology practice measure and improve the quality and value of care as measured by a portfolio of multidisciplinary metrics?SUMMARY ANSWER: We asked physicians to take the lead in choosing measures of cancer care quality that could be extracted from the electronic health record (EHR) with minimal manual augmentation. By allowing physicians to select the pertinent measures, by providing timely and accurate feedback and a small financial incentive, and by setting realistic and achievable targets, we demonstrated improvements in all measures over the study period. All cancer care programs (CCPs) met their predetermined targets and earned the financial incentive.
WHAT WE DID:We initiated a project to implement and test the initial steps of a multiyear quality improvement program in a large, academic multidisciplinary cancer center. We guided CCP leadership in choosing measures that (1) were meaningful and important to the care team and patients, (2) had data elements available in existing electronic databases, (3) had not been broadly measured at our center, (4) were multidisciplinary, and (5) would not significantly increase clinician workload. We provided monthly adherence reports to each CCP physician leader (an example is shown below for use of the EHR staging module). The leaders shared the feedback with their team. A financial incentive was provided if the CCP met its predetermined target.WHAT WE FOUND: A total of 5,604 patients or visits were included in the denominators for the metrics. The development and continuous refinement of the measurement system required two full-time equivalent roles, including quality analysts and data specialists. Our quality program had three essential elements that led to its success: (1) we engaged physicians to choose the quality measures and to prespecify goals, (2) we used automated extraction methods for rapid and timely feedback on improvement and on progress toward achieving goals, and (3) we offered the CCP a financial team-based incentive if prespecified goals were met.
BIAS, CONFOUNDING FACTOR(S), REAL-LIFE IMPLICATIONS:Our new quality program was successful in embedding and centralizing our physician leaders in its design and implementation. Although many of the metrics were easily extractable from the EHR, others required manual effort to abstract records and perform quality control on all negative results. This manual review effort was important to maintain credibility by the care teams, who could then focus on their clinical improvement efforts. The metrics chosen were measures of care processes, with outcome measures to be added in future years. Staging data completeness, which is available as a discrete data field in the EHR of this organization, will serve as the foundation for measuring value-driven care and providing our teams with data to build patient. Building and measuring specific patient cohorts will help our clinicians develop clinical outcome improvement programs, will guide program expansion, and will help ensure consis...