Improving Care With a Portfolio of Physician-Led Cancer Quality Measures at an Academic Center.

PURPOSE: Development and implementation of robust reporting processes to systematically provide quality data to care teams in a timely manner is challenging. National cancer quality measures are useful, but the manual data collection required is resource intensive, and reporting is delayed. We designed a largely automated measurement system with our multidisciplinary cancer care programs (CCPs) to identify, measure, and improve quality metrics that were meaningful to the care teams and their patients. METHODS: Each CCP physician leader collaborated with the cancer quality team to identify metrics, abiding by established guiding principles. Financial incentive was provided to the CCPs if performance at the end of the study period met predetermined targets. Reports were developed and provided to the CCP physician leaders on a monthly or quarterly basis, for dissemination to their CCP teams. RESULTS: A total of 15 distinct quality measures were collected in depth for the first time at this cancer center. Metrics spanned the patient care continuum, from diagnosis through end of life or survivorship care. All metrics improved over the study period, met their targets, and earned a financial incentive for their CCP. CONCLUSION: Our quality program had three essential elements that led to its success: (1) engaging physicians in choosing the quality measures and prespecifying goals, (2) using automated extraction methods for rapid and timely feedback on improvement and progress toward achieving goals, and (3) offering a financial team-based incentive if prespecified goals were met.

An informatics-based approach to reducing heart failure all-cause readmissions: the Stanford heart failure dashboard.

BACKGROUND: Reduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort. OBJECTIVES: We hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF. METHODS: After establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF. RESULTS: The HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001). CONCLUSIONS: In an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.