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.
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http://dx.doi.org/10.1200/JOP.2017.021139 | DOI Listing |
J Med Internet Res
January 2025
Centre for Addiction and Mental Health, Toronto, ON, Canada.
Background: The onset of the COVID-19 pandemic precipitated a rapid shift to virtual care in health care settings, inclusive of mental health care. Understanding clients' perspectives on virtual mental health care quality will be critical to informing future policies and practices.
Objective: This study aimed to outline the process of redesigning and validating the Virtual Client Experience Survey (VCES), which can be used to evaluate client and family experiences of virtual care, specifically virtual mental health and addiction care.
JAMA Netw Open
January 2025
Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Importance: People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer.
Objective: To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice.
CJEM
January 2025
Department of Emergency Medicine, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.
Objectives: This initiative assessed the integration of the Human Factors Analysis and Classification System, adapted from aviation, into emergency medicine morbidity and mortality rounds. The objective was to determine whether incorporating the Human Factors Analysis and Classification System could lead to a perceived increase in the overall quality of morbidity and mortality presentations through the standardization of classifying cause factors of medical errors.
Methods: This study involved eight emergency medicine residents who applied the adapted Human Factors Analysis and Classification System framework to their morbidity and mortality case presentations over 6 months.
J Racial Ethn Health Disparities
January 2025
School of Nursing, University of California, 700 Tiverton Ave, Los Angeles, CA, 90095, USA.
Objective: The purpose of this review was to identify relationships between social determinants of mental health service utilization and outcomes among Asian American cancer survivors in the United States (U.S.).
View Article and Find Full Text PDFCancer Causes Control
January 2025
North Valley Breast Clinic, 1335 Buenaventura Blvd, Suite 204, Redding, CA, 96001, USA.
Objectives: Automated breast ultrasound imaging (ABUS) results in a reduction in breast cancer stage at diagnosis beyond that seen with mammographic screening in women with increased breast density or who are at a high risk of breast cancer. It is unknown if the addition of ABUS to mammography or ABUS imaging alone, in this population, is a cost-effective screening strategy.
Methods: A discrete event simulation (Monte Carlo) model was developed to assess the costs of screening, diagnostic evaluation, biopsy, and breast cancer treatment.
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