Improving Wait Times and Patient Experience Through Implementation of a Provincial Expedited Diagnostic Pathway for BI-RADS 5 Breast Lesions

Factors Contributing to Disproportionate Burnout in Women Breast Imaging Radiologists: A Review

Physician burnout continues to increase in prevalence and disproportionately affects women physicians. Breast imaging is a woman-dominated subspeciality, and therefore, worsening burnout among women physicians may have significant repercussions on the future of the breast imaging profession. Systemic and organizational factors have been shown to be the greatest contributors to burnout beyond individual factors. Based on the Mayo Model, we review the evidence regarding the 7 major organizational contributors to physician burnout and their potential disproportionate impacts on women breast radiologists. The major organizational factors discussed are work-life integration, control and flexibility, workload and job demands, efficiency and resources, finding meaning in work, social support and community at work, and organizational culture and values. We also propose potential strategies for institutions and practices to mitigate burnout in women breast imaging radiologists. Many of these strategies could also benefit men breast imaging radiologists, who are at risk for burnout as well.

Timeliness of Breast Diagnostic Imaging and Biopsy in Practice: 15 Years of Collecting, Comparing, and Defining Quality Breast Cancer Care

BACKGROUND

The literature lacks well-established benchmarks for expected time between screening mammogram to diagnostic imaging and then to core needle breast biopsy.

METHODS

Timeliness of diagnostic imaging workup was evaluated using aggregate data from 2005 to 2019 submitted to The National Quality Measures for Breast Centers (NQMBC).

RESULTS

A total of 419 breast centers submitted data for 1,805,515 patients on the time from screening mammogram to diagnostic imaging. The overall time was 7 days with 75th, 25th, and 10th percentile values of 5, 10, and 13.5 days, respectively. The average time in business days decreased from 9.1 to 7.1 days (p < 0.001) over the study period with the greatest gains in poorest-performing quartiles. Screening centers and centers in the Midwest had significantly shorter time to diagnostic imaging. Time from diagnostic imaging to core needle biopsy was submitted by 406 facilities representing 386,077 patients. The average time was 6 business days, with 75th, 25th, and 10th percentiles of 4, 9, and 13.7 days, respectively. Time to biopsy improved from a mean of 9.0 to 6.3 days (p < 0.001) with the most improvement in the poorest-performing quartiles. Screening centers, centers in the Midwest, and centers in metropolitan areas had significantly shorter time to biopsy.

CONCLUSIONS

In a robust dataset, the time from screening mammogram to diagnostic imaging and from diagnostic imaging to biopsy decreased from 2005 to 2019. On average, patients could expect to have diagnostic imaging and biopsies within 1 week of abnormal results. Monitoring and comparing performance with reported data may improve quality in breast care.

Physician perspectives on delays in cancer diagnosis in Alberta: a qualitative study

BACKGROUND

Delays in cancer diagnosis have been associated with reduced survival, decreased quality of life after treatment, and suboptimal patient experience. The objective of the study was to explore the perspectives of a group of family physicians and other specialists regarding potentially avoidable delays in diagnosing cancer, and approaches that may help expedite the process.

METHODS

We conducted a qualitative study using interviews with physicians practising in primary and outpatient care settings in Alberta between July and September 2019. We recruited family physicians and specialists who were in a position to discuss delays in cancer diagnosis by email via the Cancer Strategic Clinical Network and the Alberta Medical Association. We conducted semistructured interviews over the phone, and analyzed data using thematic analysis.

RESULTS

Eleven family physicians and 22 other specialists (including 7 surgeons or surgical oncologists, 3 pathologists, 3 radiologists, 2 emergency physicians and 2 hematologists) participated in interviews; 22 were male (66.7%). We identified 4 main themes describing 9 factors contributing to potentially avoidable delays in diagnosis, namely the nature of primary care, initial presentation, investigation, and specialist advice and referral. We also identified 1 theme describing 3 suggestions for improvement, including system integration, standardized care pathways and a centralized advice, triage and referral support service for family physicians.

INTERPRETATION

These findings suggest the need for enhanced support for family physicians, and better integration of primary and specialty care before cancer diagnosis. A multifaceted and coordinated approach to streamlining cancer diagnosis is required, with the goals of enhancing patient outcomes, reducing physician frustration and optimizing efficiency.

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director’s role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda’s and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today.