Introduction: The POWER study, published in 2009 using data from 2003 examined the workload of emergency physicians using the Canadian Triage and Acuity Scale (CTAS) as a surrogate marker.  Many hospitals use a case-mix formula incorporating annual census and POWER study data to determine staffing levels. However, significant changes in emergency medicine have occurred since its publication, including the implementation of electronic medical record systems, increased patient complexity, real-time dictation software, and human health resource challenges due to the COVID-19 pandemic. Our study aimed to quantify the time required to perform tasks in the care of ambulatory emergency department patients. Our secondary objective was to stratify these times based on CTAS and provider factors.

Methods: We conducted a prospective observational time-motion study in the ambulatory section of a tertiary care academic emergency department with 90,000 visits annually, 70% of which are ambulatory.  Research assistants shadowed physicians on two 8-hour shifts daily (8 AM to 12 AM) from July 12 to August 14, 2022, tracking the time taken by physicians to perform tasks. Aggregate task times were calculated per patient.

Results: We observed 1,204 patient encounters over 65 shifts by 37 unique physicians. The mean treatment time was 21.6 minutes (95% CI 19.9-23.3) for ambulatory CTAS 2 patients, 22.5 minutes (95% CI 21.2-23.6) for CTAS 3 patients, 19.7 minutes (95% CI 17.9-21.6) for CTAS 4 patients, and 17.4 minutes (95% CI 14.9-19.9) for CTAS 5 patients. Compared to the previous POWER study data from 2003, CTAS 4 and 5 patient assessment times took 31% and 58% longer, respectively. Total assessment time by CTAS was statistically significant only comparing CTAS 5 patients to all others (p = 0.022).  Physicians who dictated their charts spent 34% less time (2.1 minutes per patient) charting than those typing.

Conclusion: The average time to see an ambulatory ED patient was 21.7 minutes.  Low-acuity ambulatory patients take longer to assess now than twenty years ago.  CTAS alone is a poor marker of workload for ambulatory patients, necessitating a reassessment of staffing and compensation formulas.

Abstract Introduction Previous studies have demonstrated that common breast cancer susceptibility alleles are differentially associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers. It is currently unknown how these alleles are associated with different breast cancer subtypes in BRCA1 and BRCA2 mutation carriers defined by estrogen (ER) or progesterone receptor (PR) status of the tumour. Methods We used genotype data on up to 11,421 BRCA1 and 7,080 BRCA2 carriers, of whom 4,310 had been affected with breast cancer and had information on either ER or PR status of the tumour, to assess the associations of 12 loci with breast cancer tumour characteristics. Associations were evaluated using a retrospective cohort approach. Results The results suggested stronger associations with ER-positive breast cancer than ER-negative for 11 loci in both BRCA1 and BRCA2 carriers. Among BRCA1 carriers, single nucleotide polymorphism (SNP) rs2981582 (FGFR2) exhibited the biggest difference based on ER status (per-allele hazard ratio (HR) for ER-positive = 1.35, 95% CI: 1.17 to 1.56 vs HR = 0.91, 95% CI: 0.85 to 0.98 for ER-negative, P-heterogeneity = 6.5 × 10-6). In contrast, SNP rs2046210 at 6q25.1 near ESR1 was primarily associated with ER-negative breast cancer risk for both BRCA1 and BRCA2 carriers. In BRCA2 carriers, SNPs in FGFR2, TOX3, LSP1, SLC4A7/NEK10, 5p12, 2q35, and 1p11.2 were significantly associated with ER-positive but not ER-negative disease. Similar results were observed when differentiating breast cancer cases by PR status. Conclusions The associations of the 12 SNPs with risk for BRCA1 and BRCA2 carriers differ by ER-positive or ER-negative breast cancer status. The apparent differences in SNP associations between BRCA1 and BRCA2 carriers, and non-carriers, may be explicable by differences in the prevalence of tumour subtypes. As more risk modifying variants are identified, incorporating these associations into breast cancer subtype-specific risk models may improve clinical management for mutation carriers.