Live Mammographic Screening Interpretation Versus Offline Same-Day Screening Interpretation at a Tertiary Cancer Center.

OBJECTIVES: The aim of this study was to compare screening mammography performance metrics for immediate (live) interpretation versus offline interpretation at a cancer center. METHODS: An institutional review board-approved, retrospective comparison of screening mammography metrics at a cancer center for January 1, 2018, to December 31, 2019 (live period), and September 1, 2020, to March 31, 2022 (offline period), was performed. Before July 2020, screening examinations were interpreted while patients waited (live period), and diagnostic workup was performed concurrently. After the coronavirus disease 2019 shutdown from March to mid-June 2020, offline same-day interpretation was instituted. Patients with abnormal screening results returned for separate diagnostic evaluation. Screening metrics of positive predictive value 1 (PPV1), cancer detection rate (CDR), and abnormal interpretation rate (AIR) were compared for 17 radiologists who interpreted during both periods. Statistical significance was assessed using χ2 analysis. RESULTS: In the live period, there were 7,105 screenings, 635 recalls, and 51 screen-detected cancers. In the offline period, there were 7,512 screenings, 586 recalls, and 47 screen-detected cancers. Comparison of live screening metrics versus offline metrics produced the following results: AIR, 8.9% (635 of 7,105) versus 7.8% (586 of 7,512) (P = .01); PPV1, 8.0% (51 of 635) versus 8.0% (47 of 586); and CDR, 7.2/1,000 versus 6.3/1,000 (P = .50). When grouped by >10% AIR or <10% AIR for the live period, the >10% AIR group showed a significant decrease in AIR for offline interpretation (from 12.7% to 9.7%, P < .001), whereas the <10% AIR group showed no significant change (from 7.4% to 6.7%, P = .17). CONCLUSIONS: Conversion to offline screening interpretation from immediate interpretation at a cancer center was associated with lower AIR and similar CDR and PPV1. This effect was seen largely in radiologists with AIR > 10% in the live setting.

Patients characteristics related to screening mammography cancellation and rescheduling rates during the COVID-19 pandemic.

PURPOSE: To identify patient characteristics associated with screening mammography cancellations and rescheduling during the COVID-19 pandemic. METHODS: Scheduled screening mammograms during three time periods were retrospectively reviewed: state-mandated shutdown (3/17/2020-6/16/2020) during which screening mammography was cancelled, a period of 2 months immediately after screening mammography resumed (6/17/2020-8/16/2020), and a representative period prior to COVID-19 (6/17/2019-8/16/2019). Relative risk of cancellation before COVID-19 and after reopening was compared for age, race/ethnicity, insurance, history of chronic disease, and exam location, controlling for other collected variables. Risk of failure to reschedule was similarly compared between all 3 time periods. RESULTS: Overall cancellation rate after reopening was higher than before shutdown (7663/16595, 46% vs 5807/15792, 37%; p < 0.001). Relative risk of cancellation after reopening increased with age (1.20 vs 1.27 vs 1.36 for ages at 25th, 50th, and 75th quartile or 53, 61, and 70 years, respectively, p < 0.001). Relative risk of cancellation was also higher among Medicare patients (1.41) compared to Medicaid and those with other providers (1.26 and 1.21, respectively, p < 0.001) and non-whites compared to whites (1.34 vs 1.25, p = 0.03). Rescheduling rate during shutdown was higher than before COVID-19 and after reopening for all patients (10,658/13593, 78%, 3569/5807, 61%, and 4243/7663, respectively, 55%, p < 0.001). Relative risk of failure to reschedule missed mammogram was higher in hospitals compared to outpatient settings both during shutdown and after reopening (0.62 vs 0.54, p = 0.005 and 1.29 vs 1.03, p < 0.001, respectively). CONCLUSION: Minority race/ethnicity, Medicare insurance, and advanced age were associated with increased risk of screening mammogram cancellation during COVID-19.

Radiology Workload Changes During the COVID-19 Pandemic: Implications for Staff Redeployment.

RATIONALE AND OBJECTIVES: Quantify changes in total and by-subspecialty radiology workload due to deferring nonurgent services during the initial COVID-19 pandemic, and describe operational strategies implemented due to shifts in priority. MATERIALS AND METHODS: This retrospective, Institutional Review Board-exempt, study was performed between February 3, 2020 and April 19, 2020 at a large academic medical center. During March 9-15 (intervention period), nonurgent outpatient service deferments began. Five-week periods pre- (baseline) and postintervention (COVID) were defined. Primary outcomes were radiology volume (reports per day) overall and in 11 subspecialty divisions. Linear regression assessed relationship between baseline vs. COVID volumes stratified by division. Secondary outcomes included changes in relative value units (RVUs), inpatient and outpatient volumes. RESULTS: There were 62,791 baseline reports vs. 23,369 during COVID; a 60% overall precipitous volume decrease (p < 0.001). Mean volume decrease pre- and during-COVID was significant (p < 0.001) amongst all individual divisions. Mean volume decrease differed amongst divisions: Interventional Radiology experienced least disruption (29% volume decrease), 7 divisions experienced 40%-60% decreases, and Musculoskeletal, Breast, and Cardiovascular imaging experienced >75% volume decrease. Total RVUs decreased 60% (71,186 baseline; 28,476 COVID). Both outpatient and inpatient report volumes decreased; 72% (41,115 baseline; 11,326 COVID) and 43% (12,626 baseline vs. 6,845 COVID), respectively. In labor pool tracking data, 21.8% (162/744) total radiology employees were reassigned to other hospital duties during the intervention period. CONCLUSION: Precipitous radiology workload reductions impacted subspecialty divisions with marked variation. Data-driven operational decisions during COVID-19 assisted workflow and staffing assignment changes. Ongoing adjustments will be needed as healthcare systems transition operations to a "new normal."