The effect of immediate reading of screening mammograms on medical care utilization and costs after false-positive mammograms

Racial Disparities in Breast Imaging Wait Times Before and After the Implementation of a Same-Day Biopsy Program

OBJECTIVE

To examine time from screening to diagnostic workup, biopsy, and surgery for non-Hispanic White (NHW) and Black women following implementation of a same-day biopsy program.

METHODS

All NHW and Black women with BI-RADS category 0 screening mammogram at Duke University Hospital were identified between August 1, 2020, and August 1, 2021. Patient characteristics were recorded. Time between screening mammogram, diagnostic workup, breast biopsy, surgical consultation, and surgery were recorded. Comparisons were made between NHW and Black women using a multivariable regression model. Diagnostic imaging to biopsy time interval was compared to historical averages before same-day biopsy implementation.

RESULTS

There were 2156 women: 69.9% NHW (1508/2156) and 30.1% Black (648/2156). Mean ± standard deviation time from screening to diagnostic imaging overall was 13.5 ± 32.5 days but longer for Black (18.0 ± 48.3 days) than for NHW women (11.5 ± 22.2 days) (P < 0.001). The mean time from diagnostic mammogram to biopsy was 5.9 ± 18.9 days, longer for Black (9.0 ± 27.9 days) than for NHW women (4.4 ± 11.8 days) (P = 0.017). The same-day biopsy program shortened the time from diagnostic imaging to biopsy overall (12.5 ± 12.4 days vs 5.9 ± 18.9 days; P < 0.001), with a significant reduction for NHW women (12.4 ± 11.7 days vs 4.4 ± 11.8 days) (P < 0.001) but not Black women (11.5 ± 9.9 days vs 9.0 ± 27.9 days) (P = 0.527).

CONCLUSION

Disparities exist along the breast imaging pathway. A same-day biopsy program benefited NHW women more than Black women.

Prioritizing Screening Mammograms for Immediate Interpretation and Diagnostic Evaluation on the Basis of Risk for Recall

PURPOSE

The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation.

METHODS

A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms.

RESULTS

The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar.

CONCLUSIONS

Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit.

Online or Offline: Does It Matter? A Review of Existing Interpretation Approaches and Their Effect on Screening Mammography Metrics, Patient Satisfaction, and Cost

The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.

Disparities in Same-Day Diagnostic Imaging in Breast Cancer Screening: Impact of an Immediate-Read Screening Mammography Program Implemented During the COVID-19 Pandemic

BACKGROUND. The need for second visits between screening mammography and diagnostic imaging contributes to disparities in the time to breast cancer diagnosis. During the COVID-19 pandemic, an immediate-read screening mammography program was implemented to reduce patient visits and decrease time to diagnostic imaging. OBJECTIVE. The purpose of this study was to measure the impact of an immediate-read screening program with focus on disparities in same-day diagnostic imaging after abnormal findings are made at screening mammography. METHODS. In May 2020, an immediate-read screening program was implemented whereby a dedicated breast imaging radiologist interpreted all screening mammograms in real time; patients received results before discharge; and efforts were made to perform any recommended diagnostic imaging during the visit (performed by different radiologists). Screening mammographic examinations performed from June 1, 2019, through October 31, 2019 (preimplementation period), and from June 1, 2020, through October 31, 2020 (postimplementation period), were retrospectively identified. Patient characteristics were recorded from the electronic medical record. Multivariable logistic regression models incorporating patient age, race and ethnicity, language, and insurance type were estimated to identify factors associated with same-day diagnostic imaging. Screening metrics were compared between periods. RESULTS. A total of 8222 preimplementation and 7235 postimplementation screening examinations were included; 521 patients had abnormal screening findings before implementation, and 359 after implementation. Before implementation, 14.8% of patients underwent same-day diagnostic imaging after abnormal screening mammograms. This percentage increased to 60.7% after implementation. Before implementation, patients who identified their race as other than White had significantly lower odds than patients who identified their race as White of undergoing same-day diagnostic imaging after receiving abnormal screening results (adjusted odds ratio, 0.30; 95% CI, 0.10-0.86; p = .03). After implementation, the odds of same-day diagnostic imaging were not significantly different between patients of other races and White patients (adjusted odds ratio, 0.92; 95% CI, 0.50-1.71; p = .80). After implementation, there was no significant difference in race and ethnicity between patients who underwent and those who did not undergo same-day diagnostic imaging after receiving abnormal results of screening mammography (p > .05). The rate of abnormal interpretation was significantly lower after than it was before implementation (5.0% vs 6.3%; p < .001). Cancer detection rate and PPV1 (PPV based on positive findings at screening examination) were not significantly different before and after implementation (p > .05). CONCLUSION. Implementation of the immediate-read screening mammography program reduced prior racial and ethnic disparities in same-day diagnostic imaging after abnormal screening mammograms. CLINICAL IMPACT. An immediate-read screening program provides a new paradigm for improved screening mammography workflow that allows more rapid diagnostic workup with reduced disparities in care.

Batch Reading and Interrupted Interpretation of Digital Screening Mammograms Without and With Tomosynthesis

OBJECTIVE

To compare batch reading and interrupted interpretation for modern screening mammography.

METHODS

We retrospectively reviewed digital mammograms without and with tomosynthesis that were originally interpreted with batch reading or interrupted interpretation between January 2015 and June 2017. The following performance metrics were compared: recall rate (per 100 examinations), cancer detection rate (per 1,000 examinations), and positive predictive values for recall and biopsy.

RESULTS

In all, 9,832 digital mammograms were batch read, yielding a recall rate of 9.98%, cancer detection rate of 4.27, and positive predictive values for recall and biopsy of 4.40% and 35.5%, respectively. There were 49,496 digital mammograms that were read with interrupted interpretation, yielding a recall rate of 11.3%, cancer detection rate of 4.44, and positive predictive values for recall and biopsy of 3.92% and 30.1%, respectively. Of the digital mammograms with tomosynthesis, 7,075 were batch read, yielding a recall rate of 6.98%, cancer detection rate of 5.37, and positive predictive values for recall and biopsy of 7.69% and 38.0%, respectively. Of the digital mammograms with tomosynthesis, 24,380 were read with interrupted interpretation, yielding a recall rate of 8.30%, cancer detection rate of 5.41, and positive predictive values for recall and biopsy of 6.52% and 33.3%, respectively. For both digital mammograms without and with tomosynthesis, recall rates improved with batch reading compared with interrupted interpretation (P < .001), but no significant differences were seen for other metrics.

DISCUSSION

Batch reading digital mammograms without and with tomosynthesis improves recall rates while maintaining cancer detection rates and positive predictive values compared with interrupted interpretation.

Communication Practices of Mammography Facilities and Timely Follow-up of a Screening Mammogram with a BI-RADS 0 Assessment

RATIONALE AND OBJECTIVES

The objective of this study was to evaluate the association of communication practices with timely follow-up of screening mammograms read as Breast Imaging Reporting and Data Systems (BI-RADS) 0 in the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium.

MATERIALS AND METHODS

A radiology facility survey was conducted in 2015 with responses linked to screening mammograms obtained in 2011-2014. We considered timely follow-up to be within 15 days of the screening mammogram. Generalized estimating equation models were used to evaluate the association between modes of communication with patients and providers and timely follow-up, adjusting for PROSPR site, patient age, and race and ethnicity.

RESULTS

The analysis included 34,680 mammography examinations with a BI-RADS 0 assessment among 28 facilities. Across facilities, 85.6% of examinations had a follow-up within 15 days. Patients in a facility where routine practice was to contact the patient by phone if follow-up imaging was recommended were more likely to have timely follow-up (odds ratio [OR] 4.63, 95% confidence interval [CI] 2.76-7.76), whereas standard use of mail was associated with reduced timely follow-up (OR 0.47, 95% CI 0.30-0.75). Facilities that had standard use of electronic medical records to report the need for follow-up imaging to a provider had less timely follow-up (OR 0.56, 95% CI 0.35-0.90). Facilities that routinely contacted patients by mail if they missed a follow-up imaging visit were more likely to have timely follow-up (OR 1.65, 95% CI 1.02-2.69).

CONCLUSIONS

Our findings support the value of telephone communication to patients in relation to timely follow-up. Future research is needed to evaluate the role of communication in completing the breast cancer screening episode.

Integrating Customer Intimacy Into Radiology to Improve the Patient Perspective: The Case of Breast Cancer Screening

OBJECTIVE

The customer intimacy business model has emerged as a key operational approach for health care organizations as they move toward patient-centered care. The question arises how the customer intimacy approach can be implemented in the clinical setting and whether it can help practitioners address problems and improve quality of care.

CONCLUSION

Breast cancer screening and its emphasis on the patient perspective provides an interesting case study for understanding how the customer intimacy approach can be integrated into radiologic practice to improve the patient experience.

Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level

Breast cancer screening holds a prominent place in public health, health care delivery, policy, and women's health care decisions. Several factors are driving shifts in how population-based breast cancer screening is approached, including advanced imaging technologies, health system performance measures, health care reform, concern for "overdiagnosis," and improved understanding of risk. Maximizing benefits while minimizing the harms of screening requires moving from a "1-size-fits-all" guideline paradigm to more personalized strategies. A refined conceptual model for breast cancer screening is needed to align women's risks and preferences with screening regimens. A conceptual model of personalized breast cancer screening is presented herein that emphasizes key domains and transitions throughout the screening process, as well as multilevel perspectives. The key domains of screening awareness, detection, diagnosis, and treatment and survivorship are conceptualized to function at the level of the patient, provider, facility, health care system, and population/policy arena. Personalized breast cancer screening can be assessed across these domains with both process and outcome measures. Identifying, evaluating, and monitoring process measures in screening is a focus of a National Cancer Institute initiative entitled PROSPR (Population-based Research Optimizing Screening through Personalized Regimens), which will provide generalizable evidence for a risk-based model of breast cancer screening, The model presented builds on prior breast cancer screening models and may serve to identify new measures to optimize benefits-to-harms tradeoffs in population-based screening, which is a timely goal in the era of health care reform.