The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison

Background Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. Objective The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. Methods We examined appointment data from 12 months of scheduled mammogram appointments, starting in September 2019 when a web and mobile app self-scheduling process for screening mammograms was made available for the Mayo Clinic primary care practice. Patients registered to the Mayo Clinic Patient Online Services could view the schedules and book their mammogram appointment via the web or a mobile app. Self-scheduling required no telephone calls or staff appointment schedulers. We examined uptake (count and percentage of patients utilizing self-scheduling), number of appointment actions taken by self-schedulers and by those using staff schedulers, no-show outcomes, scheduling efficiency, and weekend and after-hours use of self-scheduling. Results For patients who were registered to patient online services and had screening mammogram appointment activity, 15.3% (14,387/93,901) used the web or mobile app to do either some mammogram self-scheduling or self-cancelling appointment actions. Approximately 24.4% (3285/13,454) of self-scheduling occurred after normal business hours/on weekends. Approximately 9.3% (8736/93,901) of the patients used self-scheduling/cancelling exclusively. For self-scheduled mammograms, there were 5.7% (536/9433) no-shows compared to 4.6% (3590/77,531) no-shows in staff-scheduled mammograms (unadjusted odds ratio 1.24, 95% CI 1.13-1.36; P<.001). The odds ratio of no-shows for self-scheduled mammograms to staff-scheduled mammograms decreased to 1.12 (95% CI 1.02-1.23; P=.02) when adjusted for age, race, and ethnicity. On average, since there were only 0.197 staff-scheduler actions for each finalized self-scheduled appointment, staff schedulers were rarely used to redo or “clean up” self-scheduled appointments. Exclusively self-scheduled appointments were significantly more efficient than staff-scheduled appointments. Self-schedulers experienced a single appointment step process (one and done) for 93.5% (7553/8079) of their finalized appointments; only 74.5% (52,804/70,839) of staff-scheduled finalized appointments had a similar one-step appointment process (P<.001). For staff-scheduled appointments, 25.5% (18,035/70,839) of the finalized appointments took multiple appointment steps. For finalized appointments that were exclusively self-scheduled, only 6.5% (526/8079) took multiple appointment steps. The staff-scheduled to self-scheduled odds ratio of taking multiple steps for a finalized screening mammogram appointment was 4.9 (95% CI 4.48-5.37; P<.001). Conclusions Screening mammograms can be efficiently self-scheduled but may be associated with a slight increase in no-shows. Self-scheduling can decrease staff scheduler work and can be convenient for patients who want to manage their appointment scheduling activity after business hours or on weekends.

Download Full-text

The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison (Preprint)

10.2196/preprints.27072 ◽

2021 ◽

Author(s):

Frederick North ◽

Elissa M Nelson ◽

Rebecca J Buss ◽

Rebecca J Majerus ◽

Matthew C Thompson ◽

...

Keyword(s):

Mayo Clinic ◽

Odds Ratio ◽

Breast Imaging ◽

Screening Mammography ◽

Mobile App ◽

Care Practice ◽

Screening Mammogram ◽

Online Services ◽

Screening Mammograms ◽

The Web

BACKGROUND Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. OBJECTIVE The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. METHODS We examined appointment data from 12 months of scheduled mammogram appointments, starting in September 2019 when a web and mobile app self-scheduling process for screening mammograms was made available for the Mayo Clinic primary care practice. Patients registered to the Mayo Clinic Patient Online Services could view the schedules and book their mammogram appointment via the web or a mobile app. Self-scheduling required no telephone calls or staff appointment schedulers. We examined uptake (count and percentage of patients utilizing self-scheduling), number of appointment actions taken by self-schedulers and by those using staff schedulers, no-show outcomes, scheduling efficiency, and weekend and after-hours use of self-scheduling. RESULTS For patients who were registered to patient online services and had screening mammogram appointment activity, 15.3% (14,387/93,901) used the web or mobile app to do either some mammogram self-scheduling or self-cancelling appointment actions. Approximately 24.4% (3285/13,454) of self-scheduling occurred after normal business hours/on weekends. Approximately 9.3% (8736/93,901) of the patients used self-scheduling/cancelling exclusively. For self-scheduled mammograms, there were 5.7% (536/9433) no-shows compared to 4.6% (3590/77,531) no-shows in staff-scheduled mammograms (unadjusted odds ratio 1.24, 95% CI 1.13-1.36; P<.001). The odds ratio of no-shows for self-scheduled mammograms to staff-scheduled mammograms decreased to 1.12 (95% CI 1.02-1.23; P=.02) when adjusted for age, race, and ethnicity. On average, since there were only 0.197 staff-scheduler actions for each finalized self-scheduled appointment, staff schedulers were rarely used to redo or “clean up” self-scheduled appointments. Exclusively self-scheduled appointments were significantly more efficient than staff-scheduled appointments. Self-schedulers experienced a single appointment step process (one and done) for 93.5% (7553/8079) of their finalized appointments; only 74.5% (52,804/70,839) of staff-scheduled finalized appointments had a similar one-step appointment process (P<.001). For staff-scheduled appointments, 25.5% (18,035/70,839) of the finalized appointments took multiple appointment steps. For finalized appointments that were exclusively self-scheduled, only 6.5% (526/8079) took multiple appointment steps. The staff-scheduled to self-scheduled odds ratio of taking multiple steps for a finalized screening mammogram appointment was 4.9 (95% CI 4.48-5.37; P<.001). CONCLUSIONS Screening mammograms can be efficiently self-scheduled but may be associated with a slight increase in no-shows. Self-scheduling can decrease staff scheduler work and can be convenient for patients who want to manage their appointment scheduling activity after business hours or on weekends.

Download Full-text

Mammography as Screening for Coronary Artery Disease

The American Surgeon ◽

10.1177/000313480707300717 ◽

2007 ◽

Vol 73 (7) ◽

pp. 717-721 ◽

Cited By ~ 1

Author(s):

Jonathan R. Adkins ◽

T. Clark Gamblin ◽

D. Benjamin Christie ◽

Carol Collings ◽

Martin L. Dalton ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Medical Records ◽

Screening Tool ◽

Artery Bypass ◽

Screening Mammography ◽

Screening Mammogram ◽

Screening Mammograms ◽

Artery Disease ◽

The Relationship

Coronary artery disease (CAD) is the leading cause of death in American women. Screening mammograms are recommended for women starting at age 40 for the early detection of breast cancer. An additional benefit of this routine screening tool may be to detect breast arterial calcifications (BAC) as a possible sign of CAD. The purpose of this study was to determine further the relationship between mammographically detected BAC and CAD. The medical records of 44 women who had undergone coronary artery bypass grafting at our institution over 5 years were reviewed. These mammograms were examined for evidence of BAC. For all women included in the study, 18 of 44 (41%) had evidence of BAC on screening mammogram. This was statistically significant ( P < 0.0001) compared with the prevalence of BAC reported in the general population in previous studies. Most were also overweight (61.1%), had hypertension (88.8%), and hypercholesterolemia (55.5%). This is the first study to look at the direct correlation between patients with known CAD requiring revascularization and BAC. Perhaps women with BAC seen on screening mammography should undergo further workup for CAD, with the potential benefit of early intervention.

Download Full-text

Annual Mammography Screening: Is it Necessary?

The American Surgeon ◽

10.1177/000313481207800145 ◽

2012 ◽

Vol 78 (1) ◽

pp. 104-106

Author(s):

Veronica Hegar ◽

Kristin Oliveira ◽

Bharat Kakarala ◽

Alicia Mangram ◽

Ernest Dunn

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Task Force ◽

Treatment Options ◽

Retrospective Chart Review ◽

Breast Cancer Diagnosis ◽

Screening Mammography ◽

Screening Mammograms ◽

System 1 ◽

Preventative Services

Recent recommendations from the U.S. Preventative Services Task Force suggest that screening mammography for women should be biennial starting at age 50 years and continue to age 74 years. With these recommendations in mind, we proposed a study to evaluate women at our institution in whom breast cancer is diagnosed within 1 year of a previously benign mammogram. A retrospective chart review was performed over a 4-year period. Only patients who had both diagnostic mammograms and previous mammograms performed at our institution and a pathologic diagnosis of breast cancer were included. Benign mammograms were defined as either Breast Imaging Reporting And Data System 1 or 2. Analysis of the time elapse between benign mammogram and subsequent mammogram indicative of the diagnosis of breast cancer was performed. A total of 205 patients were included. The average age was 64 years. From our results, 48 patients, 23 per cent of the total, had a documented benign mammogram at 12 months or less before a breast cancer diagnosis. One hundred forty-three (70%) patients had a benign mammogram at 18 months or less prior. This study raises concern that 2 years between screening mammograms may delay diagnosis and possible treatment options for many women.

Download Full-text

A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice

Medical Decision Making ◽

10.1177/0272989x19832914 ◽

2019 ◽

Vol 39 (3) ◽

pp. 208-216

Author(s):

Jiaming Zeng ◽

Francisco Gimenez ◽

Elizabeth S. Burnside ◽

Daniel L. Rubin ◽

Ross Shachter

Keyword(s):

Clinical Practice ◽

Bayesian Network ◽

Sensitivity And Specificity ◽

Probabilistic Model ◽

Breast Imaging ◽

Screening Mammography ◽

False Positives ◽

Data System ◽

False Negatives ◽

Screening Mammograms

We developed a probabilistic model to support the classification decisions made by radiologists in mammography practice. Using the feature observations and Breast Imaging Reporting and Data System (BI-RADS) classifications from radiologists examining diagnostic and screening mammograms, we modeled their decisions to understand their judgments. Our model could help improve the decisions made by radiologists using their own feature observations and classifications while maintaining their observed sensitivities. Based on 112,433 mammographic cases from 36,111 patients and 13 radiologists at 2 separate institutions with a 1.1% prevalence of malignancy, we trained a probabilistic Bayesian network (BN) to estimate the malignancy probabilities of lesions. For each radiologist, we learned an observed probabilistic threshold within the model. We compared the sensitivity and specificity of each radiologist against the BN model using either their observed threshold or the standard 2% threshold recommended by BI-RADS. We found significant variability among the radiologists’ observed thresholds. By applying the observed thresholds, the BN model showed a 0.01% (1 case) increase in false negatives and a 28.9% (3612 cases) reduction in false positives. When using the standard 2% BI-RADS-recommended threshold, there was a 26.7% (47 cases) increase in false negatives and a 47.3% (5911 cases) reduction in false positives. Our results show that we can significantly reduce screening mammography false positives with a minimal increase in false negatives. We find that learning radiologists’ observed thresholds provides valuable information regarding the conservativeness of clinical practice and allows us to quantify the variability in sensitivity across and within institutions. Our model could provide support to radiologists to improve their performance and consistency within mammography practice.

Download Full-text

Reducing racial disparities in time to breast cancer diagnosis: Impact of immediate screening mammogram reads during the COVID pandemic.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.6508 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 6508-6508

Author(s):

Janeiro Achibiri ◽

Brian Nicholas Dontchos ◽

Sarah Mercaldo ◽

Anand Narayan ◽

Randy C. Miles ◽

...

Keyword(s):

Diagnostic Imaging ◽

Performance Metrics ◽

Screening Program ◽

Ethnic Disparities ◽

Screening Mammography ◽

Screening Mammogram ◽

Imaging Results ◽

Implementation Period ◽

Screening Mammograms ◽

Racial Ethnic

6508 Background: During the COVID-19 pandemic, barriers to access screening mammography along with goals to reduce visits supported immediate reading of screening mammograms. Typically, screening mammograms are reported after patients have left the facility. If imaging is abnormal, then an additional visit is needed for diagnostic imaging, introducing delays and potential disparities .Thus, we implemented an immediate-read screening mammography program and measured its impact on racial/ethnic disparities in time to diagnostic imaging after an abnormal screening mammogram. Methods: Responding to the COVID-19 pandemic, we implemented an immediate read screening program in late May 2020. Patients were provided imaging results before discharge and if the exam was abnormal, efforts were made to perform diagnostic imaging during that visit. We identified consecutive screening mammograms performed weekdays 8:00am-4:30pm and Saturdays 9:00am-4:00pm pre-implementation (6/1/19-10/31/19) and post-implementation (6/1/2020-10/31/2020). Exams left unread while awaiting comparison studies, due to technical factors, or for more than 10 days were excluded. Patient demographics and time from screening exam completion to report finalized were obtained from the electronic medical record. Cancer detection rate (CDR), abnormal interpretation rate (AIR), and positive predictive value (PPV) were calculated. Multivariable linear and logistic regression models were used to compare time from screening exam to report, same-day diagnostic imaging, and screening performance metrics pre- and post-implementation overall and by patient subgroups. Results: After 963 exams met exclusion criteria, a total of 8,222 pre- and 7,235 post-implementation exams were included. Median time to report finalization decreased from 61minutes (interquartile range [IQR]:24, 152) to 4 minutes (IQR:2, 7) for pre- and post-implementation periods (p < 0.001). During the pre-implementation period, non-white patients had lower odds of having same-day diagnostic imaging after an abnormal screening exam (age-adjusted odds ratio: 0.28; 95% CI: 0.10, 0.78 p = 0.015). There was no evidence of this disparity post-implementation. AIR was higher in the pre- versus post-implementation period (6.3% versus 5.0%; p < 0.001). There was no evidence of a difference in CDR (5.8 versus 4.2 cancers/1,000 exams) and PPV (9.2% versus 8.4%) for pre- versus post-implementation periods. Conclusions: An immediate read screening mammography program reduces racial/ethnic disparities in time to diagnostic imaging after an abnormal screening mammogram, thus promoting equity in access to care.[Table: see text]

Download Full-text

How to Minimize Patient Anxiety From Screening Mammography

Journal of Breast Imaging ◽

10.1093/jbi/wbab057 ◽

2021 ◽

Author(s):

Vidya R Pai ◽

Murray Rebner

Keyword(s):

Patient Flow ◽

Complex Interventions ◽

Screening Mammography ◽

Relaxation Techniques ◽

Screening Mammogram ◽

Patient Anxiety ◽

Breast Center ◽

Medical Tests ◽

The Media ◽

Factual Data

Abstract Anxiety has been portrayed by the media and some organizations and societies as one of the harms of mammography. However, one experiences anxiety in multiple different medical tests that are undertaken, including screening examinations; it is not unique to mammography. Some may argue that because this anxiety is transient, the so-called harm is potentially overstated, but for some women the anxiety is significant. Anxiety can increase or decrease the likelihood of obtaining a screening mammogram. There are multiple ways that anxiety associated with screening mammography can be diminished, including before, during, and after the examination. These include simple measures such as patient education, improved communication, being aware of the patient’s potential discomfort and addressing it, validating the patient’s anxiety as well as providing the patient with positive factual data that can easily be implemented in every breast center. More complex interventions include altering the breast center environment with multisensory stimulation, reorganization of patient flow to minimize wait times, and relaxation techniques including complementary and alternative medicine. In this article we will review the literature on measures that can be taken to minimize anxiety that would maximize the likelihood of a woman obtaining an annual screening mammogram.

Download Full-text

Mammographic features and histopathological findings of interval breast cancers

Acta Radiologica ◽

10.1080/02841850802403730 ◽

2008 ◽

Vol 49 (9) ◽

pp. 975-981 ◽

Cited By ~ 37

Author(s):

S. Hofvind ◽

B. Geller ◽

P. Skaane

Keyword(s):

Screening Program ◽

Lymph Node Involvement ◽

Screening Mammography ◽

Tumor Characteristics ◽

Breast Cancers ◽

Interval Cancers ◽

True Negative ◽

Node Involvement ◽

Screening Mammograms ◽

Baseline Screening

Background: Interval cancers are considered a shortcoming in screening mammography due to less favorable prognostic tumor characteristics compared to screening-detected cancers and consequently a lower chance of survival from the disease. Purpose: To describe the mammographic features and prognostic histopathological tumor characteristics of interval breast cancers. Material and Methods: A total of 231 interval breast cancer cases diagnosed in prevalently screened women aged 50–69 years old were examined. Thirty-five percent of the cases were retrospectively classified as missed cancers, 23% as minimal sign, and 42% as true negative (including occult cancers) in a definitive classification performed by six experienced breast radiologists. The retrospective classification described the mammographic features of the baseline screening mammograms in missed and minimal-sign interval cancers, while histopathological reports were used to describe the tumor characteristics in all the subgroups of interval cancers. Results: Fifty percent of the missed and minimal-sign interval cancers combined presented poorly defined mass or asymmetric density, and 26% calcifications with or without associated density or mass at baseline screening. Twenty-seven percent of invasive tumors were <15 mm for missed and 47% for true interval cancers ( P<0.001). Lymph node involvement was more common in missed (49%) compared with the true cases (33%, P<0.05). Conclusion: Missed interval cancers have less prognostically favorable histopathological tumor characteristics compared with true interval cancers. Improving the radiologist's perception and interpretation by establishing systematic collection of features and implementation of organized reviews may decrease the number of interval cancers in a screening program.

Download Full-text

Networked Knowledge Workers on the Web

Cyber Behavior ◽

10.4018/978-1-4666-5942-1.ch096 ◽

2014 ◽

pp. 1843-1863

Author(s):

Toni Ferro ◽

Mark Zachry

Keyword(s):

Web 2.0 ◽

Knowledge Workers ◽

Social Groups ◽

Company Size ◽

Web Content ◽

Online Services ◽

Annual Survey ◽

Survey Results ◽

Office Location ◽

The Web

With the growing popularity of online services that allow individuals to consume and contribute Web content with social groups of self-selected affiliates, the socio-technical geography of the Web has become increasingly complex. To map some of this space in a productive way for organizations and online researchers, we focus our attention on a particular segment of Web 2.0 services, publicly available online services (PAOSs) used for work purposes. After defining this segment and its relationship to other kinds of online services, we report the results of an annual survey that looks at who is using such PAOSs for work as well as the nature of that work. As our survey results indicate, how often PAOSs are used for work differs depending on the company size and office location of individuals. To frame our findings, we differentiate among the multiple PAOSs that respondents report using by classifying them as different genres of services, which we find provides a productive typology for understanding such services and their roles in organizations.

Download Full-text

Home Automation and Security System Using Internet of Things

10.4018/978-1-6684-3694-3.ch038 ◽

2022 ◽

pp. 780-802

Author(s):

Mahesh Kushwah ◽

Rajneesh Rani

Keyword(s):

Internet Of Things ◽

Mobile App ◽

Everyday Objects ◽

Video Footage ◽

Home Appliances ◽

Visually Impaired People ◽

Web Camera ◽

The Status ◽

Save Energy ◽

The Web

The internet of things (IoT) is viewed as something that connects everyday objects like smart TV and smart phones. Automation is the future of human civilization. It's like operating various devices and machinery with minimum or no human intervention. In this chapter, home appliances and other electronic devices are proposed to be controlled over internet with the help of website or smartphone. Speech recognition technology has been implemented in this chapter which will help complete or partially visually impaired people or persons with physical disability. Smart CCTV concept has been developed in this chapter which will allow to operate the web camera whenever it detects motion and sleep at other times which will save energy as well as storage unlike current CCTV scenario. Also, smart CCTV captures images and video footage and provides the real-time status of the place via a registered email address, website, and mobile app. If you are outside, you can check the status of your home and be in line with it, and you will activate the home appliances from the web site and through speech.

Download Full-text

Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽

10.7717/peerj.6201 ◽

2019 ◽

Vol 7 ◽

pp. e6201 ◽

Cited By ~ 48

Author(s):

Dina A. Ragab ◽

Maha Sharkas ◽

Stephen Marshall ◽

Jinchang Ren

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Input Data ◽

Data Augmentation ◽

Area Under The Curve ◽

Screening Mammography ◽

Support Vector ◽

Svm Classifier ◽

Deep Convolutional Neural Networks ◽

Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

Download Full-text