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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Am J Prev Med. 2023 Dec 11;66(3):399–407. doi: 10.1016/j.amepre.2023.11.002

Association of Electronic Self-Scheduling and Screening Mammogram Completion

Kimberly J Waddell 1,2,3,4, Keshav Goel 5, Sae-Hwan Park 6, Kristin A Linn 7,8,9, Amol S Navathe 10,11,12,13, Joshua M Liao 14,15, Caitlin McDonald 16, Catherine Reitz 17, Jake Moore 18, Steve Hyland 19, Shivan J Mehta 20,21,22,23
PMCID: PMC10922640  NIHMSID: NIHMS1945282  PMID: 38085196

Abstract

Introduction:

The purpose of this study was to evaluate if an electronic health record (EHR) self-scheduling function was associated with changes in mammogram completion for primary care patients who were eligible for a screening mammogram using United States Preventive Service Task Force recommendations.

Methods:

This was a retrospective cohort study (September 1, 2014 – August 31, 2019, analyses completed in 2022) using a difference-in-differences design to examine mammogram completion before versus after implementation of self-scheduling. The difference-in-differences estimate was the interaction between time (pre versus post-implementation) and group (active EHR patient portal versus inactive EHR patient portal). The primary outcome was mammogram completion among all eligible patients, with completion defined as receiving a mammogram within 6 months post-visit. The secondary outcome was mammogram completion among patients who received a clinician order during their visit.

Results:

The primary analysis included 35,257 patient visits. The overall mammogram completion rate in the pre-period was 22.2% and 49.7% in the post-period. EHR self-scheduling was significantly associated with increased mammogram completion among those with an active EHR portal, relative to patients with an inactive portal (adjusted difference 13.2 percentage points [95% CI 10.6–15.8]). For patients who received a clinician mammogram order at their eligible visit, self-scheduling was significantly associated with increased mammogram completion among patients with an active EHR portal account (adjusted difference 14.7 percentage points, [95% CI 10.9–18.5]).

Conclusions:

EHR-based self-scheduling was associated with a significant increase in mammogram completion among primary care patients. Self-scheduling can be a low-cost, scalable function for increasing preventive cancer screenings.

Keywords: electronic health record, mammogram, primary care, self-scheduling

Introduction

Breast cancer affects approximately 1 in 8 women living in the United States.1 Screening mammography is critical for the early detection and treatment of breast cancer.2 Guideline recommendations support routine breast cancer screening but mammogram completion rates are low, around 70%, with significant variability in completion across patient subpopulations.1,3,4 Mammogram screening also decreased by 40% in the early months of the COVID-19 pandemic and has not yet returned to pre-pandemic levels.5 This suggests a missed opportunity for early detection and treatment of breast cancer, which is essential for decreasing mortality and improving patient outcomes.6,7

Increasing screening mammogram completion is complex because it requires the clinician to order the test at one clinical encounter, and the patient to schedule and complete their mammogram at a separate clinical encounter. At the clinician level, a previous intervention that delivered an interruptive prompt to order a mammogram, via the electronic health record (EHR), significantly increased ordering rates but not patient completion rates.8,9 Prior work at the patient level included a range of single or multimodal interventions such as mailed reminders,10–12 telephone calls with or without motivational counseling,10,11,13,14 educational materials,15 community partnerships,16 and patient navigator support.17 These interventions were effective, especially for underserved groups, but were resource intensive and have been difficult to scale.6,11,13–17

Interventions that leverage scalable technology (e.g., EHR) to increase mammogram completion are not a panacea for the complex barriers many people experience but are well-suited to address patient-reported scheduling barriers such as prolonged wait times on the telephone or availability to call during business hours.18–20 Automated self-scheduling offers a cost-effective, scalable, and more efficient approach to patient scheduling that may help increase cancer screening rates. The potential benefits of automated self-scheduling include greater flexibility for patients to access the scheduling system, improved patient satisfaction, and reduced labor resources and costs.21–23 EHR self-scheduling also aligns with a behavioral science strategy of reducing friction along the behavioral pathway by limiting the steps in the scheduling process, making it easier to complete the task.24,25

The purpose of this study was to examine the association between implementation of an EHR self-scheduling function and mammogram completion for eligible primary care patients at a large academic health system. To examine if self-scheduling had a differential association with mammogram completion among patient subpopulations, changes in mammogram completion by patient race/ethnicity, insurance type, age, and annual household income at the neighborhood level were also explored.

Methods

This retrospective cohort study examined change in mammogram completion following the implementation of an EHR self-scheduling function. The University of Pennsylvania institutional review board approved this study. A waiver of informed consent was granted. This study followed The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting cohort studies.26

The study period comprised a total of five years (September 1, 2014 to August 31, 2019). The pre-intervention period spanned September 1, 2014- June 30, 2016 and the post-intervention period spanned July 1, 2016-August 31, 2019.

Study Sample

The study cohort was extracted using Clarity, an Epic reporting database. Data at the patient (e.g., appointment history) and clinic levels (e.g., number of clinicians) were obtained across 29 distinct primary care clinics. Patients were included if they were: 1) female sex; 2) aged 50–74 at the time of visit; 3) completed at least one eligible primary care visit after self-scheduling was implemented; and 4) were eligible for a screening mammogram at the time of their visit. Screening mammogram eligibility was determined using the United States Preventive Services Task Force (USPSTF) guidelines for breast cancer screening or a clinician modifier for more frequent screening.2 USPSTF guidelines recommend biennial screening mammography for women who are 50–74 years of age. The most common clinician modifier shortens the screening interval from two years to one year. When this occurred, the clinician modifier to determine screening eligibility was used. Criteria for an eligible primary care visit were established using Healthcare Common Procedures Coding System codes (Appendix Table 1). This approach excluded sick visits or other visit types where ordering a screening mammogram would be inappropriate or unlikely.

The exposure variable was the self-scheduling function. The health system implemented a mammogram self-scheduling function in May and June of 2016 that was accessible through the EHR patient portal. The health system required a clinician order for a mammogram and patients were not permitted to self-refer. Following implementation, if a clinician placed a mammogram order during a visit, a scheduling ticket was automatically placed, and individuals with an active patient portal account received an email message to self-schedule via their EHR portal. Prior to self-scheduling implementation, patients did not receive a notification and called during business hours to schedule their mammogram. Although the option to call and speak with a person to schedule remained available, and continued to be the method of scheduling for patients in the comparison group, the self-scheduling function offered a direct way for patients to schedule their mammogram.

Measures

The primary outcome was a binary indicator of mammogram completion (patient level), defined as completion within 6 months of the eligible primary care visit. The primary outcome was analyzed to assess whether the change in mammogram completion rates after the self-scheduling function was implemented (post-period) differed by EHR portal status. The secondary outcome was a binary indicator of mammogram completion conditional on receiving a mammogram order during the eligible visit. To explore if differential changes in completion rates varied by patient characteristics, the analysis was repeated by race/ethnicity, insurance type, age, or annual household income quartile. Income for each patient was imputed based on the median annual household income from the 2018 United States Census at the zip-code level.

Statistical Analysis

Analyses were completed in 2022. Changes in mammogram completion rates from the pre-period to post-period by EHR portal status were examined using a difference-in-differences design. To establish the exposure and comparison groups, all patients were classified into an EHR active (exposure) or EHR inactive (comparison) cohort based on portal activity in the post-period (July 1, 2016- August 31, 2019). The exposure and comparison groups were defined using data from the post-period due to the significant impact of time on patient EHR portal use. The volume of information and features available to patients through the EHR was much less during the pre-period (2014–2016), reducing the need for a portal account. This likely resulted in a low number of EHR portal users in the pre-period.

Patients were considered to have an active EHR status if they logged in to their patient portal at least once in the year prior to their eligible primary care visit.27,28 Patients who did not have an account or did not log in to their EHR portal at least once in the year prior to their eligible visit were classified as having an inactive EHR status. The comparison group for all analyses comprised all patients who had an inactive EHR portal account at the time of their eligible visit in the post-period regardless of whether their EHR account was active in the pre-period. Patients who had an active EHR portal in the post-period comprised the exposure group regardless of their EHR account status in the pre-period. Several steps were implemented to evaluate the cohort selection criteria, beginning with an evaluation of parallel trends in the pre-period, adjusting for relevant covariates that may be associated with having an EHR portal account, and sensitivity analyses. This was motivated by the possibility that individuals with an active EHR patient portal may be more likely to engage in their health relative to those who have an inactive EHR patient portal.

Unadjusted analyses examined completion rates by cohort (EHR portal active vs. EHR portal inactive) over time. An event study regression was performed for each cohort to examine if the trends in mammogram completion rates were similar in the pre-period.

Adjusted analyses fit generalized estimating equations (GEE) with an independent correlation structure clustered by primary care clinic. An independent correlation structure was specified to mitigate estimation bias due to the inclusion of endogenous covariates.29 The first eligible visit for each patient in the pre- and post-periods was used for the analysis (maximum of two visits per patient). The analysis adjusted for calendar month, race/ethnicity, age, comorbidities (Charlson Comorbidity Index), insurance type (Medicare, Medicaid, or other [commercial, private pay, uninsured]), income, and months from the self-scheduling initiation in the primary and secondary analyses. All analyses also adjusted for an active choice intervention designed to increase mammogram ordering rates among clinicians that was implemented at 3 primary care clinics within the same health system from September 1, 2016 to August 31, 2017.8

For the primary analysis, the GEE model comprised all eligible patients regardless of whether a screening mammogram was ordered at their eligible visit. As a secondary analysis, the sample was restricted to patients who received a mammogram order during their visit. The difference-in-differences (DID) estimate was obtained using an interaction term for time (pre vs. post-period) x EHR portal status (active vs. inactive) for both models. The delta-method was used to convert the DID estimate and confidence interval from the log odds scale to a percentage difference.

To examine if differential changes in completion rates varied by patient characteristics, the analysis was repeated by race/ethnicity, income, age, and insurance type. To examine changes by subgroup, a three-way interaction term was included for exploratory variable x period x EHR portal status along with all lower order terms. For each extended model, a likelihood ratio test was conducted to test whether the DID parameter differed across groups. Tests based on the extended models were considered exploratory and did not adjust for multiple comparisons.

To examine the robustness of the cohort selection criteria, the sample was restricted to patients who completed an eligible visit in both the pre-and post-periods and two additional sensitivity analyses were completed. First, the standardized mean difference for all covariates used in the primary GEE model between the full sample and restricted sample were calculated to quantify differences in the respective target populations. Second, the same GEE model used in the primary analysis was run on the restricted cohort where the treated group comprised patients with an active EHR portal status at both visits (pre- and post-period) and the control group was patients with an inactive EHR portal account at both visits. All analyses were conducted in Python (3.10.6) and Statsmodels (0.13.2) with an a priori significance level of P = 0.05.

Results

The final sample for the primary analysis included 35,257 patient visits while the secondary analysis included 17,122 patient visits. A total of 26,833 and 8,424 distinct patients were included in the primary and secondary analyses. Patients with an active EHR portal status, compared to those with an inactive EHR portal status, were more often non-Hispanic white (70.3% vs. 55.5%) and commercially-insured (70.4% vs. 62.4%), while those in the inactive EHR portal cohort were more often non-Hispanic Black (21.3% vs. 35.0%) or Hispanic (2.2% vs. 3.2%) in the pre-period (Table 1). The overall mammogram completion rate within 6 months of the primary care visit was 22.2% in the pre-period which increased to 49.7% in the post-period.

Table 1.

Sample demographics

Pre-Period (September 1, 2014-June 30,2016) Post-Period (July 1, 2016- August 31, 2019) Pre vs. Post-Periodc
EHR Active (n=2788) EHR Inactive (n=5636) P EHR Active (n=11,611) EHR Inactive (.1=15,222) P EHR Active P EHR Inactive P
Sociodemographics
Age, mean (SD) 59.6 (6.3) 59.4 (6.5) 0.303 60 6.8) 60.2 (6.9) 0.003 <0.001 0.010
Race/Ethnicity, n (%) <0.001 <0.001 <0.001 0.087
 Asian, non-Hispanic 78 (2.8) 93 (1.7) 357 (3.1) 418 (2.7)
 Black, non-Hispanic 595 (21.3) 1970 (35.0) 2556 (22.0) 5516 (36.3)
 White, non-Hispanic 1961 (70.3) 3129 (55.5) 7985 (68.8) 7828 (51.4)
 Hispanic 58 (2.2) 180 (3.2) 230 (2.0) 606 (4.0)
 Other 96 (3.4) 264 (4.6) 483 (4.1) 854 (5.6)
Insurance, n (%) <0.001 <0.001 <0.001 0.409
 Medicaid 129 (4.6) 539 (9.6) 537 (4.6) 1685 (11.1)
 Medicare 696 (25.0) 1576 (28.0) 3053 (26.3) 4683 (30.8)
 Othera 1963 (70.4) 3521 (62.5) 8021 (69.1) 8854 (58.2)
Household Income,b median [IQR] 80,525 [54,006–98,487] 72,836 [42,418-96,755] <0.001 80,791 [55,098–99,432] 72,563 [39,851–96,755] <0.001 0.174 0.157
Charlson Comorbidity Index, median [IQR] 0 [0–2] 0 [0–1] <0.001 0 [0–2] 0 [0–1] <0.001 0.151 0.033
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Note: Boldface indicates statistical significance (p<0.05); n = patient visits

a

Includes commercial insurance, private pay, or uninsured status

b

Household income was obtained using zip-code census data

c

P-values are comparison of the same group (active or inactive) between pre- and post-periods

Among all eligible patients, the unadjusted difference in completion rates between the two cohorts was 15.9 percentage points. The change in mammogram completion rates from the pre to post-period for those with an active EHR portal was 35.1 percentage points (24.4% pre, 59.5% post) and 19.2 percentage points for those with an inactive EHR portal (21.1% pre to 40.3% post). Among all patients who received a mammogram order at their visit, the unadjusted difference between the two cohorts was 14.2 percentage points. The unadjusted change in mammogram completion rate from the pre to post-period for those with an active EHR portal was 34.2 percentage points (38.6% pre, 72.8% post) and 19.9 percentage points for those with an inactive EHR portal (33.9% pre, 53.8% post). Results from the event study regression suggested that both the exposure and comparison groups did not have a significant change in mammogram completion rates during the pre-period (Appendix Figure 1). Figure 1 also confirms the parallel trends between groups in the pre-period.

Figure 1.

Figure 1.

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Adjusted mammogram completion rates for all eligible patients (A) and contingent upon receiving an order at the primary care visit (B). The significant increase in the final quarter before implementation is likely due, in part, to the staggered roll out of the self-scheduling function across the health system.

In adjusted primary analyses, the EHR self-scheduling function was associated with a 13.2 percentage point increase (95% CI [10.6–15.8], P <0.001) in mammogram completion among patients with an active EHR portal in the post-period (Figure 1A). For patients who received a clinician mammogram order at their eligible visit, those with an active EHR portal experienced a 14.7 percentage point increase (95% CI [10.9–18.5], P< 0.001) in mammogram completion over those with an inactive EHR portal (Figure 1B). There were no adjusted differential associations between self-scheduling and mammogram completion by race/ethnicity, income, or insurance type (Figure 2). There was a significant difference in mammogram completion among patients who were 60–64 years of age, relative to patients who were <55 years (−8.6 percentage points, 95% CI [−15.9, −1.3]). All values are reported in Appendix Table 2.

Figure 2.

Figure 2.

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Adjusted changes in mammogram completion rates by race/ethnicity (A) relative to patients who were non-Hispanic White; zip-code derived income (B) relative to those in the lowest income quartile; age (C) relative to those who were 50–54 years of age; and insurance status (D) relative to those with other insurance, which includes private, uninsured, or self-pay.

Results from sensitivity analyses demonstrated very small standardized mean differences in all covariates between the full and restricted samples (Appendix Table 3). The restricted sample included 6,628 patients (n=4,143 EHR inactive; n=2,485 EHR active). Additionally, adjusted results from the restricted sample demonstrated the EHR self-scheduling function was associated with a 9.1 percentage point increase (95% CI [6.6–11.5], P <0.001) in mammogram completion among patients with an active EHR portal in the post-period. These results support the robustness of the primary findings to the cohort selection criteria.

Discussion

The results from this study demonstrate that the implementation of an EHR-based self-scheduling function was associated with a significant 13.2 percentage point increase in mammogram completion rates among patients with an active EHR portal account at the time of their eligible visit, relative to patients with an inactive account. For patients who received a clinician order for a screening mammogram at their visit, the self-scheduling function was associated with a 14.7 percentage point increase in mammogram completion. The overall 22.2% screening rate in the pre-period more than doubled to 49.7% in the post-period. To the authors’ knowledge, this study is the first to examine the association of EHR self-scheduling with mammogram completion using a difference-in-differences design.

These results lend support for implementing low-cost programs to increase screening mammogram rates through the EHR patient portal. Prior interventions that did not use the EHR included mailing information,11,12 telephone or computer-based instructions,11–13 educational materials,15 or health worker support.17 These resource requirements may limit scalability and long-term implementation. In contrast, the self-scheduling function was scaled across an entire health system over a short period of time and did not require additional staffing resources, making it durable over time. The reach of this intervention will likely continue to increase as the number of patients actively using EHR patient portals continues to rise.30

Increasing mammogram completion rates among eligible patients is complex. Physician ordering of preventive screenings and patient completion are influenced by distinct factors that are difficult to address via a single intervention. An automated EHR self-scheduling function acts on a small portion of the many complex barriers that limit screening mammogram completion. Although the magnitude of change observed in this study was large, automated self-scheduling does not address all the personal, operational, or system barriers to mammogram completion. For example, barriers ranging from personal (e.g., fear of illness) to structural (e.g., lack of transportation,) and system (e.g., access to care) may require different interventions that could be coupled with automated self-scheduling to achieve a greater impact.16,31,32

These results support the implementation of self-scheduling as means to increase patient autonomy. Self-scheduling features reduce friction by affording patients greater control and independence when scheduling preventive health screenings versus the traditional model of calling a scheduling line during business hours. In order to access these features, however, patients needed to have an active EHR portal account. Moving forward, ensuring everyone has an active EHR portal account may require a more intentional effort to ensure all patients, especially those who are medically underserved, can create and access their EHR portal. For example, enhancing the language functionality to reliably serve patients with limited English proficiency would likely facilitate more equitable access. Alternatively, creating a self-scheduling function that does not require portal access (e.g., schedule via text message) would promote access for patients who may lack reliable internet or e-mail access that is required for EHR portal use.

Limitations

This study has limitations. Observational designs are vulnerable to confounding that can impact study findings, such as the observable and unobservable differences between those with active and inactive portal accounts. The implementation of self-scheduling was initiated by the health system and made available to all patients if they had an active EHR portal account. This created a natural experiment between active and inactive EHR portal groups that allowed examination of trends in screening mammogram completion using a quasi-experimental design but does not guarantee the comparison group was without limitations. Active EHR portal users may be more engaged in their health maintenance compared to those with an inactive EHR portal account. This limits the ability to determine how much of the improvement in mammogram screening was a result of self-scheduling relative to overall health awareness. Our analyses adjusted for relevant patient and clinic factors to account for potential confounding, but fully eliminating potential bias is not possible. Data related to home internet access or email capabilities were not available for this analysis, restricting the ability to adjust for personal factors that may impact portal access. The email notification encouraging patients with an active EHR portal to self-schedule their mammogram that was included in the implementation of the self-scheduling function may have also served as an intervention. Because the notification was part of the self-scheduling function, it was not possible to examine the potential association of this notification alone and mammogram completion. Lastly, the design and maintenance of EHR patient portals varies across health systems and impacts the generalizability of these findings. These results offer a blueprint for future studies that can be tailored to the needs of individual health systems and rigorously evaluated with each iteration.

Conclusions

In conclusion, an EHR-based self-scheduling function was associated with an increased screening mammogram completion rate among primary care patients with an active EHR patient portal account. These findings lend support for interventions or programs that use the EHR and self-scheduling to reduce barriers to scheduling mammogram screenings. Low-cost, EHR self-scheduling functions are a promising method for increasing preventive cancer screening rates that could ultimately result in earlier detection and treatment of certain cancers

Supplementary Material

1

Acknowledgements

This study was supported by the NIH National Institutes of Aging R61AG068947. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Footnotes

Conflict of Interest Disclosures: Dr. Mehta has received grants from the National Cancer Institute from the National Institutes of Health (K08CA234326) and National Comprehensive Cancer Network, and personal fees from the American Gastroenterological Association and Guardant Health that are unrelated to this project. Dr. Navathe reports grants from Hawaii Medical Service Association, grants from Commonwealth Fund, grants from Robert Wood Johnson Foundation, grants from Donaghue Foundation, grants from the Veterans Affairs Administration*, grants from Arnold Ventures, grants from United Healthcare, grants from Blue Cross Blue Shield of NC, grants from Humana, personal fees from Navvis Healthcare, personal fees from Singapore Ministry of Health, personal fees from Elsevier Press, personal fees from Medicare Payment Advisory Commission, personal fees from Analysis Group, personal fees from VBID Health, personal fees from Advocate Physician Partners, personal fees from the Federal Trade Commission, personal fees from Catholic Health Services Long Island, and equity from Clarify Health, personal fees and board membership for The Scan Group, and non-compensated board membership for Integrated Services, Inc. outside the submitted work in the past 3 years. Dr. Liao reports personal fees from the Washington Health Alliance outside of the submitted work in the past three years.

*

Disclaimer: This article does not necessarily represent the views of the US government or the Department of Veterans Affairs or the State of Pennsylvania

Credit Author Statement

Kimberly Waddell: conceptualization, methodology, validation, writing-original draft, review, and editing, visualization

Keshav Goel: conceptualization and writing

Sae-Hwan Park: methodology, software, formal analysis, data curation, writing- review and editing

Kristin Linn: conceptualization, methodology, supervision, writing- review and editing

Amol Navathe: conceptualization, methodology, writing- review and editing, supervision, funding acquisition

Joshua Liao: conceptualization, methodology, writing- review and editing, supervision, funding acquisition

Caitlin McDonald: conceptualization, resources, writing- review and editing

Catherine Reitz: conceptualization, resources, writing- review and editing

Jake Moore: resources, writing- review and editing

Steve Hyland: resources, writing- review and editing

Shivan J. Mehta: conceptualization, methodology, writing- review and editing, supervision, funding acquisition

Steven Hyland is affiliated with the University of Pennsylvania Perelman School of Medicine

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Kimberly J. Waddell, Perelman School of Medicine, Philadelphia, PA; Center for Health Incentives and Behavioral Economics, Philadelphia, PA; Leonard Davis Institute for Health Economics, Philadelphia, PA; University of Pennsylvania and; Corporal Michael J. Cresencz VA Medical Center, Philadelphia, PA.

Keshav Goel, Perelman School of Medicine, Philadelphia, PA.

Sae-Hwan Park, Center for Health Incentives and Behavioral Economics, Philadelphia, PA.

Kristin A. Linn, Center for Health Incentives and Behavioral Economics, Philadelphia, PA; Leonard Davis Institute for Health Economics, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, PA.

Amol S. Navathe, Perelman School of Medicine, Philadelphia, PA; Center for Health Incentives and Behavioral Economics, Philadelphia, PA; Leonard Davis Institute for Health Economics, Philadelphia, PA; University of Pennsylvania and; Corporal Michael J. Cresencz VA Medical Center, Philadelphia, PA.

Joshua M. Liao, Leonard Davis Institute for Health Economics, Philadelphia, PA; Department of Medicine, University of Washington, Seattle, WA.

Caitlin McDonald, Center for Health Care Innovation, Philadelphia, PA.

Catherine Reitz, Center for Health Care Innovation, Philadelphia, PA.

Jake Moore, Perelman School of Medicine, Philadelphia, PA.

Steve Hyland, University of Pennsylvania Perelman School of Medicine.

Shivan J. Mehta, Perelman School of Medicine, Philadelphia, PA; Center for Health Incentives and Behavioral Economics, Philadelphia, PA; Leonard Davis Institute for Health Economics, Philadelphia, PA; Center for Health Care Innovation, Philadelphia, PA.

References

  • 1.Society AC. Breast Cancer Facts & Figures 2019–2020. 2019. [Google Scholar]
  • 2.Siu AL, Force UPST. Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med. 2016;164(4):279–296. doi: 10.7326/M15-2886 [DOI] [PubMed] [Google Scholar]
  • 3.Rauscher GH, Allgood KL, Whitman S, Conant E. Disparities in screening mammography services by race/ethnicity and health insurance. J Womens Health. Feb 2012;21(2):154–160. doi: 10.1089/jwh.2010.2415 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Patel M, Malak M, Swanson J, Costa J, Turner K, Hanna K. Mammogram Order Completion Rates Among Women with Diabetes. J Am Board Fam Med. Jan-Feb 2022;35(1):158–162. doi: 10.3122/jabfm.2022.01.210239 [DOI] [PubMed] [Google Scholar]
  • 5.Oakes AH, Boyce K, Patton C, Jain S. Rates of Routine Cancer Screening and Diagnosis Before vs After the COVID-19 Pandemic. JAMA Oncol. Jan 1 2022;9(1):145–146. doi: 10.1001/jamaoncol.2022.5481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Jain AG, Guan J, FaisalUddin M, Manoucheri M, Fang C. Improving breast cancer screening rates in a primary care setting. Breast J. Sep 2019;25(5):963–966. doi: 10.1111/tbj.13377 [DOI] [PubMed] [Google Scholar]
  • 7.Institute NC. Breast Cancer Screening Progress Report. 2022. Summary of Trends in US Cancer Control Measures. April 2022. Accessed July 12, 2022. https://progressreport.cancer.gov/detection/breast_cancer [Google Scholar]
  • 8.Patel MS, Volpp KG, Small DS, et al. Using active choice within the electronic health record to increase physician ordering and patient completion of high-value cancer screening tests. Healthcare. Elsevier; 2016;4(4):340–345. doi: 10.1016/j.hjdsi.2016.04.005. Accessed 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hsiang EY, Mehta SJ, Small DS, et al. Association of an Active Choice Intervention in the Electronic Health Record Directed to Medical Assistants With Clinician Ordering and Patient Completion of Breast and Colorectal Cancer Screening Tests. JAMA Netw Open. 2019 2019;2(11):e1915619–e1915619. doi: 10.1001/jamanetworkopen.2019.15619 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Luckmann R, Costanza ME, White MJ, et al. A 4-year randomized trial comparing three outreach interventions to promote screening mammograms. Transl Behav Med. Mar 1 2019;9(2):328–335. doi: 10.1093/tbm/iby031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Feldstein AC, Perrin N, Rosales AG, et al. Effect of a multimodal reminder program on repeat mammogram screening. Am J Prev Med. Aug 2009;37(2):94–101. doi: 10.1016/j.amepre.2009.03.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Costanza ME, Luckmann R, White MJ, Rosal MC, LaPelle N, Cranos C. Moving mammogram-reluctant women to screening: a pilot study. Ann Behav Med. Jun 2009;37(3):343–349. doi: 10.1007/s12160-009-9107-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Russell KM, Champion VL, Monahan PO, et al. Randomized trial of a lay health advisor and computer intervention to increase mammography screening in African American women. Cancer Epidemiol Biomarkers Prev. Jan 2010;19(1):201–210. doi: 10.1158/1055-9965.EPI-09-0569 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Oeffinger KC, Ford JS, Moskowitz CS, et al. Promoting breast cancer surveillance: the EMPOWER study, a randomized clinical trial in the Childhood Cancer Survivor Study. J Clin Oncol. Aug 20 2019;37(24):2131. doi: 10.1200/JCO.19.00547 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Goel MS, O’Conor R. Increasing screening mammography among predominantly Spanish speakers at a federally qualified health center using a brief previsit video. Patient Educ Couns. Mar 2016;99(3):408–413. doi: 10.1016/j.pec.2015.09.007 [DOI] [PubMed] [Google Scholar]
  • 16.Kamaraju S, DeNomie M, Visotcky A, et al. Increasing mammography uptake through academic-community partnerships targeting immigrant and refugee communities in Milwaukee. Wmj. 2018;117(2):55–61. [PubMed] [Google Scholar]
  • 17.Phillips CE, Rothstein JD, Beaver K, Sherman BJ, Freund KM, Battaglia TA. Patient navigation to increase mammography screening among inner city women. J Gen Intern Med. Feb 2011;26(2):123–129. doi: 10.1007/s11606-010-1527-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Stoll CR, Roberts S, Cheng M-R, Crayton EV, Jackson S, Politi MC. Barriers to mammography among inadequately screened women. Health Educ Behav. Feb 2015;42(1):8–15. doi: 10.1177/1090198114529589 [DOI] [PubMed] [Google Scholar]
  • 19.Elkin EB, Snow JG, Leoce NM, Atoria CL, Schrag D. Mammography capacity and appointment wait times: barriers to breast cancer screening. Cancer Causes Control. Jan 2012;23(1):45–50. doi: 10.1007/s10552-011-9853-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Peipins LA, Shapiro JA, Bobo JK, Berkowitz Z. Impact of women’s experiences during mammography on adherence to rescreening (United States). Cancer Causes Control. May 2006;17(4):439–447. doi: 10.1007/s10552-005-0447-7 [DOI] [PubMed] [Google Scholar]
  • 21.Woodcock EW. Barriers to and Facilitators of Automated Patient Self-scheduling for Health Care Organizations: Scoping Review. J Med Internet Res. Jan 11 2022;24(1):e28323. doi: 10.2196/28323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Zhao P, Yoo I, Lavoie J, Lavoie BJ, Simoes E. Web-based medical appointment systems: A systematic review. J Med Internet Res. Apr 26 2017;19(4):e6747. doi: 10.2196/jmir.6747 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.North F, Nelson EM, Buss RJ, Majerus RJ, Thompson MC, Crum BA. The effect of automated mammogram orders paired with electronic invitations to self-schedule on mammogram scheduling outcomes: observational cohort comparison. JMIR Med Inform. Dec 7 2021;9(12):e27072. doi: 10.2196/27072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Sunstein CR. Sludge Audits. Behav Public Policy. Oct 2022;6(4):654–673. doi: 10.1017/bpp.2019.32 [DOI] [Google Scholar]
  • 25.Thaler RH. Nudge, not sludge. Science. Aug 3 2018;361(6401):431–431. doi:doi:10.1126/science.aau9241 [DOI] [PubMed] [Google Scholar]
  • 26.Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–1457. doi: 10.1016/S0140-6736(07)61602-X [DOI] [PubMed] [Google Scholar]
  • 27.Mehta SJ, Day SC, Norris AH, et al. Behavioral interventions to improve population health outreach for hepatitis C screening: randomized clinical trial. BMJ. May 18 2021;373:n1022. doi: 10.1136/bmj.n1022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Mehta SJ, Mallozzi C, Shaw PA, et al. Effect of Text Messaging and Behavioral Interventions on COVID-19 Vaccination Uptake: A Randomized Clinical Trial. JAMA Netw Open. Jun 1 2022;5(6):e2216649–e2216649. doi: 10.1001/jamanetworkopen.2022.16649 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Sullivan Pepe M, Anderson GL. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Commun Stat Simul Comput. 1994;23(4):939–951. doi: 10.1080/03610919408813210 [DOI] [Google Scholar]
  • 30.Gheorghiu B, Hagens S. Use and maturity of electronic patient portals. Building Capacity for Health Informatics in the Future. IOS Press; 2017:136–141. doi: 10.3233/978-1-61499-742-9-136 [DOI] [PubMed] [Google Scholar]
  • 31.Kelber ST. Exploratory study of breast cancer screening practices of urban women: a closer look at who is and is not getting screened. ABNF J. 2015;26(2):30. [PubMed] [Google Scholar]
  • 32.Aleshire ME, Adegboyega A, Escontrías OA, Edward J, Hatcher J. Access to care as a barrier to mammography for Black women. Policy Polit Nurs Pract. Feb 2021;22(1):28–40. doi: 10.1177/1527154420965537 [DOI] [PMC free article] [PubMed] [Google Scholar]

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