SAN FRANCISCO–(BUSINESS WIRE)– Commure Engage (formerly known as Rx.Health), a leading digital health and clinical care automation platform, today shared findings from a collaborative partnership with Yale New Haven Health System (YNHHS) that reveal the impact of automated and personalized patient outreach on improving exam completion rates for mammograms and breast ultrasounds performed for breast cancer screening.

Ensuring consistent adherence to breast cancer screening appointments remains challenging, with common barriers to care access and patient participation including ethnicity, language, and socioeconomic status. To address this critical issue, YNHHS and Commure Engage collaborated to develop an innovative solution: a novel and automated pre-appointment communication program, offered in both English and Spanish and delivered via text message (SMS) or interactive voice response (IVR), to effectively engage patients and provide pre-test information and wayfinding leading to improvements in exam completion rates.

“With Commure Engage, we were able to change how we communicate with patients about their upcoming breast imaging appointments across our entire health system. This has led to better informed patients and less missed care opportunities. In breast imaging, this means more patients are keeping their breast cancer screening and diagnostic imaging appointments, improving our opportunities for radiologists to catch these critical diagnoses earlier,” said Dr. Jay Pahade, medical director of radiology quality and safety for Yale New Haven Health. “We believe this type of transparent and patient-centric communication decreases anxiety, improves patient engagement, and can help reduce healthcare disparities — ultimately improving access and adherence for breast cancer screening.”

In partnership with YNHHS, the Commure Engage platform played a pivotal role in achieving a significant 49% patient engagement rate and relative reduction in patient no-shows and same-day cancellation rates for breast imaging exams by an impressive 54%. This technology partnership between YNHHS and Commure underscores the positive impact of personalized and timely patient outreach to drive patient compliance and improve the overall care experience.

“Through our partnership with Yale New Haven Health, we have an opportunity to meaningfully impact the health and wellbeing of patients and communities across Connecticut, New York, and Rhode Island,” said Richard Strobridge, head of Commure Engage. “Empowering leading providers like Yale to connect with their patients, create opportunities to engage them as collaborators in their unique care journeys, and ultimately drive better clinical outcomes is exactly why we do what we do.”

Supporting resources

• To learn more about how Commure Engage is powering clinical care coordination and patient engagement for health systems across the country, visit commure.com.
• Explore our case study to dive deeper into the partnership between Yale New Haven Health and Commure Engage and the positive results of the initiative on breast cancer screening efforts.
• These results are being shared this week at the Radiological Society of North America (RSNA) Annual Meeting in Chicago.

About Commure

Commure, Inc. is connecting disparate datasets, surfacing meaningful insights, accelerating performance through a suite of intuitive applications, and enabling seamless innovation across the healthcare industry. Commure’s mission is to empower every person in the health ecosystem to deliver exceptional care. Commure’s original applications include solutions to improve staff safety, enhance clinical workflow, and bolster revenue operations. Currently, the company enables more than 160,000 clinicians and staff across more than 500 care facilities to advance care through collaboration and supports hundreds of thousands of patients across its national network.

About Yale New Haven Health

Yale New Haven Health System (YNHHS), the largest and most comprehensive healthcare system in Connecticut, is recognized for advanced clinical care, quality, service, cost effectiveness and commitment to improving the health status of the communities it serves. YNHHS includes five hospitals – Bridgeport, Greenwich, Lawrence + Memorial, Westerly and Yale New Haven hospitals, several specialty networks and Northeast Medical Group, a non-profit medical foundation with several hundred community-based and hospital-employed physicians. Yale New Haven Hospital (YNHH) is affiliated with Yale University and Yale Medicine, the clinical practice of the Yale School of Medicine and the largest academic multi-specialty practice in New England. Yale New Haven Hospital (YNHH) is the primary teaching hospital of Yale School of Medicine.

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Breast cancer impacts require personalized screening and prevention strategies, as risk prediction models don’t correlate with mortality risk, mammography can lead to overdiagnosis, and chemoprevention effectiveness remains uncertain.

A study published in The Lancet Digital Health aims to create a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in females without a prior breast cancer diagnosis, using a large, representative dataset of over 11.6 million women.

The study used the QResearch primary care database data from 2000-2020 to identify individuals at high risk of life-threatening breast cancers in England, UK, rather than focusing solely on cancer incidence.

Key findings from the study included:

1. Dataset: Data from 11,626,969 female individuals were analyzed, totaling 70,095,574 person-years of follow-up. Among these, 1.2% received breast cancer diagnoses, 0.2% experienced breast cancer-related deaths, and 6.0% died from other causes.
2. Model Performance: Researchers utilized various modeling approaches, with the competing risks model demonstrating the highest performance, achieving a Harrell’s C-index of 0.932. This model exhibited excellent calibration across diverse age and ethnic groups.
3. Clinical Utility: Decision curve analysis indicated favorable clinical utility across all age groups, suggesting that this model could assist in stratified screening or preventive strategies.
4. Implications: The model that combines the risk of developing and dying from breast cancer on a population level has the potential to guide more effective screening and prevention approaches. Further research should assess the impact and cost-effectiveness of strategies informed by this model.

The study was funded by Cancer Research UK.

Methods and participants

This study utilized various modeling techniques, such as Cox proportional hazards, competing risks regression, XGBoost, and neural networks, to predict breast cancer mortality risk over a 10-year period in women with no prior breast cancer history. Researchers evaluated these models using an internal–external validation approach involving dataset partitioning based on time period and geographical region. They collected data from the QResearch database, which links primary care, hospital records, national cancer registry data, and mortality records. And they obtained ethical approval for this study.

The study enrolled adult females aged 20 years and older, specifically those 20–90 years old, who entered the QResearch database between January 1, 2000, and December 31, 2020. Researchers excluded women with previous diagnoses of invasive breast carcinoma or ductal carcinoma in situ from the study.

Outcomes and Candidate Predictors

The primary outcome was breast cancer mortality, defined as breast cancer recorded as a primary or contributory cause of death. Candidate predictor variables associated with breast cancer diagnosis or mortality were identified from clinical and epidemiological literature. These predictors were assessed at cohort entry or the most recent record before entry.

Procedures for Missing Data

Researchers employed multiple imputation to address missing data for variables such as alcohol intake, smoking status, BMI, deprivation score, and ethnicity. These imputed values were utilized throughout model development and evaluation.

Modelling Strategy

Researchers applied each model to the entire cohort, utilizing internal–external cross-validation, which involves data splitting by time period and geographical region. They assessed performance using metrics like Harrell’s C-index, calibration slope, and calibration in the large. Calibration plots visualized accuracy, and decision curve analysis gauged clinical value.

To develop regression models, they determined a minimum sample size of 199,500 participants with 400 outcome events, based on specific statistical parameters and a Cox-Snell R2 of 0.0045.

The analyses were carried out using Stata version 17 and R version 3.7.

Results

After excluding female individuals with a recorded history of previous or current breast cancer (n=152,870) or ductal carcinoma in situ diagnoses (n=5,409), the final study cohort comprised 11,626,969 females.
This study is the largest to develop clinical prediction models in breast cancer and the first to develop models estimating the risks of breast cancer mortality in the general female population.

This study developed prediction models for breast cancer mortality in females without breast cancer, with the competing risks regression model demonstrating the highest clinical utility. Accurate risk prediction tools can help target interventions and improve outcomes in breast cancer prevention and screening programs. Further research and validation are necessary before implementing these models in clinical practice.