Patient and Clinician Decision Support to Increase Genetic Counseling for Hereditary Breast and Ovarian Cancer Syndrome in Primary Care: A Cluster Randomized Clinical Trial.

Importance: To promote the identification of women carrying BRCA1/2 variants, the US Preventive Services Task Force recommends that primary care clinicians screen asymptomatic women for an increased risk of carrying a BRCA1/2 variant risk. Objective: To examine the effects of patient and clinician decision support about BRCA1/2 genetic testing compared with standard education alone. Design, Setting, and Participants: This clustered randomized clinical trial was conducted at an academic medical center including 67 clinicians (unit of randomization) and 187 patients. Patient eligibility criteria included women aged 21 to 75 years with no history of breast or ovarian cancer, no prior genetic counseling or testing for hereditary breast and ovarian cancer syndrome (HBOC), and meeting family history criteria for BRCA1/2 genetic testing. Interventions: RealRisks decision aid for patients and the Breast Cancer Risk Navigation Tool decision support for clinicians. Patients scheduled a visit with their clinician within 6 months of enrollment. Main Outcomes and Measures: The primary end point was genetic counseling uptake at 6 months. Secondary outcomes were genetic testing uptake at 6 and 24 months, decision-making measures (perceived breast cancer risk, breast cancer worry, genetic testing knowledge, decision conflict) based upon patient surveys administered at baseline, 1 month, postclinic visit, and 6 months. Results: From December 2018 to February 2020, 187 evaluable patients (101 in the intervention group, 86 in the control group) were enrolled (mean [SD] age: 40.7 [13.2] years; 88 Hispanic patients [46.6%]; 15 non-Hispanic Black patients [8.1%]; 72 non-Hispanic White patients [38.9%]; 35 patients [18.9%] with high school education or less) and 164 (87.8%) completed the trial. There was no significant difference in genetic counseling uptake at 6 months between the intervention group (20 patients [19.8%]) and control group (10 patients [11.6%]; difference, 8.2 percentage points; OR, 1.88 [95% CI, 0.82-4.30]; P = .14). Genetic testing uptake within 6 months was also statistically nonsignificant (13 patients [12.9%] in the intervention group vs 7 patients [8.1%] in the control group; P = .31). At 24 months, genetic testing uptake was 31 patients (30.7%) in intervention vs 18 patients (20.9%) in control (P = .14). Comparing decision-making measures between groups at baseline to 6 months, there were significant decreases in perceived breast cancer risk and in breast cancer worry (standard mean differences = -0.48 and -0.40, respectively). Conclusions and Relevance: This randomized clinical trial did not find a significant increase in genetic counseling uptake among patients who received patient and clinician decision support vs those who received standard education, although more than one-third of the ethnically diverse women enrolled in the intervention underwent genetic counseling. These findings suggest that the main advantage for these high-risk women is the ability to opt for screening and preventive services to decrease their cancer risk. Trial Registration: ClinicalTrials.gov Identifier: NCT03470402.

Patient and Provider Web-Based Decision Support for Breast Cancer Chemoprevention: A Randomized Controlled Trial.

Significant underutilization of breast cancer chemoprevention remains, despite guidelines stating that physicians should recommend chemoprevention with antiestrogen therapy to high-risk women. We randomized women, ages 35 to 75 years, who met high-risk criteria for breast cancer, without a personal history of breast cancer or prior chemoprevention use, to standard educational materials alone or combined with a web-based decision aid. All healthcare providers, including primary care providers and breast specialists, were given access to a web-based decision support tool. The primary endpoint was chemoprevention uptake at 6 months. Secondary outcomes included decision antecedents (perceived breast cancer risk/worry, chemoprevention knowledge, self-efficacy) and decision quality (decision conflict, chemoprevention informed choice) based upon patient surveys administered at baseline, 1 and 6 months after randomization. Among 282 evaluable high-risk women enrolled from November 2016 to March 2020, mean age was 57 years (SD, 9.9) and mean 5-year invasive breast cancer risk was 2.98% (SD, 1.42). There was no significant difference in chemoprevention uptake at 6 months between the intervention and control groups (2.1% vs. 3.5%). Comparing the intervention and control arms at 1 month, there were significant differences among high-risk women in accurate breast cancer risk perceptions (56% vs. 39%, P = 0.017), adequate chemoprevention knowledge (49% vs. 27%, P < 0.001), mean decision conflict (34.0 vs. 47.0, P < 0.001), and informed choice (41% vs. 23%, P = 0.003). These differences were no longer significant at 6 months. Although our decision support tools did not result in a significant increase in chemoprevention uptake, we did observe improvements in decision antecedents and decision quality measures. PREVENTION RELEVANCE: In this randomized controlled trial of decision support for 300 high-risk women and 50 healthcare providers, we did not observe a significant increase in chemoprevention uptake, which remained low at under 5%. However, these decision support tools may increase knowledge and informed choice about breast cancer chemoprevention.

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

We evaluated strategies to identify and recruit a racially/ethnically diverse cohort of women at high-risk for breast cancer to a randomized controlled trial (RCT). We enrolled 300 high-risk women and 50 healthcare providers to a RCT of standard educational materials alone or in combination with web-based decision support tools. We implemented five strategies to identify high-risk women: (i) recruitment among patients previously enrolled in a study evaluating breast cancer risk; (ii) automated breast cancer risk calculation using information extracted from the electronic health record (EHR); (iii) identification of women with atypical hyperplasia or lobular carcinoma in situ (LCIS) using International Classification of Diseases (ICD)-9/10 diagnostic codes; (iv) clinical encounters with enrolled healthcare providers; (v) recruitment flyers/online resources. Breast cancer risk was calculated using either the Gail or Breast Cancer Surveillance Consortium (BCSC) models. We identified 6,229 high-risk women and contacted 3,459 (56%), of whom 17.2% were identified from prior study cohort, 37.5% through EHR risk information, 14.8% with atypical hyperplasia/LCIS, 29.0% by clinical encounters, and 1.5% through recruitment flyers. Women from the different recruitment sources varied by age and 5-year invasive breast cancer risk. Of 300 enrolled high-risk women, 44.7% came from clinical encounters and 27.3% from prior study cohort. Comparing enrolled with not-enrolled participants, there were significant differences in mean age (57.2 vs. 59.1 years), proportion of non-Whites (41.5% vs. 54.8%), and mean 5-year breast cancer risk (3.0% vs. 2.3%). We identified and successfully recruited diverse high-risk women from multiple sources. These strategies may be implemented in future breast cancer chemoprevention trials. PREVENTION RELEVANCE: We describe five strategies to identify and successfully recruit a large cohort of racially/ethnically diverse high-risk women from multiple sources to a randomized controlled trial evaluating interventions to increase chemoprevention uptake. Findings could inform recruitment efforts for future breast cancer prevention trials to increase recruitment yield of high-risk women.

Extraction of Electronic Health Record Data using Fast Healthcare Interoperability Resources for Automated Breast Cancer Risk Assessment.

Women at high risk for breast cancer may benefit from enhanced screening and risk-reduction strategies. However, limited time during clinical encounters is one barrier to routine breast cancer risk assessment. We evaluated if electronic health record (EHR) data downloaded using Fast Healthcare Interoperability Resources (FHIR) is sufficient for breast cancer risk calculation in our decision support tools, RealRisks and BNAV. We accessed EHR data using FHIR for six patient advocates, and downloaded and parsed XML documents. We searched for relevant clinical variables, and evaluated if data was sufficient to calculate risk using validated models (Gail, Breast Cancer Screening Consortium [BCSC], BRCAPRO). While only one advocate had sufficient EHR data to calculate risk using the BCSC model only, we identified variables including age, race/ethnicity, mammographic density, and prior breast biopsy in most advocates. EHR data from FHIR could be incorporated into automated breast cancer risk calculation in clinical decision support tools.

Study protocol: Randomized controlled trial of web-based decision support tools for high-risk women and healthcare providers to increase breast cancer chemoprevention.

BACKGROUND: Chemoprevention using selective estrogen receptor modulators and aromatase inhibitors has been shown to reduce invasive breast cancer incidence in high-risk women. Despite this evidence, few high-risk women who are eligible for chemoprevention utilize it as a risk-reducing strategy. Reasons for low uptake include inadequate knowledge about chemoprevention among patients and healthcare providers, concerns about side effects, time constraints during the clinical encounter, and competing comorbidities. METHODS/DESIGN: We describe the study design of a randomized controlled trial examining the effect of two web-based decision support tools on chemoprevention decision antecedents and quality, referral for specialized counseling, and chemoprevention uptake among women at an increased risk for breast cancer. The trial is being conducted at a large, urban medical center. A total of 300 patients and 50 healthcare providers will be recruited and randomized to standard educational materials alone or in combination with the decision support tools. Patient reported outcomes will be assessed at baseline, one and six months after randomization, and after their clinic visit with their healthcare provider. DISCUSSION: We are conducting this trial to provide evidence on how best to support personalized breast cancer risk assessment and informed and shared decision-making for chemoprevention. We propose to integrate the decision support tools into clinical workflow, which can potentially expand quality decision-making and chemoprevention uptake. TRIAL REGISTRATION: NCT03069742.

An Applied Framework in Support of Shared Decision Making about BRCA Genetic Testing.

The United States Preventive Services Taskforce recommends that primary care providers screen patients for an increased risk of carrying a BRCA1 or BRCA2 mutation and refer those who meet family history criteria to genetic counseling. Such screening requires detailed and accurate family history data, which often goes uncollected during a primary care visit due to time constraints, competing priorities, and lack of awareness on behalf of both patients and providers. In order to address these barriers and promote appropriate genetic counseling referral, we developed a user-centered framework that collects and communicates relevant data in order to prepare patients and their primary care providers for an informed discussion on genetic counseling referral. This paper describes this framework and the underlining data schema that makes it possible.

Usability Testing of a Web-Based Decision Aid for Breast Cancer Risk Assessment Among Multi-Ethnic Women.

Chemoprevention with antiestrogens could decrease the incidence of invasive breast cancer but uptake has been low among high-risk women in the United States. We have designed a web-based patient-facing decision aid, called RealRisks, to inform high-risk women about the risks and benefits of chemoprevention and facilitate shared decision-making with their primary care provider. We conducted two rounds of usability testing to determine how subjects engaged with and understood the information in RealRisks. A total of 7 English-speaking and 4 Spanish-speaking subjects completed testing. Using surveys, think-aloud protocols, and subject recordings, we identified several themes relating to the usability of RealRisks, specifically in the content, ease of use, and navigability of the application. By conducting studies in two languages with a diverse multi-ethnic population, we were able to implement interface changes to make RealRisks accessible to users with varying health literacy and acculturation.

Barriers and Facilitators to Patient-Provider Communication When Discussing Breast Cancer Risk to Aid in the Development of Decision Support Tools.

The purpose of this study was to identify barriers and facilitators to patient-provider communication when discussing breast cancer risk to aid in the development of decision support tools. Four patient focus groups (N=34) and eight provider focus groups (N=10) took place in Northern Manhattan. A qualitative analysis was conducted using Atlas.ti software. The coding yielded 62.3%-94.5% agreement. The results showed that 1) barriers are time constraints, lack of knowledge, low health literacy, and language barriers, and 2) facilitators are information needs, desire for personalization, and autonomy when communicating risk in patient-provider encounters. These results will inform the development of a patient-centered decision aid (RealRisks) and a provider-facing breast cancer risk navigation (BNAV) tool, which are designed to facilitate patient-provider risk communication and shared decision-making about breast cancer prevention strategies, such as chemoprevention.