Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Crew KD

PROVIDER: S-EPMC6722284 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Crew Katherine D KD Silverman Thomas B TB Vanegas Alejandro A Trivedi Meghna S MS Dimond Jill J Mata Jennie J Sin Margaret M Jones Tarsha T Terry Mary Beth MB Tsai Wei-Yann WY Kukafka Rita R

Contemporary clinical trials communications 20190822

<h4>Background</h4>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 c ...[more]

PMID: 31497674

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Project description:BACKGROUND:Breast cancer chemoprevention can reduce breast cancer incidence in high-risk women; however, chemoprevention is underutilized in the primary care setting. We conducted a pilot study of decision support tools among high-risk women and their primary care providers (PCPs). METHODS:The intervention included a decision aid (DA) for high-risk women, RealRisks, and a provider-centered tool, Breast Cancer Risk Navigation (BNAV). Patients completed validated surveys at baseline, after RealRisks and after their PCP clinical encounter or at 6-months. Referral for high-risk consultation and chemoprevention uptake were assessed via the electronic health record. The primary endpoint was accuracy of breast cancer risk perception at 6-months. RESULTS:Among 40 evaluable high-risk women, median age was 64.5?years and median 5-year breast cancer risk was 2.19%. After exposure to RealRisks, patients demonstrated an improvement in accurate breast cancer risk perceptions (p?=?0.02), an increase in chemoprevention knowledge (p?<?0.01), and 24% expressed interest in taking chemoprevention. Three women had a high-risk referral, and no one initiated chemoprevention. Decisional conflict significantly increased from after exposure to RealRisks to after their clinical encounter or at 6-months (p?<?0.01). Accurate breast cancer risk perceptions improved and was sustained at 6-months or after clinical encounters. We discuss the side effect profile of chemoprevention and the care pathway when RealRisks was introduced to understand why patients experienced increased decision conflict. CONCLUSION:Future interventions should carefully link the use of a DA more proximally to the clinical encounter, investigate timed measurements of decision conflict and improve risk communication, shared decision making, and chemoprevention education for PCPs. Additional work remains to better understand the impact of decision aids targeting both patients and providers. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02954900 November 4, 2016 Retrospectively registered.

Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Project description: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 relevanceIn 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.

Project description: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 relevanceWe 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.

Project description:BACKGROUND:Decisional tools have demonstrated their importance in informing manufacturing and commercial decisions in the monoclonal antibody domain. Recent approved therapies in regenerative medicine have shown great clinical benefits to patients. OBJECTIVE:The objective of this review was to investigate what decisional tools are available and what issues and gaps have been raised for their use in regenerative medicine. METHODS:We systematically searched MEDLINE to identify articles on decision support tools relevant to tissue engineering, and cell and gene therapy, with the aim of identifying gaps for future decisional tool development. We included published studies in English including a description of decisional tools in regenerative medicines. We extracted data using a predesigned Excel table and assessed the data both quantitatively and qualitatively. RESULTS:We identified 9 articles addressing key decisions in manufacturing and product development challenges in cell therapies. The decision objectives, parameters, assumptions, and solution methods were analyzed in detail. We found that all decisional tools focused on cell therapies, and 6 of the 9 reviews focused on allogeneic cell therapy products. We identified no available tools on tissue-engineering and gene therapy products. These studies addressed key decisions in manufacturing and product development challenges in cell therapies, such as choice of technology, through modeling. CONCLUSIONS:Our review identified a limited number of decisional tools. While the monoclonal antibodies and biologics decisional tool domain has been well developed and has shown great importance in driving more cost-effective manufacturing processes and better investment decisions, there is a lot to be learned in the regenerative medicine domain. There is ample space for expansion, especially with regard to autologous cell therapies, tissue engineering, and gene therapies. To consider the problem more comprehensively, the full needle-to-needle process should be modeled and evaluated.

Dataset Information

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

Background

Methods/design

Discussion

Trial registration

Publications

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

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