Determinants of Breast Cancer Screening Adherence During the COVID-19 Pandemic in a Cohort at Increased Inherited Cancer Risk in the United States.

BACKGROUND: We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. METHODS: We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. RESULTS: Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. CONCLUSION: Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer.

Breast Cancer Screening Adherence in the US During COVID-19: We examined predictors of breast cancer screening adherence during COVID-19 at two large healthcare systems. Adherence was associated with older age, having more relatives with a cancer history, and living in areas with less educational attainment. Nonadherence was associated with greater neighborhood deprivation.

Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own.

OBJECTIVE: This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs); and 2) assess their impact on relative differences across sex, race, ethnicity, and language preference. MATERIALS AND METHODS: The study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data; SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language preference. RESULTS: Among 120,007 patients aged 25-60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates; NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is English. CONCLUSION: Algorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.

Comparing models of delivery for cancer genetics services among patients receiving primary care who meet criteria for genetic evaluation in two healthcare systems: BRIDGE randomized controlled trial.

BACKGROUND: Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential. METHODS: We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling. DISCUSSION: BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients. TRIAL REGISTRATION: BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .

Patient Interactions With an Automated Conversational Agent Delivering Pretest Genetics Education: Descriptive Study.

BACKGROUND: Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited. OBJECTIVE: Our primary aim is to assess user interactions with a conversational agent for pretest genetics education. METHODS: We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses. RESULTS: We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question. CONCLUSIONS: The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.

Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial.

Importance: Increasing numbers of unaffected individuals could benefit from genetic evaluation for inherited cancer susceptibility. Automated conversational agents (ie, chatbots) are being developed for cancer genetics contexts; however, randomized comparisons with standard of care (SOC) are needed. Objective: To examine whether chatbot and SOC approaches are equivalent in completion of pretest cancer genetic services and genetic testing. Design, Setting, and Participants: This equivalence trial (Broadening the Reach, Impact, and Delivery of Genetic Services [BRIDGE] randomized clinical trial) was conducted between August 15, 2020, and August 31, 2023, at 2 US health care systems (University of Utah Health and NYU Langone Health). Participants were aged 25 to 60 years, had had a primary care visit in the previous 3 years, were eligible for cancer genetic evaluation, were English or Spanish speaking, had no prior cancer diagnosis other than nonmelanoma skin cancer, had no prior cancer genetic counseling or testing, and had an electronic patient portal account. Intervention: Participants were randomized 1:1 at the patient level to the study groups at each site. In the chatbot intervention group, patients were invited in a patient portal outreach message to complete a pretest genetics education chat. In the enhanced SOC control group, patients were invited to complete an SOC pretest appointment with a certified genetic counselor. Main Outcomes and Measures: Primary outcomes were completion of pretest cancer genetic services (ie, pretest genetics education chat or pretest genetic counseling appointment) and completion of genetic testing. Equivalence hypothesis testing was used to compare the study groups. Results: This study included 3073 patients (1554 in the chatbot group and 1519 in the enhanced SOC control group). Their mean (SD) age at outreach was 43.8 (9.9) years, and most (2233 of 3063 [72.9%]) were women. A total of 204 patients (7.3%) were Black, 317 (11.4%) were Latinx, and 2094 (75.0%) were White. The estimated percentage point difference for completion of pretest cancer genetic services between groups was 2.0 (95% CI, -1.1 to 5.0). The estimated percentage point difference for completion of genetic testing was -1.3 (95% CI, -3.7 to 1.1). Analyses suggested equivalence in the primary outcomes. Conclusions and Relevance: The findings of the BRIDGE equivalence trial support the use of chatbot approaches to offer cancer genetic services. Chatbot tools can be a key component of sustainable and scalable population health management strategies to enhance access to cancer genetic services. Trial Registration: ClinicalTrials.gov Identifier: NCT03985852.

Barriers to family history collection among Spanish-speaking primary care patients: a BRIDGE qualitative study.

Objectives: Family history is an important tool for assessing disease risk, and tailoring recommendations for screening and genetic services referral. This study explored barriers to family history collection with Spanish-speaking patients. Methods: This qualitative study was conducted in two US healthcare systems. We conducted semi-structured interviews with medical assistants, physicians, and interpreters with experience collecting family history for Spanish-speaking patients. Results: The most common patient-level barrier was the perception that some Spanish-speaking patients had limited knowledge of family history. Interpersonal communication barriers related to dialectical differences and decisions about using formal interpreters vs. Spanish-speaking staff. Organizational barriers included time pressures related to using interpreters, and ad hoc workflow adaptations for Spanish-speaking patients that might leave gaps in family history collection. Conclusions: This study identified multi-level barriers to family history collection with Spanish-speaking patients in primary care. Findings suggest that a key priority to enhance communication would be to standardize processes for working with interpreters. Innovation: To improve communication with and care provided to Spanish-speaking patients, there is a need to increase healthcare provider awareness about implicit bias, to address ad hoc workflow adjustments within practice settings, to evaluate the need for professional interpreter services, and to improve digital tools to facilitate family history collection.

Association of Disparities in Family History and Family Cancer History in the Electronic Health Record With Sex, Race, Hispanic or Latino Ethnicity, and Language Preference in 2 Large US Health Care Systems.

Importance: Clinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms. Objective: To examine the availability and comprehensiveness of cancer family history information (FHI) in patients' EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021. Design, Setting, and Participants: This retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021. Exposures: Prior collection of cancer FHI in primary care settings. Main Outcomes and Measures: Availability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level. Results: Among 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups-specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])-had significantly lower availability and comprehensiveness of cancer FHI (P < .001). Conclusions and Relevance: These findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.

GARDE: a standards-based clinical decision support platform for identifying population health management cohorts.

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

Circulating T cell subsets are altered in individuals with chronic spinal cord injury.

Traumatic spinal cord injury (SCI) induces changes in the immune system, both acutely and chronically. To better understand changes in the chronic phase of SCI, we performed a prospective, observational study in a research institute and Department of Physical Medicine and Rehabilitation of an academic medical center to examine immune system parameters, including peripheral immune cell populations, in individuals with chronic SCI as compared to uninjured individuals. Here, we describe the relative frequencies of T cell populations in individuals with chronic SCI as compared to uninjured individuals. We show that the frequency of CD3+ and CD3+ CD4+ T cells are decreased in individuals with chronic SCI, although activated (HLA-DR+) CD4+ T cells are elevated in chronic SCI. We also examined regulatory T cells (Tregs), defined as CD3+ CD4+ CD25+ CD127lo and CCR4+, HLA-DR+ or CCR4+ HLA-DR+. To our knowledge, we provide the first evidence that CCR4+, HLA-DR+ or CCR4+ HLA-DR+ Tregs are expanded in individuals with SCI. These data support additional functional studies of T cells isolated from individuals with chronic SCI, where alterations in T cell homeostasis may contribute to immune dysfunction, such as immunity against infections or the persistence of chronic inflammation.