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

Video Education Is an Acceptable Alternative to Pretest Genetic Counseling for Patients With Breast, Ovarian, Pancreatic, and Metastatic Prostate Cancer: Results From a Randomized Study

PURPOSE

With increased demand for cancer genetic testing (GT), providers are exploring alternative service delivery models such as video education (VE). We compare the uptake of GT among 250 patients with breast, ovarian, pancreatic, or metastatic prostate cancer randomly assigned to receive either pretest VE or a pretest visit with a genetic counselor (GC).

MATERIALS AND METHODS

Using a 3:1 ratio, 187 patients were randomly assigned to the VE arm and 63 patients to the GC arm. GT was arranged after participants either watched an informative video (VE arm) or met with a GC (GC arm). Satisfaction, knowledge, distress, decisional regret, and family communication were assessed as secondary study end points.

RESULTS

Participants were age 39-88 years with no significant demographic differences between the two arms. In the VE arm, 170 (90.95%) participants completed GT versus 49 (77.8%) in the GC arm (P = .01). The dropout rate before the pretest visit was higher in the GC arm compared with the VE arm: 10 (15.9%) versus 9 (4.8%). In the GC arm, 97.4% of participants felt all questions and concerns had been addressed compared with 66.9% of the VE arm (P < .0001). Of the 219 participants tested, 29 (13.2%) had a pathogenic or likely pathogenic variant.

CONCLUSION

In this study, there was high acceptance of VE and it led to better GT uptake compared with the GC arm. However, it will be important for programs using VE to build-in more opportunities for patients to ask questions. Pretest VE is a viable option for patients with cancer who need their germline genetic test results to help guide surgical and medical decisions.

Social vulnerability and genetic service utilization among unaffected BRIDGE trial patients with inherited cancer susceptibility

BACKGROUND

Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility.

METHODS

We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met.

RESULTS

Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66-0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63-0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition.

CONCLUSIONS

These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists.

TRIAL REGISTRATION

BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov.

Chatbots for breast cancer education: a systematic review and meta-analysis

BACKGROUND

Effective education and awareness regarding breast cancer are critical. Traditional educational methods often fail to meet the diverse information needs of patients. Patients should be provided with tailored, accessible information to improve their retention and understanding of disease-related information.

PURPOSE

This systematic review and meta-analysis evaluated the effectiveness of chatbots for providing breast cancer education. By examining patient satisfaction with and the usability and efficacy of chatbot interventions, this study seeks to support the integration of chatbot technology into cancer education.

METHODS

This review, which was conducted in accordance with PRISMA guidelines, included studies from MEDLINE, Embase, and the Cochrane Library up to May 2024. The main inclusion criterion was chatbot interventions for breast cancer education. Meta-analysis was performed using Review Manager and Open Meta-Analyst software.

RESULTS

Of the 208 articles initially identified, 6 studies met the inclusion criteria, involving a total of 1342 women with early-stage or at-risk hereditary breast cancer. The meta-analysis revealed that most participants (85 to 99%) reported high satisfaction with chatbot interventions for breast cancer education, with no significant differences in satisfaction compared to genetic counselors or physicians. The chatbot interventions also showed positive effects on knowledge acquisition (mean proportion = 90.8%) and alleviated patients' symptoms significantly more than routine care.

CONCLUSION

This study demonstrated that chatbots can effectively provide personalized and interactive educational support, enhancing patients' understanding and retention of disease-related information. The integration of chatbot technology into educational programs can empower patients, ultimately promoting breast cancer awareness and prevention.

Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices

BACKGROUND

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies such as cancer screening and tobacco treatment, which are currently under-utilised. Polygenic risk scores (PRSs) may further motivate health behaviour change in primary care for lung cancer in diverse populations. In this work, we introduce the GREAT care paradigm, which integrates PRSs within comprehensive patient risk profiles to motivate positive health behaviour changes.

METHODS

We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardised PRS distributions across all ancestries. We validated our PRSs in 561,776 individuals of diverse ancestry from the GISC Trial, UK Biobank (UKBB), and All of Us Research Program (AoU).

FINDINGS

Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58-2.18) in UKBB and 2.39 (95% CI: 1.93-2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32-1.41) in UKBB and 1.32 (95% CI: 1.28-1.36) in AoU.

INTERPRETATION

Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations, which will be evaluated in two cluster-randomised clinical trials. This approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

FUNDING

National Institutes of Health, NIH Intramural Research Program, National Science Foundation.

Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method Using the GARDE Platform and The Utah Population Database

PURPOSE

The GARDE platform uses family history reported in the electronic health record (EHR) to systematically identify eligible patients for genetic testing for hereditary cancer syndromes. The goal of this study was to evaluate the change in effectiveness of GARDE to identify eligible individuals when more comprehensive family history data are provided, thus quantifying the impact of underdocumentation.

METHODS

A cohort of 133,764 patients at the University of Utah Health was analyzed with GARDE comparing identification rates using EHR data versus EHR plus data from a statewide population database, the Utah Population Database (UPDB).

RESULTS

Compared with EHR alone, EHR + UPDB increased the rate of individuals eligible for genetic testing from 4.1% to 9.2%. In the 44,692 individuals with the most comprehensive family history, eligibility more than quadrupled from 4.6% (EHR alone) to 19.3% (EHR + UPDB). The increase was significant across all demographics, but disparities still remained for historically marginalized minorities (9.2%-13.9% in non-White races compared with 19.7% in White races).

CONCLUSION

Augmenting EHR data with family history data from the UPDB substantially improved the detection of individuals eligible for genetic testing of hereditary cancer syndromes in all subgroups. This underscores the importance of improving methods for acquiring family history, in person or in silico. However, these increases did not ameliorate disparities. Continuous disparities are unlikely to be explained by incomplete family history alone and may also be because susceptibility genes, risk variants, and screening guidelines were discovered and developed largely in White races. Addressing disparities will require intentional data collection of family history in historically marginalized minorities and the promotion of genetic and risk assessment studies in more diverse populations to ensure equity and health care.

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.

Genetics and Primary Care: Raising Awareness and Enhancing Cooperation

The rapid evolution of the field of genetics in the past several years has opened new opportunities for diagnosis of treatment of genetic disorders. However, the limited availability of medical geneticists has led to difficulty in meeting this evolving need. Integrating awareness of genetic disorders and genetic screening into primary care may facilitate early diagnosis, while strategic support and cooperative care between primary care physicians and geneticists can improve long term management.

Precision public health in the era of genomics and big data

Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level-thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for 'precision' to enhance traditional public health approaches.

Mapping inter-professional collaboration in oncogenetics: Results from a scoping review

Inter-professional collaboration could improve timely access and quality of oncogenetic services. Here, we present the results of a scoping review conducted to systematically identify collaborative models available, unpack the nature and extent of collaboration proposed, synthesize evidence on their implementation and evaluation, and identify areas where additional research is needed. A comprehensive search was conducted in four journal indexing databases on June 13th, 2022, and complemented with searches of the grey literature and citations. Screening was conducted by two independent reviewers. Eligible documents included those describing either the theory of change, planning, implementation and/or evaluation of collaborative oncogenetic models. 165 publications were identified, describing 136 unique interventions/studies on oncogenetic models with somewhat overlapping collaborative features. Collaboration appears to be mostly inter-professional in nature, often taking place during risk assessment and pre-testing genetic counseling. Yet, most publications provide very limited information on their collaborative features, and only a few studies have set out to formally evaluate them. Better quality research is needed to comprehensively examine and make conclusions regarding the value of collaboration in this oncogenetics. We propose a definition, logic model, and typology of collaborative oncogenetic models to strengthen future planning, implementation, and evaluation in this field.

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

Background

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations.

Methods

We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry.

Results

Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU.

Conclusion

Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

Models of care and the advanced practice nurse role in caring for children and adolescents with a cancer predisposition syndrome: a scoping review protocol

OBJECTIVE

This scoping review will examine the literature describing models of care, barriers and facilitators of care, and gaps in care delivery for children and adolescents with a cancer predisposition syndrome (CPS). It will also explore how advanced practice nurses contribute to the delivery of care for children and adolescents with a CPS.

INTRODUCTION

Cancer remains a leading cause of death in children and adolescents. Pediatric CPS clinics proactively aim for early diagnosis or prevention of cancer in children and adolescents with a CPS. Additionally, the holistic well-being of individuals requires a multidisciplinary team, including advanced practice nurses, to manage their complex health care needs.

INCLUSION CRITERIA

This review will consider both published and unpublished literature exploring aspects of models of care and the role of the nurse in pediatric CPS clinics. Literature published in English from 1991 onward will be considered.

METHODS

This scoping review will follow the JBI methodology for scoping reviews. The review will include searches in MEDLINE, Embase, and CINAHL Complete. Gray literature searches will be conducted in OAIster and Social Science Research Network, as well as websites of hospitals in the USA and the UK with large pediatric cancer centers. Two reviewers will screen titles, abstracts, and full-text articles. An extraction table will be used to extract relevant data from all included articles and facilitate data analysis. Results will be presented in narrative and tabular format.

REVIEW REGISTRATION

Open Science Framework osf.io/axkp7/.

Randomized trial promoting cancer genetic risk assessment when genetic counseling cost removed: 1-year follow-up

PURPOSE

Cancer genetic risk assessment (CGRA) is recommended for women with ovarian and high-risk breast cancer. However, the underutilization of CGRA has long been documented, and cost has been a major barrier. In this randomized controlled trial, a tailored counseling and navigation (TCN) intervention significantly improved CGRA uptake at 6-month follow-up, compared with targeted print (TP) and usual care (UC). We aimed to examine the effect of removing genetic counseling costs on CGRA uptake by 12 months.

METHODS

We recruited racially and geographically diverse women with breast and ovarian cancer from cancer registries in Colorado, New Jersey, and New Mexico. Participants assigned to TCN received telephone-based psychoeducation and navigation. After 6 months, the trial provided free genetic counseling to participants in all arms.

RESULTS

At 12 months, more women in TCN obtained CGRA (26.6%) than those in TP (11.0%; odds ratio [OR] = 2.77, 95% confidence interval [CI] = 1.56 to 4.89) and UC (12.2%; OR = 2.46, 95% CI = 1.41 to 4.29). There were no significant differences in CGRA uptake between TP and UC. The Kaplan-Meier curve shows that the divergence of cumulative incidence slopes (TCN vs UC, TCN vs TP) appears primarily within the initial 6 months.

CONCLUSION

TCN significantly increased CGRA uptake at the 12-month follow-up. Directly removing the costs of genetic counseling attenuated the effects of TCN, highlighting the critical enabling role played by cost coverage. Future policies and interventions should address multilevel cost-related barriers to expand patients' access to CGRA.

TRIAL REGISTRATION

This trial was registered with the NIH clinical trial registry, clinicaltrials.gov, NCT03326713. https://clinicaltrials.gov/ct2/show/NCT03326713.

Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices

INTRODUCTION

This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers.

OBJECTIVES

By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings.

MATERIALS AND METHODS

Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements.

RESULTS

We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice.

DISCUSSION AND CONCLUSIONS

We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings.

Systematic evidence review and meta-analysis of outcomes associated with cancer genetic counseling

PURPOSE

Genetic counseling (GC) is standard of care in genetic cancer risk assessment (GCRA). A rigorous assessment of the data reported from published studies is crucial to ensure the evidence-based implementation of GC.

METHODS

We conducted a systematic review and meta-analysis of 17 patient-reported and health-services-related outcomes associated with pre- and post-test GC in GCRA in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.

RESULTS

Twenty-five of 5393 screened articles met inclusion criteria. No articles reporting post-test GC outcomes met inclusion criteria. For patient-reported outcomes, pre-test GC significantly decreased worry, increased knowledge, and decreased perceived risk but did not significantly affect patient anxiety, depression, decisional conflict, satisfaction, or intent to pursue genetic testing. For health-services outcomes, pre-test GC increased correct genetic test ordering, reduced inappropriate services, increased spousal support for genetic testing, and expedited care delivery but did not consistently improve cancer prevention behaviors nor lead to accurate risk assessment. The GRADE certainty in the evidence was very low or low. No included studies elucidated GC effect on mortality, cascade testing, cost-effectiveness, care coordination, shared decision making, or patient time burden.

CONCLUSION

The true impact of GC on relevant outcomes is not known low quality or absent evidence. Although a meta-analysis found that pre-test GC had beneficial effects on knowledge, worry, and risk perception, the certainty of this evidence was low according to GRADE methodology. Further studies are needed to support the evidence-based application of GC in GCRA.

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.

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.

The role of virtual consultations in cancer genetics: challenges and opportunities introduced by the COVID-19 pandemic

The COVID-19 pandemic changed the delivery of healthcare within the United Kingdom. A virtual model of care, utilising telephone and video consultations, was rapidly imposed upon cancer genetics teams. This large-scale change in service delivery has led to new opportunities that can be harnessed to improve patient care. There is a clear potential to mitigate geographical barriers, meet increasing patient expectations of implementing virtual consultations, reduce hospital carbon footprints, and decrease hospital costs while increasing efficiency. However, there are also significant challenges introduced by this model of care. Virtual healthcare consultations introduce another new level of digital exclusion for patients and clinicians. There are also potential challenges for maintaining patient confidentiality, and limited utility in circumstances where a physical exam may be warranted. For clinicians, there may be impacts on empathetic responses delivered and challenges in workflow and workload. Virtual consultations are likely to continue being a feature of cancer genetics services. A flexible approach is needed to allow for virtual and traditional models of care to work together and best meet patients' needs. Cancer genetics services should harness the opportunities provided by virtual processes to improve patient care, whilst collaborating with patient groups and other stakeholders to carefully examine and address the challenges that virtual consultations introduce.

The Use of Chatbots in Oncological Care: A Narrative Review

BACKGROUND

Few reports have investigated chatbots in patient care. We aimed to assess the current applications, limitations, and challenges in the literature on chatbots employed in oncological care.

METHODS

We queried the PubMed database through April 2022 and included studies that investigated the use of chatbots in different phases of oncological care. The search used five different combinations of the specific terms "chatbot", "cancer", "oncology", and "conversational agent". Inclusion criteria were chatbot use in any aspect of oncological care-prevention, patient education, treatment, and surveillance.

RESULTS

The initial search yielded 196 records, 21 of which met inclusion criteria. The identified chatbots mostly focused on breast and ovarian cancer (n=8), with the second most common being cervical cancer (n=3). Good patient satisfaction was reported among 14 of 21 chatbots. The most reported chatbot applications were cancer screening, prevention, risk stratification, treatment, monitoring, and management. Of 12 studies examining efficacy of care via chatbot, 9 demonstrated improvements compared to standard care.

CONCLUSION

Chatbots used for oncological care to date demonstrate high user satisfaction, and many have shown efficacy in improving patient-centered communication, accessibility to cancer-related information, and access to care. Currently, chatbots are primarily limited by the need for extensive user-testing and iterative improvement before widespread implementation.

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.

Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach

BACKGROUND

Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive.

OBJECTIVE

The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP.

METHODS

Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR.

RESULTS

Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria.

CONCLUSIONS

Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.

Helping Patients Understand and Cope with BRCA Mutations

Purpose of Review

Individuals carrying germline mutations in BRCA1/2 have unique psychosocial and educational needs that must be met to ensure informed clinical decision-making. In this review, we highlight the strategies used in clinical practice to support patients’ needs as well as currently available pre- and post-disclosure support interventions.

Recent Findings

Clinical risk communication is complicated by the uncertainty associated with gene penetrance, inconclusive results, variable effectiveness of surgical and screening interventions, and inadequate awareness of clinical genetics. Interventions to support patients’ psychosocial needs, and strategies for effective and scalable clinical risk communication are in routine use and largely effective at meeting patients’ needs. Research is underway to develop newer supportive resources; however, the inadequate representation of all mutation carriers persists.

Summary

Effective clinical risk communication strategies, decision support aids, written educational materials, and supportive psychosocial tools can together have a large impact on meeting BRCA carriers’ supportive needs.

OUP accepted manuscript

 

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.