The Rise of Population Genomic Screening: Characteristics of Current Programs and the Need for Evidence Regarding Optimal Implementation

Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: a Prompt Engineering Study

Background

The growing demand for genomic testing and limited access to experts necessitate innovative service models. While chatbots have shown promise in supporting genomic services like pre-test counseling, their use in returning positive genetic results, especially using the more recent large language models (LLMs) remains unexplored.

Objective

This study reports the prompt engineering process and intrinsic evaluation of the LLM component of a chatbot designed to support returning positive population-wide genomic screening results.

Methods

We used a three-step prompt engineering process, including Retrieval-Augmented Generation (RAG) and few-shot techniques to develop an open-response chatbot. This was then evaluated using two hypothetical scenarios, with experts rating its performance using a 5-point Likert scale across eight criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability.

Results

The chatbot achieved an overall score of 3.88 out of 5 across all criteria and scenarios. The highest ratings were in Tone (4.25), Usability (4.25), and Boundary management (4.0), followed by Efficiency (3.88), Clarity and Robustness (3.81), and Domain Accuracy (3.63). The lowest-rated criterion was Program Accuracy, which scored 3.25.

Discussion

The LLM handled open-ended queries and maintained boundaries, while the lower Program Accuracy rating indicates areas for improvement. Future work will focus on refining prompts, expanding evaluations, and exploring optimal hybrid chatbot designs that integrate LLM components with rule-based chatbot components to enhance genomic service delivery.

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.

The Evolution of Genetic Testing from Focused Testing to Panel Testing and from Patient Focused to Population Testing: Are We There Yet?

The field of cancer genetics has evolved significantly over the past 30 years. Genetic testing has become less expensive and more comprehensive which has changed practice patterns. It is no longer necessary to restrict testing to those with the highest likelihood of testing positive. In addition, we have learned that the criteria developed to determine who has the highest likelihood of testing positive are neither sensitive nor specific. As a result, the field is moving from testing only the highest risk patients identified based on testing criteria to testing all cancer patients. This requires new service delivery models where testing can be mainstreamed into oncology clinics and posttest genetic counseling can be provided to individuals who test positive and those with concerning personal or family histories who test negative. The use of videos, testing kiosks, chatbots, and genetic counseling assistants have been employed to help facilitate testing at a larger scale and have good patient uptake and satisfaction. While testing is important for cancer patients as it may impact their treatment, future cancer risks, and family member's cancer risks, it is unfortunate that their cancer could not be prevented in the first place. Population testing for all adults would be a strategy to identify individuals with adult-onset diseases before they develop cancer in an attempt to prevent it entirely. A few research studies (Healthy Nevada and MyCode) have offered population testing for the three Centers for Disease Control and Prevention Tier 1 conditions: hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia finding a prevalence of 1 in 70 individuals in the general population. We anticipate that testing for all cancer patients and the general population will continue to increase over the next 20 years and the genetics community needs to help lead the way to ensure this happens in a responsible manner.

Clinical Strategies in Gene Screening Counseling for the Healthy General Population

The burgeoning interest in precision medicine has propelled an increase in the use of genome tests for screening purposes within the healthy population. Gene screening tests aim to pre-emptively identify those individuals who may be genetically predisposed to certain diseases. However, as genetic screening becomes more commonplace, it is essential to acknowledge the unique challenges it poses. A prevalent issue in this regard is the occurrence of falsepositive results, which can lead to unnecessary additional tests or treatments, and psychological distress. Additionally, the interpretation of genomic variants is based on current research evidence, and can accordingly change as new research findings emerge, potentially altering the clinical significance of these variants. Conversely, a further prominent concern regards false assurances in genetic testing, as genetic tests can yield false-negative results, potentially posing a significant clinical risk. Moreover, the results obtained for the same disease can vary among different genetic testing services, due to differences in the types of variants assessed, the scope of tests, analytical methods, and the algorithms used for predicting diseases. Consequently, whereas genetic testing holds significant promise for the future of medicine, it poses unique challenges. If conducted without a full understanding of its implications, genetic testing may fail to achieve its purpose potentially hindering effective health management. Therefore, to ensure a comprehensive understanding of the implications of genetic testing within the general population, sufficient discussion and careful consideration should be given to counseling based on gene test results.

HerediGene Population Study IT infrastructure: A model to support genomic research recruitment and precision public health

The HerediGene Population Study is a large research study focused on identifying new genetic biomarkers for disease prevention, diagnosis, prognosis, and development of new therapeutics. A substantial IT infrastructure evolved to reach enrollment targets and return results to participants. More than 170,000 participants have been enrolled in the study to date, with 5.87% of those whole genome sequenced and 0.46% of those genotyped harboring pathogenic variants. Among other purposes, this infrastructure supports: (1) identifying candidates from clinical criteria, (2) monitoring for qualifying clinical events (e.g., blood draw), (3) contacting candidates, (4) obtaining consent electronically, (5) initiating lab orders, (6) integrating consent and lab orders into clinical workflow, (7) de-identifying samples and clinical data, (8) shipping/transmitting samples and clinical data, (9) genotyping/sequencing samples, (10) and re-identifying and returning results for participants where applicable. This study may serve as a model for similar genomic research and precision public health initiatives.

Evaluation of Malignant Hyperthermia Features in Patients with Pathogenic or Likely Pathogenic RYR1 Variants Disclosed through a Population Genomic Screening Program

BACKGROUND

Malignant hyperthermia (MH) susceptibility is a heritable musculoskeletal disorder that can present as a potentially fatal hypermetabolic response to triggering anesthesia agents. Genomic screening for variants in MH-associated genes RYR1 and CACNA1S provides an opportunity to prevent morbidity and mortality. There are limited outcomes data from disclosing variants in RYR1, the most common MH susceptibility gene, in unselected populations. The authors sought to identify the rate of MH features or fulminant episodes after triggering agent exposure in an unselected population undergoing genomic screening including actionable RYR1 variants.

METHODS

The MyCode Community Health Initiative by Geisinger (USA) is an electronic health record-linked biobank that discloses pathogenic and likely pathogenic variants in clinically actionable genes to patient-participants. Available electronic anesthesia and ambulatory records for participants with actionable RYR1 results returned through December 2020 were evaluated for pertinent findings via double-coded chart reviews and reconciliation. Descriptive statistics for observed phenotypes were calculated.

RESULTS

One hundred fifty-two participants had an actionable RYR1 variant disclosed during the study period. None had previous documented genetic testing for MH susceptibility; one had previous contracture testing diagnosing MH susceptibility. Sixty-eight participants (44.7%) had anesthesia records documenting triggering agent exposure during at least one procedure. None received dantrolene treatment or had documented muscle rigidity, myoglobinuria, hyperkalemia, elevated creatine kinase, severe myalgia, or tea-colored urine. Of 120 possibly MH-related findings (postoperative intensive care unit admissions, hyperthermia, arterial blood gas evaluation, hypercapnia, or tachycardia), 112 (93.3%) were deemed unlikely to be MH events; 8 (6.7%) had insufficient records to determine etiology.

CONCLUSIONS

Results demonstrate a low frequency of classic intraanesthetic hypermetabolic phenotypes in an unselected population with actionable RYR1 variants. Further research on the actionability of screening for MH susceptibility in unselected populations, including economic impact, predictors of MH episodes, and expanded clinical phenotypes, is necessary.

EDITOR’S PERSPECTIVE

Use of a multi-phased approach to identify and address facilitators and barriers to the implementation of a population-wide genomic screening program

INTRODUCTION

Population-wide genomic screening for CDC Tier-1 conditions offers the ability to identify the 1-2% of the US population at increased risk for Hereditary Breast and Ovarian Cancer, Lynch Syndrome, and Familial Hypercholesterolemia. Implementation of population-wide screening programs is highly complex, requiring engagement of diverse collaborators and implementation teams. Implementation science offers tools to promote integration of these programs through the identification of determinants of success and strategies to address potential barriers.

METHODS

Prior to launching the program, we conducted a pre-implementation survey to assess anticipated barriers and facilitators to reach, effectiveness, adoption, implementation, and maintenance (RE-AIM), among 51 work group members (phase 1). During the first year of program implementation, we completed coding of 40 work group meetings guided by the Consolidated Framework for Implementation Research (CFIR) (phase 2). We matched the top barriers to implementation strategies identified during phase 2 using the CFIR-ERIC (Expert Recommendation for Implementing Change) matching tool.

RESULTS

Staffing and workload concerns were listed as the top barrier in the pre-implementation phase of the program. Top barriers during implementation included adaptability (n = 8, 20%), complexity (n = 14, 35%), patient needs and resources (n = 9, 22.5%), compatibility (n = 11, 27.5%), and self-efficacy (n = 9, 22.5%). We identified 16 potential implementation strategies across six ERIC clusters to address these barriers and operationalized these strategies for our specific setting and program needs.

CONCLUSION

Our findings provide an example of successful use of the CFIR-ERIC tool to guide implementation of a population-wide genomic screening program.

Medical and psychosocial outcomes of state-funded population genomic screening

As the uptake of population screening expands, assessment of medical and psychosocial outcomes is needed. Through the Alabama Genomic Health Initiative (AGHI), a state-funded genomic research program, individuals received screening for pathogenic or likely pathogenic variants in 59 actionable genes via genotyping. Of the 3874 eligible participants that received screening results, 858 (22%) responded to an outcomes survey. The most commonly reported motivation for seeking testing through AGHI was contribution to genetic research (64%). Participants with positive results reported a higher median number of planned actions (median = 5) due to AGHI results as compared to negative results (median = 3). Interviews were conducted with survey participants with positive screening results. As determined by certified genetic counselors, 50% of interviewees took appropriate medical action based on their result. There were no negative or harmful actions taken. These findings indicate population genomic screening of an unselected adult population is feasible, is not harmful, and may have positive outcomes on participants now and in the future; however, further research is needed in order to assess clinical utility.

How Clinicians Conceptualize "Actionability" in Genomic Screening

Over the last decade, the concept of actionability has become a primary framework for assessing whether genetic data is useful and appropriate to return to patients. Despite the popularity of this concept, there is little consensus about what should count as "actionable" information. This is particularly true in population genomic screening, where there is considerable disagreement about what counts as good evidence and which clinical actions are appropriate for which patients. The pathway from scientific evidence to clinical action is not straightforward-it is as much social and political as it is scientific. This research explores the social dynamics shaping the integration of "actionable" genomic data into primary care settings. Based on semi-structured interviews with 35 genetics experts and primary care providers, we find that clinicians vary in how they define and operationalize "actionable" information. There are two main sources of disagreement. First, clinicians differ on the levels and types of evidence required for a result to be actionable, such as when we can be confident that genomic data provides accurate information. Second, there are disagreements about the clinical actions that must be available so that patients can benefit from that information. By highlighting the underlying values and assumptions embedded in discussions of actionability for genomic screening, we provide an empirical basis for building more nuanced policies regarding the actionability of genomic data in terms of population screening in primary care settings.

Investigating Psychological Impact after Receiving Genetic Risk Results-A Survey of Participants in a Population Genomic Screening Program

Genomic screening programs have potential to benefit individuals who may not be clinically ascertained, but little is known about the psychological impact of receiving genetic results in this setting. The current study sought to further the understanding of individuals’ psychological response to receiving an actionable genetic test result from genomic screening. Telephone surveys were conducted with patient-participants at 6 weeks and 6 months post genetic result disclosure between September 2019 and May 2021 and assessed emotional response to receiving results via the FACToR, PANAS, and decision regret scales. Overall, 354 (29.4%) study participants completed both surveys. Participants reported moderate positive emotions and low levels of negative emotions, uncertainty, privacy concern, and decision regret over time. There were significant decreases in negative emotions (p = 0.0004) and uncertainty (p = 0.0126) between time points on the FACToR scale. “Interested” was the highest scoring discrete emotion (T1 3.6, T2 3.3, scale 0−5) but was significantly lower at 6 months (<0.0001). Coupled with other benefits of genomic screening, these results of modest psychological impact waning over time adds support to clinical utility of population genomic screening programs. However, questions remain regarding how to elicit an emotional response that motivates behavior change without causing psychological harm.

From the patient to the population: Use of genomics for population screening

Genomic medicine is expanding from a focus on diagnosis at the patient level to prevention at the population level given the ongoing under-ascertainment of high-risk and actionable genetic conditions using current strategies, particularly hereditary breast and ovarian cancer (HBOC), Lynch Syndrome (LS) and familial hypercholesterolemia (FH). The availability of large-scale next-generation sequencing strategies and preventive options for these conditions makes it increasingly feasible to screen pre-symptomatic individuals through public health-based approaches, rather than restricting testing to high-risk groups. This raises anew, and with urgency, questions about the limits of screening as well as the moral authority and capacity to screen for genetic conditions at a population level. We aimed to answer some of these critical questions by using the WHO Wilson and Jungner criteria to guide a synthesis of current evidence on population genomic screening for HBOC, LS, and FH.

Lessons Learned from the Pilot Phase of a Population-Wide Genomic Screening Program: Building the Base to Reach a Diverse Cohort of 100,000 Participants

Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach can help identify the 1−2% of the US population at increased risk of developing diseases associated with these conditions and tailor prevention strategies. Our objective is to report findings from an implementation science study that evaluates multi-level facilitators and barriers to implementation of the In Our DNA SC population-wide genomic screening initiative. Methods: We established an IMPACTeam (IMPlementAtion sCience for In Our DNA SC Team) to evaluate the pilot phase using principles of implementation science. We used a parallel convergent mixed methods approach to assess the Reach, Implementation, and Effectiveness outcomes from the RE-AIM implementation science framework during the pilot phase of In Our DNA SC. Quantitative assessment included the examination of frequencies and response rates across demographic categories using chi-square tests. Qualitative data were audio-recorded and transcribed, with codes developed by the study team based on the semi-structured interview guide. Results: The pilot phase (8 November 2021, to 7 March 2022) included recruitment from ten clinics throughout South Carolina. Reach indicators included enrollment rate and representativeness. A total of 23,269 potential participants were contacted via Epic’s MyChart patient portal with 1976 (8.49%) enrolled. Black individuals were the least likely to view the program invitation (28.9%) and take study-related action. As a result, there were significantly higher enrollment rates among White (10.5%) participants than Asian (8.71%) and Black (3.46%) individuals (p < 0.0001). Common concerns limiting reach and participation included privacy and security of results and the impact participation would have on health or life insurance. Facilitators included family or personal history of a Tier 1 condition, prior involvement in genetic testing, self-interest, and altruism. Assessment of implementation (i.e., adherence to protocols/fidelity to protocols) included sample collection rate (n = 1104, 55.9%) and proportion of samples needing recollection (n = 19, 1.7%). There were no significant differences in sample collection based on demographic characteristics. Implementation facilitators included efficient collection processes and enthusiastic clinical staff. Finally, we assessed the effectiveness of the program, finding low dropout rates (n = 7, 0.35%), the identification of eight individuals with Tier 1 conditions (0.72% positive), and high rates of follow-up genetic counseling (87.5% completion). Conclusion: Overall, Asian and Black individuals were less engaged, with few taking any study-related actions. Strategies to identify barriers and promoters for the engagement of diverse populations are needed to support participation. Once enrolled, individuals had high rates of completing the study and follow-up engagement with genetic counselors. Findings from the pilot phase of In Our DNA SC offer opportunities for improvement as we expand the program and can provide guidance to organizations seeking to begin efforts to integrate population-wide genomic screening.