Precision public health is a relatively new field that integrates components of precision medicine, such as human genomics research, with public health concepts to help improve population health. Despite interest in advancing precision public health initiatives using human genomics research, current and future opportunities in this emerging field remain largely undescribed. To that end, we provide examples of promising opportunities and current applications of genomics research within precision public health and outline future directions within five major domains of public health: biostatistics, environmental health, epidemiology, health policy and health services, and social and behavioral science. To further extend applications of genomics within precision public health research, three key cross-cutting challenges will need to be addressed: developing policies that implement precision public health initiatives at multiple levels, improving data integration and developing more rigorous methodologies, and incorporating initiatives that address health equity. Realizing the potential to better integrate human genomics within precision public health will require transdisciplinary efforts that leverage the strengths of both precision medicine and public health.

The increasing demand for population-wide genomic screening and the limited availability of genetic counseling resources have created a pressing need for innovative service delivery models. Chatbots powered by large language models (LLMs) have shown potential in genomic services, particularly in pretest counseling, but their application in returning positive population-wide genomic screening results remains underexplored. Leveraging advanced LLMs like GPT-4 offers an opportunity to address this gap by delivering accurate, contextual, and user-centered communication to individuals receiving positive genetic test results. This project aimed to design, implement, and evaluate a chatbot integrated with GPT-4, tailored to support the return of positive genomic screening results in the context of South Carolina’s In Our DNA SC program. This initiative offers free genetic screening to 100,000 individuals, with over 33,000 results returned and numerous positive findings for conditions such as Lynch syndrome, hereditary breast and ovarian cancer syndrome, and familial hypercholesterolemia. A 3-step prompt engineering process using retrieval-augmented generation and few-shot techniques was used to create the chatbot. Training materials included patient frequently asked questions, genetic counseling scripts, and patient-derived queries. The chatbot underwent iterative refinement based on 13 training questions, while performance was evaluated through expert ratings on responses to 2 hypothetical patient scenarios. The 2 scenarios were intended to represent common but distinct patient profiles in terms of gender, race, ethnicity, age, and background knowledge. Domain experts rated the chatbot using a 5-point Likert scale across 8 predefined criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability. The chatbot achieved an average score of 3.86 (SD 0.89) across all evaluation metrics. The highest-rated criteria were tone (mean 4.25, SD 0.71) and usability (mean 4.25, SD 0.58), reflecting the chatbot’s ability to communicate effectively and provide a seamless user experience. Boundary management (mean 4.0, SD 0.76) and efficiency (mean 3.88, SD 1.08) also scored well, while clarity and robustness received ratings of 3.81 (SD 1.05) and 3.81 (SD 0.66), respectively. Domain accuracy was rated 3.63 (SD 0.96), indicating satisfactory performance in delivering genetic information, whereas program accuracy received the lowest score of 3.25 (SD 1.39), highlighting the need for improvements in delivering program-specific details. This project demonstrates the feasibility of using LLM-powered chatbots to support the return of positive genomic screening results. The chatbot effectively handled open-ended patient queries, maintained conversational boundaries, and delivered user-friendly responses. However, enhancements in program-specific accuracy are essential to maximize its utility. Future research will explore hybrid chatbot designs that combine the strengths of LLMs with rule-based components to improve scalability, accuracy, and accessibility in genomic service delivery. The findings underscore the potential of generative artificial intelligence tools to address resource limitations and improve the accessibility of genomic health care services.

The purpose of this study was to examine the association between neighborhood social deprivation and individual-level characteristics on breast cancer staging in African American and white breast cancer patients. We established a retrospective cohort of patients with breast cancer diagnosed from 1996 to 2015 using the South Carolina Central Cancer Registry. We abstracted sociodemographic and clinical variables from the registry and linked these data to a county-level composite that captured neighborhood social conditions—the social deprivation index (SDI). Data were analyzed using chi-square tests, Student’s t-test, and multivariable ordinal regression analysis to evaluate associations. The study sample included 52,803 female patients with breast cancer. Results from the multivariable ordinal regression model demonstrate that higher SDI (OR = 1.06, 95% CI: 1.02–1.10), African American race (OR = 1.35, 95% CI: 1.29–1.41), and being unmarried (OR = 1.17, 95% CI: 1.13–1.22) were associated with a distant stage at diagnosis. Higher tumor grade, younger age, and more recent year of diagnosis were also associated with distant-stage diagnosis. As a proxy for neighborhood context, the SDI can be used by cancer registries and related population-based studies to identify geographic areas that could be prioritized for cancer prevention and control efforts.

The fields of implementation and improvement sciences have experienced rapid growth in recent years. However, research that seeks to inform health care change may have difficulty translating core components of implementation and improvement sciences within the traditional paradigms used to evaluate efficacy and effectiveness research. A review of implementation and improvement sciences grant proposals within an academic medical center using a traditional National Institutes of Health framework highlighted the need for tools that could assist investigators and reviewers in describing and evaluating proposed implementation and improvement sciences research. We operationalized existing recommendations for writing implementation science proposals as the ImplemeNtation and Improvement Science Proposals Evaluation CriTeria (INSPECT) scoring system. The resulting system was applied to pilot grants submitted to a call for implementation and improvement science proposals at an academic medical center. We evaluated the reliability of the INSPECT system using Krippendorff’s alpha coefficients and explored the utility of the INSPECT system to characterize common deficiencies in implementation research proposals. We scored 30 research proposals using the INSPECT system. Proposals received a median cumulative score of 7 out of a possible score of 30. Across individual elements of INSPECT, proposals scored highest for criteria rating evidence of a care or quality gap. Proposals generally performed poorly on all other criteria. Most proposals received scores of 0 for criteria identifying an evidence-based practice or treatment (50%), conceptual model and theoretical justification (70%), setting’s readiness to adopt new services/treatment/programs (54%), implementation strategy/process (67%), and measurement and analysis (70%). Inter-coder reliability testing showed excellent reliability (Krippendorff’s alpha coefficient 0.88) for the application of the scoring system overall and demonstrated reliability scores ranging from 0.77 to 0.99 for individual elements. The INSPECT scoring system presents a new scoring criteria with a high degree of inter-rater reliability and utility for evaluating the quality of implementation and improvement sciences grant proposals.

Despite efforts to increase the availability of clinical genetic testing and counseling for Hereditary Breast and Ovarian (HBOC)-related cancers, these services remain underutilized in clinical settings. There have been few efforts to understand the public’s use of cancer genetic services, particularly for HBOC-related cancers. This analysis is based on data from the 2015 National Health Interview Survey (NHIS), a U.S.-based nationwide probability sample, to better understand the public’s use of HBOC-related clinical cancer genetic services. Bivariate analyses were used to compute percentages and examine the associations of familial cancer risk for three genetic services outcomes (ever had genetic counseling for cancer risk, ever discussed genetic testing for cancer risk with a provider, and ever had genetic testing for cancer risk). Multivariable logistic regression models were used to estimate the association of familial cancer risk and other demographic and health variables with genetic services. Most women (87.67%) in this study were at low risk based on self-reported family history of breast and ovarian cancer, 10.65% were at medium risk, and 1.68% were at high risk. Overall, very small numbers of individuals had ever had genetic counseling (2.78%), discussed genetic testing with their physician (4.55%) or had genetic testing (1.64%). Across all genetic services outcomes, individuals who were at higher familial risk were more likely to have had genetic counseling than those at lower risk (high risk: aOR = 5.869, 95% CI = 2.911–11.835; medium risk: aOR = 4.121, 95% CI = 2.934–5.789), discussed genetic testing (high risk: aOR = 5.133, 95% CI = 2.699–9.764; medium risk: aOR = 3.649, 95% CI = 2.696–4.938), and completed genetic testing (high risk: aOR = 8.531, 95% CI = 3.666–19.851; medium risk aOR = 3.057, 95% CI = 1.835–5.094). Those who perceived themselves as being more likely to develop cancer than the average woman were more likely to engage in genetic counseling (aOR = 1.916, 95% CI = 1.334–2.752), discuss genetic testing (aOR = 3.314, 95% CI = 2.463–4.459) or have had genetic testing (aOR = 1.947, 95% CI = 1.13–3.54). Personal cancer history was also a significant predictor of likelihood to have engaged in genetic services. Our findings highlight: (1) potential under-utilization of cancer genetic services among high risk populations in the U.S. and (2) differences in genetic services use based on individual’s characteristics such as self-reported familial risk, personal history, and beliefs about risk of cancer. These results align with other studies which have noted that awareness and use of genetic services are low in the general population and likely not reaching individuals who could benefit most from screening for inherited cancers. Efforts to promote public awareness of familial cancer risk may lead to better uptake of cancer genetic services.

Background: Health information technology (IT) is becoming increasingly utilized by cancer genetic counselors (CGCs). We sought to understand the current engagement, satisfaction, and opportunities to adopt new health IT tools among CGCs.Methods: We conducted a mixed‐mode survey among 128 board‐certified CGCs using both closed‐ and open‐ended questions. We then evaluated the utilization and satisfaction among 10 types of health IT tools, including the following: cancer screening tool, family health history (FHx) collection tools, electronic health records (EHRs), telegenetics software, pedigree drawing software, genetic risk assessment tools, gene test panel ordering tools, electronic patient education tools, patient communication tools, and family communication tools.Results: Seven of 10 health IT tools were used by a minority of CGCs. The vast majority of respondents reported using EHRs (95.2%) and genetic risk assessment tools (88.6%). Genetic test panel ordering software had the highest satisfaction rate (very satisfied and satisfied) at 80.0%, followed by genetic risk assessment tools (77.1%). EHRs had the highest dissatisfaction rate among CGCs at 18.3%. Dissatisfaction with a health IT tool was associated with desire to change: EHRs (p < .001), cancer screening tools (p = .010), genetic risk assessment tools (p = .024), and family history collection tools (p = .026). We found that nearly half of CGCs were considering adopting or changing their FHx tool (49.2%), cancer screening tool (44.9%), and pedigree drawing tool (41.8%).Conclusion: Overall, CGCs reported high levels of satisfaction among commonly used health IT tools. Tools that enable the collection of FHx, cancer screening tools, and pedigree drawing software represent the greatest opportunities for research and development.