Modernizing family health history: achievable strategies to reduce implementation gaps

Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis

BACKGROUND

Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States.

OBJECTIVE

This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx.

METHODS

This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method. KIT's design was optimized to reduce user burden. We recruited and randomized individuals from 2 crowdsourced platforms to 1 of the 2 FHx methods. All participants were asked to complete a questionnaire to assess the method's usability, the usefulness of a report summarizing their experience, user-desired chatbot enhancements, and general user experience. Engagement was studied using log data collected by the methods. We used qualitative findings from analyzing free-text comments to supplement the primary quantitative results.

RESULTS

Participants randomized to KIT reported higher usability than those randomized to the form, with a mean System Usability Scale score of 80.2 versus 61.9 (P<.001), respectively. The engagement analysis reflected design differences in the onboarding process. KIT users spent less time entering FHx information and reported more conditions than form users (mean 5.90 vs 7.97 min; P=.04; and mean 7.8 vs 10.1 conditions; P=.04). Both KIT and form users somewhat agreed that the report was useful (Likert scale ratings of 4.08 and 4.29, respectively). Among desired enhancements, personalization was the highest-rated feature (188/205, 91.7% rated medium- to high-priority). Qualitative analyses revealed positive and negative characteristics of both KIT and the form-based method. Among respondents randomized to KIT, most indicated it was easy to use and navigate and that they could respond to and understand user prompts. Negative comments addressed KIT's personality, conversational pace, and ability to manage errors. For KIT and form respondents, qualitative results revealed common themes, including a desire for more information about conditions and a mutual appreciation for the multiple-choice button response format. Respondents also said they wanted to report health information beyond KIT's prompts (eg, personal health history) and for KIT to provide more personalized responses.

CONCLUSIONS

We showed that KIT provided a usable way to collect FHx. We also identified design considerations to improve chatbot-based FHx data collection: First, the final report summarizing the FHx collection experience should be enhanced to provide more value for patients. Second, the onboarding chatbot prompt may impact data quality and should be carefully considered. Finally, we highlighted several areas that could be improved by moving from a flow-based chatbot to a large language model implementation strategy.

Leveraging Electronic Health Record Data to Understand Gaps Underlying the Underdiagnosis of Lynch Syndrome

Using the electronic health record to address the underdiagnosis of Lynch syndrome.

Automated Family Histories Significantly Improve Risk Prediction in an EHR

We recently demonstrated that electronically constructed family pedigrees (e-pedigrees) have great value in epidemiologic research using electronic health record (EHR) data. Prior to this work, it has been well accepted that family health history is a major predictor for a wide spectrum of diseases, reflecting shared effects of genetics, environment, and lifestyle. With the widespread digitalization of patient data via EHRs, there is an unprecedented opportunity to use machine learning algorithms to better predict disease risk. Although predictive models have previously been constructed for a few important diseases, we currently know very little about how accurately the risk for most diseases can be predicted. It is further unknown if the incorporation of e-pedigrees in machine learning can improve the value of these models. In this study, we devised a family pedigree-driven high-throughput machine learning pipeline to simultaneously predict risks for thousands of diagnosis codes using thousands of input features. Models were built to predict future disease risk for three time windows using both Logistic Regression and XGBoost. For example, we achieved average areas under the receiver operating characteristic curves (AUCs) of 0.82, 0.77 and 0.71 for 1, 6, and 24 months, respectively using XGBoost and without e-pedigrees. When adding e-pedigree features to the XGBoost pipeline, AUCs increased to 0.83, 0.79 and 0.74 for the same three time periods, respectively. E-pedigrees similarly improved the predictions when using Logistic Regression. These results emphasize the potential value of incorporating family health history via e-pedigrees into machine learning with no further human time.

Development of the Tool to Empower Parental Telling and Talking (TELL Tool): A digital decision aid intervention about children's origins from donated gametes or embryos

Objective

This study aimed to create and develop a well-designed, theoretically driven, evidence-based, digital, decision Tool to Empower Parental Telling and Talking (TELL Tool) prototype.

Methods

This developmental study used an inclusive, systematic, and iterative process to formulate a prototype TELL Tool: the first digital decision aid for parents who have children 1 to 16 years of age and used donated gametes or embryos to establish their families. Recommendations from the International Patient Decision Aids Standards Collaboration and from experts in decision aid development, digital health interventions, design thinking, and instructional design guided the process.

Results

The extensive developmental process incorporated researchers, clinicians, parents, children, and other stakeholders, including donor-conceived adults. We determined the scope and target audience of the decision aid and formed a steering group. During design work, we used the decision-making process model as the guiding framework for selecting content. Parents' views and decisional needs were incorporated into the prototype through empirical research and review, appraisal, and synthesis of the literature. Clinicians' perspectives and insights were also incorporated. We used the experiential learning theory to guide the delivery of the content through a digital distribution plan. Following creation of initial content, including storyboards and scripts, an early prototype was redrafted and redesigned based on feedback from the steering group. A final TELL Tool prototype was then developed for alpha testing.

Conclusions

Detailing our early developmental processes provides transparency that can benefit the donor-conceived community as well as clinicians and researchers, especially those designing digital decision aids. Future research to evaluate the efficacy of the TELL Tool is planned.

Real-World Results from Combined Screening for Monogenic Genomic Health Risks and Reproductive Risks in 300 Adults

New methods and demonstrations of feasibility guide future implementation of genomic population health screening programs. This is the first report of genomic population screening in a primary care, non-research setting using existing large carrier and health risk gene sequencing panels combined into one 432-gene test that is offered to adults of any health status. This report summarizes basic demographic data and analyses patterns of pathogenic and likely pathogenic genetic findings for the first 300 individuals tested in this real-world scenario. We devised a classification system for gene results to facilitate clear message development for our Genomic Medicine Action Plan messaging tool used to summarize and activate results for patients and primary care providers. Potential genetic health risks of various magnitudes for a broad range of disorders were identified in 16% to 34% of tested individuals. The frequency depends on criteria used for the type and penetrance of risk. 86% of individuals are carriers for one or more recessive diseases. Detecting, reporting, and guiding response to diverse genetic health risks and recessive carrier states in a single primary care genomic screening test appears feasible and effective. This is an important step toward exploring an exome or genome sequence as a multi-purpose clinical screening tool.

FHH Quick App Review: How Can a Quality Review Process Assist Primary Care Providers in Choosing a Family Health History App for Patient Care?

Family health history (FHH) is a data type serving risk assessment, diagnosis, research, and preventive health. Despite technological leaps in genomic variant detection, FHH remains the most accessible, least expensive, and most practical assessment tool for assessing risks attributable to genetic inheritance. The purpose of this manuscript is to outline a process to assist primary care professionals in choosing FHH digital tools for patient care based on the new ISO/TS 82304-2 Technical Specification (TS), which is a recently developed method to determine eHealth app quality. With a focus on eHealth in primary care, we applied the quality label concept to FHH, and how a primary care physician can quickly review the quality and reliability of an FHH app. Based on our review of the ISO TS’s 81 questions, we compiled a list of 25 questions that are recommended to be more succinct as an initial review. We call this process the FHH Quick App Review. Our ‘informative-only’ 25 questions do not produce a quality score, but a guide to complete an initial review of FHH apps. Most of the questions are straight from the ISO TS, some are modified or de novo. We believe the 25 questions are not only relevant to FHH app reviews but could also serve to aid app development and clinical implementation.

Prevalence of Americans reporting a family history of cancer indicative of increased cancer risk: Estimates from the 2015 National Health Interview Survey

The collection and evaluation of family health history in a clinical setting presents an opportunity to discuss cancer risk, tailor cancer screening recommendations, and identify people with an increased risk of carrying a pathogenic variant who may benefit from referral to genetic counseling and testing. National recommendations for breast and colorectal cancer screening indicate that men and women who have a first-degree relative affected with these types of cancers may benefit from talking to a healthcare provider about starting screening at an earlier age and other options for cancer prevention. The prevalence of reporting a first-degree relative who had cancer was assessed among adult respondents of the 2015 National Health Interview Survey who had never had cancer themselves (n = 27,999). We found 35.6% of adults reported having at least one first-degree relative with cancer at any site. Significant differences in reporting a family history of cancer were observed by sex, age, race/ethnicity, educational attainment, and census region. Nearly 5% of women under age 50 and 2.5% of adults under age 50 had at least one first-degree relative with breast cancer or colorectal cancer, respectively. We estimated that 5.8% of women had a family history of breast or ovarian cancer that may indicate increased genetic risk. A third of U.S. adults who have never had cancer report a family history of cancer in a first-degree relative. This finding underscores the importance of using family history to inform discussions about cancer risk and screening options between healthcare providers and their patients.

Primary Care Implementation of Genomic Population Health Screening Using a Large Gene Sequencing Panel

To realize the promise of genomic medicine, harness the power of genomic technologies, and capitalize on the extraordinary pace of research linking genomic variation to disease risks, healthcare systems must embrace and integrate genomics into routine healthcare. We have implemented an innovative pilot program for genomic population health screening for any-health-status adults within the largest health system in Vermont, United States. This program draws on key research and technological advances to safely extract clinical value for genomics in routine health care. The program offers no-cost, non-research DNA sequencing to patients by their primary care providers as a preventive health tool. We partnered with a commercial clinical testing company for two next generation sequencing gene panels comprising 431 genes related to both high and low-penetrance common health risks and carrier status for recessive disorders. Only pathogenic or likely pathogenic variants are reported. Routine written clinical consultation is provided with a concise, clinical “action plan” that presents core messages for primary care provider and patient use and supports clinical management and health education beyond the testing laboratory’s reports. Access to genetic counseling is free in most cases. Predefined care pathways and access to genetics experts facilitates the appropriate use of results. This pilot tests the feasibility of routine, ethical, and scalable use of population genomic screening in healthcare despite generally imperfect genomic competency among both the public and health care providers. This article describes the program design, implementation process, guiding philosophies, and insights from 2 years of experience offering testing and returning results in primary care settings. To aid others planning similar programs, we review our barriers, solutions, and perceived gaps in the context of an implementation research framework.