A thematic analysis of health information technology use among cancer genetic counselors.

As precision medicine becomes a mainstay in health care, the use of health information technology (IT) platforms will play an important role in the delivery of services across the cancer care continuum. Currently, there is both limited understanding about perceptions of health IT tools and barriers to their use among cancer genetic counselors. We assessed open-ended responses from a survey conducted among 128 board-certified cancer genetic counselors in the United States. We evaluated the utility of ten health IT tools and perceived barriers to adoption. Responses about characteristics of health IT tools that influence current use (i.e., technology-specific challenges) were deductively analyzed using the diffusion of innovations (DOI) characteristics. Responses about cancer genetic counselors' perceived challenges to adopting health IT tools (i.e., discipline-specific challenges) were inductively coded using a thematic approach. DOI innovation characteristics included mixed perceptions about the relative advantage, complexity, compatibility, trialability, and observability of tools based on the type of tool and perceived end-user. One-third of participants indicated that they were considering adopting or switching health IT tools. Common barriers to adoption included no perceived need for change, lack of organizational infrastructure, cost, and lack of decision-making power. Our findings indicate that addressing barriers to use and adoption of health IT may allow for expansion of these tools among cancer genetic counselors. Integrating health IT is critical for enhancing cancer genetic counselors' capacity to address patient needs and realizing the potential of precision medicine.

Evaluation and comparison of hereditary Cancer guidelines in the population.

BACKGROUND: Family health history (FHx) is an effective tool for identifying patients at risk of hereditary cancer. Hereditary cancer clinical practice guidelines (CPG) contain criteria used to evaluate FHx and to make recommendations for genetic consultation. Comparing different CPGs used to evaluate a common set of FHx provides insight into how well the CPGs perform, the extent of agreement across guidelines, and how well they identify patients who should consider a cancer genetic consultation. METHODS: We compare the American College of Medical Genetics and Genomics (ACMG) and the National Comprehensive Cancer Networks (NCCN) (2019) CPG criteria for FHx collected by a chatbot and evaluated by ontologies and web services in a previous study. Collected FHx met criteria from seven groups: Gene Mutation, Breast and Ovarian, Li-Fraumeni syndrome (LFS), Colorectal and Endometrial, Relative Meets Criteria, ACMG Only Criteria, and NCCN Testing. CPG Criteria were coded and matched across 12 ACMG sub-guidelines and 6 NCCN sub-guidelines for comparison purposes. RESULTS: The dataset contains 4915 records, of which 2221 met either ACMG or NCCN criteria and 2694 did not. There was significant overlap-1179 probands met both ACMG and NCCN criteria. The greatest similarities were for Gene Mutation and Breast and Ovarian criteria and the greatest disparity existed among Colorectal and Endometrial criteria. Only 156 positive gene mutations were reported and of the 2694 probands who did not meet criteria, 90.6% of them reported at least one cancer in their personal or family cancer history. CONCLUSION: Hereditary cancer CPGs are useful for identifying patients at risk of developing cancer based on FHx. This comparison shows that with the aid of chatbots, ontologies, and web services, CPGs can be more efficiently applied to identify patients at risk of hereditary cancer. Additionally this comparison examines similarities and differences between ACMG and NCCN and shows the importance of using both guidelines when evaluating hereditary cancer risk.

User experience of a family health history chatbot: A quantitative analysis.

OBJECTIVE: Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns. METHODS: We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement. RESULTS: Of 11,065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 s. Users spent the highest median time on Proband Cancer History (124.00 s) and Family Cancer History (119.00 s) subflows. Search list questions took the longest to complete (median 19.50 s), followed by free text email input (15.00 s). CONCLUSION: Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.

Automated Clinical Practice Guideline Recommendations for Hereditary Cancer Risk Using Chatbots and Ontologies: System Description.

BACKGROUND: Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling as providers lack the time and training to collect and assess their family health history. Consequently, patients at risk do not receive genetic counseling and testing that they need to determine the preventive steps they should take to mitigate their risk. OBJECTIVE: This study aims to automate clinical practice guideline recommendations for hereditary cancer risk based on patient family health history. METHODS: We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service-oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. RESULTS: The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess >5000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 (SD 1.9) seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. CONCLUSIONS: Web service-enabled, chatbot-oriented family health history collection and ontology-driven clinical practice guideline criteria risk assessment is a simple and effective method for automating hereditary cancer risk screening.

Proximal-end bias from in-vitro reconstituted nucleosomes and the result on downstream data analysis.

The most basic level of eukaryotic gene regulation is the presence or absence of nucleosomes on DNA regulatory elements. In an effort to elucidate in vivo nucleosome patterns, in vitro studies are frequently used. In vitro, short DNA fragments are more favorable for nucleosome formation, increasing the likelihood of nucleosome occupancy. This may in part result from the fact that nucleosomes prefer to form on the terminal ends of linear DNA. This phenomenon has the potential to bias in vitro reconstituted nucleosomes and skew results. If the ends of DNA fragments are known, the reads falling close to the ends are typically discarded. In this study we confirm the phenomenon of end bias of in vitro nucleosomes. We describe a method in which nearly identical libraries, with different known ends, are used to recover nucleosomes which form towards the terminal ends of fragmented DNA. Finally, we illustrate that although nucleosomes prefer to form on DNA ends, it does not appear to skew results or the interpretation thereof.

Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals.

INTRODUCTION: Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, "ItRunsInMyFamily" (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs. The extent to which non-GCs are discordant with CPGs is a gap that health information technology, such as ItRuns, can help close to facilitate the identification of individuals at risk for hereditary cancer. METHODS: We curated 18 FHx cases and surveyed GCs and non-GCs (oncologists and PCPs) to assess concordance with ItRuns CPG criteria for referring patients for GC/GT. Percent agreement was used to describe concordance, and logistic regression to compare providers and the tool's concordance with CPG criteria. RESULTS: GCs had the best overall concordance with the CPGs used in ItRuns at 82.2%, followed by oncologists with 66.0% and PCPs with 60.6%. GCs were significantly more likely to concur with CPGs (OR = 4.04, 95% CI = 3.35-4.89) than non-GCs. All providers had higher concordance with CPGs for FHx cases that met the criteria for genetic counseling/testing than for cases that did not. DISCUSSION/CONCLUSION: The risk assessment provided by ItRuns was highly concordant with that of GC's, particularly for at-risk individuals. The use of such technology-based tools improves efficiency and can lead to greater numbers of at-risk individuals accessing genetic counseling, testing, and mutation-based interventions to improve health.

Categorization of Third-Party Apps in Electronic Health Record App Marketplaces: Systematic Search and Analysis.

BACKGROUND: Third-party electronic health record (EHR) apps allow health care organizations to extend the capabilities and features of their EHR system. Given the widespread utilization of EHRs and the emergence of third-party apps in EHR marketplaces, it has become necessary to conduct a systematic review and analysis of apps in EHR app marketplaces. OBJECTIVE: The goal of this review is to organize, categorize, and characterize the availability of third-party apps in EHR marketplaces. METHODS: Two informaticists (authors JR and BW) used grounded theory principles to review and categorize EHR apps listed in top EHR vendors' public-facing marketplaces. RESULTS: We categorized a total of 471 EHR apps into a taxonomy consisting of 3 primary categories, 15 secondary categories, and 55 tertiary categories. The three primary categories were administrative (n=203, 43.1%), provider support (n=159, 33.8%), and patient care (n=109, 23.1%). Within administrative apps, we split the apps into four secondary categories: front office (n=77, 37.9%), financial (n=53, 26.1%), office administration (n=49, 24.1%), and office device integration (n=17, 8.4%). Within the provider support primary classification, we split the apps into eight secondary categories: documentation (n=34, 21.3%), records management (n=27, 17.0%), care coordination (n=23, 14.4%), population health (n=18, 11.3%), EHR efficiency (n=16, 10.1%), ordering and prescribing (n=15, 9.4%), medical device integration (n=13, 8.2%), and specialty EHR (n=12, 7.5%). Within the patient care primary classification, we split the apps into three secondary categories: patient engagement (n=50, 45.9%), clinical decision support (n=40, 36.7%), and remote care (n=18, 16.5%). Total app counts varied substantially across EHR vendors. Overall, the distribution of apps across primary categories were relatively similar, with a few exceptions. CONCLUSIONS: We characterized and organized a diverse and rich set of third-party EHR apps. This work provides an important reference for developers, researchers, and EHR customers to more easily search, review, and compare apps in EHR app marketplaces.

Using a Chatbot to Assess Hereditary Cancer Risk.

PURPOSE: We developed a Web-based chatbot (ItRunsInMyFamily.com) to help individuals collect their family health history (FHx) and determine their risk for hereditary cancer. The purpose of the current study was to assess the characteristics of users and identify opportunities to improve the FHx collection tool. METHODS: During Family Health History Month (November 2019) we launched an FHx campaign using social media advertisements to raise awareness about hereditary cancers and encourage individuals in the general population to use ItRunsInMyFamily to collect their FHx. Through this campaign, we were able to gather information about users and identify opportunities to improve the tool. RESULTS: We reached 14,140 users in November 2019 through online marketing campaigns-Facebook, Google, previous ItRuns users, and Web site marketing. Of those, 3,204 completed the full FHx assessment and received risk recommendations. The campaign targeted women between age 40 and 60 years. Users came from 3,783 counties around the United States, 48 unique cancers were reported among probands, and 79 unique cancers were reported among family members, an average of two and a half cancers per family. CONCLUSION: Our results demonstrate that it is possible to gather FHx information at the population level, with high levels of engagement and interest in the topic. There is room for future enhancements and improvements to ItRunsInMyFamily to broaden its reach and encourage individuals to learn about and record their health information.

Utilization of health information technology among cancer genetic counselors.

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.