Deevakar Rogith

Attitudes toward molecular testing for personalized cancer therapy

This study assessed attitudes of breast cancer patients toward molecular testing for personalized therapy and research.

Attitudes regarding privacy of genomic information in personalized cancer therapy

OBJECTIVE To evaluate attitudes regarding privacy of genomic data in a sample of patients with breast cancer. METHODS Female patients with breast cancer (n=100) completed a questionnaire assessing attitudes regarding concerns about privacy of genomic data. RESULTS Most patients (83%) indicated that genomic data should be protected. However, only 13% had significant concerns regarding privacy of such data. Patients expressed more concern about insurance discrimination than employment discrimination (43% vs 28%, p<0.001). They expressed less concern about research institutions protecting the security of their molecular data than government agencies or drug companies (20% vs 38% vs 44%; p<0.001). Most did not express concern regarding the association of their genomic data with their name and personal identity (49% concerned), billing and insurance information (44% concerned), or clinical data (27% concerned). Significantly fewer patients were concerned about the association with clinical data than other data types (p<0.001). In the absence of direct benefit, patients were more willing to consent to sharing of deidentified than identified data with researchers not involved in their care (76% vs 60%; p<0.001). Most (85%) patients were willing to consent to DNA banking. DISCUSSION While patients are opposed to indiscriminate release of genomic data, privacy does not appear to be their primary concern. Furthermore, we did not find any specific predictors of privacy concerns. CONCLUSIONS Patients generally expressed low levels of concern regarding privacy of genomic data, and many expressed willingness to consent to sharing their genomic data with researchers.

Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base

BACKGROUND Correlation of data within electronic health records is necessary for implementation of various clinical decision support functions, including patient summarization. A key type of correlation is linking medications to clinical problems; while some databases of problem-medication links are available, they are not robust and depend on problems and medications being encoded in particular terminologies. Crowdsourcing represents one approach to generating robust knowledge bases across a variety of terminologies, but more sophisticated approaches are necessary to improve accuracy and reduce manual data review requirements. OBJECTIVE We sought to develop and evaluate a clinician reputation metric to facilitate the identification of appropriate problem-medication pairs through crowdsourcing without requiring extensive manual review. APPROACH We retrieved medications from our clinical data warehouse that had been prescribed and manually linked to one or more problems by clinicians during e-prescribing between June 1, 2010 and May 31, 2011. We identified measures likely to be associated with the percentage of accurate problem-medication links made by clinicians. Using logistic regression, we created a metric for identifying clinicians who had made greater than or equal to 95% appropriate links. We evaluated the accuracy of the approach by comparing links made by those physicians identified as having appropriate links to a previously manually validated subset of problem-medication pairs. RESULTS Of 867 clinicians who asserted a total of 237,748 problem-medication links during the study period, 125 had a reputation metric that predicted the percentage of appropriate links greater than or equal to 95%. These clinicians asserted a total of 2464 linked problem-medication pairs (983 distinct pairs). Compared to a previously validated set of problem-medication pairs, the reputation metric achieved a specificity of 99.5% and marginally improved the sensitivity of previously described knowledge bases. CONCLUSION A reputation metric may be a valuable measure for identifying high quality clinician-entered, crowdsourced data.

User needs analysis and usability assessment of DataMed – a biomedical data discovery index

Abstract Objective To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. Materials and Methods We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers’ information and user interface needs. Results Biomedical researchers’ information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Discussion Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. Conclusion While available data and researchers’ information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers’ information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs.

Digilego: A Standardized Analytics-Driven Consumer-Oriented Connected Health Framework

Connected health solutions provide novel pathways to provide integrated and affordable care. Emerging research suggests these connected tools can result improved health outcomes and sustainable self-health management. However, current health technology frameworks limit flexibility, engagement, and reusability of underlying connected health components. The objective of this paper is to develop a data-driven consumer engagement framework, which we call Digilego, to facilitate development of connected health solutions that are targeted, modular, extensible, and engaging. The major components include social media analysis, patient engagement features, and behavioral intervention technologies. We propose implementation of these Digilego components using FHIR specification such that the resulting technology is compliant to industry standards. We apply and evaluate the proposed framework to characterize four individual building blocks (DigiMe, DigiSocial, DigiConnect, DigiEHR) for a connected health solution that is responsive to cancer survivor needs. Results indicate that the framework (a) allows identification of survivor needs (e.g. social integration, treatment side effects) through semi-automated social media analysis, (b) facilitates infusion of engagement elements (e.g. smart health trackers, integrated electronic health records), and (c) integrates behavior change constructs into the design architecture of survivorship applications (e.g. goal setting, emotional coping). End user evaluation with 16 cancer survivors indicated general user acceptance and enthusiasm to adopt the solution for self-care management. Implications for design of patient-engaging chronic disease management solutions are discussed.

DataMed – an open source discovery index for finding biomedical datasets

Abstract Objective Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

Abstract P2-12-13: Knowledge and Information seeking about personalized breast cancer therapy

INTRODUCTION: Breast cancer patients and providers are increasingly interested in personalized cancer therapy. Information-seeking behaviors and knowledge about personalized cancer therapy, cancer genetics, and molecular testing may influence patients’ participation in clinical trials and decision making regarding their care. We evaluated breast cancer patients’ knowledge and information seeking behaviors regarding personalized cancer therapy (PCT). METHODS: The study population included newly registered female breast cancer patients at The University of Texas MD Anderson Cancer Center prior to their first clinical visit. Of 308 consecutive patients who were invited to participate, 100 (32%) completed a self-administered questionnaire assessing their knowledge and information seeking preferences regarding PCT. Knowledge regarding cancer genetics and PCT research was assessed using 16 true/false questions (Cronbach’s α=0.88). A knowledge score was computed from the total number of correct responses. RESULTS: Respondents were predominantly white (70%), older (median age 55 years; SD=12.9; range 26-84), educated (78% with college degree or higher) and higher incomes (54% >

Patient knowledge and information-seeking about personalized cancer therapy.

50,000/year); 71% had been diagnosed with breast cancer for at most one year at time of participation. Knowledge regarding cancer genetics and PCT was moderate (M=8.68, SD=3.8). Although most participants (85%) could correctly identify the definition of PCT, many (59%) did not know that somatic mutations are not hereditary. Many (75%) knew that molecular testing can reveal risk for other hereditary cancers. Less than half (46.5%) knew about the availability of PCT in clinical trials. A minority (27%) indicated that they had sought information regarding PCT. They sought for information related to specific treatment options. Higher education (p CONCLUSION: Study participants could define PCT, but had limited knowledge of its availability and underlying treatment principles. This may be due, in part, to the fact that few participants had sought information about PCT. Understanding patients’ knowledge and prior information seeking regarding PCT may inform clinicians, who are likely to be patients’ initial source of information about PCT. Citation Format: Deevakar Rogith, Rafeek A Yusuf, Shelley R Hovick, Bryan M Fellman, Susan K Peterson, Allison Burton-Chase, Yisheng Li, Elmer V Bernstam, Funda Meric-Bernstam. Knowledge and Information seeking about personalized breast cancer therapy [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-12-13.