An Evaluation of the Use of a Clinical Research Data Warehouse and I2b2 Infrastructure to Facilitate Replication of Research

Replication of clinical research is requisite for forming effective clinical decisions and guidelines. While rerunning a clinical trial may be unethical and prohibitively expensive, the adoption of EHRs and the infrastructure for distributed research networks provide access to clinical data for observational and retrospective studies. Herein I demonstrate a means of using these tools to validate existing results and extend the findings to novel populations. I describe the process of evaluating published risk models as well as local data and infrastructure to assess the replicability of the study. I use an example of a risk model unable to be replicated as well as a study of in-hospital mortality risk I replicated using UNMC’s clinical research data warehouse. In these examples and other studies we have participated in, some elements are commonly missing or under-developed. One such missing element is a consistent and computable phenotype for pregnancy status based on data recorded in the EHR. I survey local clinical data and identify a number of variables correlated with pregnancy as well as demonstrate the data required to identify the temporal bounds of a pregnancy episode. Next, another common obstacle to replicating risk models is the necessity of linking to alternative data sources while maintaining data in a de-identified database. I demonstrate a pipeline for linking clinical data to socioeconomic variables and indices obtained from the American Community Survey (ACS). While these data are location-based, I provide a method for storing them in a HIPAA compliant fashion so as not to identify a patient’s location. While full and efficient replication of all clinical studies is still a future goal, the demonstration of replication as well as beginning the development of a computable phenotype for pregnancy and the incorporation of location based data in a de-identified data warehouse demonstrate how the EHR data and a research infrastructure may be used to facilitate this effort

An expanded role for clinical coordinators in investigator initiated clinical trial research

Clinical research is conducted to advance human medicine by developing efficacious treatments and improving patient outcomes when new therapies are developed and implemented. Clinical trials are a subset of the types of clinical research conducted on human volunteers in the development of new drugs, devices and other therapies. Prior to the start of a trial, a country’s regulatory authority must review the trial to ensure it is scientifically and ethically sound. In Canada, the regulatory authority is Health Canada. The International Conference on Harmonization (ICH) of technical requirements for the registration of pharmaceutics for humans aims to provide ethical and scientific quality standards for design, conduct, data collection and reporting in clinical trials. The Good Clinical Practice (GCP) Guidelines were created by the ICH Steering Committee to assure the public that rights, safety and well being of subjects are protected according to the Declaration of Helsinki, and the clinical data obtained in a ICH/GCP compliant clinical trial will meet regulatory requirements. Health Canada has adopted the ICH/GCP Guidelines and therefore, in Canada, all clinical trials involving humans must comply with these Guidelines. The clinical trial coordinator is an important and central position on the research team executing many trial duties and communications. Regulatory authorities, Research Ethics Boards and the sponsor, overlook the role and responsibilities of a highly trained clinical coordinator, despite their vital and central position. The GCP Guidelines also fail to address the role and responsibilities of a clinical coordinator. Disconnect between guidelines, regulatory expectations and actual trial conduct provides an apparent need to formalize and clearly define the role and scope of a clinical coordinator. The Registered Nurse (RN) brings professionalism, knowledge, skill and a holistic perspective to the expanded role of a clinical coordinator and to the clinical trial. Highly trained health professionals are capable of assuming more responsibilities and executing clinical trial design, setup and management as compared to the traditional administrative roles of the clinical coordinator. The expanded role of the clinical coordinator is especially beneficial for Principal Investigator initiated trials due to limited research personnel and resources. Postoperative adhesions are a common complication following pelvic surgery, therefore, this clinical trial is relevant and a response to a healthcare need. My graduate studies focused on the development and set up of the clinical trial Protocol ADE002-2013 Phase I Trial of L-Alanyl-L-Glutamine for the Reduction of Peritoneal Adhesions in Adult Females Undergoing Myomectomy. My thesis is a discussion of general Canadian clinical trial research information followed by an explanation of how we executed the information to design and set up our PI initiated clinical trial. The value of the expanded role of the clinical coordinator as a member of the research team will also be discussed

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

The Assessment of Technology Adoption Interventions and Outcome Achievement Related to the Use of a Clinical Research Data Warehouse

Introduction: While funding for research has declined since 2004, the need for rapid, innovative, and lifesaving clinical and translational research has never been greater due to the rise in chronic health conditions, which have resulted in lower life expectancy and higher rates of mortality and adverse outcomes. Finding effective diagnostic and treatment methods to address the complex challenges in individual and population health will require a team science approach, creating the need for multidisciplinary collaboration among practitioners and researchers. To address this need, the National Institutes of Health (NIH) created the Clinical and Translational Science Awards (CTSA) program. The CTSA program distributes funds to a national network of medical research institutions, known as “hubs,” that work together to improve the translational research process. With this funding, each hub is required to achieve specific goals to support clinical and translational research teams by providing a variety of services, including cutting edge use of informatics technologies. As a result, the majority of CTSA recipients have implemented and maintain data warehouses, which combine disparate data types from a range of clinical and administrative sources, include data from multiple institutions, and support a variety of workflows. These data warehouses provide comprehensive sets of data that extend beyond the contents of a single EHR system and provide more valuable information for translational research. Although significant research has been conducted related to this technology, gaps exist regarding research team adoption of data warehouses. As a result, more information is needed to understand how data warehouses are adopted and what outcomes are achieved when using them. Specifically, this study focuses on three gaps: research team awareness of data warehouses, the outcomes of data warehouse training for research teams, and how to measure objectively outcomes achieved after training. By assessing and measuring data warehouse use, this study aims to provide a greater understanding of data warehouse adoption and the outcomes achieved. With this understanding, the most effective and efficient development, implementation, and maintenance strategies can be used to increase the return on investment for these resource-intensive technologies. In addition, technologies can be better designed to ensure they are meeting the needs of clinical and translational science in the 21st century and beyond. Methods: During the study period, presentations were held to raise awareness of data warehouse technology. In addition, training sessions were provided that focused on the use of data warehouses for research projects. To assess the impact of the presentations and training sessions, pre- and post-assessments gauged knowledge and likelihood to use the technology. As objective measurements, the number of data warehouse access and training requests were obtained, and audit trails were reviewed to assess trainee activities within the data warehouse. Finally, trainees completed a 30-day post-training assessment to provide information about barriers and benefits of the technology. Results: Key study findings suggest that the awareness presentations and training were successful in increasing research team knowledge of data warehouses and likelihood to use this technology, but did not result in a subsequent increase in access or training requests within the study period. In addition, 24% of trainees completed the associated data warehouse activities to achieve their intended outcomes within 30 days of training. The time needed for adopting the technology, the ease of use of data warehouses, the types of support available, and the data available within the data warehouse may all be factors influencing this completion rate. Conclusion: The key finding of this study is that data warehouse awareness presentations and training sessions are insufficient to result in research team adoption of the technology within a three-month study period. Several important implications can be drawn from this finding. First, the timeline for technology adoption requires further investigation, although it is likely longer than 90 days. Future assessments of technology adoption should include an individual’s timeline for pursuing the use of that technology. Second, this study provided a definition for outcome achievement, which was completion o

COMPUTATIONAL PHENOTYPING AND DRUG REPURPOSING FROM ELECTRONIC MEDICAL RECORDS

Using electronic medical records (EMR) for research involves selecting cohorts and manipulating data for tasks like predictive analysis. Computational phenotyping for cohort characterization and stratification is becoming increasingly important for researchers to produce clinically relevant findings. There are significant amounts of time and effort devoted to manual chart abstraction by subject matter experts and researchers, which creates a large bottleneck for progress in clinical research. I focus on developing computational phenotyping pipelines, and I also focus on using EMR for drug repurposing in breast cancer. Drug repurposing is defined as the process of applying known drugs that are already on the market to new disease indications. Using EMR data for drug repurposing has the unique advantage of being able to observe a patient cohort over time and see drug effects on outcomes. In this dissertation, I present work on computational phenotyping and EMR-based drug repurposing. First, I use embedding models and foundational natural language processing methods to predict oral cancer risk with pathology notes. Second, I use natural language processing methods and transfer learning for breast cancer cohort selection and information extraction. Third, I present a pipeline for producing drug repurposing candidates from EMR and provide supporting evidence for predictions with biomedical literature and existing clinical trials.Doctor of Philosoph

Research Data Management Practices And Impacts on Long-term Data Sustainability: An Institutional Exploration

With the \u27data deluge\u27 leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to deposit research data into open online data repositories, which, in turn, is engendering a new set of practices to adapt formal mandates to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them \u27data articulations.\u27 This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 studies: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as articulation work in genomics; and (3) a comparative case study of genomics and social science researchers to identify factors associated with the institutionalization of research data management (RDM). The findings of this research offer an in-depth understanding of the data management and deposit practices of academic research faculty, and surfaced institutional factors associated with data deposit. Additionally, the studies led to a theoretical framework of data deposit to open research data repositories. The empirical insights into the impacts of institutionalization of RDM and data deposit on long-term data sustainability update our knowledge of the impacts of increasing guidelines for RDM. The work also contributes to the body of data management literature through the development of the data articulation framework which can be applied and further validated by future work. In terms of practice, the studies offer recommendations for data policymakers, data repositories, and researchers on defining strategies and initiatives to leverage data reuse and employ computational approaches to support data management and deposit

Social media narratives in non-communicable disease: their dynamics and value for patients, communities and health researchers

Background: Usage of social media is now widespread and growing, as is the number of people living with Non-Communicable Diseases (NCDs) such as diabetes and cancer. This thesis examines how social media are being used to share or discuss NCDs and the benefits, challenges and implications of these trends as a manifestation of digital public health. Aim and research questions: The aim of this research is to address the gap in empirical, evidence-based research into the secondary use of data from social media to understand patient health issues and inform public health research into NCDs. To this end, seven research questions, each linked to a sub-project, were defined and tested during the course of the six-year programme: 1.What is the status of the existing multi-disciplinary research literature based on analysis of data posted on social media for public health research, and where are the gaps in this research? 2.Can existing systematic review methods be re-purposed and applied to analyse data posted on social media? 3.How are research sponsors and researchers addressing the ethical challenges of analysing data posted on social media? 4.To what extent are diabetes-related posts on Twitter relevant to the clinical condition and what topics and intentions are represented in these posts? 5.In what ways do people affected by Type 1 diabetes use different social media (e.g. for social interaction, support-seeking, information-sharing) and what are the implications for researchers wishing to use these data sources in their studies? 6.Are these differences in platform usage and associated data types also seen in people affected by lung cancer? 7.Can characteristic illness trajectories be seen in a cancer patient’s digital narrative and what insights can be gained to inform palliative care services? Methods: A range of different qualitative and quantitative methods and frameworks were used to address each of the research questions listed. Arksey and O’Malley’s five-stage scoping review framework and the PRISMA guidelines are applied to the systematic scoping review of existing literature. The PRISMA guidelines and checklist are re-purposed and applied to the manual extraction and analysis of social media posts. Bjerglund-Andersen and Söderqvist’s typology of social media uses in research and Conway’s taxonomy of ethical considerations are used to classify the ethics guidelines available to researchers. The findings of these were used to inform the research design of the four empirical studies. The methods applied in the conduct of the empirical studies include a content and narrative analysis of cross-sectional and longitudinal data sourced from Twitter, Facebook, the Type 1 diabetes discussion forum on Diabetes.co.uk and the lung cancer discussion forum on Macmillan.org.uk, as well as the application of Bales’ Interaction Process Analysis and Emanuel and Emanuel’s framework for a good death. Results : Of the 49 systematic, quasi-systematic and scoping reviews identified, 24 relate to the secondary use of data from social media, with eight of these focused on infectious disease surveillance and only two on NCDs. Existing reviews tend to be fragmented, narrow in scope and siloed in different academic communities, with limited consideration of the different types of data, analytical methods and ethical issues involved, therefore creating a need for further reviews to synthesise the emerging evidence-base. The rapid increase in the volume of published research is evident, from the results of RQ1, with 87% of the eligible studies published between 2013-2017. Of the 105 eligible empirical studies that focused on NCDs, cancer (54%) and diabetes (20%) dominate the literature. Data is sourced from Twitter (26%), Facebook (14%) and blogs (10%), conducted, published and funded by the medical community. Since 2012, automated methods have increasingly been applied to extract and analyse large volumes of data. Those that use manual methods for extraction did not apply a consistent approach to doing so; the PRISMA guidelines and checklist were therefore re-purposed and applied to analyse data extracted from social media in response to RQ2. The deficit of ethical guidance available to inform research that involves social media data was also identified as a result of RQ3 and the guidelines provided by the ESRC, BPS, AoIR and NIHR were prioritised for the purposes of this research project. Results from the four empirical studies (RQ4-7) reveal that different forms of social interaction and support are represented in the variety of social media platforms available and that this is influenced by the type and nature of the condition with which people are affected, as well as the affordances offered by such platforms. In the pilot study associated with RQ4, Twitter was identified as a ‘noisy’ source of data about diabetes, with only 66% of the sample being relevant to the clinical condition. Twelve per cent of the eligible sample was associated with Type 2 diabetes, compared to 6% for Type 1, and most were information-giving in nature (49%) and correlated with the diagnosis, treatment and management of the condition (44%). A comparison of Twitter to the Type 1 Diabetes community on Facebook and the discussion forum on Diabetes.co.uk for RQ5 indicated that all three social media platforms were used to disseminate information about the condition. However, the Type 1 Diabetes Group on Facebook and the Type 1 discussion forum on Diabetes.co.uk were also used for social interaction and peer support, hence defying the generalisations made in public health studies, where social media platforms were often considered equal or synonymous. The results from the third empirical study into lung cancer (RQ6) support this, indicating that, by virtue of their digital architecture, user base and self-moderating communities, the Lung Cancer Support Group on Facebook and the lung cancer discussion forum on Macmillan.org.uk are more successful in their utility for social interaction and emotional and informational support. Meanwhile, the sample derived from Twitter hashtags showed greater companionship support. The final empirical study in this PhD research project is associated with RQ7 and used longitudinal data posted by a terminally ill patient on Twitter. This revealed that patient activity on social media mirrors the different phases of the end-of-life illness trajectory described in the literature and that it is comparable to or compliments insights garnered using more traditional qualitative research techniques. It also shows the value of such innovative methods for understanding how terminal disease is experienced by and affects individuals, how they cope, how support is sought and obtained and how patients feel about the ability of palliative care services to meet their needs at different stages. Conclusions: The analysis of health data posted on social media continues to be an expanding and evolving field of multi-disciplinary research. The results of the studies included in this thesis reveal the emergence of new methods and ethical considerations to inform research design as well as ethics policy. The re-purposed PRISMA guidelines and checklist were presented at the 2014 Medicine 2.0 Summit and World Congress whilst the review of ethical guidelines was published in the Research Ethics journal. The four empirical studies that extracted and analysed data from social media provide novel insight into the social narratives of those impacted by diabetes and cancer and can be used to inform future research and practice. The results of these studies have, to date, been presented at four international conferences and published in npj Digital Medicine and BMC Palliative Care. Although this thesis and associated publications contribute to an emerging body of knowledge, further research is warranted into the manual versus automated techniques that can be applied and the differences in social interaction and support needed by people affected by different NCDs

Waveforms and Sonic Boom Perception and Response (WSPR): Low-Boom Community Response Program Pilot Test Design, Execution, and Analysis

The Waveforms and Sonic boom Perception and Response (WSPR) Program was designed to test and demonstrate the applicability and effectiveness of techniques to gather data relating human subjective response to multiple low-amplitude sonic booms. It was in essence a practice session for future wider scale testing on naive communities, using a purpose built low-boom demonstrator aircraft. The low-boom community response pilot experiment was conducted in California in November 2011. The WSPR team acquired sufficient data to assess and evaluate the effectiveness of the various physical and psychological data gathering techniques and analysis methods

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories