Rachel L. Richesson

Data Standards in Clinical Research: Gaps, Overlaps, Challenges and Future Directions

Current efforts to define and implement health data standards are driven by issues related to the quality, cost and continuity of care, patient safety concerns, and desires to speed clinical research findings to the bedside. The Presidents goal for national adoption of electronic medical records in the next decade, coupled with the current emphasis on translational research, underscore the urgent need for data standards in clinical research. This paper reviews the motivations and requirements for standardized clinical research data, and the current state of standards development and adoption--including gaps and overlaps--in relevant areas. Unresolved issues and informatics challenges related to the adoption of clinical research data and terminology standards are mentioned, as are the collaborations and activities the authors perceive as most likely to address them.

Formal representation of eligibility criteria

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representation that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of these aspects (expression language, codification of eligibility concepts, and patient data modeling) to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward knowledge representation for sharable and computable eligibility criteria.

Variation of SNOMED CT Coding of Clinical Research Concepts Among Coding Experts

OBJECTIVE To compare consistency of coding among professional SNOMED CT coders representing three commercial providers of coding services when coding clinical research concepts with SNOMED CT. DESIGN A sample of clinical research questions from case report forms (CRFs) generated by the NIH-funded Rare Disease Clinical Research Network (RDCRN) were sent to three coding companies with instructions to code the core concepts using SNOMED CT. The sample consisted of 319 question/answer pairs from 15 separate studies. The companies were asked to select SNOMED CT concepts (in any form, including post-coordinated) that capture the core concept(s) reflected in the question. Also, they were asked to state their level of certainty, as well as how precise they felt their coding was. MEASUREMENTS Basic frequencies were calculated to determine raw level agreement among the companies and other descriptive information. Krippendorffs alpha was used to determine a statistical measure of agreement among the coding companies for several measures (semantic, certainty, and precision). RESULTS No significant level of agreement among the experts was found. CONCLUSION There is little semantic agreement in coding of clinical research data items across coders from 3 professional coding services, even using a very liberal definition of agreement.

A comparison of phenotype definitions for diabetes mellitus

OBJECTIVE This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions. MATERIALS AND METHODS Inclusion criteria from seven diabetes phenotype definitions were translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared. RESULTS Three phenotype definitions based heavily on ICD-9-CM codes identified 9-11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56-57% women, mean age range 56-57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44-48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively. DISCUSSION Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data. CONCLUSIONS Further research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research.

Use of SNOMED CT to Represent Clinical Research Data: A Semantic Characterization of Data Items on Case Report Forms in Vasculitis Research

OBJECTIVE To estimate the coverage provided by SNOMED CT for clinical research concepts represented by the items on case report forms (CRFs), as well as the semantic nature of those concepts relevant to post-coordination methods. DESIGN Convenience samples from CRFs developed by rheumatologists conducting several longitudinal, observational studies of vasculitis were selected. A total of 17 CRFs were used as the basis of analysis for this study, from which a total set of 616 (unique) items were identified. Each unique data item was classified as either a clinical finding or procedure. The items were coded by the presence and nature of SNOMED CT coverage and classified into semantic types by 2 coders. MEASUREMENTS Basic frequency analysis was conducted to determine levels of coverage provided by SNOMED CT. Estimates of coverage by various semantic characterizations were estimated. RESULTS Most of the core clinical concepts (88%) from these clinical research data items were covered by SNOMED CT; however, far fewer of the concepts were fully covered (that is, where all aspects of the CRF item could be represented completely without post-coordination; 23%). In addition, a large majority of the concepts (83%) required post-coordination, either to clarify context (e.g., time) or to better capture complex clinical concepts (e.g., disease-related findings). For just over one third of the sampled CRF data items, both types of post-coordination were necessary to fully represent the meaning of the item. CONCLUSION SNOMED CT appears well-suited for representing a variety of clinical concepts, yet is less suited for representing the full amount of information collected on CRFs.

Data standards for clinical research data collection forms: current status and challenges

Case report forms (CRFs) are used for structured-data collection in clinical research studies. Existing CRF-related standards encompass structural features of forms and data items, content standards, and specifications for using terminologies. This paper reviews existing standards and discusses their current limitations. Because clinical research is highly protocol-specific, forms-development processes are more easily standardized than is CRF content. Tools that support retrieval and reuse of existing items will enable standards adoption in clinical research applications. Such tools will depend upon formal relationships between items and terminological standards. Future standards adoption will depend upon standardized approaches for bridging generic structural standards and domain-specific content standards. Clinical research informatics can help define tools requirements in terms of workflow support for research activities, reconcile the perspectives of varied clinical research stakeholders, and coordinate standards efforts toward interoperability across healthcare and research data collection.

Patient Registries: Utility, Validity and Inference

Patient registries are essential tools for public health surveillance and research inquiry, and are a particularly important resource for understanding rare diseases. Registries provide consistent data for defined populations and can support the study of the distribution and determinants of various diseases. One advantage of registries is the ability to observe caseload and population characteristics over time, which might facilitate the evaluation of disease incidence, disease etiology, planning, operation and evaluation of services, evaluation of treatment patterns, and diagnostic classification. Any registry program must collect high quality data to be useful for its stated purpose. Registries can be developed for many different needs, and caution should be taken in interpreting registry data, which has inherent biases. We describe the methodological issues, limitations, and ideal features of registries to support various rare disease purposes. The future impact of registries on our understanding and interventions for rare diseases will depend upon technological and political solutions for global cooperation to achieve consistent data (via standards) and regulations for various registry applications.

An automated communication system in a contact registry for persons with rare diseases: scalable tools for identifying and recruiting clinical research participants.

OBJECTIVES Strategies for study recruitment are useful in clinical research network settings. We describe a registry of individuals who have self-identified with one of a multiplicity of rare diseases, and who express a willingness to be contacted regarding possible enrollment in clinical research studies. We evaluate this registry and supporting tools in terms of registry enrollment and impact on participation rates in advertised clinical research studies. METHODS A web-based automated system generates periodic and customized communications to notify registrants of relevant studies in the NIH Rare Diseases Clinical Research Network (RDCRN). The majority of these communications are sent by email. We compare the characteristics of those enrolled in the registry to the characteristics of participants enrolled in sampled RDCRN studies in order to estimate the impact of the registry on study participation in the network. RESULTS The registry currently contains over 4000 registrants, representing 40 rare diseases. Estimates of study participation range from 6-27% for all enrollees. Study participation rates for some disease areas are over 40% when considering only contact registry enrollees who live within 100 mi of a clinical research study site. CONCLUSIONS Automated notifications can facilitate consistent, customized, and timely communication of relevant protocol information to potential research subjects. Our registry and supporting communication tools demonstrate a significant positive impact on study participation rates in our network. The use of the internet and automated notifications make the system scalable to support many protocols and registrants.

Clinical Research Informatics and Electronic Health Record Data

OBJECTIVES The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. RESULTS Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. CONCLUSIONS The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRIs key role in the infrastructure of a learning healthcare system.

Illness Perceptions and Fatigue in Systemic Vasculitis

To compare illness perceptions among patients with different forms of vasculitis, identify risk factors for negative illness perceptions, and determine the association between illness perceptions and fatigue.