Evaluating the informatics for integrating biology and the bedside system for clinical research

<p>Abstract</p> <p>Background</p> <p>Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.</p> <p>Methods</p> <p>Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone.</p> <p>Results</p> <p>We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc.</p> <p>Conclusion</p> <p>The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.</p

The status of clinical trials: Cause for concern

<p>Abstract</p> <p>Background</p> <p>Americans see clinical research as important, with over 15 million American residents participating in NIH-sponsored studies in 2008 and growing yearly.</p> <p>Methods</p> <p>Documents reporting NIH supported Clinical Research projects were reviewed.</p> <p>Results</p> <p>When compared with other studies, the number of interventional Phase III and Phase IV trials have decreased from 20% to 4.4% from 1994-2008.</p> <p>Conclusions</p> <p>This finding most likely has occurred for several reasons. One reason is that the physician lacks an infrastructure for designing and carrying out trials. This lack is because of an absence of a coordinated effort to train clinical trialists. It is clear that the Nation needs a more purposeful approach to developing and maintaining the infrastructure for designing and conducting clinical trials. Building it de novo trial by trial is profoundly inefficient, to say nothing about time consuming and error prone.</p

Reengineering the clinical research enterprise to involve more community clinicians

<p>Abstract</p> <p>Background</p> <p>The National Institutes of Health has called for expansion of practice-based research to improve the clinical research enterprise.</p> <p>Methods</p> <p>This paper presents a model for the reorganization of clinical research to foster long-term participation by community clinicians.</p> <p>Based on the literature and interviews with clinicians and other stakeholders, we posited a model, conducted further interviews to test the viability of the model, and further adapted it.</p> <p>Results</p> <p>We propose a three-dimensional system of checks and balances to support community clinicians using research support organizations, community outreach, a web-based registry of clinicians and studies, web-based training services, quality audits, and a feedback mechanism for clinicians engaged in research.</p> <p>Conclusions</p> <p>The proposed model is designed to offer a systemic mechanism to address current barriers that prevent clinicians from participation in research. Transparent mechanisms to guarantee the safety of patients and the integrity of the research enterprise paired with efficiencies and economies of scale are maintained by centralizing some of the functions. Assigning other responsibilities to more local levels assures flexibility with respect to the size of the clinician networks and the changing needs of researchers.</p

Collaborative research between clinicians and researchers: a multiple case study of implementation

<p>Abstract</p> <p>Background</p> <p>Bottom-up, clinician-conceived and directed clinical intervention research, coupled with collaboration from researcher experts, is conceptually endorsed by the participatory research movement. This report presents the findings of an evaluation of a program in the Veterans Health Administration meant to encourage clinician-driven research by providing resources believed to be critical. The evaluation focused on the extent to which funded projects: maintained integrity to their original proposals; were methodologically rigorous; were characterized by collaboration between partners; and resulted in sustained clinical impact.</p> <p>Methods</p> <p>Researchers used quantitative (survey and archival) and qualitative (focus group) data to evaluate the implementation, evaluation, and sustainability of four clinical demonstration projects at four sites. Fourteen research center mentors and seventeen clinician researchers evaluated the level of collaboration using a six-dimensional model of participatory research.</p> <p>Results</p> <p>Results yielded mixed findings. Qualitative and quantitative data suggested that although the process was collaborative, clinicians' prior research experience was critical to the quality of the projects. Several challenges were common across sites, including subject recruitment, administrative support and logistics, and subsequent dissemination. Only one intervention achieved lasting clinical effect beyond the active project period. Qualitative analyses identified barriers and facilitators and suggested areas to improve sustainability.</p> <p>Conclusions</p> <p>Evaluation results suggest that this participatory research venture was successful in achieving clinician-directed collaboration, but did not produce sustainable interventions due to such implementation problems as lack of resources and administrative support.</p

Preoperative computed tomography staging of nonmetastatic colon cancer predicts outcome: implications for clinical trials

Colon cancer patients routinely undergo preoperative computed tomography (CT) scanning, but local staging is thought to be inaccurate. We aimed to determine if clinical outcome could be predicted from radiological features of the primary tumour. Consecutive patients at one hospital undergoing primary resection for colon cancer during 2000–2004 were included. Patients with visible metastases were excluded. Preoperative CT scans were reviewed independently by two radiologists blinded to histological stage and outcome. Images of the primary tumour were evaluated according to conventional TNM criteria and patients were stratified into ‘good' or ‘poor' prognosis groups. Comparison was made between prognostic group and actual clinical outcome. Hundred and twenty-six preoperative CT scans were reviewed. T-stage and nodal status was correctly predicted in only 60 and 62%, respectively. However, inter-observer agreement for prognostic group was 79% (κ=0.59) and 3-year relapse-free survival was 71 and 43% for the CT-predicted ‘good' and ‘poor' groups, respectively (P<0.0066). This compared favourably with 75 vs 43% for histology-predicted prognostic groups. Computed tomography is a robust method for stratifying patients preoperatively, with similar accuracy to histopathology for predicting outcome. Recognition of poor prognosis tumours preoperatively may permit investigation into the future use of neo-adjuvant therapy in colon cancer