Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting.

BACKGROUND: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. METHODS: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. RESULTS: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. CONCLUSIONS: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Cost-benefit assessment of using electronic health records data for clinical research versus current practices: Contribution of the Electronic Health Records for Clinical Research (EHR4CR) European Project.

INTRODUCTION: The widespread adoption of electronic health records (EHR) provides a new opportunity to improve the efficiency of clinical research. The European EHR4CR (Electronic Health Records for Clinical Research) 4-year project has developed an innovative technological platform to enable the re-use of EHR data for clinical research. The objective of this cost-benefit assessment (CBA) is to assess the value of EHR4CR solutions compared to current practices, from the perspective of sponsors of clinical trials. MATERIALS AND METHODS: A CBA model was developed using an advanced modeling approach. The costs of performing three clinical research scenarios (S) applied to a hypothetical Phase II or III oncology clinical trial workflow (reference case) were estimated under current and EHR4CR conditions, namely protocol feasibility assessment (S1), patient identification for recruitment (S2), and clinical study execution (S3). The potential benefits were calculated considering that the estimated reduction in actual person-time and costs for performing EHR4CR S1, S2, and S3 would accelerate time to market (TTM). Probabilistic sensitivity analyses using Monte Carlo simulations were conducted to manage uncertainty. RESULTS: Should the estimated efficiency gains achieved with the EHR4CR platform translate into faster TTM, the expected benefits for the global pharmaceutical oncology sector were estimated at €161.5m (S1), €45.7m (S2), €204.5m (S1+S2), €1906m (S3), and up to €2121.8m (S1+S2+S3) when the scenarios were used sequentially. CONCLUSIONS: The results suggest that optimizing clinical trial design and execution with the EHR4CR platform would generate substantial added value for pharmaceutical industry, as main sponsors of clinical trials in Europe, and beyond.

Smart Program Design Through a Common Information Model.

Although much information is already available publically from information-sharing initiatives such as ClinicalTrials.gov, information about clinical programs is unstructured, inconsistent, and incomplete. Clinical research within bioscience companies, health care, academia, and governmental agencies could benefit from easier access to best practices, historical information, and improved information sharing. Facilitating information sharing requires a standardized information model. Information standards today focus on individual clinical trials and the representation of clinical trial data. Although work is ongoing to expand standards to cover the protocol, these are insufficient to capture the objectives, rationale, and design thinking behind clinical programs. An information model is proposed to cover the rationalization and decision-making aspects of designing a clinical program and its associated trials. This paper is the output of a newly formed multicompany working group that examines the merits of a clinical program-level information standard. An example information model is presented to explain the concept.

A Model Data Management Plan Standard Operating Procedure: Results From the DIA Clinical Data Management Community, Committee on Clinical Data Management Plan.

BACKGROUND: The DIA Clinical Data Management Community created a committee to develop a model standard operating procedure (SOP) for writing a data management plan. METHODS: The goal of the committee was to develop a plan that could be used by industry and academic institutions. The model was based on contributed examples from committee members and their experiences with current practices and technologies. It is understood that as new clinical trial technology is implemented, the SOP will require modification. RESULTS: The model SOP and associated templates are presented as a starting point, and each company or institution will need to modify them to meet its individual needs. CONCLUSION: The model DMP SOP produced addresses most data management issues that are present in any phase clinical trial while providing a flexible framework.