Evaluation of the secondary use approach from Vanderbilt and its usability for Germany

Diese Arbeit befasst sich mit der Analyse der Secondary Use Systeme der Vanderbilt Universität, Nashville Tennessee (USA), und einem anknüpfenden Vergleich mit deutschen Konzepten und unter Berücksichtigung der deutschen Gesetzeslage. Dabei wurden, auf Basis einer vor Ort durchgeführten Analyse, wichtige Prozesse modelliert und im Anschluss mit den Datenschutzkonzepten der Technologie- und Methodenplattform für die vernetzte medizinische Forschung (TMF) verglichen. Weiterhin wurde darauf eingegangen, inwiefern eine Übertragung der Prozesse und Methoden mit dem deutschen Datenschutz vereinbar wäre. Die Bewertung der Ergebnisse zeigt, dass ein Großteil der zugrundeliegenden Prozesse in Vanderbilt auf Deutschland übertragen werden können, jedoch bei gewissen Methoden andere Ansätze gewählt werden müssen. Es wird ebenfalls hervorgehoben, dass es trotz Schutzmaßnahmen und -mechanismen Risiken für die Privatsphäre gibt.This work addresses the analysis of the secondary use systems from Vanderbilt University, Nashville Tennessee (USA), and a followed up comparison to German concepts under the consideration of German legislation. In doing so important processes were modeled based on an on-site visit and later on compared with data privacy concepts by Technologie- und Methodenplattform für die vernetzte medizinische Forschung (TMF). It was also addressed, to what extend an adaption of processes and methods would be compatible with German data privacy. The assessment of the results illustrates, that a better part of the underlying processes from Vanderbilt could be transferred to Germany, but that certain tasks would have to be implemented differently. It’s also emphasized, that in spite of preventive measures and mechanisms risks for the privacy exist

A European inventory of common electronic health record data elements for clinical trial feasibility

Background: Clinical studies are a necessity for new medications and therapies. Many studies, however, struggle to meet their recruitment numbers in time or have problems in meeting them at all. With increasing numbers of electronic health records (EHRs) in hospitals, huge databanks emerge that could be utilized to support research. The Innovative Medicine Initiative (IMI) funded project ‘Electronic Health Records for Clinical Research’ (EHR4CR) created a standardized and homogenous inventory of data elements to support research by utilizing EHRs. Our aim was to develop a Data Inventory that contains elements required for site feasibility analysis. Methods:The Data Inventory was created in an iterative, consensus driven approach, by a group of up to 30 people consisting of pharmaceutical experts and informatics specialists. An initial list was subsequently expanded by data elements of simplified eligibility criteria from clinical trial protocols. Each element was manually reviewed by pharmaceutical experts and standard definitions were identified and added. To verify their availability, data exports of the source systems at eleven university hospitals throughout Europe were conducted and evaluated. Results: The Data Inventory consists of 75 data elements that, on the one hand are frequently used in clinical studies, and on the other hand are available in European EHR systems. Rankings of data elements were created from the results of the data exports. In addition a sub-list was created with 21 data elements that were separated from the Data Inventory because of their low usage in routine documentation. Conclusion: The data elements in the Data Inventory were identified with the knowledge of domain experts from pharmaceutical companies. Currently, not all information that is frequently used in site feasibility is documented in routine patient care.<br

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.</p

Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned

Abstract Background The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform. Methods We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs). Results We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform. Conclusions We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset

Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system

International audienceBackgroundWith the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match.The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system.MethodsThe evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the “alloyed” gold standard, which was produced by a manual check of patient records.ResultsThe patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard’s confidence interval.In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process.ConclusionsAt the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods.Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation

Additional file 2: of Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned

Normalized Criteria for the DERENEDIAB, aXa and EWING 2008 studies (DOCX 49 kb

ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data

<div><p>Introduction</p><p>A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.</p><p>Methods</p><p>The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.</p><p>Results</p><p>The system is implemented as an open source web application (available at <a href="https://odmanalysis.uni-muenster.de" target="_blank">https://odmanalysis.uni-muenster.de</a>) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.</p><p>Discussion</p><p>Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.</p></div

Additional file 1: of Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned

Free Text Eligibility Criteria for DERENEDIAB, aXa and EWING 2008 studies (DOCX 202 kb

Schematic overview of the application’s workflow.

<p>The user can upload an ODM file via the web-based front-end. After parsing the file, its content is temporarily stored in a database. The calculated statistics and charts are presented on the result pages and are also generated as PDF. The PDF can be downloaded via the front-end and will be deleted from the server, equally the database content, after the session ends.</p