Ontology-based data integration between clinical and research systems

Data from the electronic medical record comprise numerous structured but uncoded elements, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of relevant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

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

Endovascular Treatment of Intracranial Vein and Venous Sinus Thrombosis-A Systematic Review.

BACKGROUND Cerebral venous sinus or vein thromboses (SVT) are treated with heparin followed by oral anticoagulation. Even after receiving the best medical treatment, numerous patients experience neurological deterioration, intracerebral hemorrhage or brain edema. Debate regarding whether endovascular treatment (EVT) is beneficial in such severe cases remains ongoing. This systematic review summarizes the current evidence supporting the use of EVT for SVT on the basis of case presentations, with a focus on patient selection, treatment strategies and the effects of the COVID-19 pandemic. METHODS This systemic literature review included randomized controlled trials (RCTs) and retrospective observational data analyzing five or more patients. Follow-up information (modified Rankin scale (mRS)) was required to be provided (individual patient data). RESULTS 21 records (n = 405 patients; 1 RCT, 20 observational studies) were identified. EVT was found to be feasible and safe in a highly selected patient cohort but was not associated with an increase in good functional outcomes (mRS 0-2) in RCT data. In observational data, good functional outcomes were frequently observed despite an anticipated poor prognosis. CONCLUSION The current evidence does not support the routine incorporation of EVT in SVT treatment. However, in a patient cohort prone to poor prognosis, EVT might be a reasonable therapeutic option. Further studies determining the patients at risk, choice of methods and devices, and timing of treatment initiation are warranted

Leitfaden zum Datenschutz in medizinischen Forschungsprojekten

Das Vertrauen von Patienten und Probanden ist eine unverzichtbare Voraussetzung für den Erfolg medizinischer Forschungsprojekte, die ohne die Erhebung, langfristige Speicherung und Analyse von klinischen Daten und Proben nicht durchgeführt werden können. Medizinische Forschung arbeitet heute überwiegend vernetzt in zunehmend größeren Forschungsverbünden. Entsprechend nimmt auch die Bedeutung von Datenschutz und Datensicherheit immer weiter zu. Die TMF hat bereits 2003 erstmals generische Datenschutzkonzepte für medizinische Forschungsverbünde veröffentlicht. Auf dieser Basis konnten zahlreiche Forschungsprojekte ihre Datenschutzkonzepte schneller erarbeiten und abstimmen. Die dabei gewonnenen Erfahrungen sind in die grundlegende Überarbeitung der generischen Konzepte eingeflossen. So trägt das neue Konzept der Vielschichtigkeit medizinischer Forschungsprozesse durch einen modularen Aufbau Rechnung und wurde zudem in einen umfassenden Leitfaden eingebettet

Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation

Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk