La evaluación de impacto en protección de datos en los proyectos de investigación / Impact assessment on data protection in research projects

Los recientes cambios en la normativa europea de protección de datos de carácter personal siguen permitiendo el uso de los datos sanitarios con fines de investigación, pero establecen la evaluación de impacto en protección de datos como instrumento de reflexión y análisis de riesgos en el proceso de tratamiento de datos. La publicación de una guía facilita la realización de esta evaluación de impacto, aunque no es de aplicación directa para los proyectos de investigación. Se detalla la experiencia en un proyecto concreto, y se muestra cómo el contexto del tratamiento toma relevancia respecto a las características de los datos. La realización de una evaluación de impacto es una oportunidad para asegurar el cumplimiento de los principios de la protección de datos en un entorno cada vez más complejo y con mayores desafíos éticos

Recent changes in European regulations for personal data protection still allow the use of health data for research purposes, but they have set the Impact Assessment on Data Protection as an instrument for reflection and risk analysis in the process of data processing. The publication of a guide for facilitates this impact assessment, although it is not directly applicable to research projects. Experience in a specific project is detailed, showing how the context of the treatment becomes relevant with respect to the data characteristics. Carrying out an impact assessment is an opportunity to ensure compliance with the principles of data protection in an increasingly complex environment with greater ethical challenges

Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes.

PURPOSE: Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points. METHODS: Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact. RESULTS: The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index. CONCLUSION: The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.

Validation of data from electronic data warehouse in diabetic ketoacidosis: Caution is needed.

AIMS: This study validated enterprise data warehouse (EDW) data for a cohort of hospitalized patients with a primary diagnosis of diabetic ketoacidosis (DKA). METHODS: 247 patients with 319 admissions for DKA (ICD-9 code 250.12, 250.13, or 250.xx with biochemical criteria for DKA) were admitted to Northwestern Memorial Hospital from 1/1/2010 to 9/1/2013. Validation was performed by electronic medical record (EMR) review of 10% of admissions (N = 32). Classification of diabetes type (Type 1 vs. Type 2) and DKA clinical status were compared between the EMR review and EDW data. RESULTS: Key findings included incorrect classification of diabetes type in 5 of 32 (16%) admissions and indeterminable classification in 5 admissions. DKA was not present, based on the review, in 11 of 32 (34%) admissions. DKA was not present, based on biochemical criteria, in 15 of 32 (47%) admissions. CONCLUSIONS: This study found that EDW data have substantial errors. Some discrepancies can be addressed by refining the EDW query code, while others, related to diabetes classification and DKA diagnosis, cannot be corrected without improving clinical coding accuracy, consistency of medical record documentation, or EMR design. These results support the need for comprehensive validation of data for complex clinical populations obtained through data repositories such as the EDW.

Database Quality and Access Issues Relevant to Research Using Anesthesia Information Management System Data.

For this special article, we reviewed the computer code, used to extract the data, and the text of all 47 studies published between January 2006 and August 2017 using anesthesia information management system (AIMS) data from Thomas Jefferson University Hospital (TJUH). Data from this institution were used in the largest number (P = .0007) of papers describing the use of AIMS published in this time frame. The AIMS was replaced in April 2017, making this finite sample finite. The objective of the current article was to identify factors that made TJUH successful in publishing anesthesia informatics studies. We examined the structured query language used for each study to examine the extent to which databases outside of the AIMS were used. We examined data quality from the perspectives of completeness, correctness, concordance, plausibility, and currency. Our results were that most could not have been completed without external database sources (36/47, 76.6%; P = .0003 compared with 50%). The operating room management system was linked to the AIMS and was used significantly more frequently (26/36, 72%) than other external sources. Access to these external data sources was provided, allowing exploration of data quality. The TJUH AIMS used high-resolution timestamps (to the nearest 3 milliseconds) and created audit tables to track changes to clinical documentation. Automatic data were recorded at 1-minute intervals and were not editable; data cleaning occurred during analysis. Few paired events with an expected order were out of sequence. Although most data elements were of high quality, there were notable exceptions, such as frequent missing values for estimated blood loss, height, and weight. Some values were duplicated with different units, and others were stored in varying locations. Our conclusions are that linking the TJUH AIMS to the operating room management system was a critical step in enabling publication of multiple studies using AIMS data. Access to this and other external databases by analysts with a high degree of anesthesia domain knowledge was necessary to be able to assess the quality of the AIMS data and ensure that the data pulled for studies were appropriate. For anesthesia departments seeking to increase their academic productivity using their AIMS as a data source, our experiences may provide helpful guidance.

A Fast Healthcare Interoperability Resources (FHIR) layer implemented over i2b2.

BACKGROUND: Standards and technical specifications have been developed to define how the information contained in Electronic Health Records (EHRs) should be structured, semantically described, and communicated. Current trends rely on differentiating the representation of data instances from the definition of clinical information models. The dual model approach, which combines a reference model (RM) and a clinical information model (CIM), sets in practice this software design pattern. The most recent initiative, proposed by HL7, is called Fast Health Interoperability Resources (FHIR). The aim of our study was to investigate the feasibility of applying the FHIR standard to modeling and exposing EHR data of the Georges Pompidou European Hospital (HEGP) integrating biology and the bedside (i2b2) clinical data warehouse (CDW). RESULTS: We implemented a FHIR server over i2b2 to expose EHR data in relation with five FHIR resources: DiagnosisReport, MedicationOrder, Patient, Encounter, and Medication. The architecture of the server combines a Data Access Object design pattern and FHIR resource providers, implemented using the Java HAPI FHIR API. Two types of queries were tested: query type #1 requests the server to display DiagnosticReport resources, for which the diagnosis code is equal to a given ICD-10 code. A total of 80 DiagnosticReport resources, corresponding to 36 patients, were displayed. Query type #2, requests the server to display MedicationOrder, for which the FHIR Medication identification code is equal to a given code expressed in a French coding system. A total of 503 MedicationOrder resources, corresponding to 290 patients, were displayed. Results were validated by manually comparing the results of each request to the results displayed by an ad-hoc SQL query. CONCLUSION: We showed the feasibility of implementing a Java layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources. An important part of this work was the structural and semantic mapping between the i2b2 model and the FHIR RM. To accomplish this, developers must manually browse the specifications of the FHIR standard. Our source code is freely available and can be adapted for use in other i2b2 sites.

The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience.

BACKGROUND: When developed jointly with clinical information systems, clinical data warehouses (CDWs) facilitate the reuse of healthcare data and leverage clinical research. OBJECTIVE: To describe both data access and use for clinical research, epidemiology and health service research of the "Hôpital Européen Georges Pompidou" (HEGP) CDW. METHODS: The CDW has been developed since 2008 using an i2b2 platform. It was made available to health professionals and researchers in October 2010. Procedures to access data have been implemented and different access levels have been distinguished according to the nature of queries. RESULTS: As of July 2016, the CDW contained the consolidated data of over 860,000 patients followed since the opening of the HEGP hospital in July 2000. These data correspond to more than 122 million clinical item values, 124 million biological item values, and 3.7 million free text reports. The ethics committee of the hospital evaluates all CDW projects that generate secondary data marts. Characteristics of the 74 research projects validated between January 2011 and December 2015 are described. CONCLUSION: The use of HEGP CDWs is a key facilitator for clinical research studies. It required however important methodological and organizational support efforts from a biomedical informatics department.

Protocol for a national blood transfusion data warehouse from donor to recipient.

INTRODUCTION: Blood transfusion has health-related, economical and safety implications. In order to optimise the transfusion chain, comprehensive research data are needed. The Dutch Transfusion Data warehouse (DTD) project aims to establish a data warehouse where data from donors and transfusion recipients are linked. This paper describes the design of the data warehouse, challenges and illustrative applications. STUDY DESIGN AND METHODS: Quantitative data on blood donors (eg, age, blood group, antibodies) and products (type of product, processing, storage time) are obtained from the national blood bank. These are linked to data on the transfusion recipients (eg, transfusions administered, patient diagnosis, surgical procedures, laboratory parameters), which are extracted from hospital electronic health records. APPLICATIONS: Expected scientific contributions are illustrated for 4 applications: determine risk factors, predict blood use, benchmark blood use and optimise process efficiency. For each application, examples of research questions are given and analyses planned. CONCLUSIONS: The DTD project aims to build a national, continuously updated transfusion data warehouse. These data have a wide range of applications, on the donor/production side, recipient studies on blood usage and benchmarking and donor-recipient studies, which ultimately can contribute to the efficiency and safety of blood transfusion.