How to Optimize Connection Between PACS and Clinical Data Warehouse: A Web Service Approach Based on Full Metadata Integration.

Clinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5.94 seconds and 0.9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series. We could retrieve all patients and imaging tests of the prostate cancer cohort with a precision of 0.95 and a recall of 1. By leveraging the CMOVE method, our approach based on the Dicom protocol is scalable and domain-neutral. Future improvement will focus on performance optimization and de identification.

Indexing Imaging Reports for Data Sharing: A Study of Mapping Using RadLex Playbook and LOINC.

New use cases and the need for quality control and imaging data sharing in health studies require the capacity to align them to reference terminologies. We are interested in mapping the local terminology used at our center to describe imaging procedures to reference terminologies for imaging procedures (RadLex Playbook and LOINC/RSNA Radiology Playbook). We performed a manual mapping of the 200 most frequent imaging report titles at our center (i.e. 73.2% of all imaging exams). The mapping method was based only on information explicitly stated in the titles. The results showed 57.5% and 68.8% of exact mapping to the RadLex and LOINC/RSNA Radiology Playbooks, respectively. We identified the reasons for the mapping failure and analyzed the issues encountered.

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case.

BACKGROUND: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). OBJECTIVE: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. METHODS: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. RESULTS: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. CONCLUSIONS: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data from the clinical data warehouse of Rennes and from other sources. The dataset included hospital stays based on the criteria of the French national methodology for the 30-day readmission rate (i.e., patients older than 18 years, geolocation, no iterative stays, and no hospitalization for palliative care), with a similar pre-processing for all algorithms. We calculated the area under the ROC curve (AUC) for 30-day readmission prediction by each model. In total, we included 259114 hospital stays, with a readmission rate of 8.8%. The AUC was 0.61 for the Logistic Regression, 0.69 for the Gradient Boosting, 0.69 for the Random Forest, and 0.62 for the Neural Network model. We obtained the best performance and reproducibility to predict readmissions with Random Forest, and found that the algorithms performed better when data came from different sources.

Cancer risk in dialyzed patients with and without diabetes.

BACKGROUND: The risk of cancer is higher in patients with renal diseases and diabetes compared with the general population. The aim of this study was to assess in dialyzed patients, the association between diabetes and the risk to develop a cancer after dialysis start. METHODS: All patients who started dialysis in the French region of Poitou-Charentes between 2008 and 2015 were included. Their baseline characteristics were extracted from the French Renal Epidemiology and Information Network and were linked to data relative to cancer occurrence from the Poitou-Charentes General Cancer Registry using a procedure developed by the INSHARE platform. The association between diabetes and the risk of cancer was assessed using the Fine & Gray model that takes into account the competing risk of death. RESULTS: Among the 1634 patients included, 591 (36.2 %) had diabetes and 91 (5.6 %) patients developed a cancer (n = 24 before or at dialysis start, and n = 67 after dialysis start). The risk to develop a cancer after dialysis initiation was lower in dialyzed patients with diabetes than without diabetes (SHR = 0.54; 95 %CI: 0.32-0.91). Moreover, compared with the general population, the cancer risk was higher in dialyzed patients without diabetes, but not in those with diabetes. CONCLUSION: The risk of developing a cancer in the region of Poitou-Charentes is higher in dialyzed patients without diabetes than with diabetes.

An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics.

The aim of the study was to build a proof-of-concept demonstratrating that big data technology could improve drug safety monitoring in a hospital and could help pharmacovigilance professionals to make data-driven targeted hypotheses on adverse drug events (ADEs) due to drug-drug interactions (DDI). We developed a DDI automatic detection system based on treatment data and laboratory tests from the electronic health records stored in the clinical data warehouse of Rennes academic hospital. We also used OrientDb, a graph database to store informations from five drug knowledge databases and Spark to perform analysis of potential interactions betweens drugs taken by hospitalized patients. Then, we developed a machine learning model to identify the patients in whom an ADE might have occurred because of a DDI. The DDI detection system worked efficiently and computation time was manageable. The system could be routinely employed for monitoring.

eHOP Clinical Data Warehouse: From a Prototype to the Creation of an Inter-Regional Clinical Data Centers Network.

Creation of networks such as clinical data centers within the hospital enables efficient exploitation of clinical data from a local to an inter-regional scope. This work present the structuration of the French Western Clinical Data Center Network (FWCDCN) conducted between 2016 and 2018. As of November 2018, FWCDCD is compounded with 7 institutions. CDW of the combinded Clinical Data Centers (CDC) contains the data of over 4 million patients followed since 2000.