Accuracy of using natural language processing methods for identifying healthcare-associated infections.

OBJECTIVE: There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents. MATERIALS AND METHODS: The collection of textual records in these hospitals was carried out between October 2009 and December 2010 in three French University hospitals (Lyon, Rouen and Nice). The following medical specialties were included in the study: digestive surgery, neurosurgery, orthopedic surgery, adult intensive-care units. Reference Standard surveillance was compared with the results of automatic detection using NLP. Sensitivity on 56 HAI cases and specificity on 57 non-HAI cases were calculated. RESULTS: The accuracy rate was 84% (n = 95/113). The overall sensitivity of automatic detection of HAIs was 83.9% (CI 95%: 71.7-92.4) and the specificity was 84.2% (CI 95%: 72.1-92.5). The sensitivity varies from one specialty to the other, from 69.2% (CI 95%: 38.6-90.9) for intensive care to 93.3% (CI 95%: 68.1-99.8) for orthopedic surgery. The manual review of classification errors showed that the most frequent cause was an inaccurate temporal labeling of medical events, which is an important factor for HAI detection. CONCLUSION: This study confirmed the feasibility of using NLP for the HAI detection in hospital facilities. Automatic HAI detection algorithms could offer better surveillance standardization for hospital comparisons.

TIER competency-based training course for the first receivers of CBRN casualties: a European perspective.

INTRODUCTION: Education and training are key elements of health system preparedness vis-à-vis chemical, biological, radiological and nuclear (CBRN) emergencies. Medical respondents need sufficient knowledge and skills to manage the human impact of CBRN events. OBJECTIVE: The current study was designed to determine which competencies are needed by hospital staff when responding to CBRN emergencies, define educational needs to develop these competencies, and implement a suitable delivery method. METHODS: This study was carried out from September 2014 to February 2015, using a three-step modified Delphi method. On the basis of international experiences, publications, and experts' consensus, core competencies for hospital staff - as CBRN casualty receivers - were determined, and training curricula and delivery methods were defined. RESULTS: The course consists of 10 domains. These are as follows: threat identification; health effects of CBRN agents; planning; hospital incident command system; information management; safety, personal protective equipment and decontamination; medical management; essential resources; psychological support; and ethical considerations. Expected competencies for each domain were defined. A blended approach was chosen. CONCLUSION: By identifying a set of core competencies, this study aimed to provide the specific knowledge and skills required by medical staff to respond to CRBN emergencies. A blended approach may be a suitable delivery method, allowing medical staff to attend the same training sessions despite different time zones and locations. The study output provides a CBRN training scheme that may be adapted and used at the European Union level.

Annotation methods to develop and evaluate an expert system based on natural language processing in electronic medical records.

The objective of the SYNODOS collaborative project was to develop a generic IT solution, combining a medical terminology server, a semantic analyser and a knowledge base. The goal of the project was to generate meaningful epidemiological data for various medical domains from the textual content of French medical records. In the context of this project, we built a care pathway oriented conceptual model and corresponding annotation method to develop and evaluate an expert system's knowledge base. The annotation method is based on a semi-automatic process, using a software application (MedIndex). This application exchanges with a cross-lingual multi-termino-ontology portal. The annotator selects the most appropriate medical code proposed for the medical concept in question by the multi-termino-ontology portal and temporally labels the medical concept according to the course of the medical event. This choice of conceptual model and annotation method aims to create a generic database of facts for the secondary use of electronic health records data.