[Adapting to the SARS-CoV2 pandemic: Experience of the Curie Institute]. / S'adapter face à la pandémie SARS-CoV2 : l'expérience de l'Institut Curie.

The Curie Institute exclusively cares for cancer patients, who were considered particularly "vulnerable" from the start of the SARS-CoV 2 pandemic. This pandemic, which took the medical world by surprise, suddenly required the Institute's hospital to undergo rapid and multimodal restructuring, while having an impact on everyone to varying degrees. We will examine here how this hospital has coped, with the concern for a new benefit-risk balance, in times of greater medical uncertainty and scarcity of certain resources, for these "vulnerable" patients but also for their relatives and staff. We will highlight by theme the positive aspects and difficulties encountered, and then what could be useful for other hospitals as the pandemic is ongoing.

Automatic acquisition of synonym resources and assessment of their impact on the enhanced search in EHRs.

OBJECTIVE: Currently, the use of natural language processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources. Indeed, in order to support such tasks, various types of such resources need to be collected or acquired (i.e., morphological, orthographic, synonymous). METHODS: We propose a novel method for the acquisition of synonymy resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymy relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality. RESULTS: Applied to series of synonym terms from the French subset of the UMLS , the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. In order to better integrate these resources in an EHR search system, we analyzed a sample of clinical queries submitted by healthcare professionals. CONCLUSIONS: Observation of clinical queries shows that they make a very little use of the query expansion function, and, whenever they do, synonymy relations are rarely involved.

[Shared responsibility for follow-up of breast cancer patients. Experience of the Institut Curie]. / Surveillance partagée des patientes traitées pour un cancer du sein. L'expérience de l'Institut Curie.

Routine follow-up of breast cancer patients in specialist clinics is standard practice. This follow-up involves regularly scheduled breast cancer check-ups during the disease-free period, in order to detect recurrence. However, demands on specialist resources rise with the increase in the prevalence of diagnosed breast cancer. Since September 2004, it was proposed in our Institut an alternative routine follow-up schedule. Some patients who are in remission for more than 5 years will be deferred to their general practitioner or gynaecologist for follow-up schedule, alternatively with their referent practitioner from the institut (oncologist, radiation oncologist, or surgeon). We herein present the preliminary results of this strategy, and demonstrate that partially transferring primary responsibility for follow-up does not compromise its quality.

Automatic acquisition of synonyms from French UMLS for enhanced search of EHRs.

Currently, the use of Natural Language Processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources: various types of such resources need to be collected or acquired. In this work, we propose a novel method for the acquisition of synonymous resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymous relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality. Applied to series of synonym terms from the French subset of the UMLS, the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low.