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NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds.

Purpose: Mapping clinical observations and medical test results into the standardized vocabulary LOINC is a prerequisite for exchanging clinical data between health information systems and ensuring efficient interoperability., Methods: We present a comparison of three approaches for LOINC transcoding applied to French data collected from real-world settings. These approaches include both a state-of-the-art language model approach and a classifier chains approach., Results: Our study demonstrates that we successfully improve the performance of the baselines using the classifier chains approach and compete effectively with state-of-the-art language models., Conclusions: Our approach proves to be efficient, cost-effective despite reproducibility challenges and potential for future optimizations and dataset testing.

While neural networks (NN) have been successfully applied to many NLP tasks, the way they function is often difficult to interpret. In this article, we focus on binary text classification via NNs and propose a new tool, which includes a visualization of the decision boundary and the distances of data elements to this boundary. This tool increases the interpretability of NN. Our approach uses two innovative views: (1) an overview of the text representation space and (2) a local view allowing data exploration around the decision boundary for various localities of this representation space. These views are integrated into a visual platform, EBBE-Text, which also contains state-of-the-art visualizations of NN representation spaces and several kinds of information obtained from the classification process. The various views are linked through numerous interactive functionalities that enable easy exploration of texts and classification results via the various complementary views. A user study shows the effectiveness of the visual encoding and a case study illustrates the benefits of using our tool for the analysis of the classifications obtained with several recent NNs and two datasets.

Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient's journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.

Context: We present a post-hoc approach to improve the recall of ICD classification., Method: The proposed method can use any classifier as a backbone and aims to calibrate the number of codes returned per document. We test our approach on a new stratified split of the MIMIC-III dataset., Results: When returning 18 codes on average per document we obtain a recall that is 20% better than a classic classification approach.

Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009-2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause. We used these patterns to characterize hospital healthcare flows in a visualization tool. We clustered these trajectories with kmlShape to identify time gap and tariff profiles. ACS hospital healthcare flows have three key categories: Angina pectoris, Myocardial Infarction or Ischemia . Elderly flows were more complex. Time gap profiles showed that readmissions were closer together as time goes by. Tariff profiles were different according to age and initial event. Our approach might be applied to monitoring other chronic diseases. Further work is needed to integrate these results into a medical decision-making tool.

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events' prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Jessica Pinaire et al.)

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Attention about the risks of online social networks (SNs) has been called upon reports describing their use to express emotional distress and suicidal ideation or plans. On the Internet, cyberbullying, suicide pacts, Internet addiction, and "extreme" communities seem to increase suicidal behavior (SB). In this study, the scientific literature about SBs and SNs was narratively reviewed. Some authors focus on detecting at-risk populations through data mining, identification of risks factors, and web activity patterns. Others describe prevention practices on the Internet, such as websites, screening, and applications. Targeted interventions through SNs are also contemplated when suicidal ideation is present. Multiple predictive models should be defined, implemented, tested, and combined in order to deal with the risk of SB through an effective decision support system. This endeavor might require a reorganization of care for SNs users presenting suicidal ideation., (© 2019 Wiley Periodicals, Inc.)

Today, social media is increasingly used by patients to openly discuss their health. Mining automatically such data is a challenging task because of the non-structured nature of the text and the use of many abbreviations and the slang terms. Our goal is to use Patient Authored Text to build a French Consumer Health Vocabulary on breast cancer field, by collecting various kinds of non-experts' expressions that are related to their diseases and then compare them to biomedical terms used by health care professionals. We combine several methods of the literature based on linguistic and statistical approaches to extract candidate terms used by non-experts and to link them to expert terms. We use messages extracted from the forum on ' cancerdusein.org ' and a vocabulary dedicated to breast cancer elaborated by the Institut National Du Cancer. We have built an efficient vocabulary composed of 192 validated relationships and formalized in Simple Knowledge Organization System ontology.

Background: Patients and health care professionals are becoming increasingly preoccupied in complementary and alternative medicine (CAM) that can also be called nonpharmacological interventions (NPIs). In just a few years, this supportive care has gone from solutions aimed at improving the quality of life to solutions intended to reduce symptoms, supplement oncological treatments, and prevent recurrences. Digital social networks are a major vector for disseminating these practices that are not always disclosed to doctors by patients. An exploration of the content of exchanges on social networks by patients suffering from breast cancer can help to better identify the extent and diversity of these practices., Objective: This study aimed to explore the interest of patients with breast cancer in CAM from posts published in health forums and French-language social media groups., Methods: The retrospective study was based on a French database of 2 forums and 4 Facebook groups between June 3, 2006, and November 17, 2015. The extracted, anonymized, and compiled data (264,249 posts) were analyzed according to the occurrences associated with the NPI categories and NPI subcategories, their synonyms, and their related terms., Results: The results showed that patients with breast cancer use mainly physical (37.6%) and nutritional (31.3%) interventions. Herbal medicine is a subcategory that was cited frequently. However, the patients did not mention digital interventions., Conclusions: This exploratory study of the main French forums and discussion groups indicates a significant interest in CAM during and after treatments for breast cancer, with primarily physical and nutritional interventions complementing approved treatments. This study highlights the importance of accurate information (vs fake medicine), prescription and monitoring of these interventions, and the mediating role that health professionals must play in this regard., (©Béatrice Lognos, François Carbonnel, Isabelle Boulze Launay, Sandra Bringay, Estelle Guerdoux-Ninot, Caroline Mollevi, Pierre Senesse, Gregory Ninot. Originally published in JMIR Cancer (http://cancer.jmir.org), 27.11.2019.)

Suicide is a growing public health concern in online communities. In this paper, we analyze online communications on the topic of suicide in the social networking platform, Reddit. We combine lexical text characteristics with semantic information to identify comments with features of suicide attempts and methods. Then, we develop a set of machine learning methods to automatically extract suicide methods and classify the user comments. Our classification methods performance varied between suicide experiences, with F1-scores up to 0.92 for "drugs" and greater than 0.82 for "hanging" and "other methods". Our exploratory analysis reveals that the most frequent reported suicide methods are drug overdose, hanging, and wrist-cutting.

Background: Currently, cardiovascular disease (CVD) is widely acknowledged to be the first leading cause of fatality in the world with 31% of all deaths worldwide and is predicted to remain as such in 2030. Furthermore, CVD is also a major cause of morbidity in adults worldwide. Among these diseases, the coronary artery disease (CAD) is the most common cause, accounting for over 40% of CVD deaths. Despite a decline in mortality rates, the consequences of more effective preventive and management programs, the burden of CAD remains significant. Indeed, the rise in the prevalence of modifiable risk factors due to changes in lifestyle and health behaviors has further increased the burden of this epidemic. Our objective was to evaluate the hospital burden of CAD via MI trends and Percutaneous Coronary Intervention (PCI) in the French Prospective Payment System (PPS)., Methods: MI/PCI were identified in the national PPS database from 2009 to 2014 for patients aged 20 to 99, living in metropolitan France. We examined hospitalisation, readmission and mortality trends using standardised rates., Results: Over the six-year period, we identified 678,021 patients, representing 900,121 stays of which, 215,224 had a MI and a PCI. Admission trends increased by nearly 25%. Acute MI cases increased every year, with an alarming increase in women, and more specifically in young women. Men were 3 times more hospitalised than women, who were older. A North-South divide was noted. Twenty seven percent of patients experienced readmission within 1 month. Trajectories of care were significantly different by sex and age. Overall in-hospital death was 3.3%, decreasing by 15% during the period. The highest adjusted mortality rates were observed for inpatients aged <40 or >80., Conclusion: We outlined the public health burden of this condition and the importance of improving the trajectories of care as an aid for better care., Competing Interests: Pr. Cayla reports research Grants to the Institution or Consulting/Lecture Fees from, Amgen, Abbott, AstraZeneca, Bayer, Boehringer Ingelheim, Biotronik, Bristol-Myers Squibb, Daiichi-Sankyo, Eli-Lilly, Medtronic, MSD, Pfizer, Sanofi-Aventis. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated. Detecting posts written by medical experts facilitates the quick access to posts that have more chances of being correct and informative. The main objective of this work is to learn classification models that can be used to detect posts written by medical experts in any health forum discussions. Two French health forums have been used to discover the best features and methods for this text categorization task. The obtained results confirm that models learned on appropriate websites may be used efficiently on other websites (more than 98% of F1-measure has been obtained using a Random Forest classifier). A study of misclassified posts highlights the participation of medical experts in forum discussions even if their role is not explicitly indicated.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.

Background: Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz., Objective: The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign., Methods: We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users' status. Verbatims were thematically and then statistically analyzed., Results: A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P<.001), female gender (OR 3.752, 95% CI 2.133-6.598, P<.001), and people who live in the United Kingdom (OR 2.097, 95% CI 1.447-3.038, P<.001). Nonsensitizing tweets were statistically more posted by a nonhealth or nonmedia company (OR 0.558, 95% CI 0.383-0.814, P<.001)., Conclusions: This study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated., (©Philippe Lenoir, Bilel Moulahi, Jérôme Azé, Sandra Bringay, Gregoire Mercier, François Carbonnel. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.10.2017.)

Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer., Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23])., Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life., Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients' concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus., Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life., (©Mike Donald Tapi Nzali, Sandra Bringay, Christian Lavergne, Caroline Mollevi, Thomas Opitz. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 31.07.2017.)

Background: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves., Methods: We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions., Results: Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%)., Conclusion: This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.

Ask the doctor services are personalized forums allowing patients to ask questions directly to doctors. Usually, patients must choose the most appropriate category for their question among lots of categories to be redirected to the most relevant physician. However, manual selection is tedious and error prone activity. In this work we propose to assist the patients in this task by recommending a short list of most appropriate categories.

Online health forums are increasingly used by patients to get information and help related to their health. However, information reliability in these forums is unfortunately not always guaranteed. Obviously, consequences of self-diagnosis may be severe on the patient's health if measures are taken without consulting a doctor. Many works on trust issues related to social media have been proposed, but most of them mainly focus only on the structure part of the social network (number of posts, number of likes, etc.). In the case of online health forums, a lot of trust and distrust is expressed inside the posted messages and cannot be inferred by only considering the structure. In this study, we rather suggest inferring the user's trustworthiness from the replies he receives in the forum. The proposed method is divided into three main steps: First, the recipient(s) of each post must be identified. Next, the trust or distrust expressed in these posts is evaluated. Finally, the user's reputation is computed by aggregating all the posts he received. Conducted experiments using a manually annotated corpus are encouraging.

Internet health forums are a rich textual resource with content generated through free exchanges among patients and, in certain cases, health professionals. We tackle the problem of retrieving clinically relevant information from such forums, with relevant topics being defined from clinical auto-questionnaires. Texts in forums are largely unstructured and noisy, calling for adapted preprocessing and query methods. We minimize the number of false negatives in queries by using a synonym tool to achieve query expansion of initial topic keywords. To avoid false positives, we propose a new measure based on a statistical comparison of frequent co-occurrences in a large reference corpus (Web) to keep only relevant expansions. Our work is motivated by a study of breast cancer patients' health-related quality of life (QoL). We consider topics defined from a breast-cancer specific QoL-questionnaire. We quantify and structure occurrences in posts of a specialized French forum and outline important future developments.

Background: The aim of this study was to develop an original method to extract sets of relevant molecular biomarkers (gene sequences) that can be used for class prediction and can be included as prognostic and predictive tools., Materials and Methods: The method is based on sequential patterns used as features for class prediction. We applied it to classify breast cancer tumors according to their histological grade., Results: We obtained very good recall and precision for grades 1 and 3 tumors, but, like other authors, our results were less satisfactory for grade 2 tumors., Conclusions: We demonstrated the interest of sequential patterns for class prediction of microarrays and we now have the material to use them for prognostic and predictive applications., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The epidemiology of dengue fever in French Guiana is marked by a combination of permanent transmission of the virus in the whole country and the occurrence of regular epidemics. Since 2006, a multi data source surveillance system was implemented to monitor dengue fever patterns, to improve early detection of outbreaks and to allow a better provision of information to health authorities, in order to guide and evaluate prevention activities and control measures. This report illustrates the validity and the performances of the system. We describe the experience gained by such a surveillance system and outline remaining challenges. Future works will consist in the use of other data sources such as environmental factors in order to improve knowledge on virus transmission mechanisms and determine how to use them for outbreaks prediction.

Transcriptomic technologies are promising tools for identifying new genes involved in cerebral ageing or in neurodegenerative diseases such as Alzheimer's disease. These technologies produce massive biological data, which so far are extremely difficult to exploit. In this context, we propose GeneMining, a multidisciplinary methodology, which aims at developing new strategies to analyse such data, and to design interactive tools to help biologists to identify, visualize and interpret brain ageing signatures. In order to address the specific problem of brain ageing signatures discovery, we combine and apply existing tools with emphasis to a new efficient data mining method based on sequential patterns.

Background: Health care professionals' collaboration is highly important for the medical practice. Efficient exchange of information improves good cooperation, but remains complex, due to the diversity of the medical activities. Currently, the health record is mainly used to manage structured medical information. On the one hand, such structure supports treatment that requires the documented information. On the other hand, however, the structure also imposes constraints on narrative and conversational practices of health care professionals. They use other collaboration means through phone, mail, annotations and free texts for informal strategies of communication. We focussed on informal written documents. Two different studies provided us some materials: home care charts in the context of home care and annotations in the context of the hospital health records., Purposes: We wanted to design a model of the Communication Notes to computerize the written notes so as to improve the communication and the coordination of the practitioners., Methods: We compared the results of the two studies about the various writing strategies used by the health care professionals to keep traces of their exchanges and of their acts. The first study deals with the information mentioned by the nurses in a chart during home care situations. We analysed the distribution of cooperation activities in action and in planning. The second study deals with the annotations which are written by all the practitioners to complete the documents of the health record in a paediatric ward. We analysed how annotations take part in their collaborations., Results: We found some invariable items in these two situations and we proposed a model for these Communication Notes which can be used to describe and to index them according to different points of view. Some indications on the way such descriptions are used in current computerized systems are also reported. The originality of this model comes from the way it takes into account a collaborative perspective which is not often used in the electronic medical settings., Conclusions: With our model of Communication Notes, we now dispose of a promising setting for managing all the informal and unforeseeable information produced by the health care professionals during care.

Although Health Professionals HPs are aware of the interest of the Electronic Medical Record EMR, most of them still use a Paper Medical Record PMR. In the French DocPatient project, we work on documentary functionalities to improve the use of the EMR. We made the assumption that a best integration of the way they use these paper medical documents in the EMR design will improve its utility, its use and its acceptance. We propose in this paper to add a functionality of annotations in the EMR to reinforce collaboration, coordination and awareness.

Introduction: To build relevant tools for Health Care Professionals, we must study and understand their practices. This paper discusses the way they leave traces in the Patient Record to help asynchronous collaboration, elaborating new documents or adding annotations., Methods: We compared the results of two studies about the various writing strategies used by the Health Care Professionals to capture knowledge in the Patient Records. The first study deals with the information written by the nurses in a textbook during homecare situations. The second one deals with the annotations left by all the practitioners to complete the documents of the patient record in a hospital ward., Results: We have found some invariants in these two situations. An interpretation model based on four levels: Communication Context, Communication Object, Value of Communication and Value of Cooperation, is proposed in order to describe and to index the characteristics of the Communication Notes.