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2. Teaming up in primary care: Membership boundaries, interdependence, and coordination.

Author

Everett CM, Docherty SL, Matheson E, Morgan PA, Price A, Christy J, Michener L, Smith VA, Anderson JB Jr, Viera A, and Jackson GL

Subjects
Health Personnel, Humans, Patient Care Team, Quality of Health Care, Surveys and Questionnaires, Electronic Health Records, Primary Health Care
Abstract

Objective: Increased demand for quality primary care and value-based payment has prompted interest in implementing primary care teams. Evidence-based recommendations for implementing teams will be critical to successful PA participation. This study sought to describe how primary care providers (PCPs) define team membership boundaries and coordinate tasks., Methods: This mixed-methods study included 28 PCPs from a primary care network. We analyzed survey data using descriptive statistics and interview data using content analysis., Results: Ninety-six percent of PCPs reported team membership. Team models fell into one of five categories. The predominant coordination mechanism differed by whether coordination was required in a visit or between visits., Conclusions: Team-based primary care is a strategy for improving access to quality primary care. Most PCPs define team membership based on within-visit task interdependencies. Our findings suggest that team-based interventions can focus on clarifying team membership, increasing interaction between clinicians, and enhancing the electronic health record to facilitate between-visit coordination., (Copyright © 2022 American Academy of Physician Assistants.)

Published
2022
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3. Privacy-preserving biomedical data dissemination via a hybrid approach.

Author

Jiang Y, Wang C, Wu Z, Du X, and Wang S

Subjects
Humans, Information Dissemination, Medical Records Systems, Computerized, Models, Theoretical, Privacy, Computer Security, Confidentiality, Electronic Health Records
Abstract

Sharing medical data can benefit many aspects of biomedical research studies. However, medical data usually contains sensitive patient information, which cannot be shared directly. Summary statistics, like histogram, are widely used in medical research which serves as a sanitized synopsis of the raw health dataset such as Electrical Health Records (EHR). Such synopsized representation is then be used to support advanced operations over health dataset such as counting queries and learning based tasks. While privacy becomes an increasingly important issue for generating and publishing health data based histograms. Previous solutions show promise on securely generating histogram via differential privacy, however such methods only consider a centralized solution and the accuracy is still a limitation for real world applications. In this paper, we propose a novel hybrid solution to combine two rigorous theoretical models (homomorphic encryption and differential privacy) for securely generating synthetic V-optimal histograms over distributed datasets. Our results demonstrated accuracy improvement over previous study over real medical datasets.

Published
2018

4. Privacy-preserving mimic models for clinical named entity recognition in French

Author

Nesrine Bannour, Perceval Wajsbürt, Bastien Rance, Xavier Tannier, Aurélie Névéol, Information, Langue Ecrite et Signée (ILES), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Sciences et Technologies des Langues (STL), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), This work was supported by ITMO Cancer Aviesan. The funding organization had no role in the conceptualization, design, data collection and analysis, preparation of the paper, or the decision to publish it., Tannier, Xavier, École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE)

Subjects
Computer, Narration, Mimic learning, Privacy, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Natural language processing, Confidentiality Datasets as topic, Humans, Electronic health records, Health Informatics, Neural networks, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science Applications
Abstract

International audience; A vast amount of crucial information about patients resides solely in unstructured clinical narrative notes. There has been a growing interest in clinical Named Entity Recognition (NER) task using deep learning models. Such approaches require sufficient annotated data. However, there is little publicly available annotated corpora in the medical field due to the sensitive nature of the clinical text. In this paper, we tackle this problem by building privacy-preserving shareable models for French clinical Named Entity Recognition using the mimic learning approach to enable the knowledge transfer through a teacher model trained on a private corpus to a student model. This student model could be publicly shared without any access to the original sensitive data. We evaluated three privacy-preserving models using three medical corpora and compared the performance of our models to those of baseline models such as dictionary-based models. An overall macro F-measure of 70.6% could be achieved by a student model trained using silver annotations produced by the teacher model, compared to 85.7% for the original private teacher model. Our results revealed that these privacy-preserving mimic learning models offer a good compromise between performance and data privacy preservation.

Published
2022