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Tran AD, Waller E, Mack JM, Crary SE, Citla-Sridhar D. Mental health in persons with von Willebrand disease in the United States - a large national database study. J Thromb Haemost 2024; 22:1583-1590. [PMID: 38453024 DOI: 10.1016/j.jtha.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/31/2024] [Accepted: 02/24/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND There are very few large population-based studies studying mental health in persons with von Willebrand disease (PwVWD). OBJECTIVES We aim to assess prevalence of depression and anxiety in PwVWD over a period of 20 years and identify bleeding symptoms that may be more likely associated with depression and anxiety in PwVWD. METHODS This is a retrospective cohort study using a deidentified national dataset from 1118 hospitals with 176 million patients. Cases were defined as patients aged 0-110 years, both male and female, with von Willebrand disease (VWD), without hemophilia. Controls were defined as patients aged 0-110 years, both male and female, without VWD or hemophilia. We compared rates of depression and anxiety in cases and controls and by type of bleeding symptoms. RESULTS We identified 66 367 PwVWD and 183 890 766 controls. The prevalence of depression (23.12% vs 8.62%; p ≤ .00093; relative risk = 2.68) and anxiety (32.90% vs 12.29%; p ≤ .00093; relative risk = 2.68) was higher in PwVWD. Most of the bleeding symptoms were associated with higher rates of depression and anxiety in PwVWD with the highest rates with abnormal uterine bleeding, hematemesis, hemoptysis, hematuria, and melena. CONCLUSION Our study shows that mental health disorders in PwVWD are a significant health burden, and that burden is increased with documented bleeding symptoms. It is important that primary care physicians and hematologists caring for this population recognize this increased risk and appropriately screen and refer to mental health professionals.
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Affiliation(s)
- Andrew D Tran
- Division of Hematology/Oncology, Department of Pediatrics, University of Arkansas for the Medical Sciences, Little Rock, Arkansas, USA; Arkansas Children's Hospital, Little Rock, Arkansas, USA.
| | - Emily Waller
- Division of Hematology/Oncology, Department of Pediatrics, University of Arkansas for the Medical Sciences, Little Rock, Arkansas, USA
| | - Joana M Mack
- Division of Hematology/Oncology, Department of Pediatrics, University of Arkansas for the Medical Sciences, Little Rock, Arkansas, USA; Arkansas Children's Hospital, Little Rock, Arkansas, USA
| | - Shelley E Crary
- Division of Hematology/Oncology, Department of Pediatrics, University of Arkansas for the Medical Sciences, Little Rock, Arkansas, USA; Arkansas Children's Hospital, Little Rock, Arkansas, USA
| | - Divyaswathi Citla-Sridhar
- Division of Hematology/Oncology, Department of Pediatrics, University of Arkansas for the Medical Sciences, Little Rock, Arkansas, USA; Arkansas Children's Hospital, Little Rock, Arkansas, USA
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2
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Tripathi A, Waqas A, Venkatesan K, Yilmaz Y, Rasool G. Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets. SENSORS (BASEL, SWITZERLAND) 2024; 24:1634. [PMID: 38475170 DOI: 10.3390/s24051634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/25/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024]
Abstract
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.
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Affiliation(s)
- Aakash Tripathi
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Asim Waqas
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Kavya Venkatesan
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Yasin Yilmaz
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
- Department of Neuro-Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
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3
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Zigler CK, Adeyemi O, Boyd AD, Braciszewski JM, Cheville A, Cuthel AM, Dailey DL, Del Fiol G, Ezenwa MO, Faurot KR, Justice M, Ho PM, Lawrence K, Marsolo K, Patil CL, Paek H, Richesson RL, Staman KL, Schlaeger JM, O'Brien EC. Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory. Contemp Clin Trials 2024; 137:107426. [PMID: 38160749 PMCID: PMC10922303 DOI: 10.1016/j.cct.2023.107426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/15/2023] [Accepted: 12/26/2023] [Indexed: 01/03/2024]
Abstract
The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.
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Affiliation(s)
- Christina K Zigler
- Duke University School of Medicine, Durham, NC, United States of America.
| | - Oluwaseun Adeyemi
- New York University Grossman School of Medicine, Ronald O. Perelman Department of Emergency Medicine, New York, NY, United States of America
| | - Andrew D Boyd
- Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, IL, United States of America
| | | | - Andrea Cheville
- Mayo Clinic Comprehensive Cancer Center, Rochester, MN, United States of America
| | - Allison M Cuthel
- New York University Grossman School of Medicine, Ronald O. Perelman Department of Emergency Medicine, New York, NY, United States of America
| | - Dana L Dailey
- St. Ambrose University, Davenport, IA, and University of Iowa, Iowa City, IA, United States of America
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Miriam O Ezenwa
- University of Florida College of Nursing, Gainesville, FL, United States of America
| | - Keturah R Faurot
- Department of Physical Medicine and Rehabilitation, University of North Carolina School of Medicine, Chapel Hill, NC, United States of America
| | - Morgan Justice
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - P Michael Ho
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Katherine Lawrence
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States of America
| | - Keith Marsolo
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States of America
| | - Crystal L Patil
- University of Michigan, School of Nursing, Ann Arbor, MI, United States of America
| | - Hyung Paek
- Yale University, New Haven, CT, United States of America
| | - Rachel L Richesson
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Karen L Staman
- Duke Clinical Research Institute, Durham, NC, United States of America
| | - Judith M Schlaeger
- University of Illinois Chicago, College of Nursing, Chicago, IL, United States of America
| | - Emily C O'Brien
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States of America
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Rueda M, Leist IC, Gut IG. Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data. J Biomed Inform 2024; 149:104558. [PMID: 38035971 DOI: 10.1016/j.jbi.2023.104558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023]
Abstract
Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research and enhancing patient outcomes in precision medicine and public health. To achieve this, the health data community has developed standards to promote the harmonization of variable names and values. However, the use of diverse standards across different research centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables the interconversion of common data models for phenotypic data such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap. Along with the software, we have created a detailed documentation that includes information on deployment and installation.
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Affiliation(s)
- Manuel Rueda
- Centro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028 Barcelona, Spain; Universitat de Barcelona (UB), Barcelona, Spain.
| | - Ivo C Leist
- Centro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028 Barcelona, Spain; Universitat de Barcelona (UB), Barcelona, Spain
| | - Ivo G Gut
- Centro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028 Barcelona, Spain; Universitat de Barcelona (UB), Barcelona, Spain
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Erdemir AG, Yurttutan N, Onur MR, İdilman İS, Öztürk MH, Ertürk ŞM, Çevikol C, Akpınar E. Radiological management and challenges of the twin earthquakes of February 6th. Emerg Radiol 2023; 30:659-666. [PMID: 37535144 DOI: 10.1007/s10140-023-02162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/28/2023] [Indexed: 08/04/2023]
Abstract
Two major earthquakes measuring 7.8 and 7.7 on the Richter scale struck Turkey and Northern Syria on February 6, claiming more than 50,000 lives. In such an unprecedented disaster, radiologists were confronted with very critical tasks of stepping out of the routine reporting process, performing radiological triage, managing acute adverse events, and optimizing imaging protocols. In our experience, radiologists can take three different positions in such disasters: (1) in the scene of the disaster, (2) serving in teleradiology, and (3) working in tertiary hospital for transported patients. With this article, we aimed to describe the challenges radiologists face on the three main fronts and how we manage these challenges.
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Affiliation(s)
- Ahmet Gürkan Erdemir
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey.
| | - Nursel Yurttutan
- Department of Radiology, Faculty of Medicine, Kahramanmaraş University, Kahramanmaraş, Turkey
| | - Mehmet Ruhi Onur
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | | | - Mehmet Halil Öztürk
- Department of Radiology, Faculty of Medicine, Sakarya University, Sakarya, Turkey
| | - Şükrü Mehmet Ertürk
- Department of Radiology, Faculty of Medicine, İstanbul University, Istanbul, Turkey
| | - Can Çevikol
- Department of Radiology, Faculty of Medicine, Akdeniz University, Antalya, Turkey
| | - Erhan Akpınar
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
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Hosch R, Baldini G, Parmar V, Borys K, Koitka S, Engelke M, Arzideh K, Ulrich M, Nensa F. FHIR-PYrate: a data science friendly Python package to query FHIR servers. BMC Health Serv Res 2023; 23:734. [PMID: 37415138 DOI: 10.1186/s12913-023-09498-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/03/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. METHODS The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. RESULTS As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. CONCLUSIONS FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.
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Affiliation(s)
- René Hosch
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
| | - Giulia Baldini
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany.
| | - Vicky Parmar
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
| | - Katarzyna Borys
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
| | - Sven Koitka
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
| | - Merlin Engelke
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
| | - Kamyar Arzideh
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
- Central IT Department, Data Integration Center, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Moritz Ulrich
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
- Central IT Department, Data Integration Center, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Felix Nensa
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, Essen, 45131, Germany
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Direito B, Santos A, Mouga S, Lima J, Brás P, Oliveira G, Castelo-Branco M. Design and Implementation of a Collaborative Clinical Practice and Research Documentation System Using SNOMED-CT and HL7-CDA in the Context of a Pediatric Neurodevelopmental Unit. Healthcare (Basel) 2023; 11:healthcare11070973. [PMID: 37046899 PMCID: PMC10094702 DOI: 10.3390/healthcare11070973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
This paper introduces a prototype for clinical research documentation using the structured information model HL7 CDA and clinical terminology (SNOMED CT). The proposed solution was integrated with the current electronic health record system (EHR-S) and aimed to implement interoperability and structure information, and to create a collaborative platform between clinical and research teams. The framework also aims to overcome the limitations imposed by classical documentation strategies in real-time healthcare encounters that may require fast access to complex information. The solution was developed in the pediatric hospital (HP) of the University Hospital Center of Coimbra (CHUC), a national reference for neurodevelopmental disorders, particularly for autism spectrum disorder (ASD), which is very demanding in terms of longitudinal and cross-sectional data throughput. The platform uses a three-layer approach to reduce components’ dependencies and facilitate maintenance, scalability, and security. The system was validated in a real-life context of the neurodevelopmental and autism unit (UNDA) in the HP and assessed based on the functionalities model of EHR-S (EHR-S FM) regarding their successful implementation and comparison with state-of-the-art alternative platforms. A global approach to the clinical history of neurodevelopmental disorders was worked out, providing transparent healthcare data coding and structuring while preserving information quality. Thus, the platform enabled the development of user-defined structured templates and the creation of structured documents with standardized clinical terminology that can be used in many healthcare contexts. Moreover, storing structured data associated with healthcare encounters supports a longitudinal view of the patient’s healthcare data and health status over time, which is critical in routine and pediatric research contexts. Additionally, it enables queries on population statistics that are key to supporting the definition of local and global policies, whose importance was recently emphasized by the COVID pandemic.
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Affiliation(s)
- Bruno Direito
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
- Instituto do Ambiente, Tecnologia e Vida, 3000-214 Coimbra, Portugal
| | - André Santos
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - Susana Mouga
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - João Lima
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
| | - Paulo Brás
- Coimbra Clinical Academic Center, Faculty of Medicine, Coimbra University Hospital, Pediatric Hospital, 3000-602 Coimbra, Portugal
| | - Guiomar Oliveira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
- Child Developmental Center, Research and Clinical Training Center, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, 3000-602 Coimbra, Portugal
- University Clinic of Pediatrics, Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
- Coimbra Clinical Academic Center, Faculty of Medicine, Coimbra University Hospital, Pediatric Hospital, 3000-602 Coimbra, Portugal
- Correspondence:
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Bocquet F, Raimbourg J, Bigot F, Simmet V, Campone M, Frenel JS. Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1645. [PMID: 36674399 PMCID: PMC9861145 DOI: 10.3390/ijerph20021645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Big Data and Artificial Intelligence can profoundly transform medical practices, particularly in oncology. Comprehensive Cancer Centers have a major role to play in this revolution. With the purpose of advancing our knowledge and accelerating cancer research, it is urgent to make this pool of data usable through the development of robust and effective data warehouses. Through the recent experience of Comprehensive Cancer Centers in France, this article shows that, while the use of hospital data warehouses can be a source of progress by taking into account multisource, multidomain and multiscale data for the benefit of knowledge and patients, it nevertheless raises technical, organizational and legal issues that still need to be addressed. The objectives of this article are threefold: 1. to provide insight on public health stakes of development in Comprehensive Cancer Centers to manage cancer patients comprehensively; 2. to set out a challenge of structuring the data from within them; 3. to outline the legal issues of implementation to carry out real-world evidence studies. To meet objective 1, this article firstly proposed a discussion on the relevance of an integrated approach to manage cancer and the formidable tool that data warehouses represent to achieve this. To address objective 2, we carried out a literature review to screen the articles published in PubMed and Google Scholar through the end of 2022 on the use of data warehouses in French Comprehensive Cancer Centers. Seven publications dealing specifically with the issue of data structuring were selected. To achieve objective 3, we presented and commented on the main aspects of French and European legislation and regulations in the field of health data, hospital data warehouses and real-world evidence.
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Affiliation(s)
- François Bocquet
- Data Factory & Analytics Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
- Law and Social Change Laboratory, Faculty of Law and Political Sciences, CNRS UMR 6297, Nantes University, 44313 Nantes, France
| | - Judith Raimbourg
- Oncology Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
- Center for Research in Cancerology and Immunology Nantes-Angers, INSERM UMR 1232, Nantes University and Angers University, 44035 Nantes-Angers, France
| | - Frédéric Bigot
- Oncology Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
| | - Victor Simmet
- Oncology Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
| | - Mario Campone
- Oncology Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
- Center for Research in Cancerology and Immunology Nantes-Angers, INSERM UMR 1232, Nantes University and Angers University, 44035 Nantes-Angers, France
| | - Jean-Sébastien Frenel
- Oncology Department, Institut de Cancérologie de l’Ouest, 44805 Nantes-Angers, France
- Center for Research in Cancerology and Immunology Nantes-Angers, INSERM UMR 1232, Nantes University and Angers University, 44035 Nantes-Angers, France
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Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper. J Digit Imaging 2022; 35:817-833. [PMID: 35962150 PMCID: PMC9485305 DOI: 10.1007/s10278-022-00658-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 10/28/2022] Open
Abstract
Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.
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10
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Virgolici HM, Ceban D, Raducu RC, Purcarea VL. Blockchain technology used in medicine. A brief survey. ROMANIAN JOURNAL OF MILITARY MEDICINE 2022. [DOI: 10.55453/rjmm.2022.125.3.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Blockchain is the technology that allows people to make transactions without intermediaries. Bitcoin was the first cryptocurrency launched. Cryptocurrencies are digital tokens that can be used for transactions. They use cryptography to establish their authenticity and are not linked to a central authority. Blockchain has many advantages for the healthcare industry and can be used in various fields such as: implementation and improvement of electronic patient records, in clinical trials, neuroscience, pharmaceutical industry and research. Its security can help to improve the confidentiality of patient data and can also help secure the supply chain of medicines. The security and transparency of the blockchain will play a crucial role in the medical industry. This will allow companies to register their products and conduct secure transactions. QR codes can also be placed on the back of medicine containers to help customers identify the authenticity of the products they purchase. The exchange of health information through the blockchain will also have various challenges, such as maintaining the confidentiality of patient data. At the same time, due to the different regulations in different countries, it can be difficult to establish an efficient and secure exchange. Unlike other cryptocurrencies, blockchains are usually immutable, which means that the data added in the chain will always remain. This eliminates the risk of data loss
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11
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Huang CH, Liu JS, Ho MHC, Chou TC. Towards more convergent main paths: A relevance-based approach. J Informetr 2022. [DOI: 10.1016/j.joi.2022.101317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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12
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The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127379. [PMID: 35742627 PMCID: PMC9223495 DOI: 10.3390/ijerph19127379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 02/06/2023]
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13
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Tarabichi Y, Frees A, Honeywell S, Huang C, Naidech AM, Moore JH, Kaelber DC. The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform. ACI OPEN 2022; 5:e36-e46. [PMID: 35071993 PMCID: PMC8775787 DOI: 10.1055/s-0041-1731004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Objective Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool. This work aims to describe the initiative and illustrate its potential utility through several use cases. Methods Cosmos is designed to scale rapidly by leveraging preexisting agreements, clinical health information exchange networks, and data standards. Data are stored centrally as a limited dataset, but the customer facing query tool limits results to prevent patient reidentification. Results In 2 years, Cosmos grew to contain EHR data of more than 60 million patients. We present practical examples illustrating how Cosmos could further efforts in chronic disease surveillance (asthma and obesity), syndromic surveillance (seasonal influenza and the 2019 novel coronavirus), immunization adherence and adverse event reporting (human papilloma virus and measles, mumps, rubella, and varicella vaccination), and health services research (antibiotic usage for upper respiratory infection). Discussion A low barrier of entry for Cosmos allows for the rapid accumulation of multi-institutional and mostly de-duplicated EHR data to power research and quality improvement queries characteristic of learning healthcare systems. Limitations are being vendor-specific, an “all or none” contribution model, and the lack of control over queries run on an institution’s healthcare data. Conclusion Cosmos provides a model for within-vendor data standardization and aggregation and a steppingstone for broader intervendor interoperability.
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Affiliation(s)
- Yasir Tarabichi
- Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio, United States.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, The MetroHealth System, Cleveland, Ohio, United States.,School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States
| | | | | | | | - Andrew M Naidech
- Department of Neurology, Northwestern University. Chicago, Illinois, United States
| | - Jason H Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - David C Kaelber
- Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio, United States.,Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States
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14
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Fu S, Wen A, Schaeferle GM, Wilson PM, Demuth G, Ruan X, Liu S, Storlie C, Liu H. Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:196-205. [PMID: 35854735 PMCID: PMC9285181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.
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Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Andrew Wen
- Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Gavin M Schaeferle
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Patrick M Wilson
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Gabriel Demuth
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Xiaoyang Ruan
- Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Sijia Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Curtis Storlie
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Hongfang Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA
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15
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Hughes AEO, Jackups R. Clinical Decision Support for Laboratory Testing. Clin Chem 2021; 68:402-412. [PMID: 34871351 DOI: 10.1093/clinchem/hvab201] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/24/2021] [Indexed: 01/16/2023]
Abstract
BACKGROUND As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging. CONTENT We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation. SUMMARY CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.
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Affiliation(s)
- Andrew E O Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ronald Jackups
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
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16
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Spanakis EG, Sfakianakis S, Bonomi S, Ciccotelli C, Magalini S, Sakkalis V. Emerging and Established Trends to Support Secure Health Information Exchange. Front Digit Health 2021; 3:636082. [PMID: 34713107 PMCID: PMC8521812 DOI: 10.3389/fdgth.2021.636082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
This work aims to provide information, guidelines, established practices and standards, and an extensive evaluation on new and promising technologies for the implementation of a secure information sharing platform for health-related data. We focus strictly on the technical aspects and specifically on the sharing of health information, studying innovative techniques for secure information sharing within the health-care domain, and we describe our solution and evaluate the use of blockchain methodologically for integrating within our implementation. To do so, we analyze health information sharing within the concept of the PANACEA project that facilitates the design, implementation, and deployment of a relevant platform. The research presented in this paper provides evidence and argumentation toward advanced and novel implementation strategies for a state-of-the-art information sharing environment; a description of high-level requirements for the transfer of data between different health-care organizations or cross-border; technologies to support the secure interconnectivity and trust between information technology (IT) systems participating in a sharing-data "community"; standards, guidelines, and interoperability specifications for implementing a common understanding and integration in the sharing of clinical information; and the use of cloud computing and prospectively more advanced technologies such as blockchain. The technologies described and the possible implementation approaches are presented in the design of an innovative secure information sharing platform in the health-care domain.
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Affiliation(s)
- Emmanouil G Spanakis
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Stelios Sfakianakis
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Silvia Bonomi
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Università degli Studi di Roma La Sapienza, Rome, Italy
| | - Claudio Ciccotelli
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Università degli Studi di Roma La Sapienza, Rome, Italy
| | - Sabina Magalini
- Emergency and Trauma Surgery Unit, Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
| | - Vangelis Sakkalis
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
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17
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Meliá S, Nasabeh S, Luján-Mora S, Cachero C. MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126357. [PMID: 34208252 PMCID: PMC8296168 DOI: 10.3390/ijerph18126357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/02/2021] [Accepted: 06/08/2021] [Indexed: 11/29/2022]
Abstract
The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.
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18
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Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting. J Pers Med 2021; 11:jpm11060443. [PMID: 34063850 PMCID: PMC8224063 DOI: 10.3390/jpm11060443] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/18/2021] [Accepted: 05/20/2021] [Indexed: 12/12/2022] Open
Abstract
Chronic disease management often requires use of multiple drug regimens that lead to polypharmacy challenges and suboptimal utilization of healthcare services. While the rising costs and healthcare utilization associated with polypharmacy and drug interactions have been well documented, effective tools to address these challenges remain elusive. Emerging evidence that proactive medication management, combined with pharmacogenomic testing, can lead to improved health outcomes and reduced cost burdens may help to address such gaps. In this report, we describe informatic and bioanalytic methodologies that integrate weak signals in symptoms and chief complaints with pharmacogenomic analysis of ~90 single nucleotide polymorphic variants, CYP2D6 copy number, and clinical pharmacokinetic profiles to monitor drug–gene pairs and drug–drug interactions for medications with significant pharmacogenomic profiles. The utility of the approach was validated in a virtual patient case showing detection of significant drug–gene and drug–drug interactions of clinical significance. This effort is being used to establish proof-of-concept for the creation of a regional database to track clinical outcomes in patients enrolled in a bioanalytically-informed medication management program. Our integrated informatic and bioanalytic platform can provide facile clinical decision support to inform and augment medication management in the primary care setting.
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19
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McCarthy N, Dahlan A, Cook TS, Hare NO, Ryan ML, St John B, Lawlor A, Curran KM. Enterprise imaging and big data: A review from a medical physics perspective. Phys Med 2021; 83:206-220. [DOI: 10.1016/j.ejmp.2021.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/24/2021] [Accepted: 04/06/2021] [Indexed: 02/04/2023] Open
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20
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Chen HY, Wu ZY, Chen TL, Huang YM, Liu CH. Security Privacy and Policy for Cryptographic Based Electronic Medical Information System. SENSORS 2021; 21:s21030713. [PMID: 33494288 PMCID: PMC7864482 DOI: 10.3390/s21030713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 11/23/2022]
Abstract
With the development of the internet, applications have become complicated, and the relevant technology has diversified. Compared with medical applications, the significance of information technology has been expanding to include clinical auxiliary functions of medical information. This includes electronic medical records, electronic prescriptions, medical information systems, etc. Although research on the data processing structure and format of various related systems is becoming mature, the integration is insufficient. An integrated medical information system with security policy and privacy protection, which combines e-patient records, e-prescriptions, modified smart cards, and fingerprint identification systems, and applies proxy signature and group signature, is proposed in this study. This system effectively applies and saves medical resources—satisfying the mobility of medical records, presenting the function, and security of medicine collection, and avoiding medical conflicts and profiteering to further acquire the maximum effectiveness with the least resources. In this way, this medical information system may be developed into a comprehensive function that eliminates the transmission of manual documents and maintains the safety of patient medical information. It can improve the quality of medical care and indispensable infrastructure for medical management.
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Affiliation(s)
- Hsuan-Yu Chen
- National Defense Medical Center, Department of Radiology, Tri-Service General Hospital, Taipei 10086, Taiwan;
| | - Zhen-Yu Wu
- Department of Information Management, National Penghu University of Science and Technology, Penghu 880011, Taiwan;
| | - Tzer-Long Chen
- Department of Finance, Providence University, Taichung 43301, Taiwan;
| | - Yao-Min Huang
- Department of Management Sciences, National Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Chia-Hui Liu
- Department of Applied Mathematics, Chinese Culture University, Taipei 11114, Taiwan
- Correspondence:
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21
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Theek B, Magnuska Z, Gremse F, Hahn H, Schulz V, Kiessling F. Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice. Methods 2020; 188:30-36. [PMID: 32615232 DOI: 10.1016/j.ymeth.2020.06.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 06/23/2020] [Indexed: 12/11/2022] Open
Abstract
Digitalization, especially the use of machine learning and computational intelligence, is considered to dramatically shape medical procedures in the near future. In the field of cancer diagnostics, radiomics, the extraction of multiple quantitative image features and their clustered analysis, is gaining increasing attention to obtain more detailed, reproducible, and meaningful information about the disease entity, its prognosis and the ideal therapeutic option. In this context, automation of diagnostic procedures can improve the entire pipeline, which comprises patient registration, planning and performing an imaging examination at the scanner, image reconstruction, image analysis, and feeding the diagnostic information from various sources into decision support systems. With a focus on cancer diagnostics, this review article reports and discusses how computer-assistance can be integrated into diagnostic procedures and which benefits and challenges arise from it. Besides a strong view on classical imaging modalities like x-ray, CT, MRI, ultrasound, PET, SPECT and hybrid imaging devices thereof, it is outlined how imaging data can be combined with data deriving from patient anamnesis, clinical chemistry, pathology, and different omics. In this context, the article also discusses IT infrastructures that are required to realize this integration in the clinical routine. Although there are still many challenges to comprehensively implement automated and integrated data analysis in molecular cancer imaging, the authors conclude that we are entering a new era of medical diagnostics and precision medicine.
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Affiliation(s)
- Benjamin Theek
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
| | - Zuzanna Magnuska
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany
| | - Felix Gremse
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany; Institute of Medical Informatics, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Horst Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany
| | - Volkmar Schulz
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany; Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany
| | - Fabian Kiessling
- Institute for Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Forckenbeckstrasse 55, 52074 Aachen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Am Fallturm 1, 28359 Bremen, Germany.
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22
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Abstract
OBJECTIVES Children with medical complexity (CMC) are at risk for poor outcomes during medical emergencies. Emergency information forms (EIFs) provide essential medical information for CMC during emergencies; however, they are not widely used. We sought to identify factors related to optimal care for CMC to inform development of EIFs for CMC. METHODS We interviewed 26 stakeholders, including parents of CMC, healthcare providers, health information technology, and privacy compliance experts. We inquired about barriers and facilitators to emergency care of CMC, as well as the desired content, structure, ownership, and maintenance of an EIF. Audio recordings were transcribed and analyzed inductively for common themes using thematic analysis techniques. RESULTS Providers identified problems with documentation and poor caregiver understanding as major barriers to care. Parents reported poor provider understanding of their child's condition as a barrier. All groups reported that summary documents facilitate quality care. Recommended content included demographic/contact information, medical history, medications, allergies, advance directives, information about the patient's disease, and an action plan for anticipated emergencies. Twenty-three participants indicated a preference for electronic EIFs; 19 preferred a Web-based EIF that syncs with the medical record, with paper or portable electronic copies. Although 13 participants thought that EIFs should be patient owned to ensure availability during emergencies, 19 expected medical providers to create and update EIFs. CONCLUSIONS Stakeholders interviewed reported a preference for Web-based, sync-capable EIFs with portable copies. Emergency information forms could be maintained by providers but owned by patients to optimize emergency care and align with the concept of the medical home.
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23
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Zong N, Sharma DK, Yu Y, Egan JB, Davila JI, Wang C, Jiang G. Developing a FHIR-based Framework for Phenome Wide Association Studies: A Case Study with A Pan-Cancer Cohort. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:750-759. [PMID: 32477698 PMCID: PMC7233075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.
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Affiliation(s)
- Nansu Zong
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Deepak K Sharma
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Yue Yu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jan B Egan
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Jaime I Davila
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Guoqian Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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24
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Kobayashi S, Kume N, Araki K, Yoshihara H. The Development of Medical Markup Language Version 4 as a Clinical Document Exchange Format for Nationwide EHR Systems. J Med Syst 2020; 44:69. [PMID: 32072322 DOI: 10.1007/s10916-020-1524-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 01/16/2020] [Indexed: 10/25/2022]
Abstract
Medical Markup Language (MML) is a standard format for exchange of healthcare data among healthcare providers. Following the last major update (version 3), we developed new modules and discussed the requirements for the next major updates. Subsequently, in 2016 we released MML version 4 and used it to obtain clinical data from healthcare providers for a nationwide electronic health records (EHR) system. In this article we provide an overview of this major update of MML version 4 and discuss its interoperability for clinical data.
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Affiliation(s)
- Shinji Kobayashi
- Department of Electronic Health Record, Graduate School of Medicine, Kyoto University, 15, Shimogamo-Morimoto-cho, Sakyoku, Kyoto, 600-8815, Japan.
| | - Naoto Kume
- Department of Electronic Health Record, Graduate School of Medicine, Kyoto University, 15, Shimogamo-Morimoto-cho, Sakyoku, Kyoto, 600-8815, Japan
| | - Kenji Araki
- The Institutional Research Department for Hospital Management, University of Miyazaki, Miyazaki, Japan
| | - Hiroyuki Yoshihara
- Department of Electronic Health Record, Graduate School of Medicine, Kyoto University, 15, Shimogamo-Morimoto-cho, Sakyoku, Kyoto, 600-8815, Japan
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25
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Safavi KC, Driscoll W, Wiener-Kronish JP. Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time. Anesth Analg 2019; 129:726-734. [PMID: 31425213 PMCID: PMC6693927 DOI: 10.1213/ane.0000000000003948] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2018] [Indexed: 01/11/2023]
Abstract
The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.
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Affiliation(s)
- Kyan C. Safavi
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William Driscoll
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeanine P. Wiener-Kronish
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Hoffman RA, Wu H, Venugopalan J, Braun P, Wang MD. Intelligent Mortality Reporting With FHIR. IEEE J Biomed Health Inform 2018; 22:1583-1588. [PMID: 29993991 DOI: 10.1109/jbhi.2017.2780891] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
One pressing need in the area of public health is timely, accurate, and complete reporting of deaths and the diseases or conditions leading up to them. Fast Healthcare Interoperability Resources (FHIR) is a new HL7 interoperability standard for electronic health record, while Sustainable Medical Applications and Reusable Technologies (SMART)-on-FHIR enables third-party app development that can work "out of the box." This paper demonstrates the feasibility of developing SMART-on-FHIR applications that enables medical professionals to perform timely and accurate death reporting within multiple different USA State jurisdictions. We explored how the information on a standard certificate of death can be mapped to resources defined in the FHIR standard Draft Standard for Trial Use Version 2 and common profiles. We also demonstrated analytics for potentially improving the accuracy and completeness of mortality reporting data.
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27
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Andrikos C, Rassias G, Tsanakas P, Maglogiannis I. An Enhanced Device-Transparent Real-Time Teleconsultation Environment for Radiologists. IEEE J Biomed Health Inform 2018; 23:374-386. [PMID: 29993993 DOI: 10.1109/jbhi.2018.2824312] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper describes a novel web-based platform promoting real-time advanced teleconsultation services on medical imaging. Principles of heterogeneous workflow management systems and state-of-the-art technologies such as the microservices architectural pattern, peer-to-peer networking, and the single-page application concept are combined to build a scalable and extensible platform to aid collaboration among geographically distributed healthcare professionals. The real-time communication capabilities are based on the webRTC protocol to enable direct communication among clients. This paper discusses the conceptual and technical details of the system, emphasizing on its innovative elements.
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Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability. Comput Struct Biotechnol J 2018; 16:224-230. [PMID: 30069284 PMCID: PMC6068317 DOI: 10.1016/j.csbj.2018.06.003] [Citation(s) in RCA: 347] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 06/12/2018] [Accepted: 06/18/2018] [Indexed: 11/23/2022] Open
Abstract
Interoperability in healthcare has traditionally been focused around data exchange between business entities, for example, different hospital systems. However, there has been a recent push towards patient-driven interoperability, in which health data exchange is patient-mediated and patient-driven. Patient-centered interoperability, however, brings with it new challenges and requirements around security and privacy, technology, incentives, and governance that must be addressed for this type of data sharing to succeed at scale. In this paper, we look at how blockchain technology might facilitate this transition through five mechanisms: (1) digital access rules, (2) data aggregation, (3) data liquidity, (4) patient identity, and (5) data immutability. We then look at barriers to blockchain-enabled patient-driven interoperability, specifically clinical data transaction volume, privacy and security, patient engagement, and incentives. We conclude by noting that while patient-driving interoperability is an exciting trend in healthcare, given these challenges, it remains to be seen whether blockchain can facilitate the transition from institution-centric to patient-centric data sharing.
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da Costa CA, Pasluosta CF, Eskofier B, da Silva DB, da Rosa Righi R. Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards. Artif Intell Med 2018; 89:61-69. [PMID: 29871778 DOI: 10.1016/j.artmed.2018.05.005] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 09/13/2017] [Accepted: 05/22/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. METHODS This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. RESULTS As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. CONCLUSION It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.
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Affiliation(s)
- Cristiano André da Costa
- Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil.
| | - Cristian F Pasluosta
- Machine Learning and Data Analytics Lab., Department of Computer Science, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen 91058, Germany; Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, University of Freiburg, Georges-Koehler-Allee 102, Freiburg 79110, Germany.
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab., Department of Computer Science, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen 91058, Germany.
| | - Denise Bandeira da Silva
- Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil.
| | - Rodrigo da Rosa Righi
- Software Innovation Laboratory (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil.
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Finet P, Gibaud B, Dameron O, Le Bouquin Jeannès R. Interoperable Infrastructure and Implementation of a Health Data Model for Remote Monitoring of Chronic Diseases with Comorbidities. Ing Rech Biomed 2018. [DOI: 10.1016/j.irbm.2018.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Owens RC, Bulik CC, Andes DR. Pharmacokinetics-pharmacodynamics, computer decision support technologies, and antimicrobial stewardship: the compass and rudder. Diagn Microbiol Infect Dis 2018; 91:371-382. [PMID: 29776710 DOI: 10.1016/j.diagmicrobio.2018.03.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 03/21/2018] [Accepted: 03/26/2018] [Indexed: 12/19/2022]
Abstract
The first guidelines for conducting antimicrobial stewardship in the hospitalized setting were published in 2007. These guidelines recommend that stewardship programs employ the science of pharmacokinetics-pharmacodynamics (PK-PD) as well as adopting computerized decision support technologies when possible. The United States Food and Drug Administration have adopted PK-PD as a cornerstone in the evaluation of antimicrobial agents during clinical development. The core principles of PK-PD center around describing the relationship between drug exposure indexed to the susceptibility of the infecting bacterial pathogen and patient response. Using such relationships with population pharmacokinetic models and simulation, rational drug and dosing regimens can be selected. But because PK-PD modeling and simulation programs are generally absent in clinical practice, systematic application of this science is missing. Herein we explain advances in technology that allow clinicians to apply PK-PD to optimize the agents and dosing regimens selected for the treatment of hospitalized patients with infection.
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Affiliation(s)
- Robert C Owens
- Institute for Clinical Pharmacodynamics, Schenectady, New York.
| | | | - David R Andes
- University of Wisconsin, School of Medicine, Madison, Wisconsin
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Jimenez-Molina A, Gaete-Villegas J, Fuentes J. ProFUSO: Business process and ontology-based framework to develop ubiquitous computing support systems for chronic patients' management. J Biomed Inform 2018; 82:106-127. [PMID: 29627462 DOI: 10.1016/j.jbi.2018.04.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/29/2018] [Accepted: 04/03/2018] [Indexed: 01/20/2023]
Abstract
New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns. Some of the known problems of such developments are the integration issues between applications and existing healthcare systems, the reusability of technical knowledge in the creation of new and more sophisticated systems and the usage of data gathered from multiple sources in the generation of new knowledge. This paper proposes a framework for the development of chronic disease support systems and applications as an answer to these shortcomings. Through this framework our pursuit is to create a common ground methodology upon which new developments can be created and easily integrated to provide better support to chronic patients, medical staff and other relevant participants. General requirements are inferred for any support system from the primary attention process of chronic patients by the Business Process Management Notation. Numerous technical approaches are proposed to design a general architecture that considers the medical organizational requirements in the treatment of a patient. A framework is presented for any application in support of chronic patients and evaluated by a case study to test the applicability and pertinence of the solution.
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Affiliation(s)
- Angel Jimenez-Molina
- Department of Industrial Engineering, University of Chile, Beauchef 851, Santiago 8370456, Chile.
| | - Jorge Gaete-Villegas
- Department of Industrial Engineering, University of Chile, Beauchef 851, Santiago 8370456, Chile.
| | - Javier Fuentes
- Department of Industrial Engineering, University of Chile, Beauchef 851, Santiago 8370456, Chile.
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de Vries Robbé PF, Cillessen FHJM. Modeling Problem-oriented Clinical Notes. Methods Inf Med 2018; 51:507-15. [DOI: 10.3414/me11-01-0064] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2011] [Accepted: 06/26/2012] [Indexed: 11/09/2022]
Abstract
SummaryObjectives: To develop a model as a starting-point for developing a problem-oriented clinical notes application as a generic component of an Electronic Health Record (EHR).Methods: We used the generic conceptualization of Weed’s problem-oriented medical record (POMR) to link progress notes to problems, and the Subjective, Objective, Assessment, Plan (SOAP) headings to classify elements of these notes. Health Level 7 (HL7) Version 3 and Unified Modeling Language (UML) were used for modeling. We looked especially at the role of Conditions and Concerns, and how to model these to document clinical reasoning.Results: We developed a generic HL7-based model for progress notes. In this model the specific clinical note has a condition as its reason. An assertion can be made about a condition. Any condition, observation or procedure can be a concern that has to be tracked. Utmost important is the relationship between constituting parts of a progress note and specially between progress notes by linking a progress note to conditions that are part of an earlier progress note. From this model a comprehensive hierarchical condition tree can be built. Several views, such as chronological, SOAP and condition-oriented, are possible. The clinical notes application is used in daily clinical practice. The model meets explicit design criteria and clinical needs.Conclusions: With the comprehensive HL7 standard it is possible to model and map progress notes using SOAP headings and POMR methodology. We have developed a generic, flexible and applicable paradigm by using acts for each assessment that refer to a condition (1), by separating conditions from concerns (2), and by an extensive use of the working list act (3).
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ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers. PLoS One 2018; 13:e0190028. [PMID: 29293556 PMCID: PMC5749730 DOI: 10.1371/journal.pone.0190028] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 12/06/2017] [Indexed: 11/19/2022] Open
Abstract
The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.
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Claesen S, Stone A, Rossum M, Kitney RI. Comprehensive web‐based broker for bio‐technology design and manufacturing. ENGINEERING BIOLOGY 2017. [DOI: 10.1049/enb.2017.0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Stefan Claesen
- Visbion Ltd Visbion House, Gogmore Lane Chertsey Surrey KT16 9AP UK
| | - Anna Stone
- Visbion Ltd Visbion House, Gogmore Lane Chertsey Surrey KT16 9AP UK
| | - Mark Rossum
- Visbion Ltd Visbion House, Gogmore Lane Chertsey Surrey KT16 9AP UK
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Hoffman RA, Wu H, Venugopalan J, Braun P, Wang MD. Intelligent Mortality Reporting with FHIR. ... IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS. IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS 2017; 2017:181-184. [PMID: 28804791 DOI: 10.1109/bhi.2017.7897235] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
One pressing need in the area of public health is timely, accurate, and complete reporting of deaths and the conditions leading up to them. Fast Healthcare Interoperability Resources (FHIR) is a new HL7 interoperability standard for electronic health record (EHR), while Sustainable Medical Applications and Reusable Technologies (SMART)-on-FHIR enables third-party app development that can work "out of the box". This research demonstrates the feasibility of developing SMART-on-FHIR applications to enable medical professionals to perform timely and accurate death reporting within multiple different jurisdictions of US. We explored how the information on a standard certificate of death can be mapped to resources defined in the FHIR standard (DSTU2). We also demonstrated analytics for potentially improving the accuracy and completeness of mortality reporting data.
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Affiliation(s)
- Ryan A Hoffman
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332 USA
| | - Hang Wu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Janani Venugopalan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332 USA
| | - Paula Braun
- Centers for Disease Control and Prevention (CDC), Atlanta, GA 30329 USA
| | - May D Wang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332 USA
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Chakrabarti S, Sen A, Huser V, Hruby GW, Rusanov A, Albers DJ, Weng C. An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2017; 1:1-18. [PMID: 28776047 DOI: 10.1007/s41666-017-0005-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.
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Affiliation(s)
- Shreya Chakrabarti
- Department of Biomedical Informatics, Columbia University, New York NY 10032
| | - Anando Sen
- Department of Biomedical Informatics, Columbia University, New York NY 10032
| | - Vojtech Huser
- National Institute of Health, National Library of Medicine, Bethesda, MD 20892
| | - Gregory W Hruby
- Department of Biomedical Informatics, Columbia University, New York NY 10032
| | - Alexander Rusanov
- Department of Anesthesiology, Columbia University, New York NY 10032
| | - David J Albers
- Department of Biomedical Informatics, Columbia University, New York NY 10032
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York NY 10032
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Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. BIOMED RESEARCH INTERNATIONAL 2017; 2017:2858423. [PMID: 28299322 PMCID: PMC5337312 DOI: 10.1155/2017/2858423] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 11/28/2016] [Accepted: 12/22/2016] [Indexed: 11/29/2022]
Abstract
With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
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Foran DJ, Chen W, Chu H, Sadimin E, Loh D, Riedlinger G, Goodell LA, Ganesan S, Hirshfield K, Rodriguez L, DiPaola RS. Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology. Cancer Inform 2017; 16:1176935117694349. [PMID: 28469389 PMCID: PMC5392017 DOI: 10.1177/1176935117694349] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Accepted: 01/26/2017] [Indexed: 11/16/2022] Open
Abstract
Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.
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Affiliation(s)
- David J Foran
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Wenjin Chen
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Huiqi Chu
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Evita Sadimin
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Doreen Loh
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Gregory Riedlinger
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Lauri A Goodell
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Kim Hirshfield
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Lorna Rodriguez
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
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Zhou L, Collins S, Morgan SJ, Zafar N, Gesner EJ, Fehrenbach M, Rocha RA. A Decade of Experience in Creating and Maintaining Data Elements for Structured Clinical Documentation in EHRs. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:1293-1302. [PMID: 28269927 PMCID: PMC5333263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Structured clinical documentation is an important component of electronic health records (EHRs) and plays an important role in clinical care, administrative functions, and research activities. Clinical data elements serve as basic building blocks for composing the templates used for generating clinical documents (such as notes and forms). We present our experience in creating and maintaining data elements for three different EHRs (one home-grown and two commercial systems) across different clinical settings, using flowsheet data elements as examples in our case studies. We identified basic but important challenges (including naming convention, links to standard terminologies, and versioning and change management) and possible solutions to address them. We also discussed more complicated challenges regarding governance, documentation vs. structured data capture, pre-coordination vs. post-coordination, reference information models, as well as monitoring, communication and training.
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Affiliation(s)
- Li Zhou
- Clinical Informatics, Partners HealthCare System, Boston, MA; Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Sarah Collins
- Clinical Informatics, Partners HealthCare System, Boston, MA; Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Stephen J Morgan
- Clinical Informatics, Partners HealthCare System, Boston, MA; Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Neelam Zafar
- Clinical Informatics, Partners HealthCare System, Boston, MA
| | - Emily J Gesner
- Clinical Informatics, Partners HealthCare System, Boston, MA
| | - Martin Fehrenbach
- Institute of Medical Biometry and Informatics, Heidelberg University, Germany
| | - Roberto A Rocha
- Clinical Informatics, Partners HealthCare System, Boston, MA; Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
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Banerjee I, Patané G, Spagnuolo M. Combination of visual and symbolic knowledge: A survey in anatomy. Comput Biol Med 2017; 80:148-157. [PMID: 27940289 DOI: 10.1016/j.compbiomed.2016.11.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 11/28/2016] [Accepted: 11/29/2016] [Indexed: 10/20/2022]
Abstract
In medicine, anatomy is considered as the most discussed field and results in a huge amount of knowledge, which is heterogeneous and covers aspects that are mostly independent in nature. Visual and symbolic modalities are mainly adopted for exemplifying knowledge about human anatomy and are crucial for the evolution of computational anatomy. In particular, a tight integration of visual and symbolic modalities is beneficial to support knowledge-driven methods for biomedical investigation. In this paper, we review previous work on the presentation and sharing of anatomical knowledge, and the development of advanced methods for computational anatomy, also focusing on the key research challenges for harmonizing symbolic knowledge and spatial 3D data.
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Affiliation(s)
- Imon Banerjee
- Stanford University School of Medicine, Stanford, CA, USA; Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Via De Marini, 6, 16149 Genova, Italy.
| | - Giuseppe Patané
- Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Via De Marini, 6, 16149 Genova, Italy.
| | - Michela Spagnuolo
- Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Via De Marini, 6, 16149 Genova, Italy.
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Middleton B, Sittig DF, Wright A. Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision. Yearb Med Inform 2016; Suppl 1:S103-16. [PMID: 27488402 DOI: 10.15265/iys-2016-s034] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. METHOD Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS. RESULT In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. CONCLUSION CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.
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Affiliation(s)
- B Middleton
- Blackford Middleton, Cell: +1 617 335 7098, E-Mail:
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Abstract
This article provides surgical pathologists an overview of health information systems (HISs): what they are, what they do, and how such systems relate to the practice of surgical pathology. Much of this article is dedicated to the electronic medical record. Information, in how it is captured, transmitted, and conveyed, drives the effectiveness of such electronic medical record functionalities. So critical is information from pathology in integrated clinical care that surgical pathologists are becoming gatekeepers of not only tissue but also information. Better understanding of HISs can empower surgical pathologists to become stakeholders who have an impact on the future direction of quality integrated clinical care.
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Affiliation(s)
- S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - David R Artz
- Memorial Sloan Kettering Cancer Center, 633 3rd Avenue, New York, NY 10017, USA
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Madkour M, Benhaddou D, Tao C. Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 128:52-68. [PMID: 27040831 PMCID: PMC4837648 DOI: 10.1016/j.cmpb.2016.02.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 02/16/2016] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND OBJECTIVE We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. METHODS This review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. RESULTS The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. CONCLUSIONS Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.
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Affiliation(s)
- Mohcine Madkour
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, United States.
| | - Driss Benhaddou
- Department of Engineering Technology, University of Houston, 4800 Calhoun Rd, Houston, TX 77004, United States.
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, United States.
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Finet P, Gibaud B, Dameron O, Le Bouquin Jeannès R. Relevance of health level 7 clinical document architecture and integrating the healthcare enterprise cross-enterprise document sharing profile for managing chronic wounds in a telemedicine context. Healthc Technol Lett 2016; 3:22-6. [PMID: 27222729 DOI: 10.1049/htl.2015.0053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 03/03/2016] [Accepted: 03/07/2016] [Indexed: 11/20/2022] Open
Abstract
The number of patients with complications associated with chronic diseases increases with the ageing population. In particular, complex chronic wounds raise the re-admission rate in hospitals. In this context, the implementation of a telemedicine application in Basse-Normandie, France, contributes to reduce hospital stays and transport. This application requires a new collaboration among general practitioners, private duty nurses and the hospital staff. However, the main constraint mentioned by the users of this system is the lack of interoperability between the information system of this application and various partners' information systems. To improve medical data exchanges, the authors propose a new implementation based on the introduction of interoperable clinical documents and a digital document repository for managing the sharing of the documents between the telemedicine application users. They then show that this technical solution is suitable for any telemedicine application and any document sharing system in a healthcare facility or network.
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Affiliation(s)
- Philippe Finet
- Centre Hospitalier Intercommunal Alençon-Mamers, Alençon F-61000, France; INSERM, U 1099, Rennes F-35000, France; LTSI, Université de Rennes 1, Rennes F-35000, France
| | - Bernard Gibaud
- INSERM, U 1099, Rennes F-35000, France; LTSI, Université de Rennes 1, Rennes F-35000, France
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Communicating Nursing Care Using the Health Level Seven Consolidated Clinical Document Architecture Release 2 Care Plan. Comput Inform Nurs 2016; 34:128-36. [DOI: 10.1097/cin.0000000000000214] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Lobo SEM, Rucker J, Kerr M, Gallo F, Constable G, Hotopf M, Stewart R, Broadbent M, Baggaley M, Lovestone S, McGuffin P, Amarasinghe M, Newman S, Schumann G, Brittain PJ. A comparison of mental state examination documentation by junior clinicians in electronic health records before and after the introduction of a semi-structured assessment template (OPCRIT+). Int J Med Inform 2015; 84:675-82. [PMID: 26033569 PMCID: PMC4526540 DOI: 10.1016/j.ijmedinf.2015.05.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 05/07/2015] [Accepted: 05/09/2015] [Indexed: 11/20/2022]
Abstract
OBJECTIVES The mental state examination (MSE) provides crucial information for healthcare professionals in the assessment and treatment of psychiatric patients as well as potentially providing valuable data for mental health researchers accessing electronic health records (EHRs). We wished to establish if improvements could be achieved in the documenting of MSEs by junior doctors within a large United Kingdom mental health trust following the introduction of an EHR based semi-structured MSE assessment template (OPCRIT+). METHODS First, three consultant psychiatrists using a modified version of the Physician Documentation Quality Instrument-9 (PDQI-9) blindly rated fifty MSEs written using OPCRIT+ and fifty normal MSEs written with no template. Second, we conducted an audit to compare the frequency with which individual components of the MSE were documented in the normal MSEs compared with the OPCRIT+MSEs. RESULTS PDQI-9 ratings indicated that the OPCRIT+MSEs were more 'Thorough', 'Organized', 'Useful' and 'Comprehensible' as well as being of an overall higher quality than the normal MSEs. The audit identified that the normal MSEs contained fewer mentions of the individual components of 'Thought content', 'Anxiety' and 'Cognition & Insight'. CONCLUSIONS These results indicate that a semi-structured assessment template significantly improves the quality of MSE recording by junior doctors within EHRs. Future work should focus on whether such improvements translate into better patient outcomes and have the ability to improve the quality of information available on EHRs to researchers.
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Affiliation(s)
- Sarah E M Lobo
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - James Rucker
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Madeleine Kerr
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Fidel Gallo
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Giles Constable
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Matthew Hotopf
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Robert Stewart
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Matthew Broadbent
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Martin Baggaley
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Simon Lovestone
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Peter McGuffin
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Myanthi Amarasinghe
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Stuart Newman
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Gunter Schumann
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom
| | - Philip J Brittain
- National Institute for Health Research (NIHR), Biomedical Research Centre and Dementia Unit at South London and Maudsley National Health Service Foundation Trust and King's College London, London, United Kingdom.
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Hazlehurst BL, Kurtz SE, Masica A, Stevens VJ, McBurnie MA, Puro JE, Vijayadeva V, Au DH, Brannon ED, Sittig DF. CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data. Int J Med Inform 2015; 84:763-73. [PMID: 26138036 DOI: 10.1016/j.ijmedinf.2015.06.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 02/17/2015] [Accepted: 06/02/2015] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Comparative effectiveness research (CER) requires the capture and analysis of data from disparate sources, often from a variety of institutions with diverse electronic health record (EHR) implementations. In this paper we describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations. METHODS The CER Hub platform implements a data processing pipeline that employs informatics standards for data representation and web-based tools for developing study-specific data processing applications, providing standardized access to the patient-centric electronic health record (EHR) across organizations. RESULTS The CER Hub is being used to conduct two CER studies utilizing data from six geographically distributed and demographically diverse health systems. These foundational studies address the effectiveness of medications for controlling asthma and the effectiveness of smoking cessation services delivered in primary care. DISCUSSION The CER Hub includes four key capabilities: the ability to process and analyze both free-text and coded clinical data in the EHR; a data processing environment supported by distributed data and study governance processes; a clinical data-interchange format for facilitating standardized extraction of clinical data from EHRs; and a library of shareable clinical data processing applications. CONCLUSION CER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data. By offering a range of informatics tools integrated into a framework for conducting studies using EHR data, the CER Hub provides a solution to the challenges of multi-institutional research using electronic medical record data.
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Affiliation(s)
- Brian L Hazlehurst
- Kaiser Permanente Northwest, Center for Health Research, Portland, OR, USA.
| | - Stephen E Kurtz
- Kaiser Permanente Northwest, Center for Health Research, Portland, OR, USA
| | - Andrew Masica
- Baylor Scott & White Health, Center for Clinical Effectiveness, Dallas, TX, USA
| | - Victor J Stevens
- Kaiser Permanente Northwest, Center for Health Research, Portland, OR, USA
| | - Mary Ann McBurnie
- Kaiser Permanente Northwest, Center for Health Research, Portland, OR, USA
| | | | | | - David H Au
- VA Puget Sound Health Care System, Seattle, WA, USA
| | | | - Dean F Sittig
- University of Texas Health Science Center, School of Biomedical Informatics, Houston, TX, USA
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Abstract
This article provides surgical pathologists an overview of health information systems (HISs): what they are, what they do, and how such systems relate to the practice of surgical pathology. Much of this article is dedicated to the electronic medical record. Information, in how it is captured, transmitted, and conveyed, drives the effectiveness of such electronic medical record functionalities. So critical is information from pathology in integrated clinical care that surgical pathologists are becoming gatekeepers of not only tissue but also information. Better understanding of HISs can empower surgical pathologists to become stakeholders who have an impact on the future direction of quality integrated clinical care.
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Affiliation(s)
- S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - David R Artz
- Memorial Sloan Kettering Cancer Center, 633 3rd Avenue, New York, NY 10017, USA
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