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Orjuela KD, Leppert MH, Carroll JD. Navigating the Gray: The Complex Story of PFO Closure Utilization. Circ Cardiovasc Qual Outcomes 2024; 17:e010581. [PMID: 38189124 DOI: 10.1161/circoutcomes.123.010581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Affiliation(s)
- Karen D Orjuela
- Neurology Department (K.D.O., M.H.L.), University of Colorado School of Medicine, Aurora
| | - Michelle H Leppert
- Neurology Department (K.D.O., M.H.L.), University of Colorado School of Medicine, Aurora
| | - John D Carroll
- Division of Cardiology (J.D.C.), University of Colorado School of Medicine, Aurora
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Matsuzaki K, Kitayama M, Yamamoto K, Aida R, Imai T, Ishida M, Katafuchi R, Kawamura T, Yokoo T, Narita I, Suzuki Y. A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study. JMIR Med Inform 2023; 11:e46725. [PMID: 38153801 PMCID: PMC10766166 DOI: 10.2196/46725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Background In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. Objective This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Methods Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. Results SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Conclusions Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.
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Affiliation(s)
- Keiichi Matsuzaki
- Department of Public Health, School of Medicine, Kitasato University, Sagamihara, Japan
| | - Megumi Kitayama
- Clinical Study Support Center, Wakayama Medical University Hospital, Wakayama, Japan
| | - Keiichi Yamamoto
- Translational Research Institute for Medical Innovation, Osaka Dental University, Osaka, Japan
| | - Rei Aida
- Department of Medical Statistics, Osaka Metropolitan University, Osaka, Japan
| | - Takumi Imai
- Clinical & Translational Research Center, Kobe University Hospital, Kobe, Japan
| | - Mami Ishida
- Department of Medical Informatics and Clinical Epidemiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ritsuko Katafuchi
- Kidney Unit, National Hospital Organization Fukuokahigashi Medical Center, Fukuoka, Japan
- Kidney Unit, Medical Corporation Houshikai Kano Hospital, Fukuoka, Japan
| | - Tetsuya Kawamura
- Division of Kidney and Hypertension, Department of Internal Medicine, Jikei University School of Medicine, Tokyo, Japan
| | - Takashi Yokoo
- Division of Kidney and Hypertension, Department of Internal Medicine, Jikei University School of Medicine, Tokyo, Japan
| | - Ichiei Narita
- Division of Clinical Nephrology and Rheumatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Yusuke Suzuki
- Department of Nephrology, Faculty of Medicine, Juntendo University, Tokyo, Japan
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Bazoge A, Morin E, Daille B, Gourraud PA. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Med Inform 2023; 11:e42477. [PMID: 38100200 PMCID: PMC10757232 DOI: 10.2196/42477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/16/2023] [Accepted: 09/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. OBJECTIVE The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. METHODS This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. RESULTS We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CONCLUSIONS CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.
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Affiliation(s)
- Adrien Bazoge
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
| | - Emmanuel Morin
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Béatrice Daille
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
- Nantes Université, INSERM, CHU de Nantes, École Centrale Nantes, Centre de Recherche Translationnelle en Transplantation et Immunologie, CR2TI, F-44000 Nantes, France
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Abbara S, Guillemot D, El Oualydy S, Kos M, Poret C, Breant S, Brun-Buisson C, Watier L. Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019. Clin Epidemiol 2022; 14:1547-1560. [PMID: 36540898 PMCID: PMC9759973 DOI: 10.2147/clep.s385555] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/12/2022] [Indexed: 08/15/2023] Open
Abstract
PURPOSE Antibiotic-resistant bacteremia is a leading global cause of infectious disease morbidity and mortality. Clinical data warehouses (CDWs) allow for the secure, real-time coupling of diverse data sources from real-world clinical settings, including care-based medical-administrative data and laboratory-based microbiological data. The main purpose of this study was to assess the contribution of CDWs in the epidemiological study of antibiotic resistance by constructing a database of bacteremia patients, BactHub, and describing their main clinico-microbiological features and outcomes. PATIENTS AND METHODS Adult patients with bacteremia hospitalized between January 1, 2016 and December 31, 2019 in 14 acute care university hospitals from the Greater Paris area were identified; their first bacteremia episode was included. Data describing patients, episodes of bacteremia, bacterial isolates, and antimicrobial resistance were structured. RESULTS Among 29,228 patients with bacteremia, 41% of episodes were community-onset (CO) and 59% were hospital-acquired (HA). Thirty-day and ninety-day mortality rates were 15% and 20% in CO episodes, and 18% and 36% in HA episodes. Overall resistance rates were high, including third-generation cephalosporin resistance among Klebsiella pneumoniae (CO 21%, HA 37%) and Escherichia coli (CO 13%, HA 17%), and methicillin resistance among Staphylococcus aureus (CO 11%, HA 14%). Annual incidence rates increased significantly from 2017 to 2019, from 20.0 to 20.9 to 22.1 stays with bacteremia per 1000 stays (p < 0.0001). CONCLUSION The Bacthub database provides accurate clinico-microbiological data describing bacteremia across France's largest hospital group. Data from Bacthub may inform surveillance and the clinical decision-making process for bacteremia patients, including choice of antimicrobial therapy. The database also offers opportunities for research, including analysis of hospital care pathways and significant patient outcomes such as mortality and recurrence of infection.
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Affiliation(s)
- Salam Abbara
- Anti-Infective Evasion and Pharmacoepidemiology Team, Inserm, UVSQ, University Paris-Saclay, CESP, Montigny-Le-Bretonneux, France
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), University Paris Cité, Paris, France
| | - Didier Guillemot
- Anti-Infective Evasion and Pharmacoepidemiology Team, Inserm, UVSQ, University Paris-Saclay, CESP, Montigny-Le-Bretonneux, France
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), University Paris Cité, Paris, France
- Public Health, Medical Information, Clinical Research, AP-HP, University Paris Saclay, Le Kremlin-Bicêtre, France
| | - Salma El Oualydy
- Plateforme des données de santé - Health Data Hub, Paris, France
| | - Maeva Kos
- Plateforme des données de santé - Health Data Hub, Paris, France
| | - Cécile Poret
- AP-HP, Direction des Systèmes d’Information, Pôle Innovation et Données, Paris, France
| | - Stéphane Breant
- AP-HP, Direction des Systèmes d’Information, Pôle Innovation et Données, Paris, France
| | - Christian Brun-Buisson
- Anti-Infective Evasion and Pharmacoepidemiology Team, Inserm, UVSQ, University Paris-Saclay, CESP, Montigny-Le-Bretonneux, France
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), University Paris Cité, Paris, France
| | - Laurence Watier
- Anti-Infective Evasion and Pharmacoepidemiology Team, Inserm, UVSQ, University Paris-Saclay, CESP, Montigny-Le-Bretonneux, France
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), University Paris Cité, Paris, France
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Harley JB, Pyarajan S, Partan ES, Epstein L, Wertheim JA, Diwan A, Woods CW, Davey V, Blair S, Clark DH, Kaufman KM, Khan S, Chepelev I, Devine A, Cameron P, McCann MF, Ammons MCB, Bolz DD, Battles JK, Curtis JL, Holodniy M, Marconi VC, Searles CD, Beenhouwer DO, Brown ST, Moorman JP, Yao ZQ, Rodriguez-Barradas MC, Mohapatra S, Molina De Rodriguez OY, Padiernos EB, McIndoo ER, Price E, Burgoyne HM, Robey I, Schwenke DC, Shive CL, Przygodzki RM, Ramoni RB, Krull HK, Bonomo RA. The US Department of Veterans Affairs Science and Health Initiative to Combat Infectious and Emerging Life-Threatening Diseases (VA SHIELD): A Biorepository Addressing National Health Threats. Open Forum Infect Dis 2022; 9:ofac641. [PMID: 36601554 PMCID: PMC9801224 DOI: 10.1093/ofid/ofac641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Indexed: 12/15/2022] Open
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has demonstrated the need to share data and biospecimens broadly to optimize clinical outcomes for US military Veterans. Methods In response, the Veterans Health Administration established VA SHIELD (Science and Health Initiative to Combat Infectious and Emerging Life-threatening Diseases), a comprehensive biorepository of specimens and clinical data from affected Veterans to advance research and public health surveillance and to improve diagnostic and therapeutic capabilities. Results VA SHIELD now comprises 12 sites collecting de-identified biospecimens from US Veterans affected by SARS-CoV-2. In addition, 2 biorepository sites, a data processing center, and a coordinating center have been established under the direction of the Veterans Affairs Office of Research and Development. Phase 1 of VA SHIELD comprises 34 157 samples. Of these, 83.8% had positive tests for SARS-CoV-2, with the remainder serving as contemporaneous controls. The samples include nasopharyngeal swabs (57.9%), plasma (27.9%), and sera (12.5%). The associated clinical and demographic information available permits the evaluation of biological data in the context of patient demographics, clinical experience and management, vaccinations, and comorbidities. Conclusions VA SHIELD is representative of US national diversity with a significant potential to impact national healthcare. VA SHIELD will support future projects designed to better understand SARS-CoV-2 and other emergent healthcare crises. To the extent possible, VA SHIELD will facilitate the discovery of diagnostics and therapeutics intended to diminish COVID-19 morbidity and mortality and to reduce the impact of new emerging threats to the health of US Veterans and populations worldwide.
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Affiliation(s)
- John B Harley
- Correspondence: John B. Harley, Cincinnati VA Medical Center, 3200 Vine St., John B. Harley (151), Cincinnati, OH 45220 ()
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA
| | - Elizabeth S Partan
- Center for Data and Computational Sciences, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA
| | - Lauren Epstein
- Infectious Diseases, US Department of Veterans Affairs Medical Center, Atlanta, Georgia, USA
| | - Jason A Wertheim
- Research & Development, Southern Arizona Veterans Affairs Healthcare System, US Department of Veterans Affairs, Tucson, Arizona, USA
| | - Abhinav Diwan
- Cardiology, Veterans Affairs Saint Louis Healthcare System, US Department of Veterans Affairs,Saint Louis, Missouri, USA
| | - Christopher W Woods
- Medicine, US Department of Veterans Affairs Medical Center, Durham, North Carolina, USA
| | - Victoria Davey
- Office of Research and Development, US Department of Veterans Affairs, Washington, District of Columbia, USA
| | - Sharlene Blair
- Research Services, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
| | - Dennis H Clark
- Research Services, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
| | - Kenneth M Kaufman
- Research Services, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
| | - Shagufta Khan
- Research Services, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
| | - Iouri Chepelev
- Research Services, US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
| | - Alexander Devine
- Prometheus Federal Services, Titan Alpha, Washington, District of Columbia, USA
| | - Perry Cameron
- Customer Value Partners, Titan Alpha, Washington, District of Columbia, USA
| | - Monica F McCann
- Office of Research and Development, Chesapeake Medical Communications, Contractor for the US Department of Veterans Affairs, Washington, District of Columbia, USA
| | - Mary Cloud B Ammons
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA,Idaho Veterans Research and Education Foundation, Boise, Idaho, USA
| | - Devin D Bolz
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA
| | - Jane K Battles
- Office of Research and Development, US Department of Veterans Affairs, Washington, District of Columbia, USA
| | - Jeffrey L Curtis
- Medicine Service, Veteran Affairs Ann Arbor Healthcare System, US Department of Veterans Affairs, Ann Arbor, Michigan, USA
| | - Mark Holodniy
- Public Health Surveillance, Veterans Affairs Palo Alto Healthcare System, US Department of Veterans Affairs, Palo Alto, California, USA
| | - Vincent C Marconi
- Infectious Diseases, US Department of Veterans Affairs Medical Center, Atlanta, Georgia, USA,Division of Infectious Diseases, Emory School of Medicine and Rollins School of Public Health, Atlanta, Georgia, USA
| | - Charles D Searles
- Infectious Diseases, US Department of Veterans Affairs Medical Center, Atlanta, Georgia, USA
| | - David O Beenhouwer
- Medicine, Veterans Affairs Greater Los Angeles Healthcare System, US Department of Veterans Affairs, Los Angeles, California, USA
| | - Sheldon T Brown
- Infectious Diseases, James J. Peters Veterans Affairs Medical Center, US Department of Veterans Affairs, Bronx, New York, USA
| | - Jonathan P Moorman
- Infectious Diseases, James H. Quillen Veterans Affairs Medical Center, US Department of Veterans Affairs, Mountain Home, Tennessee, USA,Center of Excellence in Inflammation, Infectious Diseases, and Immunity, East Tennessee State University, Johnson City, Tennessee, USA
| | - Zhi Q Yao
- Infectious Diseases, James H. Quillen Veterans Affairs Medical Center, US Department of Veterans Affairs, Mountain Home, Tennessee, USA,Center of Excellence in Inflammation, Infectious Diseases, and Immunity, East Tennessee State University, Johnson City, Tennessee, USA
| | - Maria C Rodriguez-Barradas
- Infectious Diseases Section, Michael E. DeBakey Veterans Affairs Medical Center, US Department of Veterans Affairs, Houston, Texas, USA,Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Shyam Mohapatra
- Medicine, James A. Haley Veterans Hospital, US Department of Veterans Affairs, Tampa, Florida, USA
| | - Osmara Y Molina De Rodriguez
- Research & Development, Southern Arizona Veterans Affairs Healthcare System, US Department of Veterans Affairs, Tucson, Arizona, USA
| | - Emerson B Padiernos
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA
| | - Eric R McIndoo
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA,Idaho Veterans Research and Education Foundation, Boise, Idaho, USA
| | - Emily Price
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA,Idaho Veterans Research and Education Foundation, Boise, Idaho, USA
| | - Hailey M Burgoyne
- Research, US Department of Veterans Affairs Medical Center, Boise, Idaho, USA,Idaho Veterans Research and Education Foundation, Boise, Idaho, USA
| | - Ian Robey
- Research & Development, Southern Arizona Veterans Affairs Healthcare System, US Department of Veterans Affairs, Tucson, Arizona, USA
| | - Dawn C Schwenke
- Research & Development, Southern Arizona Veterans Affairs Healthcare System, US Department of Veterans Affairs, Tucson, Arizona, USA
| | - Carey L Shive
- Medicine, Veterans Affairs Northeast Ohio Healthcare System, US Department of Veterans Affairs, Cleveland, Ohio, USA
| | - Ronald M Przygodzki
- Office of Research and Development, US Department of Veterans Affairs, Washington, District of Columbia, USA
| | - Rachel B Ramoni
- Office of Research and Development, US Department of Veterans Affairs, Washington, District of Columbia, USA
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Mayer DA, Rasmussen LV, Roark CD, Kahn MG, Schilling LM, Wiley LK. ReviewR: a light-weight and extensible tool for manual review of clinical records. JAMIA Open 2022; 5:ooac071. [PMID: 35936991 PMCID: PMC9350014 DOI: 10.1093/jamiaopen/ooac071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
Objectives Manual record review is a crucial step for electronic health record (EHR)-based research, but it has poor workflows and is error prone. We sought to build a tool that provides a unified environment for data review and chart abstraction data entry. Materials and Methods ReviewR is an open-source R Shiny application that can be deployed on a single machine or made available to multiple users. It supports multiple data models and database systems, and integrates with the REDCap API for storing abstraction results. Results We describe 2 real-world uses and extensions of ReviewR. Since its release in April 2021 as a package on CRAN it has been downloaded 2204 times. Discussion and Conclusion ReviewR provides an easily accessible review interface for clinical data warehouses. Its modular, extensible, and open source nature afford future expansion by other researchers.
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Affiliation(s)
- David A Mayer
- Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Christopher D Roark
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lisa M Schilling
- Division of General Internal Medicine, Department of Medicine, Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Laura K Wiley
- Corresponding Author: Laura K. Wiley, PhD, Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, 1890 N. Revere Court, Aurora, CO 80045, USA;
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7
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Esheiba L, Helal IMA, Elgammal A, El-Sharkawi ME. A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems. Sensors (Basel) 2022; 22:s22062118. [PMID: 35336288 PMCID: PMC8950267 DOI: 10.3390/s22062118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 12/04/2022]
Abstract
Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.
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Affiliation(s)
- Laila Esheiba
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt; (I.M.A.H.); (A.E.); (M.E.E.-S.)
- Correspondence:
| | - Iman M. A. Helal
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt; (I.M.A.H.); (A.E.); (M.E.E.-S.)
| | - Amal Elgammal
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt; (I.M.A.H.); (A.E.); (M.E.E.-S.)
- Faculty of Computing and Information Sciences, Egypt University of Informatics, New Administrative Capital, Cairo 11865, Egypt
| | - Mohamed E. El-Sharkawi
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt; (I.M.A.H.); (A.E.); (M.E.E.-S.)
- Faculty of Computing and Information Sciences, Egypt University of Informatics, New Administrative Capital, Cairo 11865, Egypt
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8
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Jannot AS, Messiaen C, Khatim A, Pichon T, Sandrin A. The ongoing French BaMaRa-BNDMR cohort: implementation and deployment of a nationwide information system on rare disease. J Am Med Inform Assoc 2022; 29:553-558. [PMID: 34741516 PMCID: PMC8800517 DOI: 10.1093/jamia/ocab237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/20/2021] [Accepted: 10/20/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND BaMaRa allows the secure collection and deidentified centralization of medical data from all patients followed-up in a rare disease expert network in France, based on a minimum data set (SDM-MR). The present article describes BaMaRa information system implementation and development across the whole national territory as well as data access requests through BNDMR, the data warehouse which centralizes all BaMaRa data, during the 2015-2020 period. MATERIALS AND METHODS SDM-MR is made up of 60 interoperable items and is routinely collected through BaMaRa in rare disease centers as part of care and discharged into BNDMR after deidentification and data reconciliation. Data access is regulated by a scientific committee. RESULTS In total, 668 002 affected patients had an SDM-MR recorded in BNDMR by the end of 2020 with a mean value of 3.4 activities per patients. Data access was provided for 66 projects. CONCLUSION The BaMaRa-BNDMR infrastructure provides an administrative and epidemiological resources for rare diseases in France.
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Affiliation(s)
- Anne-Sophie Jannot
- Banque Nationale de Données Maladies Rares, DSI-I&D, APHP, Paris, France
- Université de Paris, Paris, France
- HeKA team, Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université de Paris, Paris, France
| | - Claude Messiaen
- Banque Nationale de Données Maladies Rares, DSI-I&D, APHP, Paris, France
| | - Ahlem Khatim
- Banque Nationale de Données Maladies Rares, DSI-I&D, APHP, Paris, France
| | - Thibaut Pichon
- Banque Nationale de Données Maladies Rares, DSI-I&D, APHP, Paris, France
| | - Arnaud Sandrin
- Banque Nationale de Données Maladies Rares, DSI-I&D, APHP, Paris, France
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9
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Bernstam EV, Warner JL, Krauss JC, Ambinder E, Rubinstein WS, Komatsoulis G, Miller RS, Chen JL. Quantitating and assessing interoperability between electronic health records. J Am Med Inform Assoc 2022; 29:753-760. [PMID: 35015861 PMCID: PMC9006690 DOI: 10.1093/jamia/ocab289] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/13/2021] [Accepted: 12/30/2021] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES Electronic health records (EHRs) contain a large quantity of machine-readable data. However, institutions choose different EHR vendors, and the same product may be implemented differently at different sites. Our goal was to quantify the interoperability of real-world EHR implementations with respect to clinically relevant structured data. MATERIALS AND METHODS We analyzed de-identified and aggregated data from 68 oncology sites that implemented 1 of 5 EHR vendor products. Using 6 medications and 6 laboratory tests for which well-accepted standards exist, we calculated inter- and intra-EHR vendor interoperability scores. RESULTS The mean intra-EHR vendor interoperability score was 0.68 as compared to a mean of 0.22 for inter-system interoperability, when weighted by number of systems of each type, and 0.57 and 0.20 when not weighting by number of systems of each type. DISCUSSION In contrast to data elements required for successful billing, clinically relevant data elements are rarely standardized, even though applicable standards exist. We chose a representative sample of laboratory tests and medications for oncology practices, but our set of data elements should be seen as an example, rather than a definitive list. CONCLUSIONS We defined and demonstrated a quantitative measure of interoperability between site EHR systems and within/between implemented vendor systems. Two sites that share the same vendor are, on average, more interoperable. However, even for implementation of the same EHR product, interoperability is not guaranteed. Our results can inform institutional EHR selection, analysis, and optimization for interoperability.
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Affiliation(s)
- Elmer V Bernstam
- Corresponding Author: Elmer V. Bernstam, MD, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA;
| | - Jeremy L Warner
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John C Krauss
- University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Edward Ambinder
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Wendy S Rubinstein
- CancerLinQ LLC, American Society of Clinical Oncology, Alexandria, Virginia, USA
| | - George Komatsoulis
- CancerLinQ LLC, American Society of Clinical Oncology, Alexandria, Virginia, USA
| | - Robert S Miller
- CancerLinQ LLC, American Society of Clinical Oncology, Alexandria, Virginia, USA
| | - James L Chen
- Division of Medical Oncology and Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
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10
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Kahn MG, Mui JY, Ames MJ, Yamsani AK, Pozdeyev N, Rafaels N, Brooks IM. Migrating a research data warehouse to a public cloud: challenges and opportunities. J Am Med Inform Assoc 2021; 29:592-600. [PMID: 34919694 PMCID: PMC8922165 DOI: 10.1093/jamia/ocab278] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/15/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Objective Clinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud. Materials and Methods This study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories. Results Eight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise. Discussion Cloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs. Conclusions Migrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.
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Affiliation(s)
- Michael G Kahn
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA.,Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Joyce Y Mui
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Anoop K Yamsani
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nikita Pozdeyev
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ian M Brooks
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA.,Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, CO, USA
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11
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Bannay A, Bories M, Le Corre P, Riou C, Lemordant P, Van Hille P, Chazard E, Dode X, Cuggia M, Bouzillé G. Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case. JMIR Med Inform 2021; 9:e29286. [PMID: 34898457 PMCID: PMC8713098 DOI: 10.2196/29286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/12/2021] [Accepted: 07/25/2021] [Indexed: 12/13/2022] Open
Abstract
Background Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). Objective This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. Methods A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. Results The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. Conclusions This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.
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Affiliation(s)
- Aurélie Bannay
- Université de Lorraine, Centre Hospitalier Régional Universitaire de Nancy, Centre national de la recherche scientifique, Inria, Laboratoire lorrain de recherche en informatique et ses applications, Nancy, France.,Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
| | - Mathilde Bories
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France.,Pôle Pharmacie, Service Hospitalo-Universitaire de Pharmacie, Centre Hospitalier Universitaire de Rennes, Rennes, France.,Laboratoire de Biopharmacie et Pharmacie Clinique, Faculté de Pharmacie, Université de Rennes 1, Rennes, France
| | - Pascal Le Corre
- Pôle Pharmacie, Service Hospitalo-Universitaire de Pharmacie, Centre Hospitalier Universitaire de Rennes, Rennes, France.,Laboratoire de Biopharmacie et Pharmacie Clinique, Faculté de Pharmacie, Université de Rennes 1, Rennes, France.,Centre Hospitalier Universitaire de Rennes, Inserm, Ecole des hautes études en santé publique, Institut de recherche en santé, environnement et travail, UMR_S 1085, Université de Rennes 1, Rennes, France
| | - Christine Riou
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
| | - Pierre Lemordant
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
| | - Pascal Van Hille
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
| | - Emmanuel Chazard
- Centre d'Etudes et de Recherche en Informatique Médicale EA2694, Centre Hospitalier Universitaire de Lille, Université de Lille, Lille, France
| | - Xavier Dode
- Centre National Hospitalier d'Information sur le Médicament, Paris, France.,Department of Pharmacy, Hospices Civils de Lyon, University Hospital, Lyon, France
| | - Marc Cuggia
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
| | - Guillaume Bouzillé
- Inserm, Laboratoire Traitement du Signal et de l'Image - UMR 1099, Centre Hospitalier Universitaire de Rennes, Université de Rennes 1, Rennes, France
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12
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Stöhr MR, Günther A, Majeed RW. The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation. JMIR Med Inform 2021; 9:e30308. [PMID: 34847059 PMCID: PMC8669586 DOI: 10.2196/30308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/13/2021] [Accepted: 10/11/2021] [Indexed: 11/29/2022] Open
Abstract
Background In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data. Objective This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues. Methods We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories. Results A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app. Conclusions A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.
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Affiliation(s)
- Mark R Stöhr
- Justus-Liebig-University Giessen, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Gießen, Germany
| | - Andreas Günther
- Justus-Liebig-University Giessen, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Gießen, Germany
| | - Raphael W Majeed
- Justus-Liebig-University Giessen, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Gießen, Germany
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13
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Sartin EB, Metzger KB, Pfeiffer MR, Myers RK, Curry AE. Facilitating research on racial and ethnic disparities and inequities in transportation: Application and evaluation of the Bayesian Improved Surname Geocoding (BISG) algorithm. Traffic Inj Prev 2021; 22:S32-S37. [PMID: 34402327 PMCID: PMC8792156 DOI: 10.1080/15389588.2021.1955109] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Racial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field's ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research. METHODS We applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs. RESULTS We found excellent or outstanding concordance (AUC ≥0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers. CONCLUSION Findings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.
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Affiliation(s)
- Emma B. Sartin
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Kristina B. Metzger
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Melissa R. Pfeiffer
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Rachel K. Myers
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
- Division of Emergency Medicine, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Allison E. Curry
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA
- Division of Emergency Medicine, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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14
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Soudais B, Lacroix-Hugues V, Meunier F, Gillibert A, Darmon D, Schuers M. Diagnosis and management of male urinary tract infections: a need for new guidelines. Study from a French general practice electronic database. Fam Pract 2021; 38:432-440. [PMID: 33340317 DOI: 10.1093/fampra/cmaa136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The definition and the treatment of male urinary tract infections (UTIs) are imprecise. This study aims to determine the frequency of male UTIs in consultations of general practice, the diagnostic approach and the prescribed treatments. METHODS We extracted the consultations of male patients, aged 18 years or more, during the period 2012-17 with the International Classification of Primary Care, version 2 codes for UTIs or associated symptoms from PRIMEGE/MEDISEPT databases of primary care. For eligible consultations in which all symptoms or codes were consistent with male UTIs, we identified patient history, prescribed treatments, antibiotic duration, clinical conditions, additional examinations and bacteriological results of urine culture. RESULTS Our study included 610 consultations with 396 male patients (mean age 62.5 years). Male UTIs accounted for 0.097% of visits and 1.44 visits per physician per year. The UTIs most commonly identified were: undifferentiated (52%), prostatitis (36%), cystitis (8.5%) and pyelonephritis (3.5%). Fever was recorded in 14% of consultations. Urine dipstick test was done in 1.8% of consultations. Urine culture was positive for Escherichia coli in 50.4% of bacteriological tests. Fluoroquinolones were the most prescribed antibiotics (64.9%), followed by beta-lactams (17.4%), trimethoprim-sulfamethoxazole (11.9%) and nitrofurantoin (2.6%). CONCLUSIONS Male UTIs are rare in general practice and have different presentations. The definition of male UTIs needs to be specified by prospective studies. Diagnostic evidence of male cystitis may reduce the duration of antibiotic therapy and spare critical antibiotics.
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Affiliation(s)
- Benjamin Soudais
- Department of General Practice, Normandy University, Rouen, France
| | - Virginie Lacroix-Hugues
- Department of Education and Research in General Practice, Côte d'Azur University, Nice, France.,Department of Public Health, Archet 1 Hospital, Nice, France
| | - François Meunier
- Department of General Practice, Normandy University, Rouen, France
| | | | - David Darmon
- Department of Education and Research in General Practice, Côte d'Azur University, Nice, France.,INSERM, IRD, SESSTIM Sciences Economiques and Sociales de la Santé and Traitement de l'Information Médicale, Aix Marseille University, Marseille, France
| | - Matthieu Schuers
- Department of General Practice, Normandy University, Rouen, France.,CISMeF, TIBS, LITIS EA 4108, CHU Rouen, France.,INSERM U 1142, LIMICS, Paris, France
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15
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Dhalluin T, Fakhiri S, Bouzillé G, Herbert J, Rosset P, Cuggia M, Grammatico-Guillon L. Role of real-world digital data for orthopedic implant automated surveillance: a systematic review. Expert Rev Med Devices 2021; 18:799-810. [PMID: 34148465 DOI: 10.1080/17434440.2021.1943361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Data collection automation through the reuse of real-world digital data from clinical data warehouses (CDW) could represent a great opportunity to improve medical device monitoring. For instance, this approach is starting to be used for the design of automated decision support systems for joint replacement monitoring. However, a number of obstacles remains, such as data quality and interoperability through the use of common and regularly updated terminologies, and the use of a Unique Device Identifier (UDI). AREAS COVERED To present the existing models of automated surveillance of orthopedic devices, a systematic review of initiatives using real-world digital health data to monitor joint replacement surgery was performed following the PRISMA 2020 guidelines. The main objective was to identify the data sources, the target populations, the population size, the device location, and the main results of studies on such initiatives. EXPERT OPINION Analysis of the identified studies showed that real-world digital data offer many opportunities for improving the automation of monitoring in orthopedics. The contribution of real-world data, especially through natural language processing, UDI use in CDW and the integration of device databases, is needed for automated and more robust health surveillance.
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Affiliation(s)
- Thibault Dhalluin
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Sara Fakhiri
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | | | - Julien Herbert
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Philippe Rosset
- Department of Orthopedic Surgery, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
| | - Marc Cuggia
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France
| | - Leslie Grammatico-Guillon
- Department of Medical Information, University Hospital of Tours, Tours, France. Medical School, University of Tours, EA, Tours, France
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16
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Block RG, Puro J, Cottrell E, Lunn MR, Dunne MJ, Quiñones AR, Chung B, Pinnock W, Reid GM, Heintzman J. Recommendations for improving national clinical datasets for health equity research. J Am Med Inform Assoc 2021; 27:1802-1807. [PMID: 32885240 DOI: 10.1093/jamia/ocaa144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 06/05/2020] [Accepted: 06/22/2020] [Indexed: 11/12/2022] Open
Abstract
Health and healthcare disparities continue despite clinical, research, and policy efforts. Large clinical datasets may not contain data relevant to healthcare disparities and leveraging these for research may be crucial to improve health equity. The Health Disparities Collaborative Research Group was commissioned by the Patient-Centered Outcomes Research Institute to examine the data science needs for quality and complete data and provide recommendations for improving data science around health disparities. The group convened content experts, researchers, clinicians, and patients to produce these recommendations and suggestions for implementation. Our desire was to produce recommendations to improve the usability of healthcare datasets for health equity research. The recommendations are summarized in 3 primary domains: patient voice, accurate variables, and data linkage. The implementation of these recommendations in national datasets has the potential to accelerate health disparities research and promote efforts to reduce health inequities.
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Affiliation(s)
| | - Jon Puro
- Department of Research, OCHIN, Portland, Oregon, USA
| | - Erika Cottrell
- Department of Research, OCHIN, Portland, Oregon, USA.,Department of Family Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Mitchell R Lunn
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - M J Dunne
- Department of Research, OCHIN, Portland, Oregon, USA
| | - Ana R Quiñones
- Department of Research, OCHIN, Portland, Oregon, USA.,Department of Family Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Bowen Chung
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | | | - Georgia M Reid
- Department of Research, OCHIN, Portland, Oregon, USA.,Department of Sociology and Anthropology, Lewis and Clark College, Portland, Oregon, USA
| | - John Heintzman
- Department of Research, OCHIN, Portland, Oregon, USA.,Department of Family Medicine, Oregon Health and Science University, Portland, Oregon, USA
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Abstract
Data integration plays a vital role in scientific research. In biomedical research, the OMICS fields have shown the need for larger datasets, like proteomics, pharmacogenomics, and newer fields like foodomics. As research projects require multiple data sources, mapping between these sources becomes necessary. Utilized workflow systems and integration tools therefore need to process large amounts of heterogeneous data formats, check for data source updates, and find suitable mapping methods to cross-reference entities from different databases. This article presents BioDWH2, an open-source, graph-based data warehouse and mapping tool, capable of helping researchers with these issues. A workspace centered approach allows project-specific data source selections and Neo4j or GraphQL server tools enable quick access to the database for analysis. The BioDWH2 tools are available to the scientific community at https://github.com/BioDWH2.
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Affiliation(s)
- Marcel Friedrichs
- Bielefeld University, Faculty of Technology, Bioinformatics / Medical Informatics Department, Bielefeld, Germany
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18
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Cecchetti AA, Bhardwaj N, Murughiyan U, Kothakapu G, Sundaram U. Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach. JMIR Med Inform 2020; 8:e17962. [PMID: 33052114 PMCID: PMC7593861 DOI: 10.2196/17962] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. OBJECTIVE This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. METHODS The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. RESULTS The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. CONCLUSIONS The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.
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Affiliation(s)
- Alfred A Cecchetti
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Niharika Bhardwaj
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Usha Murughiyan
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Gouthami Kothakapu
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Uma Sundaram
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
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19
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Gagalova KK, Leon Elizalde MA, Portales-Casamar E, Görges M. What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions. JMIR Form Res 2020; 4:e17687. [PMID: 32852280 PMCID: PMC7484778 DOI: 10.2196/17687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 06/09/2020] [Accepted: 07/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.
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Affiliation(s)
- Kristina K Gagalova
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada.,Research Institute, BC Children's Hospital, Vancouver, BC, Canada
| | - M Angelica Leon Elizalde
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Elodie Portales-Casamar
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Matthias Görges
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
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20
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Ozaydin B, Zengul F, Oner N, Feldman SS. Healthcare Research and Analytics Data Infrastructure Solution: A Data Warehouse for Health Services Research. J Med Internet Res 2020; 22:e18579. [PMID: 32496199 PMCID: PMC7303827 DOI: 10.2196/18579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/08/2020] [Accepted: 04/16/2020] [Indexed: 12/03/2022] Open
Abstract
Background Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. Objective This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. Methods The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. Results The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. Conclusions To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.
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Affiliation(s)
- Bunyamin Ozaydin
- University of Alabama at Birmingham, Birmingham, AL, United States
| | - Ferhat Zengul
- University of Alabama at Birmingham, Birmingham, AL, United States
| | - Nurettin Oner
- University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sue S Feldman
- University of Alabama at Birmingham, Birmingham, AL, United States
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21
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Kim P, Daly JM, Berkowitz S, Levy BT. Use of the Fluoride Varnish Billing Code in a Tertiary Care Center Setting. J Prim Care Community Health 2020; 11:2150132720913736. [PMID: 32193976 PMCID: PMC7092652 DOI: 10.1177/2150132720913736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Introduction: Dental caries is the most common chronic disease in children from birth through 5 years of age. Application of fluoride varnish (FV) is recommended for children younger than 6 years every 3 to 6 months by the United States Preventive Services Task Force. The purposes of this study were to (1) assess use and reimbursement of Current Dental Terminology (CDT) D1206 and Current Procedural Terminology (CPT) 99188 codes, which are the billing codes for FV application; (2) determine when and by whom each FV code was used; and (3) summarize the associated clinical notes. Methods: Using the electronic medical record data warehouse from a single tertiary teaching hospital and its affiliated primary care clinics, the dates of service, departments, provider names, and patient identifiers associated with codes CDT D1206 and CPT 99188 were collected. The content of clinical notes was reviewed and summarized. The study period was from May 1, 2009 through May 17, 2019. Results: During the 10-year time period, CDT D1206 was used 5 times and CPT 99188 was used 35 times. FV was applied exclusively during well-child visits. Only pediatricians, and no family physicians, applied FV in this setting. Discussion: A single pediatrician championing for FV application increased both the completion of procedure and the appropriate billing in 2019. Conclusion: FV application has been likely underutilized in this Midwestern tertiary teaching hospital and its affiliated clinics. For both family medicine and pediatric offices, an advocate for caries prevention is likely needed for successful implementation of FV application at well-child visits.
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Affiliation(s)
- Peter Kim
- Genesis Health System, Davenport, IA, USA.,University of Iowa, Iowa City, IA, USA
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22
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Lelong R, Soualmia LF, Grosjean J, Taalba M, Darmoni SJ. Building a Semantic Health Data Warehouse in the Context of Clinical Trials: Development and Usability Study. JMIR Med Inform 2019; 7:e13917. [PMID: 31859675 PMCID: PMC6942180 DOI: 10.2196/13917] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 08/02/2019] [Accepted: 08/19/2019] [Indexed: 01/08/2023] Open
Abstract
Background The huge amount of clinical, administrative, and demographic data recorded and maintained by hospitals can be consistently aggregated into health data warehouses with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a semantic health data warehouse enabling both semantic description and retrieval of health information. Objective This study aimed to present a proof of concept of this semantic health data warehouse, based on the data of 250,000 patients from RUH, and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trials context. Methods The semantic health data warehouse relies on 3 distinct semantic layers: (1) a terminology and ontology portal, (2) a semantic annotator, and (3) a semantic search engine and NoSQL (not only structured query language) layer to enhance data access performances. The system adopts an entity-centered vision that provides generic search capabilities able to express data requirements in terms of the whole set of interconnected conceptual entities that compose health information. Results We assessed the ability of the system to assist the search for 95 inclusion and exclusion criteria originating from 5 randomly chosen clinical trials from RUH. The system succeeded in fully automating 39% (29/74) of the criteria and was efficiently used as a prescreening tool for 73% (54/74) of them. Furthermore, the targeted sources of information and the search engine–related or data-related limitations that could explain the results for each criterion were also observed. Conclusions The entity-centered vision contrasts with the usual patient-centered vision adopted by existing systems. It enables more genericity in the information retrieval process. It also allows to fully exploit the semantic description of health information. Despite their semantic annotation, searching within clinical narratives remained the major challenge of the system. A finer annotation of the clinical texts and the addition of specific functionalities would significantly improve the results. The semantic aspect of the system combined with its generic entity-centered vision enables the processing of a large range of clinical questions. However, an important part of health information remains in clinical narratives, and we are currently investigating novel approaches (deep learning) to enhance the semantic annotation of those unstructured data.
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Affiliation(s)
- Romain Lelong
- Department of Biomedical Informatics, Rouen University Hospital, Rouen, France.,LITIS EA 4108, TIBS, Normandy University, Rouen, France
| | - Lina F Soualmia
- Department of Biomedical Informatics, Rouen University Hospital, Rouen, France.,LITIS EA 4108, TIBS, Normandy University, Rouen, France.,LIMICS U1142, Inserm, Sorbonne University, Paris, France
| | - Julien Grosjean
- Department of Biomedical Informatics, Rouen University Hospital, Rouen, France.,LIMICS U1142, Inserm, Sorbonne University, Paris, France
| | - Mehdi Taalba
- Department of Biomedical Informatics, Rouen University Hospital, Rouen, France
| | - Stéfan J Darmoni
- Department of Biomedical Informatics, Rouen University Hospital, Rouen, France.,LIMICS U1142, Inserm, Sorbonne University, Paris, France
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23
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Bennett TD, Callahan TJ, Feinstein JA, Ghosh D, Lakhani SA, Spaeder MC, Szefler SJ, Kahn MG. Data Science for Child Health. J Pediatr 2019; 208:12-22. [PMID: 30686480 PMCID: PMC6486872 DOI: 10.1016/j.jpeds.2018.12.041] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/11/2018] [Accepted: 12/18/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO; CU Data Science to Patient Value (D2V), University of Colorado School of Medicine, Aurora, CO; Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO; Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO.
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO
| | - James A Feinstein
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO
| | - Debashis Ghosh
- CU Data Science to Patient Value (D2V), University of Colorado School of Medicine, Aurora, CO; Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO
| | - Saquib A Lakhani
- Pediatric Genomics Discovery Program, Department of Pediatrics, Yale University School of Medicine, New Haven, CT
| | - Michael C Spaeder
- Pediatric Critical Care, University of Virginia School of Medicine, Charlottesville, VA
| | - Stanley J Szefler
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO
| | - Michael G Kahn
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO; Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO
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Abstract
Research and innovation in healthcare can change existing practices aiming at constant improvement of diagnosis, treatment and prevention. As a new holistic approach Systems Medicine (SM) may revolutionize the healthcare system. This paper analyzes ethical and economic obstacles of SMs development from a niche innovation to a standard solution. We adapt a model of innovation theory to structure the barriers of adopting SM to become standard in the medical system. SM has the potential to change the medical system if barriers to this innovation can be overcome. The article discusses the potential of SM in becoming the future health paradigm considering these barriers and provides an overview of the current situation.
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Affiliation(s)
- Steffen Fleßa
- Ernst-Moritz-Arndt-Universität Greifswald, Rechts- und Staatswissenschaftliche Fakultät, Lehrstuhl für Allgemeine Betriebswirtschaftslehre und Gesundheitsmanagement, Friedrich-Loeffler-Straße 70, 17489 Greifswald, Deutschland
| | - Christin Thum
- Ernst-Moritz-Arndt-Universität Greifswald, Rechts- und Staatswissenschaftliche Fakultät, Lehrstuhl für Allgemeine Betriebswirtschaftslehre und Gesundheitsmanagement, Friedrich-Loeffler-Straße 70, 17489 Greifswald, Deutschland
| | - Susan Raths
- Ernst-Moritz-Arndt-Universität Greifswald, Rechts- und Staatswissenschaftliche Fakultät, Lehrstuhl für Allgemeine Betriebswirtschaftslehre und Gesundheitsmanagement, Friedrich-Loeffler-Straße 70, 17489 Greifswald, Deutschland
| | - Tobias Fischer
- Universitätsmedizin Greifswald, Institut für Ethik und Geschichte der Medizin, Ellernholzstraße 1-2, 17487 Greifswald, Deutschland
| | - Pia Erdmann
- Ernst-Moritz-Arndt-Universität Greifswald, TheologischeFakultät, Lehrstuhl für Systematische Theologie, Am Rubenowplatz 2-3, 17489 Greifswald, Deutschland
| | - Martin Langanke
- Universitätsmedizin Greifswald, Institut für Ethik und Geschichte der Medizin, Ellernholzstraße 1-2, 17487 Greifswald, Deutschland.,Ernst-Moritz-Arndt-Universität Greifswald, TheologischeFakultät, Lehrstuhl für Systematische Theologie, Am Rubenowplatz 2-3, 17489 Greifswald, Deutschland
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25
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Hackl WO, Ammenwerth E. SPIRIT: Systematic Planning of Intelligent Reuse of Integrated Clinical Routine Data. A Conceptual Best-practice Framework and Procedure Model. Methods Inf Med 2016; 55:114-24. [PMID: 26769124 DOI: 10.3414/me15-01-0045] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 11/11/2015] [Indexed: 12/28/2022]
Abstract
BACKGROUND Secondary use of clinical routine data is receiving an increasing amount of attention in biomedicine and healthcare. However, building and analysing integrated clinical routine data repositories are nontrivial, challenging tasks. As in most evolving fields, recognized standards, well-proven methodological frameworks, or accurately described best-practice approaches for the systematic planning of solutions for secondary use of routine medical record data are missing. OBJECTIVE We propose a conceptual best-practice framework and procedure model for the systematic planning of intelligent reuse of integrated clinical routine data (SPIRIT). METHODS SPIRIT was developed based on a broad literature overview and further refined in two case studies with different kinds of clinical routine data, including process-oriented nursing data from a large hospital group and high-volume multimodal clinical data from a neurologic intensive care unit. RESULTS SPIRIT aims at tailoring secondary use solutions to specific needs of single departments without losing sight of the institution as a whole. It provides a general conceptual best-practice framework consisting of three parts: First, a secondary use strategy for the whole organization is determined. Second, comprehensive analyses are conducted from two different viewpoints to define the requirements regarding a clinical routine data reuse solution at the system level from the data perspective (BOTTOM UP) and at the strategic level from the future users perspective (TOP DOWN). An obligatory clinical context analysis (IN BETWEEN) facilitates refinement, combination, and integration of the different requirements. The third part of SPIRIT is dedicated to implementation, which comprises design and realization of clinical data integration and management as well as data analysis solutions. CONCLUSIONS The SPIRIT framework is intended to be used to systematically plan the intelligent reuse of clinical routine data for multiple purposes, which often was not intended when the primary clinical documentation systems were implemented. SPIRIT helps to overcome this gap. It can be applied in healthcare institutions of any size or specialization and allows a stepwise setup and evolution of holistic clinical routine data reuse solutions.
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Affiliation(s)
- W O Hackl
- Dr. Werner O. Hackl, Institute of Biomedical Informatics, UMIT - University for Health Sciences, Medical Informatics and Technology, Eduard Wallnöfer Zentrum 1, 6060 Hall in Tirol, Austria, E-mail:
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26
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Branescu I, Purcarea VL, Dobrescu R. Solutions for medical databases optimal exploitation. J Med Life 2014; 7:109-18. [PMID: 24653769 PMCID: PMC3956088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 01/24/2014] [Indexed: 11/23/2022] Open
Abstract
The paper discusses the methods to apply OLAP techniques for multidimensional databases that leverage the existing, performance-enhancing technique, known as practical pre-aggregation, by making this technique relevant to a much wider range of medical applications, as a logistic support to the data warehousing techniques. The transformations have practically low computational complexity and they may be implemented using standard relational database technology. The paper also describes how to integrate the transformed hierarchies in current OLAP systems, transparently to the user and proposes a flexible, "multimodel" federated system for extending OLAP querying to external object databases.
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Affiliation(s)
- I Branescu
- Faculty of Automatic Control and Computers, Polytechnic University of Bucharest
| | - VL Purcarea
- Department of Healthcare Marketing, Technology and Medical Devices, Medical Informatics and Biostatistics, “Carol Davila" University of Medicine and Pharmacy, Bucharest
| | - R Dobrescu
- Faculty of Automatic Control and Computers, Polytechnic University of Bucharest
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27
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Anderson N, Abend A, Mandel A, Geraghty E, Gabriel D, Wynden R, Kamerick M, Anderson K, Rainwater J, Tarczy-Hornoch P. Implementation of a deidentified federated data network for population-based cohort discovery. J Am Med Inform Assoc 2012; 19:e60-7. [PMID: 21873473 PMCID: PMC3392860 DOI: 10.1136/amiajnl-2011-000133] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2011] [Accepted: 07/07/2011] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers. METHODS The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource. RESULTS By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility. DISCUSSION The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. CONCLUSION The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites.
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Affiliation(s)
- Nicholas Anderson
- Department of Biomedical Health Informatics, University of Washington, Seattle, Washington 98109, USA.
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28
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Abstract
The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.
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Affiliation(s)
- Siva Prasad Akula
- Institute of Glycoproteomics and Systems Biology, Tarnaka, Hyderbad-500017, India
- Department of Computer Sciences and Engineering, Acharya Nagarjuna University, Guntur-522510, India
| | - Raghava Naidu Miriyala
- Department of Computer Sciences and Engineering, Acharya Nagarjuna University, Guntur-522510, India
- D.M.S S.V.H. College of Engineering, Department of Computer Science, Machilipatnam - 521002, India
| | - Hanuman Thota
- Department of Computer Sciences and Engineering, Acharya Nagarjuna University, Guntur-522510, India
- D.M.S S.V.H. College of Engineering, Department of Computer Science, Machilipatnam - 521002, India
| | - Allam Appa Rao
- International Centre for Bioinformatics, Andhra University, Visakhapatnam-530003, India
| | - Srinubabu Gedela
- Institute of Glycoproteomics and Systems Biology, Tarnaka, Hyderbad-500017, India
- International Centre for Bioinformatics, Andhra University, Visakhapatnam-530003, India
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