Clinical Data Warehouse a Review

Hamoud, Alaa Khalaf; Hashim, Ali Salah; Awadh, Wid Akeel

doi:10.25195/2017/4424

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…CDWs can enhance the quality of disease management [5,6], and support clinical and translational research [7][8][9]. There are many CDW solutions such as I2B2 [10], STRIDE [11] and Vanderbilt [12] listed in a recent review [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Pressat-Laffouilhère

Balayé

Dahamna

et al. 2022

BMC Med Inform Decis Mak

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Background Unstructured data from electronic health records represent a wealth of information. Doc’EDS is a pre-screening tool based on textual and semantic analysis. The Doc’EDS system provides a graphic user interface to search documents in French. The aim of this study was to present the Doc’EDS tool and to provide a formal evaluation of its semantic features. Methods Doc’EDS is a search tool built on top of the clinical data warehouse developed at Rouen University Hospital. This tool is a multilevel search engine combining structured and unstructured data. It also provides basic analytical features and semantic utilities. A formal evaluation was conducted to measure the impact of Natural Language Processing algorithms. Results Approximately 18.1 million narrative documents are stored in Doc’EDS. The formal evaluation was conducted in 5000 clinical concepts that were manually collected. The F-measures of negative concepts and hypothetical concepts were respectively 0.89 and 0.57. Conclusion In this formal evaluation, we have shown that Doc’EDS is able to deal with language subtleties to enhance an advanced full text search in French health documents. The Doc’EDS tool is currently used on a daily basis to help researchers to identify patient cohorts thanks to unstructured data.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Pressat-Laffouilhère

Balayé

Dahamna

et al. 2022

BMC Med Inform Decis Mak

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“…However, CDW has a complex infrastructure based on a variety of databases (raw data, meta data, summary data, data marts), operational data sources, a layer to Extract, Transform and Load (ETL) information, and many data access front end (e.g., analyzing, reporting, mining, medical information, business). Besides that, CDWs also deal with policies of data backups, the workload in the system, capacity of storage and no deletion of information (Khalaf Hamoud, Salah Hashim & Akeel Awadh, 2018).…”

Section: Data Query Find Datamentioning

confidence: 99%

Open-source data management system for Parkinson’s disease follow-up

Folador

Vieira

Pereira

et al. 2021

PeerJ Computer Science

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Background Parkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients. Methods A system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall’s coefficient with a significance level of 1%. Results The free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall’s coefficient (p < 0.001). Conclusion According to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.

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“…3 Clinical data warehouses (CDWs) help to address this challenge by providing access to multiple large-scale data sources from real-world clinical settings. [4][5][6] When properly qualified and structured, they can include large numbers of patients from diverse healthcare institutions without a need to access their individual medical files, and can be used for surveillance or to conduct investigative, evaluative or observational studies at a (multi-)hospital level. [5][6][7][8][9] In particular, by allowing simultaneous access to both clinical and microbiological data, CDWs have the potential to help provide more comprehensive epidemiological descriptions of acute bacterial infections across large patient populations over time.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6] When properly qualified and structured, they can include large numbers of patients from diverse healthcare institutions without a need to access their individual medical files, and can be used for surveillance or to conduct investigative, evaluative or observational studies at a (multi-)hospital level. [5][6][7][8][9] In particular, by allowing simultaneous access to both clinical and microbiological data, CDWs have the potential to help provide more comprehensive epidemiological descriptions of acute bacterial infections across large patient populations over time. 4,10 Bacteremia is a severe acute bacterial infection which is associated with significant morbidity and mortality, and high levels of antimicrobial resistance (AMR).…”

Section: Introductionmentioning

confidence: 99%

Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019

Abbara¹,

Guillemot²,

Oualydy³

et al. 2022

CLEP

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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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Clinical Data Warehouse a Review

Cited by 13 publications

References 16 publications

Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Open-source data management system for Parkinson’s disease follow-up

Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019

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