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The Revolution of Health Data Warehouses in Hospitals: From Theory to Practice

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 10089

Special Issue Editor

Dr. François Bocquet

E-Mail Website
Guest Editor
1. The Data Factory & Analytics Department, Integrated Center for Oncology Nantes, Angers, France
2. Law and Social Change Laboratory, University of Nantes, CNRS UMR 6297, Nantes, France
3. Health and Law Institute, University of Paris, INSERM UMR S1145, Paris, France
Interests: public health; health economics; big data; data science; health organization research; health policy; pharmaceutical and drug sciences; health technology assessment; healthcare system regulations; pharmaceutical law; financing of therapeutic innovations

Special Issue Information

Dear Colleagues,

The world of healthcare is in the spotlight of a new revolution, that of the development of health data warehouses in public or private hospitals that integrates the medical and administrative data of millions of patients hospitalized (in-patients) or seen in consultation (out-patients). These data warehouses make it possible to multiply the uses of big data in healthcare. It represents a great opportunity to advance scientific research in the field of health by facilitating multicentric research not involving human subjects, feasibility studies for clinical trials and the development of artificial intelligence, algorithms and modeling.

While the large-scale use of these data can lead to progress and medical advances, it also raises many questions concerning the structuring, the standardization of the data collected, their qualification, the identification a common data models, the interoperability and consistency of the data and their meaning across applications. Researchers and clinicians face major obstacles using them because of a lack of international standards regarding data characterization and quality. Despite these barriers, the number of data sharing initiatives continues to grow.

The aim of this Special Issue in the International Journal of Environmental Research and Public Health is to try to address all these questions and to make substantial contributions to knowledge gaps in understanding the scientifical and methodological issues related to the structuration and the qualification of the data feeding the hospital data warehouses and their potential impact on research and public health.

A wide range of topics regarding HDW will be included in this issue, related to, but not limited to analysis of:

  • The structuration and the qualification of the data.
  • The standardization and the interoperability of the data
  • The identification of consistent and extensible data concepts and data models.
  • The impact of HDW to optimize the clinical research or to improve the public health.
  • International, national, regional or local initiatives or experiences of HDW involving health professionals, researchers and patient representatives.

Dr. François Bocquet
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • clinical research
  • data models
  • data science
  • data warehouses
  • healthcare
  • interoperability
  • public health
  • quality of data
  • structuration of data

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Published Papers (4 papers)

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Editorial

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6 pages, 303 KiB  
Editorial
The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals
by François Bocquet, Mario Campone and Marc Cuggia
Int. J. Environ. Res. Public Health 2022, 19(12), 7379; https://doi.org/10.3390/ijerph19127379 - 16 Jun 2022
Cited by 5 | Viewed by 2233
Abstract
Digital health, e-health, telemedicine—this abundance of terms illustrates the scientific and technical revolution at work, made possible by high-speed processing of health data, artificial intelligence (AI), and the profound upheavals currently taking place and yet to come in health systems [...] Full article
(This article belongs to the Special Issue The Revolution of Health Data Warehouses in Hospitals: From Theory to Practice)

Research

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10 pages, 1026 KiB  
Article
Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort
by Dorian Culié, Renaud Schiappa, Sara Contu, Boris Scheller, Agathe Villarme, Olivier Dassonville, Gilles Poissonnet, Alexandre Bozec and Emmanuel Chamorey
Int. J. Environ. Res. Public Health 2022, 19(19), 12200; https://doi.org/10.3390/ijerph191912200 - 26 Sep 2022
Viewed by 1290
Abstract
The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on [...] Read more.
The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification of patients according to the pathology involved in a head and neck surgery department. 24,434 Electronic Health Records (EHR) from the first visit between 2000 and 2020 were extracted. More than 6000 EHR were manually classified in ten groups of interest according to the reason for consultation with a clinical relevance. A convolutional neural network (TensorFlow, previously reported by Hsu et al.) was then used to predict the group of patients based on their pathology, using two levels of classification based on clinically relevant criteria. On the first and second level of classification, macro-average performances were: 0.95, 0.83, 0.85, 0.97, 0.84 and 0.93, 0.76, 0.83, 0.96, 0.79 for accuracy, recall, precision, specificity and F1-score versus accuracy, recall and precision of 0.580, 580 and 0.582 for Hsu et al., respectively. We validated this model to predict the pathology involved and to constitute clinically relevant cohorts in a tertiary hospital. This model did not require a preprocessing stage, was used in French and showed equivalent or better performances than other already published techniques. Full article
(This article belongs to the Special Issue The Revolution of Health Data Warehouses in Hospitals: From Theory to Practice)
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Review

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19 pages, 591 KiB  
Review
ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review
by Daniel Cuevas-González, Juan Pablo García-Vázquez, Miguel Bravo-Zanoguera, Roberto López-Avitia, Marco A. Reyna, Nestor Alexander Zermeño-Campos and María Luisa González-Ramírez
Int. J. Environ. Res. Public Health 2022, 19(19), 11941; https://doi.org/10.3390/ijerph191911941 - 21 Sep 2022
Cited by 13 | Viewed by 3335
Abstract
Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems [...] Read more.
Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2. Full article
(This article belongs to the Special Issue The Revolution of Health Data Warehouses in Hospitals: From Theory to Practice)
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Graphical abstract

12 pages, 901 KiB  
Review
Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France
by Olivier Lauzanne, Jean-Sébastien Frenel, Mustapha Baziz, Mario Campone, Judith Raimbourg and François Bocquet
Int. J. Environ. Res. Public Health 2022, 19(7), 4272; https://doi.org/10.3390/ijerph19074272 - 2 Apr 2022
Cited by 3 | Viewed by 2157
Abstract
Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic [...] Read more.
Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic approaches. We used open government data in France from 1970 to September 2021 to identify deceased patients and match them with patient data collected from the Institut de Cancérologie de l’Ouest (ICO) data warehouse (Integrated Center of Oncology—the third largest cancer center in France) between January 2015 and November 2021. To meet our objective, we evaluated algorithms to perform a deterministic record linkage: an exact matching algorithm and a fuzzy matching algorithm. Because we lacked reference data, we needed to assess the algorithms by estimating the number of homonyms that could lead to false links, using the same open dataset of deceased persons in France. The exact matching algorithm allowed us to double the number of dates of death in the ICO data warehouse, and the fuzzy matching algorithm tripled it. Studying homonyms assured us that there was a low risk of misidentification, with precision values of 99.96% for the exact matching and 99.68% for the fuzzy matching. However, estimating the number of false negatives proved more difficult than anticipated. Nevertheless, using open government data can be a highly interesting way to improve the completeness of the date of death variable for oncology patients in data warehouses Full article
(This article belongs to the Special Issue The Revolution of Health Data Warehouses in Hospitals: From Theory to Practice)
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