1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background  It is 30 years since evidence-based medicine became a great support for individual clinical expertise in daily practice and scientific research. Electronic systems can be used to achieve the goal of collecting data from heterogeneous datasets and to support multicenter clinical trials. The Ligurian Infectious Diseases Network (LIDN) is a web-based platform for data collection and reuse originating from a regional effort and involving many professionals from different fields.

          Objectives  The objective of this work is to present an integrated system of ad hoc interfaces and tools that we use to perform pseudonymous clinical data collection, both manually and automatically, to support clinical trials.

          Methods  The project comprehends different scenarios of data collection systems, according to the degree of information technology of the involved centers. To be compliant with national regulations, the last developed connection is based on the standard Clinical Document Architecture Release 2 by Health Level 7 guidelines, interoperability is supported by the involvement of a terminology service.

          Results  Since 2011, the LIDN platform has involved more than 8,000 patients from eight different hospitals, treated or under treatment for at least one infectious disease among human immunodeficiency virus (HIV), hepatitis C virus , severe acute respiratory syndrome coronavirus 2 , and tuberculosis. Since 2013, systems for the automatic transfer of laboratory data have been updating patients' information for three centers, daily. Direct communication was set up between the LIDN architecture and three of the main national cohorts of HIV-infected patients.

          Conclusion  The LIDN was originally developed to support clinicians involved in the project in the management of data from HIV-infected patients through a web-based tool that could be easily used in primary-care units. Then, the developed system grew modularly to respond to the specific needs that arose over a time span of more than 10 years.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities

          BindingDB () is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Evidence-based medicine.

            Evidence-based medicine, whose philosophical origins extend back to mid-19th century Paris and earlier, is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence-based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research. By individual clinical expertise we mean the proficiency and judgment that we individual clinicians acquire through clinical experience and clinical practice. Increased expertise is reflected in many ways, but especially in more effective and efficient diagnosis and in the more thoughtful identification and compassionate use of individual patients' predicaments, rights, and preferences in making clinical decisions about their care. By best available external clinical evidence we mean clinically relevant research, often from the basic sciences of medicine, but especially from patient centered clinical research into the accuracy and precision of diagnostic tests (including the clinical examination), the power of prognostic markers, and the efficacy and safety of therapeutic, rehabilitative, and preventive regimens. External clinical evidence both invalidates previously accepted diagnostic tests and treatment and replaces them with new ones that are more powerful, more accurate, more efficacious, and safer. Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough. Without clinical expertise, practice risks becoming tyrannized by external evidence, for even excellent external evidence may be inapplicable to or inappropriate for an individual patient. Without current best external evidence, practice risks becoming rapidly out of date, to the detriment of patients. The practice of evidence-based medicine is a process of life-long, self-directed learning in which caring for our own patients creates the need for clinically important information about diagnosis, prognosis, therapy, and other clinical and health care issues, and in which we (1) convert these information needs into answerable questions; (2) track down, with maximum efficiency, the best evidence with which to answer them (whether from the clinical examination, the diagnostic laboratory from research evidence, or other sources); (3) critically appraise that evidence for its validity (closeness to the truth) and usefulness (clinical applicability); (4) integrate this appraisal with our clinical expertise and apply it in practice; and (5) evaluate our performance.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Machine learning for clinical decision support in infectious diseases: A narrative review of current applications

              Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).
                Bookmark

                Author and article information

                Journal
                Appl Clin Inform
                Appl Clin Inform
                10.1055/s-00035026
                Applied Clinical Informatics
                Georg Thieme Verlag KG (Rüdigerstraße 14, 70469 Stuttgart, Germany )
                1869-0327
                11 January 2023
                January 2023
                1 January 2023
                : 14
                : 1
                : 16-27
                Affiliations
                [1 ]Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
                [2 ]Infectious Diseases Unit, Policlinico San Martino Hospital, IRCCS for Oncology and Neuroscience, Genoa, Italy
                [3 ]Department of Infectious Disease, IRCCS AOU San Martino IST, (DISSAL), University of Genoa, Italy
                [4 ]Infectious Diseases Unit, ASL-1 Imperiese, Sanremo, Imperia, Italy
                Author notes
                Address for correspondence Sara Mora, Eng Department of Informatics, Bioengineering, Robotics and System Engineering, (DIBRIS), University of Genoa Italy sara.mora@ 123456edu.unige.it
                Article
                ACI-2022-04-RA-0123
                10.1055/s-0042-1760081
                9833953
                36631000
                02556af7-b11e-4b17-98ec-30640444fd3f
                The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. ( https://creativecommons.org/licenses/by/4.0/ )

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 May 2022
                : 16 November 2022
                Categories
                Research Article

                data sharing,ehrs and systems,clinical data reuse,hl7 standards,clinical data management

                Comments

                Comment on this article