For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
28
views
102
references
 Top references
cited by
410
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      72
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      An overview of clinical decision support systems: benefits, risks, and strategies for success

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      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

          Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

          Related collections

          Most cited references102

          • Record: found
          • Abstract: found
          • Article: not found

          Dermatologist-level classification of skin cancer with deep neural networks

          Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)
          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
          Article 0 comments Cited 2023 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

            Varun Gulshan, Lily Peng, Marc Coram … (2016)
            Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
            Article 0 comments Cited 1296 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Machine Learning in Medicine.

              Rahul Deo (2015)
              Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
              Article 0 comments Cited 744 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Karen I. Kroeker: kkroeker@ualberta.ca
                Journal
                Journal ID (nlm-ta): NPJ Digit Med
                Journal ID (iso-abbrev): NPJ Digit Med
                Title: NPJ Digital Medicine
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2398-6352
                Publication date (Electronic): 6 February 2020
                Publication date PMC-release: 6 February 2020
                Publication date Collection: 2020
                Volume: 3
                Electronic Location Identifier: 17
                Affiliations
                [1 ]GRID grid.17089.37, Department of Medicine, Division of Gastroenterology, , University of Alberta, ; Edmonton, Canada
                [2 ]ISNI 0000 0001 0693 8815, GRID grid.413574.0, Chief Medical Information Office, Alberta Health Services, ; Edmonton, Canada
                Author information
                http://orcid.org/0000-0002-3009-1914
                Article
                Publisher ID: 221
                DOI: 10.1038/s41746-020-0221-y
                PMC ID: 7005290
                PubMed ID: 32047862
                SO-VID: a0b6cc30-6102-449f-930a-6333aa655a96
                Copyright © © The Author(s) 2020
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 21 February 2019
                Date accepted : 19 December 2019
                Categories
                Subject: Review Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                Keywords: health services,diagnosis,drug regulation,medical imaging
                Data availability:
                Keywords: health services, diagnosis, drug regulation, medical imaging

                Comments

                Comment on this article

                scite_

                Similar content72

                See all similar

                Cited by414

                See all cited by

                Most referenced authors1,307

                See all reference authors