Clinical Decision Support Systems for Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3050

Special Issue Editor


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Guest Editor
Department of Emergency Medicine, University of Florida, Gainesville, FL 32611, USA
Interests: clinical and patient decision making; complex system process improvement and design; system impacts on human performance

Special Issue Information

Dear Colleagues,

This Special Issue is focused on clinical decision support systems for healthcare. Submissions of original articles, systematic reviews, short communications, and other types of articles on related topics are welcome. All manuscripts will undergo standard journal peer-review practices. We look forward to receiving your contributions.

Dr. Jessica M. Ray
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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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

  • clinical decision support
  • biomedical data
  • model evaluation
  • electronic health records
  • healthcare systems

Published Papers (2 papers)

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Research

17 pages, 1469 KiB  
Article
Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network
by Yi-Cheng Chiang, Yin-Chia Hsieh, Long-Chuan Lu and Shu-Yi Ou
Healthcare 2023, 11(11), 1598; https://doi.org/10.3390/healthcare11111598 - 30 May 2023
Cited by 1 | Viewed by 1072
Abstract
Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are [...] Read more.
Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. Full article
(This article belongs to the Special Issue Clinical Decision Support Systems for Healthcare)
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13 pages, 910 KiB  
Article
Detection of Drug-Related Problems through a Clinical Decision Support System Used by a Clinical Pharmacy Team
by Laurine Robert, Elodie Cuvelier, Chloé Rousselière, Sophie Gautier, Pascal Odou, Jean-Baptiste Beuscart and Bertrand Décaudin
Healthcare 2023, 11(6), 827; https://doi.org/10.3390/healthcare11060827 - 11 Mar 2023
Cited by 2 | Viewed by 1683
Abstract
Clinical decision support systems (CDSSs) are intended to detect drug-related problems in real time and might be of value in healthcare institutions with a clinical pharmacy team. The objective was to report the detection of drug-related problems through a CDSS used by an [...] Read more.
Clinical decision support systems (CDSSs) are intended to detect drug-related problems in real time and might be of value in healthcare institutions with a clinical pharmacy team. The objective was to report the detection of drug-related problems through a CDSS used by an existing clinical pharmacy team over 22 months. It was a retrospective single-center study. A CDSS was integrated in the clinical pharmacy team in July 2019. The investigating clinical pharmacists evaluated the pharmaceutical relevance and physician acceptance rates for critical alerts (i.e., alerts for drug-related problems arising during on-call periods) and noncritical alerts (i.e., prevention alerts arising during the pharmacist’s normal work day) from the CDSS. Of the 3612 alerts triggered, 1554 (43.0%) were critical, and 594 of these 1554 (38.2%) prompted a pharmacist intervention. Of the 2058 (57.0%) noncritical alerts, 475 of these 2058 (23.1%) prompted a pharmacist intervention. About two-thirds of the total pharmacist interventions (PI) were accepted by physicians; the proportion was 71.2% for critical alerts (i.e., 19 critical alerts per month vs. 12.5 noncritical alerts per month). Some alerts were pharmaceutically irrelevant—mainly due to poor performance by the CDSS. Our results suggest that a CDSS is a useful decision-support tool for a hospital pharmacist’s clinical practice. It can help to prioritize drug-related problems by distinguishing critical and noncritical alerts. However, building an appropriate organizational structure around the CDSS is important for correct operation. Full article
(This article belongs to the Special Issue Clinical Decision Support Systems for Healthcare)
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