Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice

Abstract Background Medication review is time-consuming and not exhaustive in most French hospitals. We routinely use an innovative hybrid decision support system using Artificial Intelligence to prioritize medication review by scoring prescriptions by their risk of containing at least one medication error.Aim We aimed to demonstrate the digital tool’s ability to improve prescription safety by ruling out prescriptions that are effectively risk-free in daily practice.Methods We conducted a case-control study to compare the rate of pharmaceutical interventions (PI) between low and high-risk prescriptions defined by the tool’s calculated score. Medication orders were reviewed daily by a clinical pharmacist. Proportion of prescriptions with at least one severe medication error was calculated in both groups. Severe medication errors were characterized through a multidisciplinary approach.Results Four hundred and twenty (107 low score and 313 high score) prescriptions were analyzed. A significant difference in the percentage of PI was found between the “low score” (29%) and “high score” (51%) prescriptions (p < 0.001). The percentage of prescriptions with severe medication errors was dramatically decreased in low score prescriptions (2.8% vs. 15,3% respectively; p < 0.05). During the study period, the use of this tool allowed to rule out 55% of all prescriptions in our hospital.Conclusion This new decision support tool is an accurate method to rule out “low score” prescriptions, with an acceptable risk of missing medication errors and can be improved by the integration of future features. It offers a solution to focus pharmaceutical expertise on the most at-risk prescriptions and considerably improve the safety of patients’ care.

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Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

Ingénierie des systèmes d information ◽

10.18280/isi.260109 ◽

2021 ◽

Vol 26 (1) ◽

pp. 87-93

Author(s):

Sandeep Patalay ◽

Madhusudhan Rao Bandlamudi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Support ◽

Stock Market ◽

Decision Support System ◽

Portfolio Selection ◽

Support System ◽

Financial Analysis ◽

Traditional Approach ◽

Stock Portfolio

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

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Sardanashvili S.A., Shvechkov V.A. Artificial intelligence technologies application in the development of innovative domestic dispatching decision support system in the field of modeling, planning and management of inter-regional gas flows in the unified gas supply system (UGSS)

Automation Telemechanization and Communication in Oil Industry ◽

10.33285/0132-2222-2019-8(553)-31-37 ◽

2019 ◽

pp. 31-37

Author(s):

S.A. Sardanashvili ◽

◽

V.A. Shvechkov ◽

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Supply System ◽

Gas Flows ◽

Planning And Management ◽

Gas Supply

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Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

10.2196/preprints.19282 ◽

2020 ◽

Author(s):

Avishek Choudhury

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Decision Support ◽

Literature Review ◽

Decision Support System ◽

Support System ◽

Clinical Setting ◽

Clinical Decision Making ◽

Theoretical Perspective ◽

Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

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