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

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.

Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription

2021 ◽  
Vol 10 (1) ◽  
pp. 121-125
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Gait Analysis ◽  
Support System ◽  
Patient Specific ◽  
Evidence Based ◽  
Extensive Literature ◽  
Analysis Tool ◽  
Care Providers

With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.

A structural design of clinical decision support system for chronic diseases risk management

Open Medicine ◽  
2007 ◽  
Vol 2 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chuen-Sheng Cheng
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Structural Design ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision

AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.

scholarly journals Implications for the design of a Diagnostic Decision Support System (DDSS) to reduce time and cost to diagnosis in paediatric shoulder instability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fraser Philp ◽  
Alice Faux-Nightingale ◽  
Sandra Woolley ◽  
Ed de Quincey ◽  
Anand Pandyan
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Shoulder Instability ◽  
Clinical Decision Making ◽  
Perceived Usefulness ◽  
Novel Technologies ◽  
Diagnostic Decision

Abstract Background Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis and improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability. Methods A modified nominal focus group technique, involving three clinical vignettes, was used to elicit physiotherapists decision-making processes. Results Twenty-five physiotherapists, (18F:7 M) from four separate clinical sites participated. The themes identified related to ‘Variability in diagnostic processes and lack of standardised practice’ and ‘Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making’. Conclusion No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed. Trial Registraty This was not a clinical trial and so no clinical trial registry is needed.

scholarly journals Developing A Diagnostic Decision Support System (DDSS) to Reduce Time To Diagnosis in Paediatric Shoulder Instability

2020 ◽  
Author(s):  
Fraser Philp ◽  
Alice Faux-Nightingale ◽  
Sandra Wooley ◽  
Ed De Quincey ◽  
Anand Pandyan
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Shoulder Instability ◽  
Clinical Decision Making ◽  
Perceived Usefulness ◽  
Diagnostic Decision ◽  
Time To Diagnosis

Abstract Background: Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability.Methods: A modified nominal focus group technique, involving three clinical vignettes was used to elicit information physiotherapists decision-making processes.Results: Twenty-five physiotherapists, (18F:7M) from four separate clinical sites participated. The themes identified related to ‘Variability in diagnostic processes and lack of standardised practice’ and ‘Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making’. Conclusion: No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed.Trial Registration: This was not a clinical trial and so no clinical trial registry is needed.

scholarly journals RETRACTED : Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP) Method

2017 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Solikhun Solikhun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Research Publication ◽  
Artificial Intelligence Research ◽  
Multi Attribute Decision Making

Following a rigorous, carefully concerns and considered review of the article published in International Journal of Artificial Intelligence Research to article entitled “Decision support system in Predicting the Best teacher with Multi-Attribute Decision Making Weighted Product (MADMWP) Method” Vol 1, No 1, pp. 47-53, June 2017, DOI: https://doi.org/10.29099/ijair.v1i1.1This paper has been found to be in violation of the International Journal of Artificial Intelligence Research Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in JURASIK(Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol. 1, No. 1, pp. 56-63, 2016,  http://ejurnal.tunasbangsa.ac.id/index.php/jurasik/article/view/9The document and its content have been removed from International Journal of Artificial Intelligence Research, and reasonable effort should be made to remove all references to this article.

Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre

2018 ◽  
Vol 3 ◽  
pp. 14
Author(s):  
Lidia K Simanjuntak ◽  
Tessa Y M Sihite ◽  
Mesran Mesran ◽  
Nuning Kurniasih ◽  
Yuhandri Yuhandri
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Action Selection ◽  
Multi Criteria Decision Making ◽  
Prospective Students ◽  
College Entrance ◽  
Electre Method ◽  
Selection Of

All colleges each year organize the selection of new admissions. Acceptance of prospective students in universities as education providers is done by selecting prospective students based on achievement in school and college entrance selection. To select the best student candidates based on predetermined criteria, then use Multi-Criteria Decision Making (MCDM) or commonly called decision support system. One method in MCDM is the Elimination Et Choix Traduisant la Reality (ELECTRE). The ELECTRE method is the best method of action selection. The ELECTRE method to obtain the best alternative by eliminating alternative that do not fit the criteria and can be applied to the decision SNMPTN invitation path.

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