scholarly journals Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases

2021 ◽  
Author(s):  
Akram Redjdal ◽  
Jacques Bouaud ◽  
Gilles Guézennec ◽  
Joseph Gligorov ◽  
Brigitte Seroussi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Gold Standard ◽  
Compliance Rate ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Tumor Board ◽  
Complex Cases ◽  
Clinical Cases

The guideline-based decision support system (GL-DSS) of the DESIREE project and OncoDoc are two clinical decision support systems applied to the management of breast cancer. In order to evaluate the DESIREE GL-DSS, we decided to reuse a sample of clinical cases previously resolved by the multidisciplinary tumor board (MTB) of the Tenon Hospital (Paris, France) when using OncoDoc. Since we had two different knowledge representation models to represent clinical parameters and decisions, and two formalisms to represent guidelines, we developed a transformation sequence, involving the creation of synthetic patients, the enrichment of DESIREE ontology, and the translation of clinical cases and their decisions, to transform OncoDoc data into the DESIREE representation. Considering MTB decisions as the gold standard, the 84% compliance rate of DESIREE recommendations was rather satisfactory. Some situations (0.7%) concerned clinical cases that were compliant neither with OncoDoc nor with DESIREE that we defined as complex cases, not handled by guidelines, which necessitate effective MTB discussions.

Massive transfusion experience, current practice and decision support: A survey of Australian and New Zealand anaesthetists

2021 ◽  
pp. 0310057X2097403
Author(s):  
Brenton J Sanderson ◽  
Jeremy D Field ◽  
Lise J Estcourt ◽  
Erica M Wood ◽  
Enrico W Coiera
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Response Rate ◽  
Massive Transfusion ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Transfusion Practice

Massive transfusions guided by massive transfusion protocols are commonly used to manage critical bleeding, when the patient is at significant risk of morbidity and mortality, and multiple timely decisions must be made by clinicians. Clinical decision support systems are increasingly used to provide patient-specific recommendations by comparing patient information to a knowledge base, and have been shown to improve patient outcomes. To investigate current massive transfusion practice and the experiences and attitudes of anaesthetists towards massive transfusion and clinical decision support systems, we anonymously surveyed 1000 anaesthetists and anaesthesia trainees across Australia and New Zealand. A total of 228 surveys (23.6%) were successfully completed and 227 were analysed for a 23.3% response rate. Most respondents were involved in massive transfusions infrequently (88.1% managed five or fewer massive transfusion protocols per year) and worked at hospitals which have massive transfusion protocols (89.4%). Massive transfusion management was predominantly limited by timely access to point-of-care coagulation assessment and by competition with other tasks, with trainees reporting more significant limitations compared to specialists. The majority of respondents reported that they were likely, or very likely, both to use (73.1%) and to trust (85%) a clinical decision support system for massive transfusions, with no significant difference between anaesthesia trainees and specialists ( P = 0.375 and P = 0.73, respectively). While the response rate to our survey was poor, there was still a wide range of massive transfusion experience among respondents, with multiple subjective factors identified limiting massive transfusion practice. We identified several potential design features and barriers to implementation to assist with the future development of a clinical decision support system for massive transfusion, and overall wide support for a clinical decision support system for massive transfusion among respondents.

Methodologies of Legacy Clinical Decision Support System

2017 ◽  
Vol 2 (2) ◽  
pp. 20-37
Author(s):  
Meenakshi Sharmi ◽  
Himanshu Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

scholarly journals Smart Diabetic Screening and Managing Software, A Novel Decision Support System

2018 ◽  
Vol 8 (3Sep) ◽  
Author(s):  
M Ghoddusi Johari ◽  
M H Dabaghmanesh ◽  
H Zare ◽  
A R Safaeian ◽  
Gh Abdollahifard
Keyword(s):  
Diabetes Mellitus ◽  
Decision Support ◽  
Chronic Disease ◽  
Decision Support System ◽  
Support System ◽  
Diabetes Care ◽  
Diabetes Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Diabetic Screening

Background: Diabetes is a serious chronic disease, and its increasing prevalence is a global concern. If diabetes mellitus is left untreated, poor control of blood glucose may cause long-term complications. A big challenge encountered by clinicians is the clinical management of diabetes. Many IT-based interventions such ad CDSS have been made to improve the adherence to the standard care for chronic diseases.Objective: The aim of this study is to establish a decision support system of diabetes management based on diabetes care guidelines in order to reduce medical errors and increase adherence to guidelines.Materials and Methods: To start the process, at first the existing guidelines in the field of diabetes mellitus such as ADA 2017 and AACE guideline 2017 were reviewed, and accordingly, flowcharts and algorithms for screening and managing of diabetes were designed. Then, it was passed on to the information technology team to design software.Results: The most significant outcome of this research was to establish a smart diabetic screening and managing software, which is an important stride to promote patients' health status, control diabetes and save patients' information as an important and reliable source. Conclusion: Health care technologies have the potential to improve the quality of diabetes care through IT-based intervention, such as clinical decision support systems. In a chronic disease like diabetes, the critical component is the disease management. The advantages of this web-based system are on-time registration, reports of diabetic prevalence, uncontrolled diabetes, diabetic complications and reducing the rate of mismanagement of diabetes, so that it helps the physicians in order to manage the patients in a better way.

scholarly journals Technical Requirement of Clinical Decision Support system for Diabetic Patients

2020 ◽  
Vol 9 (1) ◽  
pp. 31
Author(s):  
Shamim Kiyani ◽  
Sanaz Abasi ◽  
Zahra Koohjani ◽  
Azam Aslani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
Design Metrics

Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system. Results: 32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and ect), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and ect. ) and 31 item information items (such as Content guidance, diagnostic support and concise and ect ).Discussion: This study identified important aspects of designing decision support system, It can be applied not only to diabetic patients but also to other decision support systems.Conclusion: Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.

scholarly journals Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Youlu Zhao ◽  
Xizi Zheng ◽  
Jinwei Wang ◽  
Damin Xu ◽  
Shuangling Li ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Kidney Injury ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Clinical decision support systems including both electronic alerts and care bundles have been developed for hospitalized patients with acute kidney injury. Methods Electronic databases were searched for randomized, before-after and cohort studies that implemented a clinical decision support system for hospitalized patients with acute kidney injury between 1990 and 2019. The studies must describe their impact on care processes, patient-related outcomes, or hospital length of stay. The clinical decision support system included both electronic alerts and care bundles. Results We identified seven studies involving 32,846 participants. Clinical decision support system implementation significantly reduced mortality (OR 0.86; 95 % CI, 0.75–0.99; p = 0.040, I2 = 65.3 %; n = 5 studies; N = 30,791 participants) and increased the proportion of acute kidney injury recognition (OR 3.12; 95 % CI, 2.37–4.10; p < 0.001, I2 = 77.1 %; n = 2 studies; N = 25,121 participants), and investigations (OR 3.07; 95 % CI, 2.91–3.24; p < 0.001, I2 = 0.0 %; n = 2 studies; N = 25,121 participants). Conclusions Nonrandomized controlled trials of clinical decision support systems for acute kidney injury have yielded evidence of improved patient-centered outcomes and care processes. This review is limited by the low number of randomized trials and the relatively short follow-up period.

Methodologies of Legacy Clinical Decision Support System

2020 ◽  
pp. 553-568
Author(s):  
Meenakshi Sharmi ◽  
Himanshu Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

scholarly journals Implementation of a Clinical Decision Support System for Precision Oncology across an Academic Network

2021 ◽  
Author(s):  
David Tamborero ◽  
Rodgrigo Dienstmann ◽  
Maan Rachid ◽  
Jorrit Boekel ◽  
Adria Lopez-Fernandez ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Tumor Board ◽  
Next Generation Sequencing Data ◽  
Precision Oncology ◽  
Molecular Tumor Board

Abstract There is a growing need for systems that efficiently support the work of medical teams at the precision oncology point-of-care. Here we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data across the Cancer Core Europe network. Automating the interpretation and reporting of sequencing results decreased drastically the need for manual procedures that are time consuming and prone to errors. In addition, the adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promoted consistent decision-making and structured data capture across centers. Finally, the use of information-rich patient reports with interactive content facilitated collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, we believe that streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.

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