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

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 Sustained effects of a clinical decision support system for acute kidney injury

2020 ◽  
Vol 35 (10) ◽  
pp. 1819-1821
Author(s):  
Ayham Bataineh ◽  
Dilhari Dealmeida ◽  
Andrew Bilderback ◽  
Richard Ambrosino ◽  
Mohammed J Al-Jaghbeer ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Sustained Effects

scholarly journals Integration of Explicit Criteria in a Clinical Decision Support System Through Evaluation of Acute Kidney Injury Events

2021 ◽  
Author(s):  
Laurine Robert ◽  
Chloé Rousseliere ◽  
Jean-Baptiste Beuscart ◽  
Sophie Gautier ◽  
Emmanuel Chazard ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Health Professionals ◽  
Clinical Decision Support System ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Rule Based

In Clinical Decision Support System (CDSS), relevance of alerts is essential to limit alert fatigue and risk of overriding relevant alerts by health professionals. Detection of acute kidney injury (AKI) situations is of great importance in clinical practice and could improve quality of care. Nevertheless, to our knowledge, no explicit rule has been created to detect AKI situations in CDSS. The objective of the study was to implement an AKI detection rule based on KDIGO criteria in a CDSS and to optimize this rule to increase its relevance in clinical pharmacy use. Two explicit rules were implemented in a CDSS (basic AKI rule and improved AKI rule), based on KDIGO criteria. Only the improved rule was optimized by a group of experts during the two-month study period. The CDSS provided 1,125 alerts on AKI situations (i.e. 643 were triggered for the basic AKI rule and 482 for the improved AKI rule). As the study proceeds, the pharmaceutically and medically relevance of alerts from the improved AKI rule increased. A ten-fold increase was shown for the improved AKI rule compared to the basic AKI rule. The study highlights the usefulness of a multidisciplinary review to enhance explicit rules integrated in CDSS. The improved AKI is able to detect AKI situations and can improve workflow of health professionals.

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.

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.

scholarly journals Development A Guideline-based Decision Support System to Diagnosis of Primary Immunodeficiency Diseases

2019 ◽  
Vol 8 (1) ◽  
pp. 11
Author(s):  
Fateme Sepehri ◽  
Mostafa Langarizadeh ◽  
Laleh Sharifi ◽  
Gholamreza Azizi ◽  
Reza Safdari ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Primary Immunodeficiency ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Primary Immunodeficiency Diseases ◽  
Inference Model

Introduction: Primary immunodeficiency diseases (PID) are generally rare genetic disorders affecting the immune system. Overlapping PIDs symptoms and signs is a challenge to diagnosis and treatment. On the other hand, remembering of all diagnosis criteria is difficult for practitioners. The purpose of this research is developing guideline-based clinical decision support system for diagnosis of primary immune deficiency diseases, to assist practitioner in order to diagnose of disease in early stage and to minimize complications of such diseases.Material and Methods: To provide data a checklist was used and most important demographic information, symptoms, family history, physical findings and laboratory findings to diagnose eight common PIDs extracted from guidelines and literature under specialists opinion. The diagnosis inference model design and develop in Protégé (version 3.4.8) frame based ontology modeling using "Noy and McGuinness" method. Then the mobile based inference model in Eclipse (SDK version 3.7.1) software has been developed and clinical decision support system of primary immunodeficiency has been created.Results:  To design the diagnosis inference model in Protégé software, data were classified in 5 main classes and 24 subclasses as hierarchical. Then, specific properties of each class, and determine the value of each property. Then define Instances of each class and initialized instance properties. Then use this model to develop CDSS based on mobile in Eclipse software. At the end, the inference model and the CDSS test with 110 patient’s record data and both of them recognized all 110 patient correctly such as specialist recognition.Conclusion:Guideline-based decision support systems help to detect diseases correctly, quickly and early. Guideline-based decision support systems are very reliable to practitioner, because guidelines are accepted to their. These systems reduce the forget probability of diagnosis stages and percentage error of diagnosis by practitioner and increase the accuracy of diagnosis.

scholarly journals The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

2017 ◽  
Vol 141 (4) ◽  
pp. 585-595 ◽  
Author(s):  
Nicolas Delvaux ◽  
Katrien Van Thienen ◽  
Annemie Heselmans ◽  
Stijn Van de Velde ◽  
Dirk Ramaekers ◽  
...  
Keyword(s):  
Decision Support ◽  
Laboratory Test ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Test Ordering

Context.— Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. Objective.— To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. Data Sources.— We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. Conclusions.— Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

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.

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