Methodologies of Legacy Clinical Decision Support System

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.

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

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2016-0115-ra ◽

2017 ◽

Vol 141 (4) ◽

pp. 585-595 ◽

Cited By ~ 14

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.

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Technical Requirement of Clinical Decision Support system for Diabetic Patients

Frontiers in Health Informatics ◽

10.30699/fhi.v9i1.217 ◽

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

BMC Nephrology ◽

10.1186/s12882-021-02459-y ◽

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.

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

Anaesthesia and Intensive Care ◽

10.1177/0310057x20974035 ◽

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.

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

Frontiers in Health Informatics ◽

10.30699/fhi.v8i1.184 ◽

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.

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Clinical Decision Support Systems

Advances in Healthcare Information Systems and Administration - Developing Maternal Health Decision Support Systems in Developing Countries ◽

10.4018/978-1-7998-3958-3.ch005 ◽

2021 ◽

pp. 64-73

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Medical Informatics ◽

Clinical Decision Support ◽

Support System ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Health Decision ◽

Software Development Methodologies

The BACIS program is an example of an e-health decision support system, and therefore a chapter focusing on the topic of decision support systems is needed as part of the background and context to the BACIS program study. The chapter begins with a discussion of the design of decision support systems. In this discussion, the software development methodologies used in their development is explained. Then various architectures for their design are considered. This is followed by a section on implementation of decision support systems in developing country contexts. The chapter closes with a discussion of the Clinical Decision Support (CDS) roadmap of the International Medical Informatics Association.

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Determining attitude and readiness of Medical students in Kerman University of Medical Sciences concerning acceptance of Decision Support Systems for Electronic Prescription in 2017

Medical Technologies Journal ◽

10.26415/2572-004x-vol1iss4p113-114 ◽

2017 ◽

Vol 1 (4) ◽

pp. 113-114

Author(s):

Reza Khajouei Khajouei ◽

Maryam Eslami Jahromi ◽

Fatemeh Tabatabaei

Keyword(s):

Decision Support ◽

Medical Students ◽

Decision Support System ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support System ◽

Support Systems ◽

Clinical Decision ◽

Medical Sciences ◽

Electronic Prescription

Introduction: Clinical Decision Support Systems (CDSS) are any computer program designed to assist health care professionals in making clinical decisions, especially at the moment of action. These information systems are flexible and interactive, helping people by making use of models and rules of a comprehensive database, or on the basis of clinical guidelines, to make applicable decisions, especially solving difficult problems with common scientific methods. Therefore, these systems support complex medical decisions, increase their effectiveness, and can lead to a reduction in the types of medical errors, such as medication errors. However, knowledge and attitude of the future physicians in the country about these systems is unclear. The purpose of this study was Determining attitude and readiness of Medical students concerning acceptance of Decision Support Systems for Electronic Prescription. Methods: This research is descriptive-analytic. The research population included general medical students of Kerman University of Medical Sciences in 2017. Out of these students, a sample of 226 people was selected by proportional sampling. Data collection was done using a standard questionnaire. The questionnaire was first translated into Persian and then its validity was confirmed by three medical informatics specialists. Data analysis was done descriptively-analytically using SPSS 19. Results: The findings of this study showed that 17% of students (n=36) knew about this system and how to use it, and 83% of them (n=177) were willing to learn and use this system to practice their profession in the future. More than half of them (n=121) believed that using this system would Reduce errors in prescribing. 80% of them (n=170) lacked a strategic plan to implement a decision support system for electronic prescription and a lack of standard and acceptable software, and about 60% (n=128) lack of financial incentives and lack of sufficient evidence Concerned about the usefulness of using it as a potential obstacle to the implementation of this system. There was no significant difference between the age, sex and term of the students with their familiarity with this system (P>0.05). Conclusion: The results of this study showed that while many students lack sufficient knowledge about the clinical decision support system, they are interested in learning and using it, especially in the field of prescribing drugs. This result indicates that the system is likely to be admitted if it is implemented and educated to medical students and doctors.

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Overview of Clinical Decision Support Systems in Healthcare

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch064 ◽

2017 ◽

pp. 1501-1527 ◽

Cited By ~ 2

Author(s):

Jane Dominique Moon ◽

Mary P. Galea

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Support System ◽

Medical Information ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Set Up ◽

Ongoing Support

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of self-management using social computing technologies, in particular in the management of chronic disease.

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Overview of Clinical Decision Support Systems in Healthcare

Improving Health Management through Clinical Decision Support Systems - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-4666-9432-3.ch001 ◽

2016 ◽

pp. 1-27 ◽

Cited By ~ 4

Author(s):

Jane Dominique Moon ◽

Mary P. Galea

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Decision Support System ◽

Medical Information ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Set Up ◽

Ongoing Support

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of self-management using social computing technologies, in particular in the management of chronic disease.

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