Modeling of Computerized Physician Order Entry with Decision Support System for Diabetic Patients

Introduction: Providing care for patients and preventing complications is one of the major subjects in medical sciences. Computerized Physician Order Entry (CPOE) with a decision support system is expected to deliver many benefits. A system with decision support system may help clinicians, patients, and others to suggest patient-appropriate evidence-based treatment options.The present study was conducted to prepare a conceptual model for a CPOE system of diabetic patients (Type 2) using Unified Modeling Language (UML). Then, a software program was designed accordingly.Method: This cross-sectional study was conducted in 2017. A minimum data set of patient records was used as the patient profile in the system, and a list of drugs and functional requirements of the CPOE system for diabetic patients was provided. Following the confirmation of the minimum data set by diabetes specialists, UML figures were drawn and the software was designed.Results: The minimum data set of patient records included demographic and clinical information as well as laboratory tests. Functional requirements of the CPOE system for type 2 diabetic patients consisted of the possibility of recording simple and complicated orders, connecting the system to the pharmacy or other auxiliary information systems, controlling drug side effects, etc.Conclusion: A CPOE system should have minimum errors in documentations and provide information on allergies, drug interactions, and side effects in a timely manner to reduce medical errors, especially drug errors, increase physician efficiency and patient satisfaction, and finally promote the quality of healthcare services.

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952-P: Improving Glycemic Management of Patients with Type 2 Diabetes in Acute Geriatric Care by Using a Basal Insulin Algorithm Incorporated into a Workflow and Decision Support System

Diabetes ◽

10.2337/db19-952-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 952-P

Author(s):

ANGELA LIBISELLER ◽

KATHARINA M. LICHTENEGGER ◽

JULIA KOPANZ ◽

ANTONELLA DE CAMPO ◽

TATJANA WIESINGER ◽

...

Keyword(s):

Type 2 Diabetes ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Basal Insulin ◽

Geriatric Care ◽

Glycemic Management ◽

Acute Geriatric Care

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A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

Current Diabetes Reviews ◽

10.2174/1573399815666190531100012 ◽

2020 ◽

Vol 16 (3) ◽

pp. 262-269

Author(s):

Tahere Talebi Azad Boni ◽

Haleh Ayatollahi ◽

Mostafa Langarizadeh

Keyword(s):

Decision Support ◽

Cardiovascular Diseases ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Diabetic Patients ◽

K Value ◽

Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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Improvements in Diabetic Patients’ Outcomes in a Clinical Decision Support System

European Journal for Biomedical Informatics ◽

10.24105/ejbi.2018.14.1.6 ◽

2018 ◽

Vol 14 (1) ◽

Author(s):

Sakiko Ota ◽

Kaoru Mogushi ◽

Aizan Hirai ◽

Yoshihito Niimura ◽

Hiroshi Tanaka

Keyword(s):

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Diabetic Patients

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A Decision Support System Proposition for Type-2 Diabetes Mellitus Treatment Using Spherical Fuzzy AHP Method

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-85577-2_88 ◽

2021 ◽

pp. 749-756

Author(s):

Sezi Cevik Onar ◽

Enes Hakan Ibil

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Fuzzy Ahp ◽

Ahp Method ◽

Diabetes Mellitus Treatment

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A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams

Symmetry ◽

10.3390/sym11111426 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1426

Author(s):

Mehmet Erkan Yuksel ◽

Huseyin Fidan

Keyword(s):

Decision Making ◽

Decision Support ◽

Text Mining ◽

Decision Support System ◽

Support System ◽

Grey System Theory ◽

Database Management System ◽

Data Set ◽

Relational Analysis ◽

Grey Relational

Grey relational analysis (GRA) is a part of the Grey system theory (GST). It is appropriate for solving problems with complicated interrelationships between multiple factors/parameters and variables. It solves multiple-criteria decision-making problems by combining the entire range of performance attribute values being considered for every alternative into one single value. Thus, the main problem is reduced to a single-objective decision-making problem. In this study, we developed a decision support system for the evaluation of written exams with the help of GRA using contextual text mining techniques. The answers obtained from the written exam with the participation of 50 students in a computer laboratory and the answer key prepared by the instructor constituted the data set of the study. A symmetrical perspective allows us to perform relational analysis between the students’ answers and the instructor’s answer key in order to contribute to the measurement and evaluation. Text mining methods and GRA were applied to the data set through the decision support system employing the SQL Server database management system, C#, and Java programming languages. According to the results, we demonstrated that the exam papers are successfully ranked and graded based on the word similarities in the answer key.

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