decision support system in clinics
210860352
Trends and Future Direction of the Clinical Decision Support System in Traditional Korean Medicine
Objectives The Clinical Decision Support System (CDSS) which analyzes and uses electronic health records (EHR) for medical care, pursues patient centered medical care. It is necessary to establish the CDSS in Korean medical services for objectification and standardization. For this purpose, analyses were performed on the points to be followed for CDSS implementation with a focus on herbal medicine prescription. Methods To establish the CDSS in the prescription of Traditional Korean Medicine, the current prescription practices of Traditional Korean Medicine doctors were analyzed. We also analyzed whether the prescription support function of the electronic chart was implemented. A questionnaire survey was conducted querying Traditional Korean Medicine doctors working at Traditional Korean Medicine clinics and hospitals, to investigate their desired CDSS functions, and their perceived effects on herbal medicine prescription. The implementation of the CDSS among the audit software developers used by the Korean medical doctors was examined. Results On average, 41.2% of Traditional Korean Medicine doctors working in Traditional Korean Medicine clinics manipulated 1 to 4 herbs, and 31.2% adjusted 4 to 7 herbs. On average, 52.5% of Traditional Korean Medicine doctors working in Traditional Korean Medicine hospitals adjusted 1 to 4 herbs, and 35.5% adjusted 4 to 7 herbs. Questioning the desired prescription support function in the electronic medical record system, the Traditional Korean Medicine doctors working at Korean medicine clinics desired information on 'medicine name, meridian entry, flavor of medicinals, nature of medicinals, efficacy, 'herb combination information' and 'search engine by efficacy of prescription. The doctors also desired compounding contraindications (eighteen antagonisms, nineteen incompatibilities) and other contraindicatory prescriptions, 'medicine information' and 'prescription analysis information through basic constitution analyses. The implementation of prescription support function varied by clinics and hospitals. Conclusion In order to implement and utilize the CDSS in a medical service, clinical information must be generated and managed in a standardized form. For this purpose, standardization of terminology, coding of prescriptions using a combination of herbal medicines, and unification such as the preparation method and the weights and measures should be integrated.
Journal of pharmacopuncture
Hyun Kyung Sung Boyung Jung Kyeong Han Kim Soo-Hyun Sung Angela Dongmin Sung Jang-Kyung Park
3367518
Clinical Decision Support System in Virtual Clinic
Healthcare facilities in Pakistan are not sufficient, particularly in rural or remote areas. These areas show poorer health outcomes than urban areas due to lack of trained physicians, clinics and healthcare services. This is mainly because, most of the healthcare practitioners don't like to practice in these areas. In contrast, a huge number of lady doctors don't practice after their graduation due to various reasons, thus major portion of medical human resource is not utilized. Moreover, the information and communication technologies (ICT) in Pakistan are becoming popular and affordable day by day. Many developing countries like Pakistan have started the implementation of telemedicine projects. This research work focuses on connecting the remote areas of Pakistan to the urban areas' practices and doctors especially the non practicing lady doctors through ICT by establishing virtual clinics in remote areas. In addition to that, the virtual clinic decision support system (VCDSS) facilitates the users in two ways. Firstly, automatically assigning the appropriate most doctor for patient based on his ranking, experience and specialization etc. using a fuzzy rule based system (FRBS) Secondly, it helps the registered doctors at the time of prescribing a patient by suggesting the medicines according to the patient's symptoms and history. It is done by using the knowledge base of past diagnoses/prescriptions by the same doctor as well as other doctors and British National Formulary (BNF) using Apriori Algorithm and Inductive Learning Algorithm. The results are shown using sample dataset. Keyword: CDSS, Virtual Clinic, Apriori Algorithm, Data mining, WEKA
Aghiad Bakry Kiran Sultan Mohammad Aftab Alam Khan Mehwash Farooqui Dhiaa Musleh
225242412
Trends in the Use of Clinical Decision Support by Health System Affiliated Ambulatory Clinics in the United States, 2014 2016
Robert S Rudin Shira H Fischer Yunfeng Shi Paul Shekelle Alejandro Amill-Rosario M Susan Ridgely Dennis P Scanlon Cheryl L Damberg
221599788
A CLINICAL DECISION SUPPORT SYSTEM FOR THE DIAGNOSIS OF GYNECOLOGICAL DISEASES
Gynaecological diseases diagnosis is one of the important issues in the medical field globally because gynaecologists have to analyze and diagnose the disease according to various and similar symptoms. They may accidentally miss some symptoms that lead to a misdiagnosis. Hence, this paper aimed for developing a clinical decision support system (CDSS) to assist the gynaecologists in the diagnosis process for eleven types of gynaecological diseases that represent the most important diseases that are frequently diagnosed in gynaecologist's clinics which are: Polyps, Infections, Fibroids, Prolapse, Cancer, Endometrial hyperplasia, Migrants, Amenorrhea, Abortions, Dysmenorrhea and Infertility. In the proposed system, a multilayer perceptron (MLP) feed forward neural network was used. The input layer of the proposed system included 54 input variables. An iterative process was used to determine the number of neurons and hidden layers. Furthermore, a resilient backpropagation algorithm (Rprop) was used to train the system. In particular, a 10 fold cross validation scheme was used to access the generalization of the proposed system. We obtained 94.5% classification accuracy from the experiments made on the data that were taken from 550 patients' medical records suffering from eleven gynaecological diseases managed at the gynaecological clinics at Jordan University Hospital (JUH)
Elham F Ahmad Mohammad Alshraideh Kamil M Fram
213670335
Decision support system for diagnosing Rheumatic Musculoskeletal Disease using fuzzy cognitive map technique
Abstract Rheumatic Musculoskeletal Disease (RMD) is a leading cause of disability worldwide. It causes inflammation of the connecting body structures, affects joints, tendons, ligaments, bones, and muscles, and is responsible for thousands of deaths per year worldwide. It is prevalent in Africa. In Nigeria, RMD constitutes 10 15% of rheumatology cases in most clinics, with a ratio of 2.4:1 (Female:Male) killing over 1652 people per year. The similarity of its symptoms with other diseases often cause misdiagnosis at an early stage among infected patients, and existing computational techniques used for diagnosis cannot address the confusability of the symptoms. Hence, there is an inability to determine the causality of symptoms. Moreover, a dearth of Rheumatologists prevents many RMD infected persons from obtaining an early and accurate diagnosis. Therefore, the need arises to develop a decision support system (DSS) for diagnosing RMD, such as by using a fuzzy cognitive map (FCM) technique. Our study focuses on the development and implementation of FCM based DSS for RMD diagnosis (RMD FCMDSS) RMD FCMDSS serves as a software tool complementing physician decision making. Our evaluation of RMD FCMDSS suggests that it has an improved diagnostic value as compared to earlier traditional and conventional medical methods. Performance results indicate an 87% accuracy, 90% sensitivity, and 80% specificity. RMD FCMDSS was tested on limited data due to a dearth of Rheumatologists in Nigeria and a limited population of rheumatic patients interacted with; thus in future work we shall need to increase the test sample. Hybridization of Fuzzy Logic and Cognitive Mapping techniques helps to guarantee accuracy, specificity, sensitivity, and also speed diagnosis.
Boluwaji A Akinnuwesi Blessing A Adegbite Femi Adelowo U Ima-Edomwonyi Gbenga Fashoto Olaseni Taiwo Amumeji
218517068
BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment
Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context specific evidence. In this paper we present an overview of BigO (http:/bigoprogram.eu) a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.
2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
Christos Diou Ioannis Sarafis Vassilis Papapanagiotou Leonidas Alagialoglou Irini Lekka Dimitrios Filos Leandros Stefanopoulos Vasileios Kilintzis Christos Maramis Youla Karavidopoulou Nicos Maglaveras Ioannis Ioakimidis Evangelia Charmandari Penio Kassari Athanasia Tragomalou Monica Mars Thien-An Ngoc Nguyen M Tahar Kechadi Shane O' Donnell Gerardine Doyle Sarah Browne Grace O' Malley Rachel Heimeier Katerina Riviou Evangelia Koukoula Konstantinos Filis Maria Hassapidou Ioannis Pagkalos Daniel Ferri Isabel Perez Anastasios Delopoulos
201622506
The effect of a clinical decision support system on prompting an intervention for risky alcohol use in a primary care smoking cessation program: a cluster randomized trial
BackgroundClinical decision support systems (CDSSs) may promote practitioner adherence to evidence based guidelines. This study examined if the addition of a CDSS influenced practitioner delivery of a brief intervention with treatment seeking smokers who were drinking above recommended alcohol consumption guidelines, compared with practitioners who do not receive a CDSS prompt.MethodsThis was a cluster randomized controlled trial conducted in primary health care clinics across Ontario, Canada, implementing the Smoking Treatment for Ontario Patients (STOP) smoking cessation program. Clinics randomized to the intervention group received a prompt when a patient reported consuming alcohol above the Canadian Cancer Society (CCS) guidelines; the control group did not receive computer alerts. The primary outcome was an offer of an appropriate educational alcohol resource, an alcohol reduction workbook for patients drinking above the CCS guidelines, and an abstinence workbook to patients scoring above 20 points in the AUDIT screening tool; the secondary outcome was patient acceptance of the resource. The tertiary outcome was patient abstinence from smoking, and alcohol consumption within CCS guidelines, at 6 month follow up. Results were analyzed using a generalized estimation approach for fitting logistic regression using a population averaged method.ResultsTwo hundred and twenty one clinics across Ontario were randomized for this study; 110 to the intervention arm and 111 to the control arm. From the 15,222 patients that enrolled in the smoking cessation program, 15,150 (99.6% of patients) were screened for alcohol use and 5715 patients were identified as drinking above the CCS guidelines. No statistically significant difference between groups was seen in practitioner offer of an educational alcohol resource to appropriate patients (OR 1.19, 95% CI 0.88 1.64, p 0.261) or in patient abstinence from smoking and drinking within the CCS guidelines at 6 month follow up (OR 0.93, 95% CI 0.71 1.22, p 0.594) However, a significantly greater proportion of patients in the intervention group accepted the alcohol resource offered to them by their practitioner (OR 1.48, 95% CI 1.01 2.16, p 0.045).ConclusionA CDSS may not increase the likelihood of practitioners offering an educational alcohol resource, though it may have influenced patients' acceptance of the resource.Trial registrationThis trial is registered with ClinicalTrials.gov, number NCT03108144, registered on April 11, 2017, "retrospectively registered"
Implementation Science
Nadia Minian Dolly Baliunas Aliya Noormohamed Laurie Zawertailo Norman Giesbrecht Christian S Hendershot Bernard Le Foll Jurgen Rehm Andriy V Samokhvalov Peter L Selby
10068061
Feasibility of a computerized clinical decision support system for treating tobacco use in dental clinics.
This study tested the feasibility of using information technology to improve dentists' adherence to the Treating Tobacco Use and Dependence Clinical Practice Guidelines by offering a web based computer mediated clinical decision support system (CDSS) for tobacco use treatment in dental clinics. The authors developed a CDSS tool, deployed the software in one of New York University College of Dentistry's general practice clinics, interviewed associate student dentists, and reviewed a random selection of patients' charts to determine if, after implementation, there were changes in tobacco use screening and referral to treatment. Students reported that the CDSS was easy to use, increased their efficiency, and provided better quality of evidence than was available prior to the intervention. Chart reviews demonstrated that, after CDSS implementation, patients in the target clinic were significantly more likely to be screened for tobacco use <0.001) and tobacco using patients were more likely to be advised <0.001) referred to the state's Quit Line <0.001) and prescribed nicotine replacement therapy (0.035) This study concludes that it is feasible to implement a CDSS for tobacco use treatment in dental clinics. The CDSS is a promising method for improving adherence to tobacco use treatment guidelines and warrants further study.
Journal of dental education
Theresa Montini Andrew Schenkel Donna R Shelley
196810767
Randomized Clinical Trial of a Clinical Decision Support Tool for Improving the Appropriateness Scores for Ordering Imaging Studies in Primary and Specialty Care Ambulatory Clinics.
OBJECTIVE. The objective of our study was to evaluate whether the use of a clinical decision support (CDS) tool improved the appropriateness scores of orders for advanced imaging in clinical practice. MATERIALS AND METHODS. We used a stepped wedge, cluster randomized clinical trial to evaluate the effectiveness of a CDS tool in an integrated health care system. Clinicians entered structured indications for each CT and MRI order, and the indications were electronically scored against appropriateness criteria to assign an appropriateness score. We compared the proportion of orders with adjusted appropriateness scores of 7 or greater (on a 1 9 scale) before and after activation of best practice alerts (BPAs) triggered for orders with low or marginal appropriateness scores. Secondary outcomes included the rate per month of orders for advanced imaging and the proportion of orders for which the radiology department requested changes. RESULTS. Between October 2015 and February 2016, 941 clinicians ordered 22,279 CT or MRI studies that met eligibility criteria. Before activation of the BPA, the mean proportion of appropriate orders (adjusted for time and clinic effect) was 77.0% (95% CI, 75.5 78.4% which increased to 80.1% (95% CI, 78.7 81.5% after activation (p 0.001) There was no significant change in the rate of orders per month for advanced imaging. The proportion of order changes requested by the radiology department decreased from 5.7% (95% CI, 5.6 5.9% before CDS implementation to 5.3% (95% CI, 5.1 5.5% after CDS implementation (p 0.001) CONCLUSION. Using an evidence based CDS tool in clinical practice was associated with a modest but significant improvement in the appropriateness scores of advanced imaging orders.
AJR. American journal of roentgenology
Ted E Palen Richard E Sharpe Susan Shetterly John F Steiner
51717981
Decision support system for detection of hypertensive retinopathy using arteriovenous ratio
Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).The presented system includes three modules i.e. main component extraction, artery/vein (A/V) classification and finally AVR calculation and grading of HR. Proposed system uses vascular map and a set of hybrid features for A/V classification. The evaluation of proposed system is carried out using three datasets. The proposed system shows average accuracies of 95.14% for images of INSPIRE AVR database, 96.82% for images of VICAVR database and 98.76% for local dataset AVRDB. These results support that the proposed system is trustworthy for clinical use in detection and grading of HR disease. Main contribution of proposed system is that it utilizes complete blood vessel map for A/V classification. These arteries and veins are then used to calculate AVR and grade HR cases based on AVR values. Another contribution of this article is that it presents a new dataset AVRDB for A/V classification and HR detection.
Artif. Intell. Medicine
Shahzad Akbar Muhammad Usman Akram Muhammad Sharif Anam Tariq Shoab Ahmad Khan