Clinical decision-making in acute paediatrics : evaluation of the impact of an internet-delivered paediatric decision support system

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Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

تم عمل نظام دعم القرار السريري الآلي (CDSS) كنموذج جديد في الخدمات الطبية. بحيث يتم استخدام CDSSs لمساعدة الأخصائيين (الأطباء) في اتخاذ قراراتهم المحيرة. ولهذا السبب ، تم بناء DSS اعتمادًا على معرفة الأطباء وباستخدام استخراج البيانات لمساعدة خلية الازمة الطبية للسيطرة على جائحة فيروس COVID-19 ، وبشكل عام ، لتحديد الفئة من العدوى وتقديم علاج بروتوكول مناسب حسب أعراض المريض. في البداية لتشخيص المرض تم الاعتماد على ثلاث اعراض اولية هي ( الحمى, التعب والسعال الجاف) لمعرفة الشخص المصاب وعند تحديد أي من هذه الاعراض يتم تقسيم الاشخاص المصابين الى اربعة اصناف حسب مناعة الاشخاص ( اصابة طفيفة , اصابة عالية , اصابة شديدة جدا و طبيعي). وايضا يتم التشخيص باستخدام عاملين هما ( عمر المريض و الامراض المزمنة للمريض مثل السكر ومشاكل القلب وضغط الدم ) ثم يتم تقدير حالة المصاب حيث توجد ستة مستويات للاشخاص المصابين بفيروس كورونا 2019 وتحتاج الى عناية حسب حالة المصاب. عندما يكون الفحص موجب واعتمادا على عمر المريض والامراض المزمنة يتم تحديد في أي مستوى من المستويات الستة يكون المريض حسب الاعراض . وبذلك يتم تحديد درجة حالة المريض من الدرجات الاربع ثم يتم اقتراح اربعة بروتوكولات للعلاج ويتم اختيار الانسب حسب اختيار الاطباء وايضا يوفر النظام معلومات كاملة عن الوقاية وتجنب الوباء واخيرا يتم ارسال ايميل يحتوي جميع المعلومات من مركز السيطرة لللاشخاص المسؤولين . تم اعتماد خوارزمية C4.5  في شجرة اتخاذ القرار لبناء هذا التطبيق.                                                                                                                                              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree

Syndromic Surveillance for Bioterrorism-related Inhalation Anthrax in an Emergency Department Population

Objective: To utilize clinical data from emergency department admissions and published clinical case reports from the 2001 bioterrorism-related inhalation anthrax (IA) outbreak to develop a detection algorithm for syndromic surveillance. Methods: A comprehensive review of case reports and medical charts was undertaken to identify clinical characteristics of IA. Eleven historical cases were compared to 160 patients meeting a syndromic case definition based on acute respiratory failure and the presence of mediastinal widening or lymphadenopathy on a chest radiograph. Results: The majority of syndromic group patients admitted were due to motor vehicle accident (52%), followed by fall (10%), or other causes (4%). Positive culture for a gram positive rod was the most predictive feature for anthrax cases. Among signs and symptoms, myalgias, fatigue, sweats, nausea, headache, cough, confusion, fever, and chest pain were found to best discriminate between IA and syndromic patients. When radiological findings were examined, consolidation and pleural effusions were both significantly higher among IA patients. A four step algorithm was devised based on combinations of the most accurate clinical features and the availability of data during the course of typical patient care. The sensitivity (91%) and specificity (96%) of the algorithm were found to be high. Conclusions: Surveillance based on late stage findings of IA can be used by clinicians to identify high risk patients in the Emergency Department using a simple decision tree. Implications for public health: Monitoring pre-diagnostic indicators of IA can provide enough credible evidence to initiate an epidemiological investigation leading to earlier outbreak detection and more effective public health response

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Appraisal of free online symptom checkers and applications for self-diagnosis and triage: An Australian evaluation

The internet has impacted society and changed the way companies and individuals operate on a daily basis. Seeking information online via computer or mobile device is common practice. The phrase ‘Google it’ is now part of modern vernacular and is a resource increasingly utilised by young and old alike. Around 80% of Australian’s search health-related information online as it is convenient, cheap, and available 24/7. Symptom checkers are one tool used by consumers to investigate their health issues. Symptom checkers are automated online programs which use computerised algorithms, asking a series of questions to help determine a potential diagnosis and/or provide suitable triage advice. Recent evidence suggests symptom checkers may not work the way they are intended. Inferior or incorrect healthcare information can potentially have serious consequences on the consumer’s wellbeing and may not have the desired effect of directing consumers to the appropriate point of care. This research evaluated the clinical performance of 36 symptom checkers found on websites and smartphone applications that are freely available for use by the Australian general public. Symptom checkers were exposed to 48 clinical vignettes, generating 1858 symptom checker vignette tests (SCVT). Diagnosis was assessed on the inclusion of the correct diagnosis in the first, the top three or top ten differential diagnoses (n = 1,170 SCVT). Triage advice was assessed on whether the triage category recommended was concordant with our assessment (n = 688 SCVT). The correct diagnosis was listed first in 36% (95% CI 31–42) of SCVT, within the top three in 52% (95% CI 47–59) and within the top ten in 58% (95% CI 53–65). Symptom checkers which claimed to utilise artificial intelligence (AI) outperformed non-AI with the first listed diagnosis being accurate in 46% (95% CI 40–57) versus 32% (95% CI 26–38) of SCVT. Individual symptom checker performance varied considerably, with the average rate of correct diagnosis provided first ranging between 12%–-61%. Triage advice provided was concordant with our assessment in 49% (95% CI 44–54) of SCVT. Appropriate triage advice was provided more frequently for emergency care SCVT at 63% (95% CI 52–71) than for non-urgent SCVT at 30% (95% CI 11–39). Symptom checker performance varied considerably in relation to diagnosis. Triage advice was risk-averse, typically recommending more urgent care pathways than necessary. Given this, symptom checkers may not be working to alleviate demand for health services (particularly emergency services) within Australia—counter to marketing materials of some organisations’ symptom checkers. It is important that symptom checkers do not further burden the healthcare system with inappropriate referrals or incorrect care advice. Although, a balance must be struck as avoiding unsuitable triage advice could potentially result in life-threatening consequences for consumers. Nonetheless, the results of this research make clear that the accuracy of diagnosis and triage advice provided from readily available symptom checkers for the Australian public require improvements before everyday consumers can rely entirely on health information provided via these mediums