A Case for Enterprise Interoperability in Healthcare IT

2014 ◽  
pp. 192-215 ◽  
Author(s):  
Mustafa Yuksel ◽  
Asuman Dogac ◽  
Cebrail Taskin ◽  
Anil Yalcinkaya
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Devices ◽  
Support Services ◽  
Clinical Decision ◽  
Medical Data ◽  
Physiological Status ◽  
Healthcare Applications ◽  
It Systems ◽  
Healthcare It

The PHR systems need to be integrated with a wide variety of healthcare IT systems including EHRs, electronic medical devices, and clinical decision support services to get their full benefit. It is not possible to sustain the integration of PHRs with other healthcare IT systems in a proprietary way; this integration has to be achieved by exploiting the promising interoperability standards and profiles. This chapter provides a survey and analysis of the interoperability standards and profiles that can be used to integrate PHRs with a variety of healthcare applications and medical data resources, including EHR systems to enable access of a patient to his own medical data generated by healthcare professionals; personal medical devices to obtain the patient’s instant physiological status; and the clinical decision support services for patient-physician shared decision making.

A Case for Enterprise Interoperability in Healthcare IT

2016 ◽  
pp. 1073-1096 ◽  
Author(s):  
Mustafa Yuksel ◽  
Asuman Dogac ◽  
Cebrail Taskin ◽  
Anil Yalcinkaya
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Devices ◽  
Support Services ◽  
Clinical Decision ◽  
Medical Data ◽  
Physiological Status ◽  
Healthcare Applications ◽  
It Systems ◽  
Healthcare It

The PHR systems need to be integrated with a wide variety of healthcare IT systems including EHRs, electronic medical devices, and clinical decision support services to get their full benefit. It is not possible to sustain the integration of PHRs with other healthcare IT systems in a proprietary way; this integration has to be achieved by exploiting the promising interoperability standards and profiles. This chapter provides a survey and analysis of the interoperability standards and profiles that can be used to integrate PHRs with a variety of healthcare applications and medical data resources, including EHR systems to enable access of a patient to his own medical data generated by healthcare professionals; personal medical devices to obtain the patient's instant physiological status; and the clinical decision support services for patient-physician shared decision making.

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

scholarly journals Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

2017 ◽  
Vol 25 (3) ◽  
pp. 1091-1104 ◽  
Author(s):  
Mirza Mansoor Baig ◽  
Hamid GholamHosseini ◽  
Aasia A Moqeem ◽  
Farhaan Mirza ◽  
Maria Lindén
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Hospital Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Smartphone Applications ◽  
Clinical Workflow ◽  
Healthcare Applications ◽  
Wide Range

Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians’ acceptability, as well as the low impact on the medical professionals’ decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Event ◽  
Support Services ◽  
Medication Safety ◽  
Point Of Care ◽  
Clinical Decision ◽  
Substantial Contribution ◽  
Drug Event

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

scholarly journals AI in Healthcare practice: opportunities and challenges for clinical decision support systems

2019 ◽  
Author(s):  
David R. Millen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

A Discussion of Clinical Decision Support Services

2013 ◽  
Vol 56 (3) ◽  
pp. 446-452 ◽  
Author(s):  
CORENTHIAN “COREY J. BOOKER ◽  
PAIGE N. ANDREWS
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support Services ◽  
Clinical Decision

scholarly journals Delivering clinical decision support services: There is nothing as practical as a good theory

2010 ◽  
Vol 43 (5) ◽  
pp. 831-843 ◽  
Author(s):  
John Fox ◽  
David Glasspool ◽  
Vivek Patkar ◽  
Mark Austin ◽  
Liz Black ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support Services ◽  
Clinical Decision ◽  
Good Theory
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