scholarly journals Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: A literature review and case study

2010 ◽  
Vol 79 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Thomas R. Campion ◽  
Lemuel R. Waitman ◽  
Addison K. May ◽  
Asli Ozdas ◽  
Nancy M. Lorenzi ◽  
...  
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Insulin Therapy ◽  
Clinical Decision Support ◽  
Intensive Insulin Therapy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Contextual Characteristics ◽  
Social Organizational

scholarly journals Development of a Scalable Tele-Monitoring Platform based on the Clinical Decision Support Systems

2020 ◽  
Author(s):  
azita yazdani ◽  
Reza Safdari ◽  
Roxana Sharifian ◽  
maryam zahmatkeshan
Keyword(s):  
Health Care ◽  
Performance Evaluation ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Monitoring Systems ◽  
Tele Monitoring

Abstract Background: One of the most important types of information systems that play important role today in providing quality health care services are clinical decision support systems (CDSSs). These systems are effective in overcoming human resource constraint and intelligent analysis of information generated by Tele-monitoring systems. In spite of the many advantages of this architectures, these are single-purpose, meaning that only the CDSS of a disease is located on them. If we want to use the same model of architecture in the decision-making process of another disease, all the components of this architecture should be redevelopment with a new CDSS, which is time-consuming and costly. Due to the increasing demand for health information technology at low cost and mobile access in the health care industry, in this article, a scalable software platform(Patient Tele monitoring: PATEL) based on SOA for implementing and use different CDSSs on a common platform, for use in Tele-monitoring Systems, was created.Implementation: To develop PATEL platform, the component-based software development approach and hybrid programming approach to implementing various components used. In the evaluation phase of the proposed platform, the case study, accuracy and performance evaluation (transmission delays, patient data fetch, parsing overhead and inference time) used.Results: The results of the case study evaluation confirmed the scalability and interoperability between CDSSs on the platform. Based on performance evaluation, the proposed platform has responded to 89% of the requests in less than one second. Also, based on accuracy evaluation, the platform presented in this article was successful in diagnosing 91.6% of the cases.Conclusion: The proposed platform can support CDSSs of various diseases simultaneously and provides the necessary scalability to add a new CDSS. Tele-monitoring systems will be capable of service by connecting to this platform. Using this infrastructure is expected to be a lot of duplication in the implementation of tele-monitoring systems based CDSSs will be reduced.

scholarly journals Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study

2019 ◽  
Vol 10 (02) ◽  
pp. 237-246 ◽  
Author(s):  
Jeritt Thayer ◽  
Jeffrey Miller ◽  
Alexander Fiks ◽  
Linda Tague ◽  
Robert Grundmeier
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
High Volume ◽  
Clinical Decision ◽  
Clinical Workflow ◽  
System Integrity ◽  
Electronic Health

Background With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem. Objective This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR. Methods In an academic health system’s primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams. Results We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts—loss of individual features with straightforward workarounds—arose from three malfunctions, which affected users on 53 days. Discussion Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow. Conclusion This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.

Human Factors and Technological Characteristics Influencing the Interaction with AI-enabled Clinical Decision Support Systems: A Literature Review (Preprint)

2021 ◽  
Author(s):  
Michael Knop ◽  
Sebastian Weber ◽  
Marius Mueller ◽  
Bjoern Niehaves
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Professionals ◽  
Technological Characteristics ◽  
Theoretical Foundations

BACKGROUND The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, while undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSS) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information, but still suffer from lack of adoption and standardized evaluation. With the rise of artificial intelligence (AI), CDSS became adaptive human-like technologies, able to learn and destined to change their characteristics over time. Yet, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSS. OBJECTIVE Our study seeks to summarize the factors influencing an effective collaboration between medical professionals and AI-enabled CDSS within a direct, individual interaction. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of AI-enabled CDSS. METHODS Following the PRISMA guidelines, we conducted a literature review including three different meta-databases, screening over 1000 articles and including 101 of them for full-text assessment. In the end, seven met our inclusion criteria and were analyzed for our synthesis. RESULTS We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSS in accordance with our research objective, namely training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSS, some characteristics/factors retain their importance, while others gain or lose relevance due to the uniqueness of human-AI interaction. However, only few studies mention theoretical foundations and patient outcomes related to AI-enabled CDSS. CONCLUSIONS Our study provides a comprehensive overview of relevant characteristics and factors that influence the interaction and collaboration of medical professionals and AI-enabled CDSS. Rather limited theoretical foundations are currently hindering the possibility of creating adequate concepts and models to explain and predict interrelations between these characteristics and factors. For an appropriate evaluation of human-AI collaboration, patient outcomes and the role of patients within the decision-making process should be taken into consideration.

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