Decision-Making and Decision Support in Acute Care

Evaluation Process ◽

Clinical Decision ◽

Medical Decision ◽

Information Tools ◽

Novices And Experts ◽

And Function ◽

Medical Choice

Clinical Decision Support Systems (CDSS) are information tools intended to optimize medical choice, promising better patient outcomes, faster care, reduced resource expenditure, or some combination of all three. Clinical trials of CDSS have provided only insipid evidence of benefit to date. This chapter reviews the theory of medical decision-decision making, identifying the different decision support needs of novices and experts, and demonstrates that discipline, objective and setting, and affect of the nature of support that is required. A discussion on categorization attempts to provide metrics by which systems can be compared and evaluated, in particular with regard to decision support mechanics and function. Throughout, the common theme is the placement of clinical decision makers at the center of the design or evaluation process.

Clinical Decision Making by Emergency Room Physicians and Residents

Encyclopedia of Healthcare Information Systems ◽

10.4018/978-1-59904-889-5.ch030 ◽

2008 ◽

pp. 217-223

Author(s):

Ken J. Farion ◽

Michael J. Hine ◽

Wojtek Michalowski ◽

Szymon Wilk

Keyword(s):

Decision Making ◽

Information Technology ◽

Decision Support ◽

Clinical Decision ◽

Medical Community ◽

Medical Decision ◽

Best Interest ◽

Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

Computational Methods and Algorithms for Medicine and Optimized Clinical Practice - Advances in Medical Technologies and Clinical Practice ◽

Mental Health Clinical Decision Support Exploiting Big Data

10.4018/978-1-5225-8244-1.ch008 ◽

2019 ◽

pp. 160-184 ◽

Cited By ~ 1

Author(s):

Jan Kalina

Keyword(s):

Mental Health ◽

Decision Making ◽

Big Data ◽

Decision Support ◽

Support Systems ◽

Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Robotic Systems ◽

10.4018/978-1-7998-1754-3.ch008 ◽

2020 ◽

pp. 167-186

Author(s):

Steven Walczak

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Decision Support ◽

Support Systems ◽

Clinical Decision ◽

Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Cancers ◽

10.3390/cancers12020369 ◽

2020 ◽

Vol 12 (2) ◽

pp. 369 ◽

Cited By ~ 4

Author(s):

Claudia Mazo ◽

Cathriona Kearns ◽

Catherine Mooney ◽

William M. Gallagher

Keyword(s):

Breast Cancer ◽

Systematic Review ◽

Decision Making ◽

Decision Support ◽

Support Systems ◽

Clinical Decision ◽

Major Advance

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

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

Health Informatics Journal ◽

10.1177/1460458217740722 ◽

2017 ◽

Vol 25 (3) ◽

pp. 1091-1104 ◽

Cited By ~ 7

Author(s):

Mirza Mansoor Baig ◽

Hamid GholamHosseini ◽

Aasia A Moqeem ◽

Farhaan Mirza ◽

Maria Lindén

Keyword(s):

Decision Making ◽

Decision Support ◽

Hospital Care ◽

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.

Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx118 ◽

2017 ◽

Vol 25 (5) ◽

pp. 585-592 ◽

Cited By ~ 26

Author(s):

Kristen Miller ◽

Danielle Mosby ◽

Muge Capan ◽

Rebecca Kowalski ◽

Raj Ratwani ◽

...

Keyword(s):

Decision Support ◽

Quality Care ◽

Clinical Decision ◽

The Body ◽

Narrative Review ◽

Functional Requirements ◽

Design Features ◽

And Function

Abstract Objective Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care. Material and Methods A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems. Results Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Discussion Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. Conclusion This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.

Desenvolvimento e Avaliação de uma Plataforma Colaborativa Digital para Educação e Tomada de Decisão Médica Baseada em Evidências

Revista Brasileira de Educação Médica ◽

10.1590/1981-5271v43suplemento1-20190083 ◽

2019 ◽

Vol 43 (1 suppl 1) ◽

pp. 513-524

Author(s):

Álisson Oliveira dos Santos ◽

Alexandre Sztajnberg ◽

Tales Mota Machado ◽

Daniel Magalhães Nobre ◽

Adriano Neves de Paula e Souza ◽

...

Keyword(s):

Decision Making ◽

Clinical Practice ◽

Real Life ◽

Evaluation Process ◽

Clinical Decision ◽

Medical Decision ◽

Alternative Form ◽

Evidence Based ◽

Life Problems

ABSTRACT The medical education for clinical decision-making has undergone changes in recent years. Previously supported by printed material, problem solving in clinical practice has recently been aided by digital tools known as summaries platforms. Doctors and medical students have been using such tools from questions found in practice scenarios. These platforms have the advantage of high-quality, evidence-based and always up-to-date content. Its popularization was mainly due to the rise of the internet use and, more recently, of mobile devices such as tablets and smartphones, facilitating their use in clinical practice. Despite this platform is widely available, the most of them actually present several access barriers as costs, foreign language and not be able to Brazilian epidemiology. A free national platform of evidence-based medical summaries was proposed, using the crowdsourcing concept to resolve those barriers. Furthermore, concepts of gamification and content evaluation were implemented. Also, there is the possibility of evaluation by the users, who assigns note for each content created. The platform was built with modern technological tools and made available for web and mobile application. After development, an evaluation process was conducted by researchers to attest to the valid of content, usability, and user satisfying. Consolidated questionnaires and evaluation tools by the literature were applied. The process of developing the digital platform fostered interdisciplinarity, from the involvement of medical and information technology professionals. The work also allowed the reflection on the innovative educational processes, in which the learning from real life problems and the construction of knowledge in a collaborative way are integrated. The assessment results suggest that platform can be real alternative form the evidence-based medical decision-making.

Decision Making by Emergency Room Physicians and Residents

New Technologies for Advancing Healthcare and Clinical Practices ◽

10.4018/978-1-60960-780-7.ch008 ◽

2011 ◽

pp. 131-148

Author(s):

Michael J. Hine ◽

Ken J. Farion ◽

Wojtek Michalowski ◽

Szymon Wilk

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Decision Support ◽

Physical Examination ◽

Tacit Knowledge ◽

Expert Knowledge ◽

Clinical Decision ◽

Clinical Decisions ◽

Input Variables

Clinical Decision Support Systems (CDSS) are typically constructed from expert knowledge and are often reliant on inputs that are difficult to obtain and on tacit knowledge that only experienced clinicians possess. Research described in this article uses empirical results from a clinical trial of a CDSS with a decision model based on expert knowledge to show that there are differences in how clinician groups of the same specialty, but different level of expertise, elicit necessary CDSS input variables and use said variables in their clinical decisions. This article reports that novice clinicians have difficulty eliciting CDSS input variables that require physical examination, yet they still use these incorrectly elicited variables in making their clinical decisions. Implications for the design of CDSS are discussed.

Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs ◽

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

10.4018/978-1-4666-1755-1.ch006 ◽

2012 ◽

pp. 84-110 ◽

Cited By ~ 1

Author(s):

Reza S. Kazemzadeh ◽

Kamran Sartipi ◽

Priya Jayaratna

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Support ◽

Clinical Guideline ◽

Support Systems ◽

Clinical Decision ◽

Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.