Application of Artificial Intelligence to Overcome Clinical Information Overload in Urologic Cancer

2021 ◽  
Author(s):  
Arnulf Stenzl ◽  
Cora N. Sternberg ◽  
Jenny Ghith ◽  
Lucile Serfass ◽  
Bob J.A. Schijvenaars ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Clinical Information ◽  
Urologic Cancer

Clinical Practice Ontology Automatic Learning from SOAP Reports

2016 ◽  
pp. 349-363
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos Fernandes Baeta
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Natural Language ◽  
Knowledge Base ◽  
Clinical Information ◽  
Knowledge Structure ◽  
Daily Routine ◽  
Automatic Learning ◽  
Controlled Natural Language ◽  
Practice Knowledge

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

scholarly journals Novel Representation of Clinical Information in the ICU

2010 ◽  
Vol 01 (02) ◽  
pp. 116-131 ◽  
Author(s):  
V. Herasevich ◽  
A. Ahmed ◽  
O. Gajic ◽  
B.W. Pickering
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
User Interfaces ◽  
Information Overload ◽  
Care Delivery ◽  
Tertiary Care ◽  
Clinical Information ◽  
Care Providers ◽  
Effective Use ◽  
Physician Order Entry

SummaryThe introduction of electronic medical records (EMR) and computerized physician order entry (CPOE) into the intensive care unit (ICU) is transforming the way health care providers currently work. The challenge facing developers of EMR’s is to create products which add value to systems of health care delivery. As EMR’s become more prevalent, the potential impact they have on the quality and safety, both negative and positive, will be amplified. In this paper we outline the key barriers to effective use of EMR and describe the methodology, using a worked example of the output. AWARE (Ambient Warning and Response Evaluation), is a physician led, electronic-environment enhancement program in an academic, tertiary care institution’s ICU. The development process is focused on reducing information overload, improving efficiency and eliminating medical error in the ICU. Citation: Pickering BW, Herasevich V, Ahmed A, Gajic O. Novel representation of clinical information in the ICU – developing user interfaces which reduce information overload. Appl Clin Inf 2010; 1: 116–131 http://dx.doi.org/10.4338/ACI-2009-12-CR-0027

scholarly journals A Virtual Assistant for Websites

2004 ◽  
Vol 3 (1) ◽  
Author(s):  
José Luiz Andrade Duizith ◽  
Lizandro Kirst Da Silva ◽  
Daniel Ribeiro Brahm ◽  
Gustavo Tagliassuchi ◽  
Stanley Loh
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Web Pages ◽  
Human Intervention ◽  
Web Browser ◽  
Cd Rom ◽  
Matching Process ◽  
Management Processes ◽  
The Web ◽  
Supply Information

This work presents a Virtual Assistant (VA) whose main goal is to supply information for Websites users. AVA is a software system that interacts with persons through a Web browser, receiving textual questions and answering automatically without human intervention. The VA supplies information by looking for similar questions in a knowledge base and giving the corresponding answer. Artificial Intelligence techniques are employed in this matching process, to compare the user’s question against questions stored in the base. The main advantage of using the VA is to minimize information overload when users get lost in Websites. The VA can guide the user across the web pages or directly supply information. This is especially important for customers visiting an enterprise site, looking for products, services or prices or needing information about some topic. The VA can also help in Knowledge Management processes inside enterprises, offering an easy way for people storing and retrieving knowledge. An extra advantage is to reduce the structure of Call Centers, since the VA can be given to customers in a CD-ROM. Furthermore, the VA provides Webmasters with statistics about the usage of the VA (themes more asked, number of visitants, time of conversation).

scholarly journals Overview of Evidence-based Medicine: Challenges for Evidence-based Laboratory Medicine

2001 ◽  
Vol 47 (8) ◽  
pp. 1536-1546 ◽  
Author(s):  
Matthew J McQueen
Keyword(s):  
Evidence Based Medicine ◽  
Information Overload ◽  
Clinical Information ◽  
Laboratory Medicine ◽  
Evidence Based ◽  
Efficiency And Effectiveness ◽  
Care Maps ◽  
Critical Appraisal Skills ◽  
Based Medicine ◽  
The Impact

Abstract Evidence-based medicine (EBM) has been driven by the need to cope with information overload, by cost-control, and by a public impatient for the best in diagnostics and treatment. Clinical guidelines, care maps, and outcome measures are quality improvement tools for the appropriateness, efficiency, and effectiveness of health services. Although they are imperfect, their value increases with the quality of the evidence they incorporate. Laboratory professionals must direct more effort to demonstrating the impact of laboratory tests on a greater variety of clinical outcomes. Laboratory and clinical practitioners must be familiar with many of the accessible electronic and paper tools for searching for evidence. Detailed statistical and epidemiologic knowledge is not essential, but critical appraisal skills and a competent understanding of the strengths and weaknesses of systematic review and metaanalysis are necessary. Overemphasis on complexity and failure to recognize time limitations are major barriers to translating EBM into everyday practice. Emphasizing and practicing the role of the laboratory professional as a skilled clinical consultant strongly grounded in evidence as well, in addition to better integration of laboratory and clinical information and improved laboratory reports will overcome most barriers. There is a poverty of good, primary studies of test evaluations. Institution of more consistent standards for the design and reporting of studies on diagnostic accuracy should improve the situation. If nothing else, systematic reviews have demonstrated the need for more good-quality primary research in laboratory medicine.

scholarly journals Information needs for the OR and PACU electronic medical record

2014 ◽  
Vol 05 (03) ◽  
pp. 630-641 ◽  
Author(s):  
V. Herasevich ◽  
J.R. Hebl ◽  
M.J. Brown ◽  
B.W. Pickering ◽  
M.A. Ellsworth
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Medical Error ◽  
Information Needs ◽  
Clinical Decision Making ◽  
Information Overload ◽  
Clinical Information ◽  
Clinical Decision ◽  
Anesthesia Providers ◽  
Survey Responses

Summary Objective: The amount of clinical information that anesthesia providers encounter creates an environment for information overload and medical error. In an effort to create more efficient OR and PACU EMR viewer platforms, we aimed to better understand the intraoperative and post-anesthesia clinical information needs among anesthesia providers. Materials and Methods: A web-based survey to evaluate 75 clinical data items was created and distributed to all anesthesia providers at our institution. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the OR and PACU settings. Results: There were 107 survey responses with distribution throughout all clinical roles. 84% of the data items fell within the top 2 proportional quarters in the OR setting compared to only 65% in the PACU. Thirty of the 75 items (40%) received an absolutely necessary rating by more than half of the respondents for the OR setting as opposed to only 19 of the 75 items (25%) in the PACU. Only 1 item was rated by more than 20% of respondents as not needed in the OR compared to 20 data items (27%) in the PACU. Conclusion: Anesthesia providers demonstrate a larger need for EMR data to help guide clinical decision making in the OR as compared to the PACU. When creating EMR platforms for these settings it is important to understand and include data items providers deem the most clinically useful. Minimizing the less relevant data items helps prevent information overload and reduces the risk for medical error. Citation: Herasevich V, Ellsworth MA, Hebl JR, Brown MJ, Pickering BW. Information needs for the OR and PACU electronic medical record. Appl Clin Inf 2014; 5: 630–641http://dx.doi.org/10.4338/ACI-2014-02-RA-0015

Combating Information Overload by Means of Information Customization Systems

2011 ◽  
pp. 60-92
Author(s):  
Mohamed Salah Hamdi
Keyword(s):  
Artificial Intelligence ◽  
Collaborative Filtering ◽  
Web Mining ◽  
Information Overload ◽  
The Internet ◽  
Global Information ◽  
Intelligent Interfaces ◽  
Information Matching ◽  
Available Information ◽  
Information Customization

The evolution of the Internet into the Global Information Infrastructure has led to an explosion in the amount of available information. The result is the “information overload” of the user, i.e., users have too much information to make a decision or remain informed about a topic. Information customization systems are supposed to be the answer for information overload. They allow users narrowcast what they are looking for and get information matching their needs. Information customization systems are also a bargain of consummate efficiency. The value proposition of such systems is reducing the time spent looking for information. We hold the view that information customization could be best done by combining various artificial intelligence technologies such as collaborative filtering, intelligent interfaces, agents, bots, web mining, and intermediaries. MASACAD, the system described in this chapter, is an example of an information customization system that combines many of the technologies already mentioned and others to approach information customization and combat information overload.

scholarly journals The Potential of AI in Care Optimization: Insights from the User-Driven Co-Development of a Care Integration System

2021 ◽  
Vol 58 ◽  
pp. 004695802110179
Author(s):  
Anna Schneider-Kamp
Keyword(s):  
Artificial Intelligence ◽  
Information Overload ◽  
Relevant Information ◽  
Missing Information ◽  
User Experiences ◽  
Complex Processes ◽  
Care Integration ◽  
Qualitative Health Research ◽  
Care Processes ◽  
User Groups

Transitions from one level of care to another are complex processes that pose medical and organizational risks and depend on care integration between different providers. This qualitative study investigated user experiences with an existing digital system for care integration between hospitals and nursing homes, and the potential of artificial intelligence to contribute to its optimization. The findings reveal challenges regarding (a) untimely information, (b) irrelevant information, (c) confusing information, (d) missing information, (e) information overload, and (f) information multiplicity. Artificial intelligence could address these by (i) identifying and verifying low-quality information, (ii) targeting information for different user groups, (iii) visually summarizing relevant information, and (iv) jointly presenting multiple versions. The implications of these findings extend beyond the context of care integration, presenting empirical evidence for the importance of qualitative health research in, and a model for, determining the scope and design of future artificial intelligence solutions to optimize (health)care processes.

scholarly journals Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zubair Ahmad ◽  
Shabina Rahim ◽  
Maha Zubair ◽  
Jamshid Abdul-Ghafar
Keyword(s):  
Artificial Intelligence ◽  
Clinical Information ◽  
Accurate Diagnosis ◽  
Surgical Pathology ◽  
Computer Algorithms ◽  
Comprehensive Review ◽  
Future Role ◽  
Personalized Care ◽  
High Level

Abstract Background The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world. Main text Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted. Conclusion AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

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