The Current Status and Path Forward on Clinicians’ Assisted Decision Making by Artificial Intelligence-Enabled Technology: Mixed Method Approach (Preprint)

2021 ◽  
Author(s):  
Hyeyoung Hah ◽  
Deana Goldin
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Reasoning ◽  
Hierarchical Linear Modeling ◽  
Rapid Development ◽  
Current Status ◽  
Healthcare Decision ◽  
Current Form ◽  
Patient Diagnosis ◽  
Diagnostic Capability

BACKGROUND With the potential and rapid development of artificial intelligence and related technologies, AI algorithms are being embedded into various health information technologies to assist clinicians’ decision making in clinician-patient encounters. OBJECTIVE The objective of this study is to explore how clinicians perceive AI assistance in their diagnosis decision making and suggest paths forward as to what necessitates to achieve AI-human teaming in healthcare decision making. METHODS This study uses a mixed methods approach utilizing hierarchical linear modeling (HLM) and sentiment analysis through natural language understanding (NLU) techniques. RESULTS A total of 114 clinicians who practice in family medicine and interact with AI algorithm to make patient diagnosis participated in online simulation surveys during 2020- 2021. Our qualitative results show a promise that clinicians’ overall sentiment toward AI-assisted patient diagnosis was positive and comparable to those of live patient encounters. However, it also showed that the process of diagnosis decision making by the given AI physiology algorithms did not align with the way clinicians make diagnosis decision. In the follow-up quantitative survey, clinicians perceive that current AI assistance was not likely to enhance their diagnostic capability and rather negatively affect their overall task performance (β=-0.421, p=0.016). Interestingly, clinician’s level of clinical diagnosis capability is rather associated with clinicians’ ex ante quality such as education (β=1.880, p=0.072) and age (β=2.428, p=0.071) on diagnostic capability as well as existing technology habit on both dependent variables (β=0.232, p=0.009 and β=0.244, p=0.003, respectively). CONCLUSIONS This paper sheds light on clinicians’ current perception and sentiment toward AI-enabled diagnosis technology in healthcare decision makings. We showed here that while overall sentiment toward the AI assistance was positive, current form of AI assistance is not linked to efficient decision-making in that AI algorithms are not aligned with humans’ subjective clinical reasoning. We suggest that health policy makers and HIT developers need to gather behavioral data from clinicians in various disciplines and specialties to make clinical AI algorithms to be aligned with humans’ subjective and unique clinical reasoning patterns.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

The effectiveness of physical education teaching in college based on Artificial intelligence methods

2020 ◽  
pp. 1-11
Author(s):  
Zihao Li ◽  
Hejin Wang
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Rapid Development ◽  
Participation Rate ◽  
Colleges And Universities ◽  
The Body ◽  
Current Status ◽  
Innovative Teaching ◽  
Artificial Intelligence Technology ◽  
Low Activity

Traditional physical education in colleges and universities is difficult to arouse students’ interest in sports, resulting in low activity participation rate and inability to exercise the body. How to effectively improve the effectiveness of physical education in colleges and universities has become one of the hot topics of most concern from all walks of life. In physical education, innovative teaching concepts and methods, teaching methods and processes, and teaching evaluation methods are all conducive to improving the classroom atmosphere of physical education and successfully improve the effectiveness of physical education. This article focuses on analyzing the current status of physical education in colleges and universities. Based on the rapid development of artificial intelligence technology, how to improve the effectiveness of physical education is studied, and an experimental method is used to compare and analyze physical education in a college. The analysis results show that artificial intelligence-based physical education can obviously improve students’ strength quality, speed quality, endurance quality, and agility quality, which provides a more important reference and reference for improving the effectiveness of college physical education.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

PP138 Current Status Of Healthcare Decision Making And Future Perspective Of The Health Technology Assessment Implementation Roadmap In Turkey

2020 ◽  
Vol 36 (S1) ◽  
pp. 16-17
Author(s):  
Enver Kagan Atikeler ◽  
Ahmad Nader Fasseeh ◽  
Bert Leufkens ◽  
Wim Goettsch
Keyword(s):  
Decision Making ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Current Status ◽  
Healthcare Decision ◽  
Health Technologies ◽  
Economic Decision Making ◽  
Healthcare Decision Making ◽  
Decision Making Processes

IntroductionTurkey's health reforms, which started in 2003, have led to increased access to health care and pharmaceuticals as well as rising public pharmaceutical expenditures. The need to improve healthcare decision making by implementing health technology assessment (HTA) has become an important priority for Turkey. This study sought to provide a tailor-made HTA implementation roadmap, drawing on insights from national stakeholders.Our study aimed to describe the current HTA environment in Turkey and to explore long-term perspectives and suggestions from a wide spectrum of Turkish stakeholders regarding the preferred status of HTA in ten years (by 2029).MethodsWe conducted an online survey using a questionnaire previously applied in other HTA research. We assessed the current evaluation of medical and economic decision-making processes and examined the need for HTA. We also ascertained stakeholder perspectives on potential developments that can be done together with policymakers, representatives of pharmaceutical companies, and patient organizations. We also included general information about the pharmaceutical market and decision making processes in Turkey.ResultsThe survey was sent to various stakeholders from different areas within the health system. Additional face-to-face interviews were conducted with a few respondents to clarify some of their answers. A total of twenty-seven Turkish stakeholders completed the survey. Of these, twenty-one (78%) participants were employed in the public sector and six (22%) were from the private sector. The majority of the participants would introduce HTA for all new health technologies being considered for public reimbursement and institute an additional review process for currently reimbursed technologies. Most of the respondents considered that only new technologies with significant budget impact should be evaluated in the next ten years.ConclusionsIt is clear that Turkey needs to implement an HTA process in the future. Our study shows stakeholder expectations, which will be helpful for creating an HTA implementation roadmap, and it is clear that different stakeholders have different views and expectations about HTA implementation in Turkey. The experiences of other countries will also be helpful during the implementation process.

Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect

2020 ◽  
pp. 002224292095734
Author(s):  
Chiara Longoni ◽  
Luca Cian
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Natural Language Processing ◽  
Transaction Costs ◽  
Language Processing ◽  
Word Of Mouth ◽  
Rapid Development ◽  
Policy Makers ◽  
Trade Offs

Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel “word-of-machine” effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person’s unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).

Is Artificial Intelligence a New Dawn or Challenge for Corporate Decision Making?

2019 ◽  
pp. 20-42
Author(s):  
Maria Igorevna Nikishova ◽  
Mikhail E. Kuznetsov
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Decision Making Process ◽  
Fourth Industrial Revolution ◽  
Stakeholder Interaction ◽  
The Board Of Directors

The Fourth Industrial Revolution provides companies with new opportunities, and business picks up allies represented by technologies that can change mechanisms of corporate decision making in corporations. Rapid development of technologies, which allows working more efficiently with information, can lead to the creation of a new system of stakeholder interaction, thanks to better analytics, transparency, and speed of decisions. In this regard, the analyst based on big data with the use of artificial intelligence (AI) is able to significantly affect the quality of decisions. How can the application of AI for analysis of big data be able to influence the decision-making process and to what extent can it influence the system of corporate relationships? To answer this question, the authors will try to describe how transformation of decision-making methodology at the Board of Directors level under the influence of the Fourth Industrial Revolution and the development of AI technologies and big data, and what are the opportunities, limitations, and risks of the decision-making process with AI.

scholarly journals Administrative Decisions in the Era of Artificial Intelligence

2020 ◽  
Vol 11 ◽  
pp. 253-271
Author(s):  
Mateusz Pszczyński
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Public Administration ◽  
Rapid Development ◽  
The State ◽  
Economic Life ◽  
Decision Makers ◽  
Administrative Discretion ◽  
Building Trust

The rapid development of cybernetics allows the use of artificial intelligence in many areas of social and economic life. The State can also harness algorithms and machine learning for its actions. Automatic decision making should be one of the stages in the development and improvement of public administration. While it is easy to implement these solutions in the case of related decisions, decisions made under administrative discretion, general clauses or valuation standards pose a challenge. The correct transformation of paper-based public administration into automatic public administration requires a change in decision makers’ thinking, the introduction of new solutions, and building trust in artificial intelligence. Therefore, new solutions have to be built in accordance with the principles of transparency, accountability, equality, goodness and justice. Artificial intelligence making automatic decisions on behalf of the State must be a tool to support the execution of public tasks concerning citizens which is based on trust towards AI and public administration.

scholarly journals Artificial Intelligence in Endoscopy

2021 ◽  
pp. 1-5
Author(s):  
Alexander Hann ◽  
Alexander Meining
Keyword(s):  
Artificial Intelligence ◽  
Rapid Development ◽  
Image Interpretation ◽  
Colorectal Polyps ◽  
Screening Colonoscopy ◽  
Current Status ◽  
Great Promise ◽  
Polyp Detection ◽  
Open Questions ◽  
Adenoma Detection

<b><i>Background:</i></b> Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. <b><i>Summary:</i></b> In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. <b><i>Key Messages:</i></b> The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.

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