scholarly journals Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

10.2196/26611 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e26611
Author(s):  
Thomas Ploug ◽  
Anna Sundby ◽  
Thomas B Moeslund ◽  
Søren Holm
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Public Policy ◽  
Decision Making ◽  
Decision Support ◽  
Health Care Professionals ◽  
High Performance ◽  
Fractional Factorial ◽  
Final Decision ◽  
System Use

Background Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public’s interests in such features of AI. Objective This study elicited the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. Methods We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents’ views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. Results Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents’ trust in health and technology, and respondents’ fears and hopes regarding AI, do not play a significant role in the majority of cases. Conclusions The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.

scholarly journals Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey (Preprint)

2020 ◽  
Author(s):  
Thomas Ploug ◽  
Anna Sundby ◽  
Thomas B Moeslund ◽  
Søren Holm
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Public Policy ◽  
Decision Making ◽  
Decision Support ◽  
Health Care Professionals ◽  
High Performance ◽  
Fractional Factorial ◽  
Final Decision ◽  
System Use

BACKGROUND Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public’s interests in such features of AI. OBJECTIVE This study elicited the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. METHODS We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents’ views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. RESULTS Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents’ trust in health and technology, and respondents’ fears and hopes regarding AI, do not play a significant role in the majority of cases. CONCLUSIONS The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

Caring for Gravely Ill Children

PEDIATRICS ◽  
1994 ◽  
Vol 94 (4) ◽  
pp. 433-439 ◽  
Author(s):  
Alan R. Fleischman ◽  
Kathleen Nolan ◽  
Nancy N. Dubler ◽  
Michael F. Epstein ◽  
Mary Ann Gerben ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Professionals ◽  
Treatment Plan ◽  
Ethics Committees ◽  
Well Being ◽  
Pediatric Health Care ◽  
Spiritual Well Being ◽  
Primary Focus

Background. Much has been written about the care of the hopelessly ill adult, but there is little guidance for pediatric health care professionals in the management of children who are critically or terminally ill. Methods. Through a 3-day meeting in Tarrytown, NY, attended by a group of pediatricians and others directly involved in these issues, a principled approach was developed for the treatment of, and health care decision-making for, children who are gravely ill. Results. The group agreed that the needs and interests of the child must be the central focus of any treatment plan and that the child should be involved to as great extent possible, consistent with developmental maturity, in the decision-making process. Quality of future life should be viewed as being relevant in all decisions. Parents are believed to be the natural guardians of children and ought to have great latitude in making decisions for them. However, parental discretion is not absolute and professionals must maintain an independent obligation to protect the child's interests. Conclusions. Decision-making should be collaborative among patient, parents, and professionals. When conflict arises, consultation and ethics committees may assist in resolution. When cure or restoration of function is no longer possible, or reasonable, promotion of comfort becomes the primary goal of management. Optimal use of pain medication and compassionate concern for the physical, psychological, and spiritual well-being of the child and family should be the primary focus of the professionals caring for the dying child.

scholarly journals Understanding the perspectives of older adults and elderly regarding vaccination; a snapshot in four European countries

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
A Timen ◽  
R Eilers ◽  
S Lockhart ◽  
R Gavioli ◽  
S Paul ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Decision Making ◽  
Infectious Diseases ◽  
Health Care Professionals ◽  
Elderly Care ◽  
European Countries ◽  
Decision Making Process ◽  
Ageing Population ◽  
Syncytial Virus

Abstract Prevention of infectious diseases in elderly by immunization is a prerequisite to ensuring healthy ageing. However, in order for the vaccine programs to be effective, these need to be provided by health care professionals who have up-to-date knowledge and high motivation. Furthermore, the knowledge and attitudes towards vaccination in the targeted age groups needs to be fully understood. When focusing on the information provision, it is important to know from whom or which institution older adults and elderly would like to receive and in which form. In January 2019, an international project called the VITAL (The Vaccines and InfecTious diseases in the Ageing population) project was started, within the framework of IMI (Innovative Medicines Initiatives). One of the goals of the VITAL project is to develop strategies to educate and train health care professionals (HCPs) and to promote awareness among stakeholders involved in elderly care management. We briefly focus on the results of studies undertaken in four European countries (Italy, France, The Netherlands and Hungary), which reveal the perspective of older adults and elderly regarding influenza, pneumococcal, herpes zoster vaccination and respiratory syncytial virus (RSV) as well as generic characteristics of the vaccines and diseases. We will show how attitudes towards vaccination are represented in our study population and which determinants influence the decision-making process of accepting vaccination. Furthermore, we shall elaborate on how the decision-making process towards vaccination takes place and which additional information is needed. In the second part of the session, we shall invite the audience to reflect on the findings and identify the factors they consider most important for setting up a training and education programme on vaccination.

scholarly journals Penerapan Metode Profile Matching Sistem Pendukung Keputusan Demosi Karyawan (Studi Kasus: PT Nafasindo)

2020 ◽  
Vol 7 (2) ◽  
pp. 290
Author(s):  
Daniel F S P Sitohang ◽  
Berto Nadeak ◽  
Putri Ramadani
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Accurate Information ◽  
Profile Matching ◽  
System Use ◽  
Computerized System

One effort in the development of information technology today requires fast and accurate information in its implementation. in the assessment of the work ability of well-performing and poor employees with the support of a decision support system it produces one of the implementations of the development of information technology in improving the quality of the company's work. where the decision making process determines employee demotion is still done manually. still there are often a number of errors such as misdirection. Therefore, to make an assessment in making a decision to choose a decent demotion employee based on the assessment carried out in the field. Then the decision support system that will be built with a computerized system so that decision making is done quickly and accurately. For this decision support system, use the Profile Matching method or matching the demotion of employee demos with the profile of the employee assessed with the specified Criteria. Making an application program must be made carefully, so that it looks easy to understand and proven useful and useful for users. the system built can help PT. Nafasindo in determining the demotion of employees who are decent and can reduce errors in determining the demotion of employees

Creating Superior Knowledge Discovery Solutions

2011 ◽  
pp. 795-799 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Decision Support ◽  
Knowledge Discovery ◽  
High Performance ◽  
New Technologies ◽  
Relevant Information ◽  
Customer Relationship ◽  
Business Strategies ◽  
Competitive Edge

The information age has made information communication technology (ICT) a necessity for conducting business. This in turn has led to the exponential increase in the electronic capture of data and its storage in vast data warehouses. In order to respond quickly to fast changing markets, organizations must maximize these raw data and information resources. Specifically, they need to transform them into germane knowledge to aid superior decision-making (Wickramasinghe & von Lubitz, 2006). To do this effectively not only involves the analysis of the data and information but also requires the use of sophisticated tools to enable such analyses to occur. Knowledge discovery technologies represent a spectrum of new technologies that facilitate the analysis of data to find relationships from the data to finding reasons behind observable patterns (i.e., transform the data into relevant information and germane knowledge). Such new discoveries can have a profound impact on decision making in general and the designing of business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning, and predictive forecasting, these knowledge discovery technologies are becoming competitive necessities for providing a high performance and feature rich intelligent application servers for intelligent enterprises. Knowledge management (KM) tools and technologies are the systems that integrate various legacy systems, databases, ERP systems, and data warehouse to help facilitate an organization’s knowledge discovery process. Integrating all of these with advanced decision support and online real time events enables an organization to understand customers better and devise business strategies accordingly. Creating a competitive edge is the goal of all organizations employing knowledge discovery for decision support (Thorne & Smith, 2000). The following provides a synopsis of the major tools and critical considerations required to enable an organization to successfully effect appropriate knowledge sharing, knowledge distribution, knowledge creation, as well as knowledge capture and codification processes and hence embrace effective knowledge management (KM) techniques and advanced knowledge discovery.

Decision Support Software

2009 ◽  
pp. 440-447
Author(s):  
John Wang ◽  
Huanyu Ouyang ◽  
Chandana Chakraborty
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Mathematical Logic ◽  
Decision Support ◽  
Support Systems ◽  
Scientific Methods ◽  
Simulation And Optimization ◽  
Business Software ◽  
The Right ◽  
Support Software

Throughout the years many have argued about different definitions for DSS; however they have all agreed that in order to succeed in the decision-making process, companies or individuals need to choose the right software that best fits their requirements and demands. The beginning of business software extends back to the early 1950s. Since the early 1970s, the decision support technologies became the most popular and they evolved most rapidly (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002). With the existence of decision support systems came the creation of decision support software (DSS). Scientists and computer programmers applied analytical and scientific methods for the development of more sophisticated DSS. They used mathematical models and algorithms from such fields of study as artificial intelligence, mathematical simulation and optimization, and concepts of mathematical logic, and so forth.

scholarly journals Advanced CKD Care and Decision Making: Which Health Care Professionals Do Patients Rely on for CKD Treatment and Advice?

2020 ◽  
Vol 2 (5) ◽  
pp. 532-542.e1 ◽  
Author(s):  
Tyler M. Barrett ◽  
Jamie A. Green ◽  
Raquel C. Greer ◽  
Patti L. Ephraim ◽  
Sarah Peskoe ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Professionals
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