scholarly journals Understanding, Explaining, and Utilizing Medical Artificial Intelligence

2021 ◽  
Author(s):  
Romain Cadario ◽  
Chiara Longoni ◽  
Carey K Morewedge
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Decision Making ◽  
Healthcare Providers ◽  
Cost Effective ◽  
Decision Processes ◽  
Medical Decision ◽  
Medical Decisions ◽  
Important Barrier ◽  
Nationally Representative

Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre- registered experiments with convenience and nationally representative samples (N = 2,699), we find that people exhibit such an illusory understanding of human medical decision making (Study 1). This leads people to claim greater understanding of decisions made by human than algorithmic healthcare providers (Studies 2A-B), which makes people more reluctant to utilize algorithmic providers (Studies 3A-B). Fortunately, we find that asking people to explain the mechanisms underlying medical decision making reduces this illusory gap in subjective understanding (Study 1). Moreover, we test brief interventions that, by increasing subjective understanding of algorithmic decision processes, increase willingness to utilize algorithmic healthcare providers without undermining utilization of human providers (Studies 3A-B). Corroborating these results, a study on Google testing ads for an algorithmic skin cancer detection app shows that interventions that increase subjective understanding of algorithmic decision processes lead to a higher ad click-through rate (Study 4). Our findings show how reluctance to utilize medical algorithms is driven both by the difficulty of understanding algorithms, and an illusory understanding of human decision making.

scholarly journals Influence of response shift and disposition on patient-reported outcomes may lead to suboptimal medical decisions: a medical ethics perspective

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Iris D. Hartog ◽  
Dick L. Willems ◽  
Wilbert B. van den Hout ◽  
Michael Scherer-Rath ◽  
Tom H. Oreel ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Patient Reported Outcomes ◽  
Medical Decision Making ◽  
Response Shift ◽  
Cost Effective ◽  
Medical Decision ◽  
Medical Decisions ◽  
Patient Reports ◽  
Patient Reported

Abstract Background Patient-reported outcomes (PROs) are frequently used for medical decision making, at the levels of both individual patient care and healthcare policy. Evidence increasingly shows that PROs may be influenced by patients’ response shifts (changes in interpretation) and dispositions (stable characteristics). Main text We identify how response shifts and dispositions may influence medical decisions on both the levels of individual patient care and health policy. We provide examples of these influences and analyse the consequences from the perspectives of ethical principles and theories of just distribution. Conclusion If influences of response shift and disposition on PROs and consequently medical decision making are not considered, patients may not receive optimal treatment and health insurance packages may include treatments that are not the most effective or cost-effective. We call on healthcare practitioners, researchers, policy makers, health insurers, and other stakeholders to critically reflect on why and how such patient reports are used.

Support for management medical decisions and the nature of uncertainty in them

2021 ◽  
Author(s):  
S.Yu. Zhuleva ◽  
A.V. Kroshilin ◽  
S.V. Kroshilina
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Information ◽  
General Scheme ◽  
The Body ◽  
Medical Decision ◽  
Problem Situation ◽  
Medical Decisions ◽  
Inaccurate Information ◽  
Individual Decisions

The process of making a medical decision is characterized by a lack of knowledge and inconsistency of the available information, the lack of the possibility of attracting competent medical experts, limited time resources, incomplete or inaccurate information about the patient's condition. These aspects may be the causes of medical errors, which lead to further aggravation of the problem situation. Purpose – it is necessary to define and justify managerial medical decisions and types of medical information in conditions of uncertainty, when each variant of the sets of outcomes of the situation (recommendations) has its own unique set of values. The fundamental difference between this process for medical use is the concept of the "best medical solution", in which the key role is given to the patient's state of health in obtaining and evaluating alternatives, as well as the need to take into account the time, adverse reactions of the body and the costs of implementing this solution. In the medical field, support for medical decision-making can be classified as organizational-managerial and therapeutic-diagnostic, but both are determined by the position of the person making the medical decision and are aimed at effective management of the medical institution as a whole. The article describes the causes and factors of the nature of uncertainty in the tasks of supporting medical decision-making in medical-diagnostic and organizational-managerial areas. The analysis of the features of supporting medical decision-making in conditions of uncertainty is carried out. Approaches and directions in this area, as well as the concept of “solution”, are considered. The essence of the management medical decision is reflected. The classification of management medical decisions is given, the requirements that are imposed on them are highlighted. The features of the development of management medical solutions in the conditions of incompleteness and uncertainty, the problems that arise when they are implemented in information systems are presented. The general scheme of the process of creating a management medical solution is shown. The features of making group and individual decisions are reflected. The algorithm of actions of the person making the medical decision in the conditions of uncertainty, incompleteness and risk in medical subject areas is presented.

scholarly journals Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence

2020 ◽  
Vol 176 ◽  
pp. 1703-1712
Author(s):  
Georgy Lebedev ◽  
Eduard Fartushnyi ◽  
Igor Fartushnyi ◽  
Igor Shaderkin ◽  
Herman Klimenko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Evidence Based Medicine ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Evidence Based ◽  
Based Medicine

Limits of trust in medical AI

2020 ◽  
Vol 46 (7) ◽  
pp. 478-481 ◽  
Author(s):  
Joshua James Hatherley
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Clinical Practice ◽  
Medical Decision Making ◽  
Hospital Readmissions ◽  
New Drugs ◽  
Medical Decision ◽  
Practice Of Medicine

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

scholarly journals 41st Annual North American Meeting of the Society for Medical Decision Making; October 21, 2019; Portland, Oregon Keynote Address The More Who Die, the Less We Care: Confronting the Deadly Arithmetic of Compassion

2020 ◽  
Vol 5 (1) ◽  
pp. 238146832091431
Author(s):  
Paul Slovic
Keyword(s):  
Decision Making ◽  
North American ◽  
Medical Decision Making ◽  
Human Life ◽  
Cost Effective ◽  
Medical Decision ◽  
Keynote Address ◽  
Shared Decision ◽  
Daily Lives ◽  
The Face

In this keynote address delivered at the 41st Annual North American Meeting of the Society for Medical Decision Making, I discuss the psychology behind valuing human lives. Research confirms what we experience in our daily lives. We are inconsistent and sometimes incoherent in our valuation of human life. We value individual lives greatly, but these lives lose their value when they become part of a larger crisis. As a result, we do too little to protect human lives in the face of catastrophic threats from violence, natural disasters, and other causes. In medicine, this may pose difficult choices when treating individual patients with expensive therapies that keep hope alive but are not cost-effective for the population, for example, with end of life. Lifesaving judgments and decisions are highly context-dependent, subject to many forms of response mode and framing effects and affective biases. This has implications for risk communication and the concept of shared decision making. Slower, more introspective decision making may reduce some of the biases associated with fast, intuitive decisions. But slow thinking can also introduce serious biases. Understanding the strengths and weaknesses of fast and slow thinking is a necessary first step toward valuing lives humanely and improving decisions.

scholarly journals Value-impregnated factual claims may undermine medical decision-making

2018 ◽  
Vol 13 (3) ◽  
pp. 151-158 ◽  
Author(s):  
Niels Lynøe ◽  
Gert Helgesson ◽  
Niklas Juth
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Decision Making ◽  
Negative Impact ◽  
Personal Values ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Medical Decision ◽  
Clinical Decisions ◽  
Soft Laws

Clinical decisions are expected to be based on factual evidence and official values derived from healthcare law and soft laws such as regulations and guidelines. But sometimes personal values instead influence clinical decisions. One way in which personal values may influence medical decision-making is by their affecting factual claims or assumptions made by healthcare providers. Such influence, which we call ‘value-impregnation,’ may be concealed to all concerned stakeholders. We suggest as a hypothesis that healthcare providers’ decision making is sometimes affected by value-impregnated factual claims or assumptions. If such claims influence e.g. doctor–patient encounters, this will likely have a negative impact on the provision of correct information to patients and on patients’ influence on decision making regarding their own care. In this paper, we explore the idea that value-impregnated factual claims influence healthcare decisions through a series of medical examples. We suggest that more research is needed to further examine whether healthcare staff’s personal values influence clinical decision-making.

scholarly journals The Gray Zone: Adolescent and Young Adult Decision Support Needs for Ulcerative Colitis

2020 ◽  
Vol 5 (2) ◽  
pp. 238146832094070
Author(s):  
Andrea Meisman ◽  
Nancy M. Daraiseh ◽  
Phil Minar ◽  
Marlee Saxe ◽  
Ellen A. Lipstein
Keyword(s):  
Ulcerative Colitis ◽  
Decision Making ◽  
Decision Support ◽  
Focus Groups ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Support Needs ◽  
Final Decision ◽  
Care Providers ◽  
Medical Decisions

Purpose. To understand the medical decision support needs specific to adolescents and young adults (AYAs) with ulcerative colitis (UC) and inform development of a decision support tool addressing AYAs’ preferences. Methods. We conducted focus groups with AYAs with UC and mentors from a pediatric inflammatory bowel disease clinic’s peer mentoring program. Focus groups were led by a single trained facilitator using a semistructured guide aimed at eliciting AYAs’ roles in medical decision making and perceived decision support needs. All focus groups were audio recorded, transcribed, and coded by the research team. Data were analyzed using content analysis and the immersion crystallization method. Results. The facilitator led six focus groups: one group with peer mentors aged 18 to 24 years, three groups with patients aged 14 to 17 years, and two groups with patients aged 18 to 24 years. Decision timing and those involved in decision making were identified as interacting components of treatment decision making. Treatment decisions by AYAs were further based on timing, location (inpatient v. outpatient), and family preference for making decisions during or outside of clinic. AYAs involved parents and health care providers in medical decisions, with older participants describing themselves as “final decision makers.” Knowledge and experience were facilitators identified to participating in medical decision making. Conclusions. AYAs with UC experience changes to their roles in medical decisions over time. The support needs identified will inform the development of strategies, such as decision support tools, to help AYAs with chronic conditions develop and use skills needed for participating in medical decision making.

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.

‘Evidence-based’, ‘Cost-Effective’ and ‘Preference-Driven’ Medicine: Decision Analysis Based Medical Decision Making is the Pre-Requisite

1996 ◽  
Vol 1 (2) ◽  
pp. 104-113 ◽  
Author(s):  
Jack Dowie
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Evidence Based Medicine ◽  
Medical Decision Making ◽  
Clinical Effectiveness ◽  
Cost Effective ◽  
Medical Decision ◽  
Evidence Based ◽  
Meta Analyses ◽  
Based Medicine

Three broad movements are seeking to change the world of medicine. The proponents of ‘evidence-based medicine’ are mainly concerned with ensuring that strategies of proven clinical effectiveness are adopted. Health economists are mainly concerned to establish that ‘cost-effectiveness’ and not ‘clinical effectiveness’ is the criterion used in determining option selection. A variety of patient support and public interest groups, including many health economists, are mainly concerned with ensuring that patient and public preferences drive clinical and policy decisions. This paper argues that decision analysis based medical decision making (DABMDM) constitutes the pre-requisite for the widespread introduction of the main principles embodied in evidence-based medicine, cost-effective medicine and preference-driven medicine; that, in the light of current modes of practice, seeking to promote these principles without a prior or simultaneous move to DABMDM is equivalent to asking the cart to move without the horse; and that in fact DABMDM subsumes and enjoins the valuable aspects of all three. Particular attention is paid to differentiating between DABMDM and EBM, by way of analysis of various expositions of EBM and examination of two recent empirical studies. EBM, as so far expounded, reflects a problem-solving attitude that results in a heavy concentration on RCTs and meta-analyses, rather than a broad decision making focus that concentrates on meeting all the requirements of a good clinical decision. The latter include: Ensuring that inferences from RCTs and meta-analyses to individual patients (or patient groups) are made explicitly; paying equally serious attention to evidence on values and costs as to clinical evidence; and accepting the inadequacy of ‘taking into account and bearing in mind’ as a way of integrating the multiple and distinct elements of a decision.

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