Medical Informatics and Clinical Decision Making: The Science and the Pragmatics

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S2-S14 ◽  
Author(s):  
Edward H. Shortliffe
Keyword(s):  
Decision Making ◽  
Medical Informatics ◽  
Ad Hoc ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Makers ◽  
Medical Decision ◽  
Normative Models ◽  
Outcomes Management ◽  
Collaborative Efforts

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

scholarly journals Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

2018 ◽  
Vol 38 (5) ◽  
pp. 593-600
Author(s):  
Marco Boeri ◽  
Alan J. McMichael ◽  
Joseph P. M. Kane ◽  
Francis A. O’Neill ◽  
Frank Kee
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Discrete Choice Experiments ◽  
Medical Decision ◽  
Random Parameters ◽  
Acceptable Risk ◽  
Syndrome Scale ◽  
Unit Increase ◽  
Negative Syndrome

Background. In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR. Methods. Psychiatrists were given information about a group of patients’ responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable. Results. Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms – measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists. Limitations. We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients. Conclusions. This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

Clinical Decision Making by Emergency Room Physicians and Residents

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 Making ◽  
Clinical Decision Support Systems ◽  
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).

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.

Bias: a normal operating characteristic of the diagnosing brain

Diagnosis ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Pat Croskerry
Keyword(s):  
Decision Making ◽  
Process Model ◽  
Clinical Decision Making ◽  
Conventional Medicine ◽  
Clinical Decision ◽  
Blind Spot ◽  
Research Area ◽  
Dual Process ◽  
Medical Decision ◽  
The Impact

AbstractPeople diagnose themselves or receive advice about their illnesses from a variety of sources ranging from their family or friends, alternate medicine, or through conventional medicine. In all cases, the diagnosing mechanism is the human brain which normally operates under the influence of a variety of biases. Most, but not all biases, reside in intuitive decision making, and no individual or group is immune from them. Two biases in particular, bias blind spot and myside bias, have presented obstacles to accepting the impact of bias on medical decision making. Nevertheless, there is now a widespread appreciation of the important role of bias in the majority of medical disciplines. The dual process model of decision making now seems well accepted, although a polarization of opinions has arisen with some arguing the merits of intuitive approaches over analytical ones and vice versa. We should instead accept that it is not one mode or the other that enables well-calibrated thinking but the discriminating use of both. A pivotal role for analytical thinking lies in its ability to allow decision makers the means to detach from the intuitive mode to mitigate bias; it is the gatekeeper for the final diagnostic decision. Exploring and cultivating such debiasing initiatives should be seen as the next major research area in clinical decision making. Awareness of bias and strategies for debiasing are important aspects of the critical thinker’s armamentarium. Promoting critical thinking in undergraduate, postgraduate and continuing medical education will lead to better calibrated diagnosticians.

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

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 ◽  
Clinical Decision Making ◽  
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.

Supporting Knowledge-Based Decision Making in the Medical Context

2011 ◽  
Vol 1 (1) ◽  
pp. 42-60 ◽  
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Theory Analysis ◽  
Knowledge Based ◽  
Therapeutic Decisions ◽  
Computer Based ◽  
Based Medicine ◽  
Domain Independent

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

scholarly journals TSH Measurement and Its Implications for Personalised Clinical Decision-Making

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Rudolf Hoermann ◽  
John E. M. Midgley
Keyword(s):  
Decision Making ◽  
Thyroid Function ◽  
Clinical Decision Making ◽  
Reference Range ◽  
Clinical Decision ◽  
Population Based ◽  
Medical Decision ◽  
Basic Question ◽  
Screening Parameter ◽  
Log Linear

Advances in assay technology have promoted thyrotropin (TSH) measurements from participation in a multi-analyte assessment of thyroid function to a statistically defined screening parameter in its own right. While this approach has been successful in many ways, it has some grave limitations. This includes the basic question of what constitutes an agreed reference range and the fact that the population-based reference range by far exceeds the variation of the intraindividual set point. Both problems result in a potential misdiagnosis of normal and pathological thyroid function in a substantial proportion of patients. From a physiological perspective, TSH plays an integrated role in thyroid homeostasis. Few attempts have been made to adopt physiological insights into thyroid homeostasis for medical decision-making. Some emerging novel findings question the widely assumed log-linear TSH-FT4relationship over the entire thyroid function spectrum. This data favours more complex hierarchically structured models. With a better understanding of its role in thyroid homeostasis in thyroid health and disease, TSH can be revisited in the context of thyroid regulation. This, in turn, could help overcome some of the limitations arising from its isolated statistical use and offer new prospects towards a more personalised interpretation of thyroid test results.

DISCUSSION AGENDA FOR THE SESSION ON MEDICAL DECISION MAKING and MINUTES OF A GROUP DISCUSSION ON CLINICAL DECISION MAKING

1988 ◽  
pp. 599-612
Author(s):  
Milton C. Weinstein ◽  
Harvey V. Fineberg ◽  
Barbara J. McNeil ◽  
Stephen G. Pauker ◽  
Robert J. Quinn
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Decision Making ◽  
Group Discussion ◽  
Clinical Decision ◽  
Medical Decision

scholarly journals Uncertainty Quantification in Radiogenomics: EGFR Amplification in Glioblastoma

2020 ◽  
Author(s):  
Leland S. Hu ◽  
Lujia Wang ◽  
Andrea Hawkins-Daarud ◽  
Jennifer M. Eschbacher ◽  
Kyle W. Singleton ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Probability Distributions ◽  
Model Performance ◽  
Clinical Decision ◽  
Medical Decision ◽  
Transductive Learning ◽  
Egfr Amplification ◽  
Gp Model

ABSTRACTBACKGROUNDRadiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions.METHODSWe developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor - a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation.RESULTSPredictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n=95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n=72) compared to predictions with higher uncertainty (48% accuracy, n=23), due largely to data interpolation (rather than extrapolation).CONCLUSIONWe present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Interactive Video Assists in Clinical Decision Making

1998 ◽  
Vol 37 (02) ◽  
pp. 201-205 ◽  
Author(s):  
B. E. Waitzfelder ◽  
E. P. Gramlich
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Interactive Video ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Treatment Decision ◽  
Clinical Decision ◽  
Medical Decision ◽  
Treatment Decision Making ◽  
Interactive Videodisk

AbstractThe Hawaii Quality and Cost Consortium began a project in 1993 to implement and evaluate interactive videodisk programs to assist in clinical decision-making for breast cancer. Communication problems between physicians and patients, limitations of available outcomes data and varying preferences of individual patients can result in treatment outcomes that are less than satisfactory. Shared Decision-making Programs (SDPs) were developed by the Foundation for Informed Medical Decision Making (FIMDM) in Hanover, New Hampshire, to assist in the treatment decision-making process. Utilizing interactive videodisks, the programs provide patients with clear, unbiased information about available treatment options. With this information, patients can become more active participants in making treatment decisions. The SDPs for breast cancer were implemented at two sites in Hawaii. Evaluation data from 103 patients overwhelmingly indicate that patients find the programs clear, balanced and very good or excellent in terms of the amount of information presented and overall rating.

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