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PubMed1988

[Receiver operating curve, an aid in decision making. Principles and applications illustrated with some examples].

The practitioner has many difficult questions to resolve when he has placed in front of him more and more diagnostic and screening tests. Which is, in a given situation, the best test, by which is meant useful, least costly and most acceptable by the patient? Having chosen the test, at what level is the result to be considered positive or negative? The authors, having explained the principles and having taken some examples out of the literature of gynaecology and obstetrics, present a technique that shows on a receiver operating characteristic curve sensitivity and specificity sharing as a function of the possible levels for making decisions according to the test. The curve inordinate shows the sensitivity of the test or the number of true positives and on the abscissa the complement of specificity or the rate of false positives ford each decision threshold of the test. A simple glance gives the practitioner a view of the characteristics of the test over the whole band that this test can use. Using these curves it is possible to adopt more elaborate techniques to select the best threshold and to compare different tests.

Decision Making↗
PubMed1997

Making decisions about end-stage renal disease treatment: a review of theories and issues.

Multiple theories and models of decision-making have been reported in the popular and scientific literature. Decisions and choices about health care are some of the most dramatic and difficult that humans make. The interactive role of patient and health care professional in circumstances of urgency, stress, and possible unpredictability is complex. This article reviews some of the theories of decision-making and their implementation or usefulness in the end-stage renal disease setting. In caring for patients, health professionals need to identify their approach to the decision-making process with special concern for the patient role in this process.

Decision Making↗
PubMedDate not supplied

Small-group decision making can be effective management tool.

One of the key components of effective management is the ability to make decisions. To more effectively assist hospital supervisors and managers in dealing with and making appropriate judgments, educators need to examine carefully the processes involved in decision making and how they impact on both the individual and the group. The following discussion of structured small-group decision making is adapted from an independent study of the literature undertaken by this author to dissect these processes and how they impact on the decisions made.

Decision Making↗
PubMed1994

Clinical decision making: describing the decision rules of practicing speech-language pathologists.

The diagnostic decision-making standards used by practicing clinicians to determine language impairment were investigated. Randomly selected ASHA members who worked with children were asked to review hypothetical and real case profiles of children ages 4 to 9:11 (years: months) with language performance skills ranging from slightly above average to substantially below average. Based on the child's age and language and intelligence test information, clinicians were asked to decide if the child was language impaired (LI) and, if so, to provide a severity rating. Results show significant interrater agreement among the 27 clinicians' LI decisions (generalized kappa = 0.14, p < .0001) and moderate intrarater reliability within clinician's LI decisions (phi = .68). Most of the clinicians' diagnostic decision-making standards could be modeled using stepwise logistic regression. These decision rules can provide guidance for those who wish to employ diagnostic standards that reflect those used in clinical practice. Also, these results provide insight into the manner in which clinicians use information for the determination of language impairment.

Adult↗
PubMed1997

The limitations of decision trees and automatic learning in real world medical decision making.

The decision tree approach is one of the most common approaches in automatic learning and decision making. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. But in real life it is often impossible to find the desired number of representative training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, and unavailability of all attributes at the same time are the typical representatives. For this reason we decided to use the decision trees not for their primary task--the decision making--but for outlining the most important attributes. This was possible by using a well-known property of the decision trees--their knowledge representation, which can be easily understood by humans. In a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions and the "tips," provided by the decision trees, can be of a great assistance. Our main interest was to discover a predisposition to two forms of acidosis: the metabolic acidosis and respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects. Instead of using a test set for evaluation of a decision tree, we asked medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments show that trees generated from the available training set mainly have surprisingly good branches, but on the other hand, for some, no medical explanation could be found.

Acidosis↗
PubMed2002

Thinking processes used by nurses in clinical decision making.

Clinical decision making forms the basis of expert clinical practice. The purpose of this study was to investigate and document the thinking processes used by nurses in clinical decision making situations so the processes could guide educational practice. Clinical data was analyzed to reveal that clinical decision making is complex and requires a variety of thinking processes. Medical and surgical nurses used different thinking processes, showing the importance of context in clinical decision making. The nursing exemplars and working vocabulary developed in this study to describe the thinking processes used in clinical decision making can be used in nursing education.

Decision Making↗
PubMed2002

Error rate and outcome predictability affect neural activation in prefrontal cortex and anterior cingulate during decision-making.

Decision-making in the presence of uncertainty is a complex process that involves both affective and cognitive factors. Both error rate and predictability have been implicated in the process of response selection during decision-making. This study examined the hypothesis that the rate of errors during decision-making differentially affects the activation in prefrontal and cingulate cortex. BOLD echo-planar signal intensity was obtained during a two-choice prediction task across 90-s blocks with 20, 50, or 80% error rates. This study yielded three main findings. First, at chance level error rates, activation of the right dorsolateral (BA 9, 46), inferior prefrontal (BA 44), and precuneus (BA 7) during the two-choice prediction task replicated the finding previously reported. Second, premotor (BA 6) and parahippocampal (BA 36) areas were relatively more active at high error rates, and dorsolateral (BA 9, 46) and inferior prefrontal cortex (BA 44) as well as parietal (BA 40) and cingulate cortex (BA 25, 32) were more active during low error rates. Third, the relationship between the frequency of the dominant strategy underlying decision-making (win-stay/lose-shift) and the activation in the dorsolateral prefrontal cortex and the anterior cingulate was dependent on error rate or outcome predictability. These results support the hypothesis that error rates and predictability affect the activation patterns in the neural systems underlying decision-making because these structures maintain a representation of the reinforcement history for the available response alternatives to select an "optimal strategy."

Adult↗
PubMed2006

Adults with autism show increased sensitivity to outcomes at low error rates during decision-making.

Decision-making is an important function that can be quantified using a two-choice prediction task. Individuals with Autistic Disorder (AD) often show highly restricted and repetitive behavior that may interfere with adaptive decision-making. We assessed whether AD adults showed repetitive behavior on the choice task that was unaffected by changing task demands, by examining the influence of experimenter-determined error rates on decision-making. Sixteen AD adults and 14 typically developed subjects were administered a two-choice task using three error rate conditions. Although AD subjects showed occurrences of stereotyped responding, their decision-making behavior was strongly affected by changes in task demands, especially when they experienced frequent success. Thus, behavioral paradigms that provide frequent reinforcement may be helpful in modifying decision-making abilities in AD.

Adult↗
PubMed2005

Advance directives and cancer decision making near the end of life.

Seriously ill individuals, including those seriously ill with cancer, are frequently encouraged to complete instructional advance directives (i.e., living wills) to ensure that their wishes about the use of life-sustaining treatment are honored if they should lose the ability to make decisions for themselves. The authors present a social psychological analysis making explicit a series of steps that must necessarily take place if living wills are to honor the wishes of incapacitated patients. They then focus on 3 key steps in the analysis and review relevant research from the medical and psychological literatures. In each case, this research raises serious questions about the psychological assumptions underlying the effective use of living wills in end-of-life decision making. Discussion focuses on the need for policy and law guiding the use of advance directives to be informed by both basic and applied research on judgment and decision making.

Advance Directives↗
PubMed2005

Model of evidence-based dental decision making.

Shared decision making empowers patients as informed consumers, helping them to make clinical decisions that optimize their personal oral health. Evidence-based dentistry supports this process by providing best evidence that, when presented in visual and interactive formats, focuses consultation time on treatment or therapy options and their trade-offs. Currently, an explosion of evidence and technological advancements have necessitated a cooperation that translates into an interdisciplinary approach to care delivery. For evidence-based dentistry, this interdisciplinary approach includes the interaction of researchers, clinicians, and patients in promoting individual patient care. To facilitate this interaction, a computerized model of evidence-based dental decision making is presented to manage knowledge in its application to clinical practice. This model includes the use of decision aids and a decision tree composed of multiple clinical practice guidelines. These aids allow for quantifying treatment options in terms of estimates using probability, utility, and cost data. With these estimates, decision analysis and the flexibility to manipulate data provide patients with increased control and acceptance of the decisions that they make about their personal oral health.

Editorial↗
PubMed1994

Values clarification and ethical decision making.

Ethical decision making at the bedside has become an integral part of nursing care delivery. Health care providers have long been practicing clinical ethics as evidenced by their codes of practice. Still, bedside ethical decision making has really just become more formalized in the 1980s through the establishment of hospital ethics committees. The decisions of the 1980s and 1990s have become more difficult as well mostly because of advanced medical technology, the increased number of people now involved in the decision-making process, and most importantly, the recognition of patient autonomy in the decision-making process.

Advance Directives↗
PubMed1998

The limitations of decision trees and automatic learning in real world medical decision making.

The decision tree approach is one of the most common approaches in automatic learning and decision making. It is popular for its simplicity in constructing, efficient use in decision making and for simple representation, which is easily understood by humans. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. The training sets are usually large enough for learning algorithm to construct a hypothesis consistent with the underlying concept. But in real life it is often impossible to find the desired number of training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, unavailability of all attributes at the same time are the typical representatives. There are different ways to deal with some of these problems, but in a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions. We have measured the values of 24 attributes before and after the 82 operations of children in age between 2 and 10 years. The aim was to find the dependencies between attribute values and a child's predisposition to acidemia--the decrease of blood's pH. Our main interest was in discovering predisposition to two forms of acidosis, the metabolic acidosis and the respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects, which was complete (there were no missing attribute values), but on the other hand not large enough to avoid the effect of overfitting. A common approach to evaluation of a decision tree is the use of a test set. In our case we decided that instead of using a test set, we ask medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments on the generated trees can be found in this paper. The comments show, that trees generated from available training set mainly have surprisingly good branches, but on the other hand some are very "stupid" and no medical explanation could be found. Thereafter we can conclude, that the decision tree concept and automatic learning can be successfully used in real world situations, constrained with the real world limitations, but they should be used only with the guidelines of appropriate medical experts.

Acidosis↗
PubMed2002

Schizophrenia subjects show intact success-related neural activation but impaired uncertainty processing during decision-making.

Decision-making is a complex process that is important for everyday life. This study examined the effect of the degree of success, and outcome uncertainty, on decision-making and associated neural substrate activation in schizophrenia subjects (SZS) and normal comparison subjects (NCS). A total of 15 subjects with a diagnosis of schizophrenia and 15 age- and education-matched NCS participated in this study. These subjects completed the two-choice prediction task during functional magnetic resonance imaging. Decision-making characteristics and activation of neural substrates were obtained at 20, 50, or 80% error rate. Success and uncertainty influenced the behavioral characteristics on the two-choice prediction task, and the task-related activation in SZS and NCS. Neither success nor uncertainty differentially affected the behavioral characteristics of SZS relative to NCS during the two-choice prediction task. Nonetheless, there was a significant interaction between group and error rate in bilateral parietal cortex. The activation in NCS was the highest when the outcome was most uncertain. In contrast, task-related activation in SZS was not modulated by the degree of uncertainty. Thus, SZS failed to utilize the parietal cortex to process decision-making situations with highly uncertain outcomes.

Adult↗
PubMed2001

Rationality in medical decision making: a review of the literature on doctors' decision-making biases.

The objectives of this study were to describe ways in which doctors make suboptimal diagnostic and treatment decisions, and to discuss possible means of alleviating those biases, using a review of past studies from the psychological and medical decision-making literatures. A number of biases can affect the ways in which doctors gather and use evidence in making diagnoses. Biases also exist in how doctors make treatment decisions once a definitive diagnosis has been made. These biases are not peculiar to the medical domain but, rather, are manifestations of suboptimal reasoning to which people are susceptible in general. None the less, they can have potentially grave consequences in medical settings, such as erroneous diagnosis or patient mismanagement. No surefire methods exist for eliminating biases in medical decision making, but there is some evidence that the adoption of an evidence-based medicine approach or the incorporation of formal decision analytic tools can improve the quality of doctors' reasoning. Doctors' reasoning is vulnerable to a number of biases that can lead to errors in diagnosis and treatment, but there are positive signs that means for alleviating some of these biases are available.

Attitude of Health Personnel↗
PubMed2006

The politics of end-of-life decision-making: computerised decision-support tools, physicians' jurisdiction and morality.

With the increasing corporate and governmental rationalisation of medical care, the mandate of efficiency has caused many to fear that concern for the individual patient will be replaced with impersonal, rule-governed allocation of medical resources. Largely ignored is the role of moral principles in medical decision-making. This analysis comes from an ethnographic study conducted from 1999-2001 in three US Intensive Care Units, two of which were using the computerised decision-support tool, APACHE III (Acute Physiological and Chronic Health Evaluation III), which notably predicts the probability that a patient will die. It was found that the use of APACHE presents a paradox regarding concern for the individual patient. To maintain jurisdiction over the care of patients, physicians share the data with the payers and regulators of care to prove they are using resources effectively and efficiently, yet they use the system in conjunction with moral principles to justify treating each patient as unique. Thus, concern for the individual patient is not lessened with the use of this system. However, physicians do not share the data with patients or surrogate decision-makers because they fear they will be viewed as more interested in profits than patients.

APACHE↗