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Can medical decisions be standardized? Should they be?

After a variety of regulatory and payment schemes have failed to stem the rising tide of health care cost, the Omnibus Budget Reconciliation Act of 1989 mandates the creation of a system of decision rules called practice parameters for appropriate medical action in many circumstances. A large body of practice guidelines already exists, but lacks the internal coherence required of a policy tool. Professional organizations therefore have developed attributes to achieve uniform style. However, little has been said or published about the constraints that might be imposed on the structure and content of an efficient and coherent system. The arguments presented here lead to the following conclusions: (a) process control is an inefficient tool to manage outcome--standards should reflect product control; (b) guidelines that proscribe are more likely to be supported by scientific data and consensus than those that prescribe; (c) the decision thresholds contained in such directives are policy choices rather than scientific imperatives; (d) neither decision analysis nor artificial intelligence is likely to readily influence medical decisions; and (e) as suggested by operations research, the development of practice parameters should concentrate on issues of therapeutic management in preference to issues of diagnostic activity.

Chemistry, Clinical↗

Confessions and expert testimony.

In this clinical paper, the author discusses criminal confessions from the point of view of the expert witness who may be asked to comment on the reliability of the statement and waiver of rights. From the time a suspect is in police custody, constitutional protections against self-incrimination and for due process are in place. The Supreme Court set the standard for these situations in the 1966 Miranda v. Arizona decision. Although it has long been criticized by law enforcement, the decision was upheld in the 2000 decision in Dickerson v. U.S. For a waiver of rights to be valid, it must be a knowing, intelligent, and voluntary decision. Voluntariness is an equation of objective and subjective variables. Treatment by police, physical conditions of interrogation, the suspect's experience and mental state can alter the reliability of a confession. Accordingly, the author has devised a mnemonic for the recognition of conditions that may give rise to expert testimony. The conditions are: Mental illness, Intoxication, Retardation, Acquiescence, Narcotic withdrawal, Deception, and Abuse. These are discussed, supported by examples from the author's practice.

Civil Rights↗

[Principles of computer-assisted classification in psychiatry].

The use of computer based diagnostic decision and artificial intelligence are discussed in relation to the problems of validity and reliability of psychiatric diagnoses. The authors suggest a definition of a computer aided classificator as an existent decision system for an automatically analysis of findings.

Artificial Intelligence↗

An expert system for nursing practice. Clinical decision support.

An artificial-intelligence-based nursing knowledge base can serve as an expert clinical decision support system in the areas of standards of care, care plans, continuing education, and others for patient conditions with both medical and nursing diagnoses.

Computers↗

Effect of cognitive impairment and premorbid intelligence on treatment preferences for life-sustaining medical therapy.

OBJECTIVE: This study examines the influence of cognitive impairment, premorbid intelligence, and decision-making capacity to complete advance directives on the treatment preferences for life-sustaining medical therapy in the elderly. METHOD: One hundred elderly individuals were recruited. Fifty were first referrals to specialist services with a DSM-IV diagnosis of dementia, and 50 were volunteers. Each person was asked about treatment preferences in three clinical vignettes. RESULTS: Elderly individuals who had cognitive impairment and were incapable of completing advance directives were significantly more likely to opt for life-sustaining interventions. There was no association between premorbid intelligence and treatment preferences. CONCLUSIONS: Cognitive impairment appears to influence treatment preferences for life-sustaining medical therapy. With increasing cognitive impairment, elderly individuals tend to opt for treatment interventions.

Advance Directives↗

Using decision tree induction to model oculomotor data.

Decision tree induction is a machine learning method used to generate classification models from data sets. Numerous decision trees were constructed to examine relationships between oculomotor test parameters and lesion sites in a data set containing cases with operated cerebello-pontine angle tumour, operated hemangioblastoma, infarction of cerebello-brainstem and Ménière's disease, and control subjects. The aim was to find useful parameter combinations with discriminatory power. Decision trees constructed using both pursuit eye movements and saccadic eye movements yielded the best classification results. This is reasonable: oculomotor test results vary according to the site of the lesion and so the performance ability of subjects has to be taken into account in the classification. The decision tree program was able to generate classification models from the oculomotor data set. Generated decision trees were intelligible and can be utilized in physicians' research work.

Decision Making↗

Intelligent purchasing in Trent: information for decision-making in the region's health authorities.

The realization of an evidence-based health service presents opportunities for libraries to make a direct contribution to the decision-making process in the delivery of health care. An example of such an opportunity is given through a description of the Trent Working Group on Acute Purchasing, a network of health authority purchasers that takes a research-based approach to making purchasing decisions. The Working Group is supported by a multidisciplinary research team, which includes an Information Officer. The role of the Information Officer in supporting the process of the Working Group is described below; namely in the setting of research priorities, in contributing to research projects and in disseminating the outputs of the Working Group. The benefits of being involved in such a project, both to the information service and to the Information Officer, are summarized.

Decision Making, Organizational↗

Decision support system for medical triage.

The paper explains the application of an artificial intelligence tool for the purpose of medical decision-making. The first product of this application is a triage engine, available on the Internet, to help laypersons make a decision about the urgency of their situation by providing tailored and accurate information. The expansion of this tool can lead to diagnostic tools for professionals or for educational purposes.

Artificial Intelligence↗

Decision support for tendon tissue engineering.

The aim of this study is to provide decision support with artificial intelligence for tendon tissue engineering strategies. The experimental data of tissue-engineered tendons were integrated and standardized with a centralized database, and a decision support system was developed using both artificial neural networks and decision trees. The decision support system was trained with existing cases in the database, and then was used to generate tissue engineering schemes for new experimental animals. Following the schemes generated by the artificial intelligent system, we cured 28 of the 30 experimental animals. In conclusion, artificial intelligence is a powerful method for decision support in the tendon tissue engineering realm.

Animals↗

Challenges facing the distribution of an artificial-intelligence-based system for nursing.

The marketing and successful distribution of artificial-intelligence-based decision-support systems for nursing face special barriers and challenges. Issues that must be confronted arise particularly from the present culture of the nursing profession as well as the typical organizational structures in which nurses predominantly work. Generalizations in the literature based on the limited experience of physician-oriented artificial intelligence applications (predominantly in diagnosis and pharmacologic treatment) must be modified for applicability to other health professions.

Attitude of Health Personnel↗

VALAB: expert system for validation of biochemical data.

In large laboratories that use "high-throughput" equipment, it is now possible to use artificial intelligence techniques to aid decision making and validation of data. This paper describes an artificial intelligence project, VALAB, that has been carried out in our laboratory. VALAB, an expert system that permits real-time validation of data, is designed to be equivalent to validation by the laboratory director. The decision produced by the expert system is based on several factors, including correlation between repeated laboratory results, physiological association between different variables, the hospital department from which the test was ordered, and the patient's age and sex. In 200 abnormal chemistry profiles randomly selected, VALAB's ability to detect abnormal cases (i.e., sensitivity = 0.75) was exceeded by only one of seven laboratory experts. However, all seven experts outperformed VALAB's measured specificity of 0.63. The VALAB system incorporates greater than 4000 rules. Operational since November 1988, it has validated greater than 50,000 medical patients' reports in real time.

Artificial Intelligence↗

ADEMA: a decision support system for asthma health care.

Asthma is a distressing disease, affecting up to 7% of the French population and causing considerable morbidity and mortality. A medical decision support system such can help physicians to control this chronic disease. Thanks to the health care network (RESALIS) of Fedialis Médica (disease management branch from GlaxoSmithKline), asthma consultation data were collected to exploit them. We chose Case-Based Reasoning paradigm to develop our medical decision support system. Intelligent data analysis methods have been used to determine the case model for our system. Our similarity metric is based on the MVDM method. We developed two methods to reuse retrieved cases. We present our data analysis results and similarity metric from which we designed our Case Based System for asthmatic patients health care: ADEMA. To conclude, an evaluation of ADEMA is presented.

Algorithms↗

Intelligent dialogue based on statistical models of clinical decision-making.

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.

Abdomen↗

Feature extraction for systolic heart murmur classification.

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an "intelligent stethoscope" with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil's sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Aged↗

Effects of information on patient stereotyping.

The influence of information on nurse attitudes and behavior toward a member of a stereotyped group was investigated. Responsibility for the decision to be childless and the intelligence of the person making that decision were manipulated. Student nurses individually interacted with a hypothetical, childless, female patient, presented via audiotape, who was about to undergo surgery for sterilization. The patient was described (a) as seeking sterilization either voluntarily or because of medical necessity, and (b) as either retarded or not classified intellectually. It was hypothesized that the patient who freely chose childlessness would be viewed and treated less positively when thought to be of normal intelligence. When the patient was mildly retarded or seeking sterilization out of medical necessity, however, more positive attitudes and behavior were expected. Both of these hypotheses were supported. The results of this study support the importance of contextual variables on perceptions of patients.

Adult↗

Initial clinical experience with a partly autonomous robotic surgical instrument server.

BACKGROUND: The authors believe it would be useful to have surgical robots capable of some degree of autonomous action in cooperation with the human members of a surgical team. They believe that a starting point for such development would be a system for delivering and retrieving instruments during a surgical procedure. METHODS: The described robot delivers instruments to the surgeon and retrieves the instruments when they are no longer being used. Voice recognition software takes in requests from the surgeon. A mechanical arm with a gripper is used to handle the instruments. Machine-vision cameras locate the instruments after the surgeon puts them down. Artificial intelligence software makes decisions about the best response to the surgeon's requests. RESULTS: A robot was successfully used in surgery for the first time June 16, 2005. The operation involved excision of a benign lipoma. The procedure lasted 31 min, during which time the robot performed 16 instrument deliveries and 13 instrument returns with no significant errors. The average time between verbal request and delivery of an instrument was 12.4 s. CONCLUSIONS: The described robot is capable of delivering instruments to a surgeon at command and can retrieve them independently using machine vision. This robot, termed a "surgical instrument server," represents a new class of information-processing machines that will relieve the operating room team of repetitive tasks and allow the members to focus more attention on the patient.

Adult↗

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗