Improving Diagnosis in Health Care

The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Improving Diagnosis in Health Care

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Error Reduction and Prevention in Surgical Pathology

The 1st edition of Error Reduction and Prevention in Surgical Pathology was an opportunity to pull together into one place all the ideas related to errors in surgical pathology and to organize a discipline in error reduction.

Error Reduction and Prevention in Surgical Pathology

The 1st edition of Error Reduction and Prevention in Surgical Pathology was an opportunity to pull together into one place all the ideas related to errors in surgical pathology and to organize a discipline in error reduction. This 2nd edition is an opportunity to refine this information, to reorganize the book to improve its usability and practicality, and to include topics that were not previously addressed. This book serves as a guide to pathologists to successfully avoid errors and deliver the best diagnosis possible with all relevant information needed to manage patients. The introductory section includes general principles and ideas that are necessary to understand the context of error reduction. In addition to general principles of error reduction and legal and regulatory responsibilities, a chapter on regulatory affairs and payment systems which increasingly may be impacted by error reduction and improvement activities was added. This later chapter is particularly important in view of the implementation of various value-based payment programs, such as the Medicare Merit-Based Incentive Payment System that became law in 2015. The remainder of the book is organized in a similar manor to the 1st edition with chapters devoted to all aspects of the test cycle, including pre-analytic, analytic and post-analytic. The 2nd Edition of Error Reduction and Prevention in Surgical Pathology serves as an essential guide to a successfully managed laboratory and contains all relevant information needed to manage specimens and deliver the best diagnosis.

Improving Diagnosis in Health Care

The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Improving Diagnosis in Health Care


Error in Diagnosis

Innocent lives are put in jeopardy in this terrifying new thriller that asks the question: Are we prepared for the next epidemic?

Error in Diagnosis

Innocent lives are put in jeopardy in this terrifying new thriller that asks the question: Are we prepared for the next epidemic? A mysterious illness with disturbing symptoms is plaguing women across the United States. It begins with memory loss and confusion and ends with the patient falling into a coma. Medical professionals are at a loss for the cause, but one thing remains constant: All of the victims are pregnant. Called in to consult on the case of his best friend’s wife, neurologist Jack Wyatt has never seen anything like it. Now, with the nation on the brink of panic, Jack and his colleagues are in race against time to find a cure. The disease they are calling Gestational Neuropathic Syndrome (GNS) is spreading. Patients are dying—and no one can guess what will happen next…

Design error diagnosis in logic circuits using diagnosis oriented test patterns

Abstract: "We present a new diagnostic algorithm, based on backward-propagation, for localising design errors in combinational logic circuits.

Design error diagnosis in logic circuits using diagnosis  oriented test patterns

Abstract: "We present a new diagnostic algorithm, based on backward-propagation, for localising design errors in combinational logic circuits. Diagnosis-oriented test patterns are generated in order to rapidly reduce the suspected area where the error lies. A theorem shows that, in favourable cases, only two such patterns suffice to get a correction. The method has been implemented, and performances are given on benchmarks."

Error and Uncertainty in Diagnostic Radiology

This book is essential for radiologists, members of the Society to Improve Diagnosis in Medicine, emergency physicians, medical educators, medical and hospice administrators, especially quality and safety officers, as well as malpractice ...

Error and Uncertainty in Diagnostic Radiology

Over the past decade, radiological imaging tests - including CT scanning, MRI, PET, X-rays, ultrasound, fluoroscopy and other modalities - have become essential to the routine diagnostic process. While these modern advanced medical images and their striking anatomic detail have discovered underlying issues, they have also contributed to a false impression of infallibility. Unlike other straightforward diagnostic tests, such as the EKG or blood chemistry panel, radiological imaging tests are highly variable and complex, often yielding uncertain results, as well as frequent false-negatives and false-positives. The experts who interpret the images (the diagnostic radiologists) sometimes make mistakes: the practice of diagnostic radiology is a fallible, human endeavour, one involving complex perceptual, neuro-physiological and cognitive processes employed under a wide range of circumstances, and with a great deal of variability. Error and Uncertainty in Diagnostic Radiology opens the 'black box,' of medical imaging, exposing the remarkable inner workings of the process of diagnostic radiology-including how and why it can sometimes go tragically wrong. The occurrence of radiological error is shown to be fundamentally intertwined with the underlying high level of uncertainty known to be present in the diagnostic process. As a foremost expert on radiology quality and safety, Dr. Bruno provides insight into the various types of radiologist error, along with a conceptual framework for understanding error and uncertainty in radiology, leading to practical strategies for error prevention and for reducing the risk of harm to patients when errors inevitably occur. This book is essential for radiologists, members of the Society to Improve Diagnosis in Medicine, emergency physicians, medical educators, medical and hospice administrators, especially quality and safety officers, as well as malpractice insurance carriers.

Logical Error Diagnosis

Logical Error Diagnosis


Fault Diagnosis Systems

This book gives an introduction into the field of fault detection, fault diagnosis and fault-tolerant systems with methods which have proven their performance in practical applications.

Fault Diagnosis Systems

With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.

Intention based Diagnosis of Novice Programming Errors

This book is intended for people interested in the application of artificial-intelligence techniques to computer-aided instruction, and to automatic program analysis, debugging, and synthesis.

Intention based Diagnosis of Novice Programming Errors

Accurate identification and explication of program bugs requires an understanding of the programmer's intentions. Otherwise it is not possible to determine exactly what part of the program is erroneous, and how best to correct it. Understanding the programmer's intentions is doubly necessary if the programmer is a novice, and the diagnostician is a teacher who is trying to find out why the student is having difficulties. Intention-based error diagnosis has been implemented in a program called PROUST, which identifies non-syntactic bugs in programs written by novice Pascal programmers. Empirical studies of PROUST's performance show that it achieves high performance in finding bugs in non-trivial student programs. This book is intended for people interested in the application of artificial-intelligence techniques to computer-aided instruction, and to automatic program analysis, debugging, and synthesis.

Toward an Integration of Item Response Theory and Cognitive Error Diagnosis

In this study, a new methodology that is capable of diagnosing cognitive errors and analyzing different methods for solving problems will be introduced, illustrated with fraction subtraction problems.

Toward an Integration of Item Response Theory and Cognitive Error Diagnosis

In this study, a new methodology that is capable of diagnosing cognitive errors and analyzing different methods for solving problems will be introduced, illustrated with fraction subtraction problems. The new approach called Rule space integrates Item Response Theory (IRT) and the algebraic theory of databases (Lee, 1983). The rule space model is general enough to apply to any domain of interest, where classifications and selections of students, diagnoses of misconceptions are done by Bayes' decision rules for minimum errors (Fukunagawa, 1972) or any other equivalent decision rules with respect to a set of systematic errors determined prior to analyses. The first section discusses important objectives which the construction of cognitive diagnostic tests must follow. Then the introduction of the rule space model starts with its brief concept, connection to a distribution theory, construction of a bug library and the rule space, and finally introduction of the operational classification scheme.

Diagnostic Error

Annotation Despite diagnosis being the key feature of a physician's clinical performance, this is the first book that deals specifically with the topic.

Diagnostic Error

Despite diagnosis being the key feature of a physician's clinical performance, this is the first book that deals specifically with the topic. In recent years, however, considerable interest has been shown in this area and significant developments have occurred in two main areas: a) an awareness and increasing understanding of the critical role of clinical decision making in the process of diagnosis, and of the multiple factors that impact it, and b) a similar appreciation of the role of the healthcare system in supporting clinicians in their efforts to make accurate diagnoses. Although medicine has seen major gains in knowledge and technology over the last few decades, there is a consensus that the diagnostic failure rate remains in the order of 10-15%. This book provides an overview of the major issues in this area, in particular focusing on where the diagnostic process fails, and where improvements might be made.

Data Driven Techniques for Type Error Diagnosis

Our results show that these are practical, lightweight techniques for improving the error messages produced by type checkers.

Data Driven Techniques for Type Error Diagnosis

Static type systems are a powerful tool for reasoning about the safety of programs. Global type inference eliminates one of the prime complaints against static types, that the annotation burden is too high. However, this introduces its own problems as the type checker must now make assumptions about what the programmer intended to do. A single incorrect assumption can lead the type checker to erroneously blame an expression far from the actual error the programmer made, which can be particularly confusing for newcomers who have not yet constructed a mental model for how the type checker works. In this dissertation we present a pair of complementary techniques to localize and explain type errors, with an emphasis on the errors encountered by novice users. We tackle the localization problem by using machine learning to learn a model of the errors made by students in an introductory course. Then, we use the model to produce a ranked list of likely error locations in new programs. Our models can be trained on a modest amount of data, e.g. a single instance of a course, and we envision a future where each introductory course is accompanied by a model of its students' errors. To better explain the error to novice users, we present a runtime error that the type system would have prevented. We interleave type-checking and execution to search for a set of program inputs that would lead execution to a bad state, and present the execution trace to the user in an interactive debugger. This allows the user to explore why their program was rejected, and connects the dynamic (runtime) semantics to the static (typing) semantics. We have evaluated our techniques empirically using a new dataset of ill-typed student programs collected from two instances of an undergraduate programming languages course at UC San Diego. We have also performed user studies with novice users, comparing the output of our techniques with the state of the art in type error diagnosis. Our results show that these are practical, lightweight techniques for improving the error messages produced by type checkers.

Fault Detection and Diagnosis in Industrial Systems

This book presents the theoretical background and practical techniques for data-driven process monitoring.

Fault Detection and Diagnosis in Industrial Systems

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Error and Uncertainty in Diagnostic Radiology

In their report, the NAM called national attention to the problem of diagnostic error, defined the sorts of events thought to constitute diagnostic error, and estimated the prevalence and potential impact of diagnostic error in the ...

Error and Uncertainty in Diagnostic Radiology

Over the past decade, radiological imaging tests - including CT scanning, MRI, PET, X-rays, ultrasound, fluoroscopy and other modalities - have become essential to the routine diagnostic process. While these modern advanced medical images and their striking anatomic detail have discovered underlying issues, they have also contributed to a false impression of infallibility. Unlike other straightforward diagnostic tests, such as the EKG or blood chemistry panel, radiological imaging tests are highly variable and complex, often yielding uncertain results, as well as frequent false-negatives and false-positives. The experts who interpret the images (the diagnostic radiologists) sometimes make mistakes: the practice of diagnostic radiology is a fallible, human endeavour, one involving complex perceptual, neuro-physiological and cognitive processes employed under a wide range of circumstances, and with a great deal of variability. Error and Uncertainty in Diagnostic Radiology opens the 'black box,' of medical imaging, exposing the remarkable inner workings of the process of diagnostic radiology-including how and why it can sometimes go tragically wrong. The occurrence of radiological error is shown to be fundamentally intertwined with the underlying high level of uncertainty known to be present in the diagnostic process. As a foremost expert on radiology quality and safety, Dr. Bruno provides insight into the various types of radiologist error, along with a conceptual framework for understanding error and uncertainty in radiology, leading to practical strategies for error prevention and for reducing the risk of harm to patients when errors inevitably occur. This book is essential for radiologists, members of the Society to Improve Diagnosis in Medicine, emergency physicians, medical educators, medical and hospice administrators, especially quality and safety officers, as well as malpractice insurance carriers.