Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.

/pdf/to-err-is-human-building-a-safer-health-system-1nfl34wxl6.pdf

To Err Is Human Building a Safer Health System

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directio...

Toward a Theory of Situation Awareness in Dynamic Systems

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

For Resilience Engineering, 'failure' is the result of the adaptations necessary to cope with the complexity of the real world, rather than a breakdown or malfunction. The performance of individuals and organizations must continually adjust to current conditions and, because resources and time are finite, such adjustments are always approximate. This definitive new book explores this groundbreaking new development in safety and risk management, where 'success' is based on the ability of organizations, groups and individuals to anticipate the changing shape of risk before failures and harm occur. Featuring contributions from many of the worlds leading figures in the fields of human factors and safety, Resilience Engineering: Concepts and Precepts provides thought-provoking insights into system safety as an aggregate of its various components, subsystems, software, organizations, human behaviours, and the way in which they interact. The book provides an introduction to Resilience Engineering of systems, covering both the theoretical and practical aspects. It is written for those responsible for system safety on managerial or operational levels alike, including safety managers and engineers (line and maintenance), security experts, risk and safety consultants, human factors professionals and accident investigators.

Resilience Engineering: Concepts and Precepts

PREFACE THE DRIVING FORCES Introduction On Terminology Computerization and Growing Complexity Self-Reinforcing Complexity Cycle Complexity and Unpredictability Conspicuousness of the Human Factor The Constraining Paradigm Input-Output Models The Shannon-Weaver Model Communication Model Prototypical Information Processing From Human-Machine Interaction to Joint Systems The Cognitive Viewpoint The Classical Human-Machine View The Disintegrated View Changing the Paradigm Definition of a Cognitive System The Scope of CSE THE EVOLUTION OF WORK Technological Systems as Amplification 'Examples of Amplification Amplification of Control Effects of Amplification Amplification and Interpretation Tools and Prostheses A Short History of Human-Machine Interaction The Conspicuousness of Cognition Changing Balance Between Doing and Thinking Loss of Work-Specific Information The Law of Requisite Variety Models of the Human as Controller The Joint Cognitive System (JCS) Control and Cognition Disjoint and Joint Systems Amplifying the Ability to Control THE BASICS OF A SCIENCE Model-Classification-Method Requirements to Measurements Theory-Driven Measurements Theory-Begging Measurements The Meaning of Measurements The Elusiveness of Cognition Cognition in the Mind Cognition in the Wild The Focus of CSE Cognition and Context Cognition and Control Modeling Cognition and Context Sequentiality in Reality and in Models The Threads of CSE Coping With Complexity Use of Artifacts Joint Cognitive Systems Merging the Threads COPING WITH COMPLEXITY Introduction About Coping Sources of Complexity Losing Control Lack of Time Lack of Knowledge Lack of Readiness or Preparedness (Competence) Lack of Resources Coping Strategies Information Input Overload Information Input Underload Designing for Simplicity Simplicity-Complexity Trade-Off Information Structuring How Should the Interaction Be Designed? Designing for Complexity Support for Coping Time Predictability Summary USE OF ARTIFACTS Introduction Phenomenology of Coagency Tools and Prostheses Artifacts in CSE Range of Artifacts Cognitive Artifacts The Substitution Myth Consequences of Technology Change Traffic Safety Typical User Responses to New Artifacts Failure Modes of an Artefact The Accidental User User Models and Accidental Users JOINT COGNITIVE SYSTEMS Introduction On the Nature of System Boundaries Automation and Joint Cognitive Systems Degrees of Human-Machine Dependency Automation Philosophies Ironies of Automation Lesons of Automation Function Analysis and Goal Achievement Goals and Means Simple Test-Operate-Test-Exit (TOTE) Recursive Goals-Means Descriptions CONTROL AND COGNITION Introduction Feedback and Control Feedforward and Control The Substance of Modeling Cognition Cognition Without Context Cognition in Context Mental Models and the Law of Requisite Variety COCOM-Contextual Control Model Model Constituents Control Modes ECOM-Extended Control Model Tracking Regulating Monitoring Targeting ECOM Structure and Parameters Interaction Between Loops Modeling the Loss of Control TIME AND CONTROL Orthodoxy in Modeling Model Minimization Two Neglected Issues Modeling Control Predictability Available Time The Modeling of Time Representation of Time in COCOM Time and Control Modes How to Enhance Control Technological and Organizational Solutions Human Solutions Conclusions CSE AND ITS APPLICATIONS What Should CSE Be About? Extensions to Human Factors/Ergonomics Design of Work (Cognitive Task Design) Making Work Easy Making Work Safe Problems in the Design of Work Control Rooms Adaptation Decision Support The Last Words BIBLIOGRAPHY AUTHOR INDEX SUBJECT INDEX

Joint Cognitive Systems: Foundations of Cognitive Systems Engineering

New technology is flexible in the sense that it provides practitioners with a large number of functions and options for carrying out a given task under different circumstances. However, this flexib...

How in the world did we ever get into that mode? Mode error and awareness in supervisory control

This paper presents an approach to the description and analysis of complex Man-Machine Systems (MMSs) called Cognitive Systems Engineering (CSE). In contrast to traditional approaches to the study of man-machine systems which mainly operate on the physical and physiological level, CSE operates on the level of cognitive functions. Instead of viewing an MMS as decomposable by mechanistic principles, CSE introduces the concept of a cognitive system: an adaptive system which functions using knowledge about itself and the environment in the planning and modification of actions. Operators are generally acknowledged to use a model of the system (machine) with which they work. Similarly, the machine has an image of the operator. The designer of an MMS must recognize this, and strive to obtain a match between the machine's image and the user characteristics on a cognitive level, rather than just on the level of physical functions. This article gives a presentation of what cognitive systems are, and of how CSE can contribute to the design of an MMS, from cognitive task analysis to final evaluation.

Cognitive systems engineering: new wine in new bottles

The patient safety movement includes a wide variety of approaches and views about how to characterise patient safety, study failure and success, and improve safety. Ultimately all these approaches make reference to the nature of technical work of practitioners at the “sharp end” in the complex, rapidly changing, intrinsically hazardous world of health care. 1 2 It is clear that a major activity of technical workers (physicians, nurses, technicians, pharmacists, and others) is coping with complexity and, in particular, coping with the gaps that complexity spawns.3 Exploration of gaps and the way practitioners anticipate, detect, and bridge them is a fruitful means of pursuing robust improvements in patient safety.

#### Summary points

Complex systems involve many gaps between people, stages, and processes

Analysis of accidents usually reveals the presence of many gaps, yet only rarely do gaps produce accidents

Safety is increased by understanding and reinforcing practitioners' normal ability to bridge gaps

This view contradicts the normal view that systems need to be isolated from the unreliable human element

We know little about how practitioners identify and bridge new gaps that occur when systems change

The notion of gaps is simple. Gaps are discontinuities in care. They may appear as losses of information or momentum or interruptions in delivery of care. In practice gaps rarely lead to overt failure. Rather, most gaps are anticipated, identified, and bridged andtheir consequences nullified by the technical work done at the sharp end. These gap driven activities are so intimately woven into the fabric of technical work that neither outsiders nor insiders recognise them as distinct from other technical work.

Gaps are most readily seen when they are aligned with organisational and institutionalboundaries that mark changes in responsibility or authority, different roles of professionals, or formal divisions of labour. For example, the loss of coherence in …

https://www.bmj.com/content/bmj/320/7237/791.full.pdf

David D. Woods

Papers

Resilience Engineering: Concepts and Precepts

Joint Cognitive Systems: Foundations of Cognitive Systems Engineering

How in the world did we ever get into that mode? Mode error and awareness in supervisory control

Cognitive systems engineering: new wine in new bottles

Gaps in the continuity of care and progress on patient safety