ABSTRACT
OBJECTIVES: Developing a care plan for a patient is a complex task, requiring an understanding of interactions and dependencies between procedures and of their possible outcomes for an individual patient. Decision support for planning has broader requirements than are typically considered in medical informatics applications. We consider the appropriate design of software to assist medical planning. METHODS: The likely cognitive loads imposed by planning tasks were assessed with a view to directly supporting these via software. RESULTS: Five types of cognitive load are likely to be important. A planning support system, REACT, was designed to ameliorate these cognitive loads by providing targeted dynamic feedback during planning. An initial evaluation study in genetic counselling indicates that the approach is successful in that role. CONCLUSIONS: The approach provides the basis of a general aid for visualizing, customizing and evaluating care plans.
Subject(s)
Cognition , Communication , Decision Making , Decision Support Techniques , Patient Care Planning , Physician-Patient Relations , Software , Comprehension , Genetic Counseling , Humans , User-Computer InterfaceABSTRACT
This article describes an investigation into the residual writing skills of a severely dysgraphic patient (DA). We found that they were powerfully influenced by a number of lexical variables (lexicality, frequency, imageability, length and geminates). His error pattern was characterized by semantic, lexical, substitution, deletion errors and fragment responses that preserved the first letter. Thus, DA's written spelling was characterized by both deep dysgraphic and graphemic output buffer effects. It is proposed that this pattern of performance represents a new"putative functional syndrome."
Subject(s)
Agraphia/psychology , Language Disorders/psychology , Agraphia/etiology , Anomia/etiology , Anomia/psychology , Carotid Stenosis/complications , Carotid Stenosis/psychology , Humans , Language Disorders/etiology , Language Tests , Male , Memory, Short-Term/physiology , Middle Aged , Models, Neurological , Neuropsychological Tests , Psychomotor Performance/physiology , Reading , Semantics , SpeechABSTRACT
Public awareness of genetic predisposition to diseases such as breast cancer threatens to put severe strain on genetics services. Computer-based decision support for general practitioners (GPs) has the potential to reduce unnecessary referrals, but issues of communicating about levels of risk and uncertainty must be addressed. An argumentation logic formalism can subsume both traditional probability theory and more qualitative, reason-based approaches to communicating uncertainty, and we propose that qualitative, argument-based presentation will make uncertainty information more accessible and comprehensible to both patient and GP. We describe software that uses an argumentation approach to assess genetic risk during a GP consultation and provide referral advice along with detailed qualitative explanations for its advice. The software was evaluated in real-life GP consultations in which actors played patients concerned about genetic risk, and in use by GPs evaluating simulated cases. Significant improvement in accuracy of assessment and appropriateness of referrals was found. GPs viewed the software and the qualitative reporting approach highly favourably.
Subject(s)
Breast Neoplasms/genetics , Decision Making, Computer-Assisted , Genetic Counseling , Risk Assessment/methods , Software , Family Practice , Humans , PedigreeABSTRACT
OBJECTIVES: To assist general practitioners in evaluating patients' genetic risk of cancer on the basis of family history data. METHODS: A new computer application, RAGs (Risk Assessment in Genetics), has been developed to help doctors create graphical family trees and assess the genetic risk of breast and colorectal cancer. RAGs possesses two features that distinguish it from similar software: (i) a user-centred design, which takes into account the requirements of the doctor-patient encounter; (ii) effective and accessible risk reporting by employing qualitative evidence for or against increased risk, which is more easily understood than numerical probabilities. The system allows any rule-based genetic risk guideline to be implemented, and may be readily modified to cater for the varying degrees of information required by different specialists. RESULTS: RAGs permits fast, accurate data entry, and results in more appropriate management decisions than those made via other techniques. In addition, RAGs enables both the clinician and the patient to understand how it arrives at its conclusions, since the use of qualitative evidence allows the program to provide explanations for its reasoning. CONCLUSIONS: The RAGs system promises to help practitioners be more effective gatekeepers to genetic services. It may empower doctors both to make an informed choice when deciding to refer patients who are at increased genetic risk of breast or colorectal cancer, and to reassure those who are at low risk.
