Source:http://linkedlifedata.com/resource/pubmed/id/10566423
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2000-2-1
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| pubmed:abstractText |
We are examining ways in which a clinical information system can favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. In order to do so, we have developed a theoretically based cognitive model for patient decision making. Our model includes somatic and emotional awareness, perceived threat (vulnerability and susceptibility), expectations of symptoms, self-efficacy and response efficacy to explain the response of an individual their symptoms. Variables are explained within a framework that details how they are interrelated in the context of other moderating variables. With an understanding of the decision process, we are able to collect, maintain and access patient specific data to tailor technology-based interventions unique to the requirements of each individual at various phases of the decision process. Existing clinical information systems at Columbia-Presbyterian Medical Center already address issues related to patient relevant on-line data. Other patient specific information will be collected through on-line questionnaires. By basing our approach on the use of a cognitive model, we can assess the capacity of our interventions to modify variables important to the decision-making process, allowing us to pinpoint which interventions are effective and the reasons why they are ineffective.
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| pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-1934372,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-3378902,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-3837277,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-7899950,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-8944590,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9054819,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9103125,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9215423,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9656660,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9678398,
http://linkedlifedata.com/resource/pubmed/commentcorrection/10566423-9827784
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
1531-605X
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
570-4
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| pubmed:dateRevised |
2009-11-18
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| pubmed:meshHeading |
pubmed-meshheading:10566423-Attitude to Health,
pubmed-meshheading:10566423-Cognition,
pubmed-meshheading:10566423-Decision Making,
pubmed-meshheading:10566423-Humans,
pubmed-meshheading:10566423-Models, Psychological,
pubmed-meshheading:10566423-Myocardial Infarction,
pubmed-meshheading:10566423-Patient Acceptance of Health Care
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| pubmed:year |
1999
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| pubmed:articleTitle |
Modeling patient response to acute myocardial infarction: implications for a tailored technology-based program to reduce patient delay.
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| pubmed:affiliation |
Department of Medical Informatics, Columbia University, New York, New York, USA.
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| pubmed:publicationType |
Journal Article
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