Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The concept of thresholds plays a vital role in decisions involving the initiation, continuation, and completion of diagnostic testing. Much research has focused on the development of explicit thresholds, in the form of practice guidelines and decision analyses. However, these tools are used infrequently; most medical decisions are made at the bedside, using implicit thresholds. Study of these thresholds can lead to a deeper understanding of clinical decision making. The authors examine some factors constituting individual clinicians' implicit thresholds. They propose a model for static thresholds using the concept of situational gravity to explain why some thresholds are high, and some low. Next, they consider the hypothetical effects of incorrect placement of thresholds (miscalibration) and changes to thresholds during diagnosis (manipulation). They demonstrate these concepts using common clinical scenarios. Through analysis of miscalibration of thresholds, the authors demonstrate some common maladaptive clinical behaviors, which are nevertheless internally consistent. They then explain how manipulation of thresholds gives rise to common cognitive heuristics including premature closure and anchoring. They also discuss the case where no threshold has been exceeded despite exhaustive collection of data, which commonly leads to application of the availability or representativeness heuristics. Awareness of implicit thresholds allows for a more effective understanding of the processes of medical decision making and, possibly, to the avoidance of detrimental heuristics and their associated medical errors. Research toward accurately defining these thresholds for individual physicians and toward determining their dynamic properties during the diagnostic process may yield valuable insights.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/ACM.0b013e3181ccd59b | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!