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
Objective: Detecting adverse events is pivotal for measuring and improving medical safety, yet current techniques discourage routine screening. The authors hypothesized that discharge summaries would include information on adverse events, and they developed and evaluated an electronic method for screening medical discharge summaries for adverse events.
Design: A cohort study including 424 randomly selected admissions to the medical services of an academic medical center was conducted between January and July 2000. The authors developed a computerized screening tool that searched free-text discharge summaries for trigger words representing possible adverse events.
Measurements: All discharge summaries with a trigger word present underwent chart review by two independent physician reviewers. The presence of adverse events was assessed using structured implicit judgment. A random sample of discharge summaries without trigger words also was reviewed.
Results: Fifty-nine percent (251 of 424) of the discharge summaries contained trigger words. Based on discharge summary review, 44.8% (327 of 730) of the alerted trigger words indicated a possible adverse event. After medical record review, the tool detected 131 adverse events. The sensitivity and specificity of the screening tool were 69% and 48%, respectively. The positive predictive value of the tool was 52%.
Conclusion: Medical discharge summaries contain information regarding adverse events. Electronic screening of discharge summaries for adverse events using keyword searches is feasible but thus far has poor specificity. Nonetheless, computerized clinical narrative screening methods could potentially offer researchers and quality managers a means to routinely detect adverse events.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC181984 | PMC |
http://dx.doi.org/10.1197/jamia.M1201 | DOI Listing |
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