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: 3122
Function: getPubMedXML
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
Background: One-third of injured patients treated in the emergency department (ED) have an alcohol use disorder (AUD). Few are screened and receive counseling because ED staff have little time for additional tasks. We hypothesized that computer technology can screen and provide an intervention that reduces at-risk drinking (British Medical Association criteria) in injured ED patients.
Methods: In all, 3,026 subcritically injured patients admitted to an ED were screened for an AUD using a laptop computer that administered the AUD Identification Test (AUDIT) and assessed motivation to reduce drinking. Patients with a positive AUDIT (n = 1,139) were randomized to an intervention (n = 563) or control (n = 576) condition. The computer generated a customized printout based on the patient's own alcohol use pattern, level of motivation, and personal factors, which was provided in the form of feedback and advice.
Results: Most patients (85%) used the computer with minimal assistance. At study entry, a similar proportion in each group met criteria for at-risk drinking (49.6% versus 46.8%, p = 0.355). At 6 months, 21.7% of intervention and 30.4% of control patients met criteria for at-risk drinking (p = 0.008). Intervention patients also had a 35.7% decrease in alcohol intake, compared with a 20.5% decrease in controls (p = 0.006). At 12 months, alcohol intake decreased by 22.8% in the intervention group versus 10.9% in controls (p = 0.023), but the proportion of at-risk drinkers did not significantly differ (37.3% versus 42.6%, p = 0.168).
Conclusions: The computer-generated intervention was associated with a significant decrease in alcohol use and at-risk drinking. Research is needed to further evaluate and adapt information technology to provide preventive clinical services in the ED.
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Source |
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http://dx.doi.org/10.1097/01.ta.0000196399.29893.52 | DOI Listing |
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