Severity: Warning
Message: file_get_contents(https://...@remsenmedia.com&api_key=81853a771c3a3a2c6b2553a65bc33b056f08&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
Unified Modelling Language (UML) models of the patient journey in a regional Australian emergency department (ED) were used to develop an accurate, complete representation of ED processes and drive the collection of comprehensive quantitative and qualitative service delivery and patient treatment data as an evidence base for hospital service planning. The focus was to identify bottle-necks that contribute to over-crowding. Data was collected entirely independently of the routine hospital data collection system. The greatest source of delay in patient flow was the waiting time from a bed request to exit from the ED for hospital admission. It represented 61% of the time that these patients occupied ED cubicles. The physical layout of the triage area was identified as counterproductive to efficient triaging, and the results of investigations were often observed to be available for some time before clinical staff became aware. The use of independent primary data to construct UML models of the patient journey was effective in identifying sources of delay in patient flow, and aspects of ED activity that could be improved. The findings contributed to recent department re-design and informed an initiative to develop a business intelligence system for predicting impending occurrence of access block.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1016/j.ienj.2010.03.003 | DOI Listing |
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