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
Background: Over 90 million Americans suffer from advanced illness (AI) and spend their last days of life in critical care units receiving costly, unwanted, aggressive medical care.
Objective: Evaluate the impact of a specialized care model in medical/surgical units for hospitalized geriatric patients and patients with complex care requirements where designated AI beds align care with patient's wishes/goals, minimize aggressive interventions, and influence efficient resource utilization.
Design: US based multi-facility retrospective, longitudinal descriptive study of screened positive AI patients in AI Beds (N = 1,237) from 3 facilities from 2015 to 2017.
Results: Patient outcomes included 60% referrals to AI beds from ICU, a decrease of 39-49% in average ICU LOS, a 23% reduction of AI bed patient expirations, 9.0% referrals to hospice, and projected cost savings of $4,361.66/patient, US dollars.
Conclusion: Allocating AI beds to deliver care to AI patients resulted in a decreased cost of care by reducing overall hospital LOS, mortality, and efficient use of both critical care and hospital resources.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1177/1049909120984384 | DOI Listing |
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